Genetic variation of hypoxia tolerance in farmed fish: a systematic review for selective breeding purposes

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Barahona, Nicolas Salinas-Parra, Rodrigo Pulgar, José Gallardo-Matus This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6010748/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The acceleration of climate change and increasing water pollution have contributed to a global increase in hypoxic events in the oceans. As a result, this environmental stressor has had significant economic repercussions for the marine aquaculture sector. Consequently, selective breeding for hypoxia-tolerant fish is being explored as a promising strategy to mitigate climate change effects. In this context, the present systematic review synthesizes and critically evaluates current knowledge regarding the genetic variation associated with hypoxia tolerance in farmed fish species. A literature search was conducted in Scopus and Web of Science, following the PRISMA 2020 guidelines. In total, 963 articles were identified, of which 40 met the inclusion criteria, encompassing 29 species and three hybrid lines. Among the farmed fish, the blunt snout bream ( Megalobrama amblycephala ), rainbow trout ( Oncorhynchus mykiss ), common carp ( Cyprinus carpio ) and Nile tilapia ( Oreochromis niloticus ) were the most extensively studied. The most commonly used traits to measure hypoxia tolerance included: 1) time of loss of equilibrium (t LOE ), 2) survival time or status (alive/dead) and 3) critical oxygen partial pressure (P crit ), measured via respirometry. Notably, 22 studies reported substantial variability in hypoxia tolerance across families, strains, gynogenetic lines, growth-transgenic lines, hybrids, and species. Moreover, 15 studies identified SNP markers significantly associated with hypoxia tolerance; however, heritability estimates, reported in only two studies, ranged from 0.28 to 0.65. Furthermore, candidate genes were frequently identified as downstream effectors of the HIF pathway or as components of signaling pathways such as VEGF and mTOR, which are critical for angiogenesis and energy conservation, respectively. Additionally, genes involved in erythropoiesis, ion regulation, glucose metabolism, DNA repair, and iron metabolism, key processes in the hypoxia response, were identified. Given that aquatic environments are becoming increasingly hypoxic, these findings underscore the potential of the inherent genetic diversity present in farmed fish populations. In this context, genomic selection and gene editing emerge as promising tools for developing hypoxia-tolerant fish lines. Nevertheless, further research is warranted to implement such lines under field conditions, particularly because the correlations between hypoxia tolerance and other economically important traits, such as growth and pathogen resistance, remain largely unknown. Aquaculture and Mariculture Epigenetics & Genomics Hypoxia tolerance Selective breeding Aquaculture Genomic selection GWAS. Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Hypoxia is a complex environmental stressor defined by reduced levels of dissolved oxygen in water [ 1 , 2 ]. Rising temperatures associated with climate change are expected to increase frequency and intensity of hypoxic events worldwide [ 3 , 4 ]. As a result, many coastal regions are experiencing an expansion of hypoxic zones [ 5 , 6 ]. Since dissolved oxygen is the primary limiting factor of the aerobic metabolism of aquatic organisms [ 7 ], its depletion critically reduces the yield, quality, and survival of farmed fish [ 8 ], thereby threatening the sustainability of the aquaculture industry. Hypoxia arises from multiple factors within aquaculture production systems, prompting the implementation of mitigation measures at the cage or culture pond level (Fig. 1). Environmental drivers include climate change [ 9 , 10 ], thermal stratification due to diurnal and seasonal temperature fluctuations [ 11 – 14 ], upwelling of oxygen-depleted deep waters [ 15 , 16 ], coastal eutrophication [ 1 , 17 , 18 ], and microbial decomposition of harmful algal blooms [ 19 – 23 ]. Additionally, gill damage caused by harmful algal blooms [ 24 – 27 ], jellyfish [ 28 , 29 ] or amoebae [ 30 ] can further impair the ability of fish to tolerate hypoxia. Production-related factors contributing to hypoxia events include high stocking densities in ponds or culture cages [ 31 ], cage size, the accumulation of organic matter from excreta and uneaten food, biofouling that clogs cage nets and obstructs proper water flow [ 32 ], nocturnal algal respiration, system failures, and human errors [ 2 , 33 – 35 ]. These factors are often transient and difficult to predict, causing sharp and unexpected fluctuations in dissolved oxygen levels across time and space [ 12 , 36 ]. Since farmed fish, particularly in marine environments, cannot escape hypoxic conditions, those unable to tolerate low oxygen levels often experience high mortality rates. Indeed, mass mortality events triggered by hypoxia have been reported in various production systems worldwide [ 37 – 40 ], highlighting the severity of this stressor [ 41 , 42 ]. To mitigate hypoxia, aquaculture facilities commonly employ aeration technologies such as nanobubble diffusion systems, water jets, paddles, air injection [ 43 – 47 ] or artificial upwelling [ 48 ] (Fig. 1). However, these technologies can be costly to implement and maintain on a large scale [ 49 ] and remain susceptible to human error [ 50 ]. Hypoxic events do not always lead to fish mortality. Their impact depends on the nature of hypoxia, whether it is acute (short-term and extreme) or chronic (prolonged exposure). In some cases, hypoxia induces sublethal effects, altering physiological, tissue, and molecular processes [ 9 ]. At the physiological level, hypoxia reduces metabolic rate, growth rate, feed conversion efficiency, and immune response [ 51 – 54 ]. These adverse effects increase fish susceptibility to pathogens and reduce their ability to adapt to environmental changes, ultimately compromising survival [ 55 ]. At the tissue-level, hypoxia can cause gill damage [ 56 ], while chronic exposure may lead to cell cycle arrest or apoptosis in response to extensive genetic damage [ 57 ]. However, fish have evolved both physiological and metabolic mechanisms to tolerate hypoxia [ 58 ]. For instance, they can enhance O₂ uptake and transport, upregulate anaerobic ATP production pathways such as glycolysis, and suppress high-ATP-demanding molecular processes such as protein synthesis [ 7 , 52 , 59 , 60 ]. Most of these hypoxia tolerance mechanisms are mediated by hypoxia-responsive genes activated by specific pathways. The HIF-1 pathway, regulated by the heterodimeric transcription factor HIF-1α/HIF-1β, controls the expression of multiple hypoxia-sensitive genes, such as VEGF , EPO , LDH and GLUT [ 60 – 63 ]. Additional signaling pathways, such as MAPK and PI3K/AKT/mTOR, also contribute to hypoxia tolerance in fish [ 64 – 66 ]. Since fish exhibit varying degrees of hypoxia tolerance [ 67 , 68 ], even within the same species [ 69 ], understanding the genetic architecture of this trait could inform artificial selection strategies to enhance hypoxia resilience in aquaculture [ 70 ]. A simple and cost-effective method for assessing hypoxia tolerance in fish is the loss-of-equilibrium (LOE) test [ 71 ]. This protocol consists of gradually reducing oxygen levels by injecting nitrogen, recording the time at which the fish loses its ability to maintain dorsoventral balance. A short time-to-LOE (t LOE ) indicates a hypoxia-sensitive individual, whereas a longer t LOE denotes greater hypoxia tolerance [ 69 , 72 , 73 ]. Additional LOE-based metrics include LOE crit (O 2 LOE), which quantifies the oxygen concentration (mg/L), saturation (%) or partial pressure (KPa or torr) at which equilibrium loss occurs [ 70 , 74 ], and LOE 50 , which represents the time or oxygen concentration (mg/L) at which 50% of fish in a group lose equilibrium [ 68 , 75 ]. Beyond LOE-based assessments, hypoxia tolerance can be evaluated using critical oxygen tension (P crit ), which reflects a fish’s ability to uptake dissolved oxygen from water [ 76 – 79 ], incipient lethal oxygen saturation (ILOS) [ 80 , 81 ], survival time [ 82 ], and binary survival/mortality traits [ 8 ]. Additional physiological and molecular indicator of hypoxia tolerance include cardiac function parameters [ 69 , 83 ], gill remodeling [ 70 ], metabolic activity [ 84 ], enzymatic activities [ 85 ], differential gene expression [ 62 ] and transcriptomic analysis [ 86 , 87 ]. Heritability (h²) estimates for hypoxia tolerance have been reported in several commercially important fish species, ranging from moderate in rainbow trout ( Oncorhynchus mykiss , h² = 0.28) [ 88 ], to medium in common carp ( Cyprinus carpio , h² = 0.50) [ 89 ] and high in large yellow croaker ( Larimichthys crocea , h² = 0.61–0.65) [ 8 , 90 ]. This reported genetic variability in hypoxia tolerance suggests an adaptive potential of fish populations facing expanding hypoxic environments. Understanding the genetic basis of this variation is key to designing artificial selection strategies that improve the resilience of farmed species (Fig. 2B). At the molecular level, genetic variation associated with hypoxia tolerance has been documented at the single nucleotide polymorphism (SNP) marker level, with specific SNPs and candidate genes identified in rainbow trout [ 88 ] and channel catfish [ 91 ]. SNPs located in regulatory regions—such as hypoxia-responsive elements (HREs) within promoters of HIF-target genes, as well as in introns, enhancers, UTRs, or exons—can directly or indirectly influence gene transcription or expression efficiency, thereby modulating the hypoxia tolerance phenotype (Fig. 2A) [ 91 , 92 ]. Given the critical role of hypoxia tolerance in aquaculture sustainability, this systematic review consolidates and critically evaluates current knowledge on the genetic basis of this trait in farmed fish. It synthesizes advances ranging from phenotypic assessments to the identification of molecular markers, highlighting their potential applications in selective breeding programs to enhance resilience to low-oxygen environments. METHODS This systematic review adhered to the guidelines of the PRISMA 2020 statement [ 93 ]. Articles indexed in databases and focusing on hypoxia tolerance in farmed fish and containing data on (i) heritability (h 2 ), (ii) QTLs, (iii) SNPs, (iv) G×E interaction analysis, (v) genome-wide association studies (GWAS), (vi) candidate genes, (vii) phenotypic variability among families, strains, populations, or species, or (viii) genetic correlations were included. Articles focusing on traits unrelated to hypoxia tolerance (e.g. thermal tolerance) were excluded. The following types of publications were also excluded: (i) graduate theses, editorials, letters to the editor, reviews, book chapters, conference proceedings and abstracts, (ii) non-English language manuscripts, (iii) articles without full-text availability, (iv) studies focusing exclusively on transcriptomics, gene expression, biomarkers and/or epigenetic markers, (v) studies on non-fish species or on non-farmed fish, (vi) studies that did not compare tolerance to hypoxia across groups (e.g., gynogenetic lines or strains), (vii) studies analyzing growth rate, immunity, biochemical parameters, and/or physiological responses under varying hypoxic or anoxic conditions or hypoxia-induced stress or (viii) studies examining the effects of diets or pharmaceuticals on hypoxia tolerance. A systematic search for relevant articles was conducted in the Web of Science (WoS) and Scopus databases in September 2024, using the following strategy: (“genetic variation” or “genetic variant” or “genetic variability” or “genetic architecture” or “geographic variation” or genotype* or allele* or polymorphism or “genomic variant*” or “phenotypic plasticity” or “phenotypic variation” or strain* or inter-strain* or inter-population* or “population differences” or line* or family* or inter-family* or correlation or “genetic correlation” or GWAS or “genome-wide association study” or “genome-wide association analysis” or QTL* or “genomic prediction” or “single nucleotide polymorphism” or “candidate SNPs” or “novel SNP*” or “SNP chip” or SNP* or “SNP array” or “SNP panel*” or marker* or “genomic selection” or GS or “candidate genes” or “annotated candidate genes” or imput* or heritability* or “putative candidate genes” or “genetic improvement” or “artificial selection” or “selective breeding”) AND (“hypoxia tolerance” or “tolerance to hypoxia” or “oxygen stress” or “oxygen tolerance” or “oxygen variability” or “hypoxic stress” or “hypoxic conditions” or “low oxygen tolerance” or “hypoxia-resilient” or “oxygen metabolism” or “hypoxia tolerance trait*” or LOE or “loss of equilibrium”) AND (fish* or finfish* or “teleost fish*” farmed or “farmed fish” or aquaculture or fisher* or inland or marine or “grass carp” or “silver carp” or “Nile tilapia” or “common carp” or catla or “bighead carp” or Carassius or “striped catfish” or “roho labeo” or “clarias catfish*” or “tilapias nei” or “Wuchang bream” or “rainbow trout” or “black carp” or “largemouth black bass” or “Atlantic salmon” or “milkfish” or “mullets nei” or “githead seabream” or “large yellow croaker” or “European seabass” or “groupers nei” or “coho salmon” or “Japanese seabass” or “giant seaperch” or “red drum”) The third block of the search strategy includes the common names of the top 15 inland and top 15 marine/coastal fish species with the highest global production according to FAO 2022’s report [ 94 ]. However, all farmed fish species identified through the search strategy were included, regardless of their explicit mention in the FAO 2022’s report. The Endnote software package was used to manage retrieved references, automatically remove duplicates, and organize sources by theme. Study selection was conducted manually based on titles and abstracts, following predefined inclusion/exclusion criteria. Articles that met these criteria were subjected to a full-text review. One reviewer conducted the initial screening, and a second reviewer verified the selection. Any discrepancies in inclusion or exclusion were resolved through consensus. RESULTS AND DISCUSSION This systematic review synthesizes current knowledge on genetic variation underlying hypoxia tolerance in farmed fish. A comprehensive search across two databases retrieved 963 articles. After removing 143 duplicates and excluding 780 articles that did not meet the inclusion criteria, 40 studies were selected for analysis (Fig. 3). The selected articles were categorized into two groups: (a) traditional genetics and selective breeding studies, which assessed hypoxia tolerance at the phenotypic level by comparing strains, genetic lines, families, or hybrids (Table 1 ), or (b) modern genomics and biotechnology studies, which investigated hypoxia tolerance using advanced genomic approaches such as genome-wide association studies (GWAS), genomic selection or mutagenesis (Table 2 ). In total, the review identified 26 farmed fish and three catfish hybrid lines, including blunt snout bream ( Megalobrama amblycephala ), rainbow trout ( Oncorhynchus mykiss ), common carp ( Cyprinus carpio ), Nile tilapia ( Oreochromis niloticus ), bighead carp ( Aristichthys nobilis ), grass carp ( Ctenopharyngodon idellus ), Atlantic salmon ( Salmo salar ), channel catfish ( Ictalurus punctatus ), among others. These findings highlight the growing interest in understanding the genetic basis of hypoxia tolerance and its potential applications in selective breeding for aquaculture sustainability Table 1 Overview of the phenotypic studies of hypoxia tolerance identified in this review (n = 23) Common name Species Method for measuring hypoxia tolerance Trait(s) evaluated Number of groups compared Types of groups compared Reference Rainbow trout Oncorhynchus mykiss Hypoxia challenge test t LOE (min) 3 Genetically identical lines with different growth rates Borey et al . (2018) Rainbow trout Oncorhynchus mykiss Hypoxia challenge test t LOE (min) 2 Strains with different growth rates Roze et al . (2013) Rainbow trout Oncorhynchus mykiss Hypoxia challenge test, Respirometry Incipient lethal oxygen saturation ILOS (% O 2 Saturation), critical oxygen level (O 2crit ). 3 Strains Zhang et al . (2018) Nile tilapia Oreochromis niloticus Hypoxia challenge test ASR (mgO 2 /L) 2 Strains Obirikorang et al . (2023) Nile tilapia Oreochromis niloticus Hypoxia challenge test ASR (KPa) 5 Groups Akrokoh et al . (2024) Atlantic salmon Salmo salar Hypoxia challenge test t LOE (min) 41 Families Anttila et al . (2013) Atlantic salmon Salmo salar Mortality hypoxia test Survival of embryos (Number) 4 Wild genetically different populations from different rivers Côte et al . (2012) Giant seapearch Lates calcarifer Respirometry P crit (% air sat) 2 Two populations geographically separated Collins et al . (2016) Channel catfish Ictalurus punctatus Hypoxia challenge test t LOE (min) 6 Strains Wang et al . (2017) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test LOE crit (mg/L) 2 Groups (control strain vs genetically improved strain) Wu et al . (2020) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test LOE crit (mg/L) 2 Groups (control strain vs genetically improved strain) Zhao et al . (2022) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test Mean O 2 LOE (mg/L) at fish began to lose equilibrium, Average number of fish that lost equilibrium 2 Groups (control vs hybrid) Gong et al . (2024) Qing bo / Hiina astelparrak Spinibarbus sinensis Respirometry P crit (torr) 6 Species Chen et al . (2019) Chinese bream Parabramis pekinensis Respirometry P crit (torr) 6 Species Chen et al . (2019) Grass carp Ctenopharyngodon idellus Respirometry P crit (torr) 6 Species Chen et al . (2019) Big head carp Aristichthys nobilis Respirometry P crit (torr) 6 Species Chen et al. (2019) Silver carp Hypophthalmichthys molitrix Respirometry P crit (torr) 6 Species Chen et al. (2019) Common carp Cyprinus carpio var Jian Respirometry P crit (torr) 6 Species Chen et al. (2019) Qing bo / Hiina astelparrak Spinibarbus sinensis Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Common carp Cyprinus carpio Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Crucian carp Carassius carassius Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Thick-jawed bream Megalobrama pellegrini Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Silver carp Hypophthalmichthys molitrix Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Bighead carp Aristichthys nobilis Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Grass carp Ctenopharyngodon idellus Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Black carp Mylopharyngodon piceus Hypoxia challenge test, Respirometry LOE crit (KPa), P crit (KPa) 8 Species Dhillon et al . (2013) Sablefish Anoplopoma fimbria Hypoxia challenge test, Respirometry P crit (% air sat), O 2 LOE (% air sat), t LOE (min) 2 Groups (juvenile vs. adult) Leeuwis et al . (2019) Mountain carp Schizothorax prenanti Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Sharp-jaw barbell Onychostoma sima Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Qing bo / Hiina astelparrak Spinibarbus sinensis Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Crucian carp Carassius carassius Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Common carp Cyprinus carpio Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Bighead carp Aristichthys nobilis Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Silver carp Hypophthalmichthys molitrix Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Chinese bream Parabramis pekinensis Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Grass carp Ctenopharyngodon idellus Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Black carp Mylopharyngodon piceus Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Chinese hook snout carp Zacco platypus Hypoxia challenge test, Respirometry LOE 50 (mgO 2 L − 1 ), P crit (mgO 2 L − 1 ), ASR50 11 Species Fu et al. (2014) Rainbow trout Oncorhynchus mykiss Hypoxia challenge test t LOE (min) 3 Triploid and diploid strains Scott et al. (2015) Brook trout Salvelinus fontinalis Hypoxia challenge test t LOE (min), PO 2 LOE (KPa) 2 Triploid and diploid strains Jensen & Benfey (2022) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test O 2 LOE (mg/L) 3 Gynogenetic lines Gong et al. (2019) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test O 2 LOE (mg/L) 2 Gynogenetic lines Fu et al. (2022) Common carp Cyprinus carpio Mortality hypoxia test Survival time (min), Survival status (alive/dead) 8 Families (Growth hormone (GH)-transgenic common carp v/s Control) Dunham et al. (2002) Rainbow trout Oncorhynchus mykiss Hypoxia challenge test, Mortality hypoxia test Initial loss of equilibrium (ILOE), final loss of equilibrium (FLOE), and mortality. All traits measured as (mgO 2 L − 1 ) 4 Parasite resistant strains Fetherman et al. (2016) Hybrid catfish Pelteobagrus fulvidraco × Leiocassis longirostris Hypoxia challenge test Floating head 50 (mgO 2 /L) 5 Hybrid types Wang et al. (2023) Hybrid catfish Ictalurus punctatus × Ictalurus furcatus Hypoxia challenge test t LOE (min) 4 Strains from different rivers Dunham et al. (2014) Table 2 General information concerning hypoxia tolerance from genetic articles identified in this review (n = 17). Common name Species Method for measuring hypoxia tolerance Trait(s) Type of genetics study Genotyping method Number of SNPs evaluated Number of genotyped fish Number of significant (a) or suggestive (b) marker associated with hypoxia tolerance trait(s) Phenotypic variance explained (PVE) of the most significant marker Heritability (h 2 ) Reference Nile tilapia Oreochromis niloticus Hypoxia challenge test t LOE (sec) SNP association study (Wald test) Sanger sequencing 5 192 1 b Not provided Not provided Li et al . (2017) Giant seapearch Lates calcarifer Mortality hypoxia test Survival status (alive/dead) SNP association study (Chi-squared test) Sanger sequencing 3 280 1 Not provided Not provided Yang et al . (2020) Channel catfish Ictalurus punctatus Hypoxia challenge test t LOE (min) Genome-wide Association Study (GWAS) 250K SNP array 176,798 376 Across strains: 1 a and 16 b ; within strains: Kansas (26 a ), Kmix (4 a ), Thompson (1 a ) Across strains: 5.71%, Within strains: Kansas (25.32%), Kmix (23.04%), Thompson (32.04%) Not provided Wang et al . (2017) Catfish hybrid I. punctatus × I. furcatus Hypoxia challenge test t LOE (min) Genome-wide Association Study (GWAS) 250K SNP array 208,598 208 9 a y 31 b 12.44% Not provided Zhong et al . (2017) Large yellow croaker Larimichthys crocea Mortality hypoxia test Survival time (min), Survival status (alive/dead) Genome-wide Association Study (GWAS) ddRAD-Seq 54,224 396 Survival time: 2 a* , Survival status: 4 a* Survival time: 18.04%, Survival status: 8.49% Survival time: 0.65, Binary trait: 0.61 Ding et al . (2022) Large yellow croaker Larimichthys crocea Mortality hypoxia test Survival time (min), Survival status (alive/dead) Genome-wide Association Study (GWAS) 55K SNP array 120,815 372 Survival time: 5 b , Survival status: 2 b Survival time: 4.98%, Survival status: 5.37% Not provided Ding et al . (2023) Rainbow trout Oncorhynchus mykiss Hypoxia challenge test t LOE (min) Genome-wide Association Study (GWAS) 57K SNP array 418,925 1297 7 a y 2 b Not provided 0.28 Prchal et al . (2023) Nile tilapia Oreochromis niloticus Hypoxia challenge test Survival time (min) QTL mapping analysis ddRAD-Seq 924 96 2 a Not provided Not provided Li et al . (2017) Pompano Trachinotus ovatus Mortality hypoxia test Survival time (min) Genome-wide Association Study (GWAS) Whole-genome resequencing 706,991 100 4 b 32.1% Not provided San et al . (2021) Catfish Pelteobagrus vachelli Hypoxia challenge test t LOE (min) QTL mapping analysis ddRAD-Seq 5,059 200 1 QTL 11.3% Not provided Zhang et al . (2020)] Silver sillago Sillago sihama Hypoxia challenge test Time until gasping (min) QTL mapping analysis and SNP association study (Linear regression) Genotyping-by-sequencing (GBS) method and Sanger Sequencing GBS: 4,735 SNP, Sanger: 13 SNP 162 6 QTL, 5 SNP QTL: 7.17% Not provided Ye et al . (2024) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test LOE crit (mg/L) Haplotype analysis (Chi-squared tes) Sanger Sequencing 2 SNP 100 1 haplotype Not provided Not provided Wang et al . (2020) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test LOE crit (mg/L) Association analysis of diplotypes (Anova) Sanger Sequencing 2 SNP 90 1 haplotype Not provided Not provided Zhao et al . (2023) Golden pompano Trachinotus blochii Hypoxia challenge test t LOE (min) Bulked Segregant RNA-Seq (BSR-seq) SNP calling from RNA-seq 821,398 42 16 QTL Not provided Not provided Liu et al . (2021) Blunt snout bream Megalobrama amblycephala Hypoxia challenge test LOE crit (mg/L) Whole-genomic mutagenesis to increase hypoxia tolerance Whole-genome resequencing 3,195,434 6 Not provided Not provided Not provided Su et al . (2022) Large yellow croaker Larimichthys crocea Mortality hypoxia test Survival time (min), Survival status (alive/dead) Genomic selection 55 K SNP array 38, 472 753 Not provided Not provided Survival time: 0.62 ± 0.05, Survival status: 0.65 ± 0.08 Ding et al . (2024) Traditional genetics and selective breeding studies of Hypoxia Tolerance Since accurate and cost-effective measurement of hypoxia tolerance is crucial for implementing selective breeding programs in aquaculture, it is relevant to first discuss the detected methods. In the traditional genetics and selective breeding studies retrieved by our search strategy (Table 1 ), hypoxia tolerance was primarily assessed using three key traits: (1) time to loss of equilibrium (t LOE ) measured by hypoxia challenge test, (2) survival time or survival status (alive/dead) determined by mortality hypoxia test, and (3) critical oxygen partial pressure (P crit ) measured via respirometry. Less commonly used indicators included aquatic surface respiration (ASR) and aerial emergence, sometimes referred to as “floating head”. ASR describes the selective uptake of oxygen-rich water from the surface layer, while aerial emergence involves leaving the water to breathe air directly. The loss-of-equilibrium (LOE) test is widely used due to its simplicity, speed, and minimal equipment requirements (e.g., nitrogen tanks or standard farm equipment). This method is also scalable for high-throughput screening in a short time [ 71 ]. However, observer bias can influence results, requiring standardized training for personnel conducting the assessments. In contrast, the survival time or survival status (alive/dead) measured by the mortality hypoxia test provides an unambiguous outcome and enables the selection of individuals with extreme hypoxia tolerance, which is relevant to real-world survival scenarios. However, ethical concerns arise due to the use of lethal hypoxia levels, particularly in large-scale trials involving hundreds or thousands of fish. A common limitation of the aforementioned methods is that they assess acute hypoxia responses rather than long-term adaptation to low oxygen environments. By contrast, the critical oxygen partial pressure (P crit ) measured via respirometry provides detailed physiological insight into oxygen uptake efficiency under hypoxic conditions. Additionally, it is non-lethal, allowing for repeated measurements on the same individuals [ 78 ]. However, despite these advantages, respirometry is complex, expensive, and time-consuming, restricting its feasibility in large-scale breeding programs. The efficient implementation of selection breeding programs for hypoxia tolerance requires understanding its genetic correlations with other economically important traits, such as growth and pathogen resistance. These relationships can be either favorable or antagonistic, influencing the feasibility of breeding strategies. For instance, the genetically improved farmed Tilapia (GIFT) strain, known for its fast growth, was compared with the native, slower-growing Akosombo strain to evaluate differences in hypoxia tolerance [ 95 ]. The GIFT strain exhibited a significantly higher oxygen consumption rate (MO₂), engaged in aquatic surface respiration (ASR) at higher oxygen levels, and showed an increased ventilatory frequency (fᵥ) compared to the Akosombo strain. Based on these physiological responses, the authors concluded that the GIFT strain had lower hypoxia tolerance [ 95 ]. However, this negative correlation between growth rate and hypoxia tolerance is not always observed. In a study on common carp ( Cyprinus carpio ), researchers compared survival under low dissolved oxygen between a normal strain and a transgenic F₂ strain carrying a trout growth hormone (rtGH) transgene [ 96 ]. Interestingly, the transgenic carp exhibited a longer mean survival time under hypoxic conditions, suggesting a beneficial pleiotropic effect of the inserted transgene on hypoxia tolerance. Similarly, a study in rainbow trout ( Oncorhynchus mykiss ) compared two strains with differing growth rates for their time to loss of equilibrium (t LOE ) under hypoxia [ 84 ]. Significant inter-individual variation was observed, but overall, the fast-growing strain demonstrated greater hypoxia tolerance (t LOE : 180–410 min) compared to the slow-growing strain (t LOE : 130–280 min). In contrast, the relationship between hypoxia tolerance and pathogen resistance remains poorly studied [ 97 ]. One of the few available studies, conducted by Fetherman et al., compared hypoxia tolerance between a strain resistant to Myxobolus cerebralis , the causative agent of whirling disease, and a non-resistant strain. No significant differences in hypoxia tolerance were detected between the two groups [ 97 ]. This highlights the need for further research to determine whether genetic selection for hypoxia tolerance affects disease resistance in aquaculture species. Several studies identified in our search have highlighted the importance of evaluating hypoxia tolerance across different strains and populations. This is expected, as understanding inter-strain and inter-population variation is essential for optimizing breeding programs that enhance resilience to low-oxygen environments. For instance, significant differences of hypoxia tolerance have been documented among strains of rainbow trout [ 80 ]. Similarly, a study on Atlantic salmon ( Salmo salar ) assessed embryo survival under hypoxia across four genetically distinct populations from different rivers in France, revealing significant differences among them [ 98 ]. In channel catfish ( Ictalurus punctatus ), a broad range of hypoxia tolerance has also been reported, with time to loss of equilibrium (t LOE ) ranging from 8 to 104 minutes across six different strains [ 99 ]. These findings suggest a strong genetic component underlying hypoxia tolerance, potentially influenced by local adaptation or historical selection pressures. However, not all species exhibit clear interpopulation differences. A study on giant seaperch ( Lates calcarifer ), a farmed catadromous species, compared hypoxia tolerance between two geographically distinct populations: one from a tropical river with severe oxygen fluctuations and another from a subtropical river. No significant differences in critical oxygen partial pressure (P crit ) were found between populations [ 100 ]. The authors suggested that in this species, physiological plasticity may play a more dominant role than local genetic adaptation in determining hypoxia tolerance [ 100 ]. Overall, these findings highlight the significant genetic contribution to hypoxia tolerance and emphasize the importance of strain selection in breeding programs aimed at improving fish survival and performance under low-oxygen conditions. Other studies detected by our search highlighted the potential of triploid and gynogenetic strains for improving hypoxia tolerance in fish. For example, a study in rainbow trout found that triploid (3n) strains exhibited lower hypoxia tolerance than diploid (2n) strains [ 101 ], primarily due to reduced gill surface area. Similarly, another study in brook trout ( Salvelinus fontinalis ) found the triploids (3n) less tolerant to hypoxia than diploids (2n) although this difference was very small [ 102 ]. In contrast, gynogenetic strains have shown enhanced hypoxia tolerance. In blunt-snouted bream ( Megalobrama amblycephala ), two gynogenetic lines were developed by activating sperm-activated eggs using UV-C irradiation to induce chromosome duplication [ 103 ]. Both lines exhibited greater hypoxia tolerance than the normal strain, though the specific mechanisms underlying this improvement remain unclear. Likewise, a separate study generated a gynogenetic strain by activating eggs with UV-inactivated red crucian carp spermatozoa, producing individuals with superior hypoxia tolerance compared to the control group [ 104 ]. These findings highlight the potential of gynogenesis as a tool for enhancing hypoxia tolerance in aquaculture. However, further research is needed to elucidate the genetic and physiological mechanisms driving these improvements and to assess their practical applications in selective breeding programs. Hybridization has long been recognized as a powerful tool for enhancing desirable traits in aquaculture, including hypoxia tolerance. By crossing strains within the same species or closely related species, researchers have explored whether hybrid vigor (heterosis) can improve resilience to low-oxygen conditions. For instance, a study comparing the hypoxia tolerance of blunt snout bream ( Megalobrama ampblycephala , BSB) and its hybrid (M. amblycephala ♀ × Culter alburnus ♂, BTBB) found that the hybrid exhibited significantly greater tolerance. The BTBB hybrid had a lower mean oxygen loss of equilibrium (LOE) threshold (0.51 ± 0.01 mg/L) and fewer individuals losing equilibrium (5.33 ± 0.58 fish) compared to BSB (0.92 ± 0.03 mg/L, 24.67 ± 1.5 fish), suggesting a strong heterotic effect [ 105 ]. Similarly, artificial hybridization between Pelteobagrus fulvidraco and Leiocassis longirostris produced two hybrid lines: PL ( P. fulvidraco ♀ × L. longirostris ♂) and LP ( L. longirostris ♀ × P. fulvidraco ♂). The PL hybrid exhibited a higher hatching rate, expected morphological traits, and greater hypoxia tolerance, as evidenced by increased enzyme activity and upregulation of HIF-related genes [ 106 ]. In catfish, hybrids of Ictalurus punctatus × Ictalurus furcatus displayed varying degrees of hypoxia tolerance depending on the geographical origin of the I. furcatus parent strain, suggesting that local thermal regimes influence this trait [ 107 ]. A separate study by Chen et al. [ 108 ] reinforced these findings, reporting that hybrids of M. amblycephala × Culter alburnus inherited broad hypoxia tolerance from C. alburnus . Lastly, hybridization has also been explored in disease-resistant strains. A cross between resistant and non-resistant Oncorhynchus mykiss strains for Myxobolus cerebralis exhibited greater hypoxia tolerance than one of the pure lines, further supporting the potential of hybridization in selective breeding programs [ 97 ]. Overall, these studies underscore the potential of hybridization as a viable strategy to enhance hypoxia tolerance in aquaculture species, leveraging both hybrid vigor and genetic contributions from specific parental lineages to develop more resilient fish strains. Hypoxia tolerance varies not only within species but also across different aquaculture-relevant species, influencing their suitability for specific culture conditions. Several studies have explored these interspecific differences, providing valuable insights into aquaculture management. In China, a study evaluating six commercially important cyprinid species from the Yangtze River found significant variation in their critical oxygen partial pressure (P crit ), indicating that each species has different oxygen demands and culture requirements [ 109 ]. Similarly, another study [ 67 ] assessed hypoxia tolerance in 10 cyprinid species, including two strains of Cyprinus carpio , widely used in Chinese aquaculture. The results showed a broad range of hypoxia tolerances: Carassius carassius (crucian carp) and Carassius auratus (goldfish), both adapted to slow-moving water bodies, displayed extreme tolerance (LOE crit ~ 0 KPa). Six species exhibited moderate tolerance (LOE crit : 0.1–0.3 KPa), whereas Megalobrama pellegrini (thick-jawed bream) and Spinibarbus sinensis (qingbo), which typically inhabit fast-flowing rivers, were the most sensitive (LOE crit ~ 0.6 KPa). Interestingly, hypoxia tolerance (LOE crit ) did not correlate with oxygen uptake capacity (P crit , Pearson’s correlation = 0.004, P = 0.60), reinforcing that P crit alone is not a reliable indicator of hypoxia tolerance. The authors concluded that, at least in cyprinids, hypoxia tolerance is independent of phylogenetic relationships [ 67 ]. A follow-up study by Fu et al. [ 68 ] expanded this analysis to 12 cyprinid species from habitats with different flow regimes (rapid, slow, and intermediate). Consistent with previous findings, species from fast-flowing environments exhibited lower hypoxia tolerance than those from slow-moving waters, further supporting the idea that hypoxia tolerance is more strongly influenced by habitat than by phylogenetic lineage [ 68 ]. Beyond cyprinids, hypoxia tolerance has also been studied in Anoplopoma fimbria , a species gaining interest in aquaculture. A study comparing juveniles and adults found that adults, which naturally inhabit deep oxygen minimum zones (~ 1500 m), exhibited significantly higher hypoxia tolerance (O₂ LOE: ~5.4% oxygen saturation) than juveniles (O₂ LOE: ~8.3% oxygen saturation) [ 110 ]. These findings underscore the complexity of hypoxia tolerance across species, highlighting the importance of considering ecological adaptations and life stage differences when developing aquaculture strategies. Selective Breeding Results Despite the growing interest in improving hypoxia tolerance through selective breeding, only one study identified in this review successfully implemented a multi-generational breeding program [ 70 ]. This initiative was launched in April 2007 by the Bream Genetics and Breeding Center (BGBC) at Shanghai Ocean University, China. The base population (F 0 ) was sourced from wild fish in Poyang Lake, and subsequent generations were selectively bred for increased hypoxia tolerance. By 2015, the F₄ generation demonstrated a significantly lower loss of equilibrium threshold (LOE crit : 0.54 mg/L at 10°C) compared to the control group, ‘Pujiang No. 1’ (LOE crit : 0.72 mg/L at 10°C), confirming that selective breeding had successfully enhanced hypoxia tolerance in this lineage [ 70 ]. More recently, in 2022, a study evaluated the ‘Pujiang No. 2’ line, which was developed in 2020 by the same institution. This new strain exhibited a 27% greater tolerance to hypoxia than its predecessor, further validating the effectiveness of selective breeding in improving this trait [ 111 ]. These findings highlight the potential of long-term breeding programs to enhance hypoxia tolerance in aquaculture species, paving the way for the development of more resilient fish strain Modern genetics and selective breeding studies of Hypoxia Tolerance Recent advances in genetics have significantly contributed to the understanding of hypoxia tolerance in farmed fish. In this systematic review, a total of 17 modern genetic studies were identified. They have employed diverse methodologies, including genetic association studies (n = 2), genome-wide association studies (GWAS) (n = 7), quantitative trait locus (QTL) mapping (n = 3), SNP screening (n = 2), Bulked Segregant Analysis coupled with RNA sequencing (BSR-Seq) (n = 1), transgenic approaches (n = 1), and whole-genome mutagenesis (n = 1). (Table 2 ). In most cases, hypoxia tolerance was assessed through loss-of-equilibrium (LOE) experiments. However, the classification criteria for hypoxia-sensitive (HS) and hypoxia-tolerant (HT) individuals varied widely among studies. For instance, some studies defined HS individuals as the first 10% to lose equilibrium and HT individuals as the last 10% [ 8 , 82 ], while others used broader thresholds, such as the first and last 35% [ 91 ], the first and last 5% [ 86 ], or the first and last 6% [ 90 ]. In certain cases, researchers selected a fixed number of individuals instead of percentages, such as the first and last 48 [ 112 ] or the first and last 14 [ 113 ] to lose equilibrium, to avoid challenges in identifying intermediate phenotypes. Despite these methodological differences, all genotypic studies reviewed identified QTLs, SNPs, potential candidate genes, and the biological pathways in which these genes might be involved (Table 3 ). These findings underscore the genetic basis of hypoxia tolerance and provide valuable insights for selective breeding programs aimed at improving resilience to low-oxygen environments in aquaculture. Table 3 Number of candidate genes proposed (n) and signaling pathways and cellular processes involved for hypoxia tolerance. Species n Some candidate genes identified Signaling pathways and Cellular Processes involved Reference Nile tilapia ( Oreochromis niloticus ) 192 HIF1αn (also known as FIH) HIF-1 signaling pathway Li et al . (2017) Giant seapearch ( Lates calcarifer ) 280 HIF1αn (also known as FIH) HIF-1 signaling pathway Yang et al . (2020) Channel catfish ( Ictalurus punctatus ) 15 lrrcl , tceb3 , mlip , fam83b , gclc , fgfr2 , plpp4 , fbxo9 , bmp5 , bag2, nf1 , lgals9 , ucp2 , gdnf , dhrs13 Mitogen-Activated Protein Kinase (MAPK) pathway, PI3K/AKT/mTOR (PAM) signaling pathway, hypoxia-mediated angiogenesis, cellular proliferation, apoptosis, survival Wang et al . (2017) ♂ Hybrid F × ♀ Channel catfish Hybrid F1: ♀ Channel catfish × ♂ Blue catfish ( I. punctatus × I. furcatus ) 125 dmbx1a , pif1 , ptger4 , artn , st3gal3a , kdm4a , ptprf , pkib, cyp1a1 , ccbe1 , sema7a , arid3a , arid3b , fam219b , p2ry1 , rap2b , klhl5 , fam114a1 , klf3 Vascular endothelial growth factor (VEGF) pathway, Mitogen-Activated Protein Kinase (MAPK) pathway, PI3K/AKT/mTOR (PAM) signaling pathway, p53-mediated apoptosis, damage checkpoint Zhong et al . (2017) Large yellow croaker ( Larimichthys crocea ) 12 mybpc1 , atp1a1 , prmt5 , psmb5 , mybpc1 , atp1a1 , egln2 , pygm , camk2d , arsj , trmt10a , aco1 HIF signaling pathway, oxidative stress, energy metabolism, ion regulation Ding et al . (2022) Large yellow croaker ( Larimichthys crocea ) 22 pds5a , smin14 , ugdh , lias , rfc1 , klf3 , tbc1d1 , pgm2 , melk , thsd4 , polq , camk2d2 , ankb , pgd , wfs1a , gpi , stim2a , slc34a2a , ho1 , nfix , ccna2 , grik4 DNA replication and repair, glucose metabolism, erythropoiesis, glucose transport, iron metabolism, ion regulation, pentose phosphate pathway Ding et al . (2023) Rainbow trout ( Oncorhynchus mykiss ) 15 ids , fmr1 , arx , lonrf3 , commd5 , map4k4 , smu1 , b4galt1 , re1 , abca1 , noa1 , igfbp7, noxo1, bcl2a, mylk3 Glycogen metabolism, glucose metabolism, cation regulation, DNA repair, HIF-1α regulation, mitochondrial metabolism, hematopoiesis, angiogenesis, oxidative response pathways, apoptosis Prchal et al . (2023) Nile tilapia ( Oreochromis niloticus ) 2 gpr132 , abcg4 Lactate sensing and signaling, oxidative stress protection Li et al . (2017) Pompano ( Trachinotus ovatus ) 16 lonrf3 , commd5 , fam199x , gpr137 , pld7 , syvn1 , smad5 , mdga1 , gabra4 , ap1ar , cfap100 , pold1 , zgc:55558 , trit1 , kif18a , NEK3 glycolysis, DNA repair, acid balance, apoptosis San et al . (2021) Golden pompano ( Trachinotus blochii ) 1116 PTGS2, CYLD, Ifih1 Anaerobic metabolism, stress response, immune response, waste discharge, cell death Liu et al . (2021) Pelteobagrus vachelli 1 sema7a Immune processes Zhang et al . (2020) Silver sillago ( Sillago sihama ) 7 cyp20a1 , mgst3b , kcnh2 , cluh , adk , xdh , and slc19a2 Xenobiotic biodegradation, defense against oxidative stress, membrane potential equilibrium, mitochondrial integrity, ribose metabolism, nucleotide metabolism, ion transport and caption Ye et al . (2024) Blunt snout bream ( Megalobrama amblycephala ) 1 egln2 HIF-1 signaling pathway Wang et al . (2020) Blunt snout bream ( Megalobrama amblycephala ) 1 hif2αb HIF-1 signaling pathway Zhao et al . (2023) Blunt snout bream ( Megalobrama amblycephala ) 4 Epo X1 , VEGFR1 , HO-1a , LPAR6 HIF-1 signaling pathway, Vascular endothelial growth factor (VEGF) pathway, Forkhead box O (FOXO) signaling pathway, Janus kinases (JAKs), signal transducer and activator of transcription proteins (STATs), Mitogen-Activated Protein Kinase (MAPK) Pathway, PI3K/AKT/mTOR (PAM) signaling pathway Su et al . (2022) Analysis of association between single genes and hypoxia tolerance Two studies identified in our search explored the genetic basis of hypoxia tolerance by analyzing associations between single nucleotide polymorphisms (SNPs) in key genes and hypoxia-related traits. Li et al. (2017) identified a significant association between a SNP (8892953 G > A), located in the fourth intron of the hypoxia-inducible factor inhibitor gene ( HIF1αn , also known as FIH1 ), and hypoxia tolerance in Nile tilapia ( Oreochromis niloticus ) [ 114 ]. Out of 248 fish phenotyped for time to loss of equilibrium (t LOE ), only the 192 individuals with extreme phenotypes (most sensitive and most tolerant) were genotyped via Sanger sequencing to avoid challenges in categorizing intermediate phenotypes. The SNP showed a significant genotype-phenotype association ( F -test, P < 0.01), with further confirmation using PLINK analysis ( T = -1.856, P = 0.07). Fish with the A/A genotype (n = 77) exhibited the longest t LOE under hypoxic stress (15,140 seconds), followed by A/G heterozygotes (11,655 seconds, n = 87) and G/G homozygotes (11,495 seconds, n = 17) [ 114 ]. Similarly, a study on giant seaperch ( Lates calcarifer ) identified three SNPs in the third and fourth introns of HIF1αn , with SNP 9331841 (C/T) in the fourth intron being significantly associated with hypoxia tolerance [ 115 ]. Sanger sequencing of this region in 140 hypoxia-dead and 140 surviving fish revealed a significant difference in allele frequencies ( P < 0.001). In hypoxia-dead fish, the T allele frequency was 91.19%, compared to 89.79% in tolerant fish, whereas the C allele frequency was 8.91% in hypoxia-dead fish and 18.21% in tolerant fish. The other two SNPs (9331841 and 9331890) showed no significant association with hypoxia tolerance. Haplotype analysis of the three SNPs identified six haplotypes and nine genotype combinations. Notably, the GGT/GAT genotype was the most prevalent among hypoxia-tolerant fish. Based on these findings, the authors suggest that SNP 9332241 (C/T) and the GGT/GAT genotype could serve as markers for genetic improvement programs in giant seaperch [ 115 ]. GWAS and QTL Mapping Genome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping are powerful tools for identifying genetic variants associated with hypoxia tolerance. These approaches have been applied in multiple fish species, revealing a complex genetic architecture underlying this trait. A GWAS study on six strains of channel catfish ( Ictalurus punctatus ) employed both “within-strain” and “between-strain” approaches to identify significant QTLs and SNPs associated with hypoxia tolerance [ 91 ]. Several of these SNPs were found in haplotypic blocks, suggesting that hypoxia tolerance in this species may be linked to the co-segregation of genomic segments [ 91 ]. In the within-strain analysis, the Kansas strain showed 26 significant SNPs, while the Kmix and Thompson strains exhibited four and one significant SNP, respectively. The most significant SNPs in each strain accounted for high percentages of phenotypic variation (25.32%, 23.04%, and 32.04%, respectively). In the remaining three strains (103KS, Marion, and MarionS), no SNPs were significantly associated with hypoxia tolerance, although some were suggestively associated. In the across-strain analysis, only one significant SNP was detected, which explained 5.71% of phenotypic variance. According to the authors, these findings emphasize the complex genetic architecture of hypoxia tolerance. Since the SNPs analyzed between strains are not necessarily the same as those analyzed within individual strains, it appears that each strain has accumulated its own set of mutations, leading to strong genetic differentiation, as previously reflected in other phenotypic studies [ 99 ]. Given that the SNPs identified in the strain-specific analyses appeared more important and numerous than those detected in the combined analysis, the authors suggest that strain-by-strain GWAS analysis is the preferred approach for breeding purposes [ 91 ]. Another GWAS study examining hypoxia tolerance in the hybrid of channel catfish × blue catfish [ 66 ] detected significant SNPs; however, these SNPs differed from those identified in the six channel catfish strains [ 91 ]. The authors attributed this discrepancy to the complex genetic architecture of the trait, the contribution to phenotypic variation, and the influence of sample size and the number of families used in the analysis [ 66 ]. A recent genomic selection study in large yellow croaker, phenotyped 753 individuals using time survival (hours) and survival status (binary trait: hypoxia-sensitive HS, hypoxia-tolerant HT), and genotyped then using 38,472 high-quality SNPs [ 90 ]. A great phenotype variation was detected in this population (time survival: 20.13–38.6 h) and 200 individuals HS and 200 individuals HT. This study does not provide information concerning genome-wide significant SNPs or QTLs, but provides heritability estimates for both hypoxia-tolerance related traits (h 2 survival status = 0.65, h 2 survival time = 0.62) [ 90 ]. In rainbow trout, a GWAS study on hypoxia tolerance identified three significant QTLs: two on chromosome 31 (Omy31) and one on chromosome 20 (Omy20) [ 88 ]. Additionally, two putative QTLs with suggestively associated SNPs were found, one located on chromosome 15 (Omy15) and the other on chromosome 28 (Omy28), each with a single SNP. The most significant SNPs from each QTL explained between 0.17% and 0.78% of the genetic variance. The authors note that, unlike other species where the most significant (“peak”) SNPs explained a large percentage of phenotypic variance (see Table 2 ), in rainbow trout these SNPs had minimal effects, reinforcing the idea that hypoxia tolerance in this species has a highly polygenic nature, governed by multiple loci with small individual effects [ 88 ]. An intriguing hypothesis proposed by the authors is that one of the QTLs on chromosome 31 (Omy31_1) could be a “supergene,” given its high linkage disequilibrium (r 2 = 0.45). This term refers to clusters of neighboring genes that segregate together and contribute to a complex trait [ 116 ]. In large yellow croaker ( Larimichthys crocea ), a study used de novo SNP genotyping by ddRAD-Seq to analyze hypoxia tolerance through GWAS [ 8 ]. This study, which focused on two traits related to hypoxia tolerance (survival time and binary trait), identified two significant SNPs for survival time and four for the binary trait. Another study on the same species, combining GWAS and transcriptomic analysis, detected suggestively significant SNPs and 22 candidate genes identified by both approaches [ 86 ]. In Nile tilapia, a 2017 study using ddRAD-Seq for genotyping identified four highly significant QTLs related to hypoxia tolerance and two candidate genes [ 112 ]. In Golden pompano ( T. ovatus ), a whole-genome sequencing study conducted to genotype and perform a GWAS for hypoxia tolerance [ 8 ] did not detect significant SNPs, which the authors attributed to the small sample size (n = 100) and the high number of quality SNPs used in the analysis. However, four suggestively significant SNPs were identified. In Pelteobagrus vachelli , ddRAD-Seq was used to develop a high-resolution genetic linkage map, which was then used to analyze three traits of interest: growth, sex determination, and hypoxia tolerance [ 117 ]. The authors identified a single significant QTL, and a candidate gene associated with hypoxia tolerance (Table 3 ). The potential candidate genes identified in the reviewed studies are often downstream effectors of the HIF pathway or are involved in signaling pathways such as VEGF and mTOR, which are critical for angiogenesis and energy conservation, respectively. Genes related to erythropoiesis, ion regulation, glucose metabolism, DNA repair, and iron metabolism - key processes in the hypoxia response - were also identified (Table 3 ). Some candidate genes, including klf3 , egln2 , lonrf3 , commd5 , gpr132 , and gpr137 may be conserved across species. A recent study investigated the genetic basis of hypoxia tolerance in silver sillago ( Sillago sihama ) using QTL mapping [ 118 ], in which 162 individuals were phenotyped based on the time from the start of the experiment to the onset of gasping (average: 398.73 ± 94.68 min). Six QTLs associated with hypoxia tolerance were identified across five linkage groups (chromosomes: LG8, LG12, LG15, LG20, and LG23). Of the 567 genes detected, only seven were considered potential candidates after GO and KEGG enrichment analyses: cyp20a1 , mgst3b , kcnh2 , cluh , adk , xdh , and slc19a2 . The mgst3b gene, part of the glutathione-S-transferase family, was highlighted as particularly important based on prior research. A SNP association analysis of the mgst3b gene revealed five intronic SNPs (g.583 T > C, g.611 A > G, g.629 T > A, g.633 T > A, g.937 A > G), with heterozygous genotypes of four SNPs showing higher hypoxia tolerance compared to homozygous genotypes. Haplotype analysis indicated that the TATT haplotype was predominant. The authors suggest that these markers in mgst3b gene could serve as molecular markers for selective breeding in silver sillago [ 118 ]. In a F5 strain of a genetically improved strain for hypoxia tolerance of Megalobrama amblycephala , a study [ 85 ] based on a previous transcriptomic analysis [ 119 ], investigated two SNPs located at positions 397 and 715 of the egln2 gene’s cDNA. These SNPs produced haplotypes with distinct phenotypes. Diplotype II (T 397 T 397 T 715 T 715 ) exhibited higher hypoxia tolerance compared to other diplotype combinations. This was reflected in its increased erythrocyte and hemoglobin production under hypoxic conditions, higher catalase and superoxide dismutase enzyme activity, a lower LOE crit , and reduced need for increased gill lamellar surface area [ 85 ]. The authors suggested that haplotype II (T 397 T 715 ) could serve as a marker for selecting hypoxia-tolerant individuals. Similarly, in the same species, another study explored two SNPs located at positions 203 and 752 of the hif2αb gene cDNA, which is involved in regulating the hypoxia response [ 120 ]. The study found that haplotype II (A 203 A 752 ), forming diplotype II (A 203 A 203 A 752 A 752 ), conferred greater hypoxia resistance than diplotypes I and III, as evidenced by a lower LOE crit , higher erythrocyte count, and elevated catalase and superoxide dismutase activity. The authors proposed this haplotype II as another marker for hypoxia tolerance selection [ 120 ]. Bulked Segregant Analysis - RNA Seq (BSR-Seq) A technique known as BSR-Seq analysis, which combines RNA-Seq with Bulked Segregant Analysis (BSA) to correlate global gene expression patterns with SNPs [ 113 ], was applied in golden pompano ( Trachinotus blochii ). Authors found that hypoxia tolerance-related SNPs in this fish are determined by multiple linkage groups, mainly the linkage groups (LG) 18 and 22. Numerous candidate genes were proposed (768 and 348, for brain and liver respectively), which are involved in anaerobic energy metabolism, stress response, immune response, waste discharge, and cell death. These findings highlight the utility of BSR-Seq in identifying genetic variants and molecular mechanisms underlying hypoxia tolerance, providing potential markers for genetic selection in aquaculture. Gene editing and hypoxia tolerance mutant lines Precise genomic modification using gene editing techniques and others mutagenesis techniques presents new opportunities for developing hypoxia tolerance fish (Fig. 2C). For example, gene editing has been applied to blunt snout bream ( Megalobrama amblycephala ) to create knockout mutants of the erythropoietin (EPO) gene [ 121 ]. EPO is a glycoprotein hormone that plays a key role in regulating erythropoiesis and is a classic hypoxia-responsive gene in the HIF-1 pathway. Interestingly, EPO −/− mutants exhibited reduced red blood cell counts, lower hemoglobin levels, and increased oxygen tension thresholds under hypoxia, highlighting the crucial role of EPO in hypoxia tolerance in this species [ 121 ]. Also, there is evidence that it is feasible to develop hypoxia-tolerant fish using techniques such as Atmospheric and Room Temperature Plasma (ARTP) mutagenesis. For example, in blunt snout bream ( Megalobrama amblycephala ), atmospheric and room temperature plasma (ARTP) mutagenesis was used to generate complete genomic mutants [ 122 ]. By applying ARTP to semen from a gynogenetic male, researchers produced 2,026 mutant offspring, of which 384 showed improved critical oxygen levels (LOE crit : 0.45 mg/L) compared to the control group (LOE crit : 0.86 mg/L) after three months of culture. Genome resequencing revealed 3,651 nonsynonymous mutations across 1,223 genes in ARTP mutants, with four genes ( Epo X1 , VEGFR1 , HO-1a , LPAR6 ) showing differential expression under hypoxia. These four genes are involved in key pathways such as HIF-1, VEGF, FoxO, JAK-STAT, MAPK, mTOR, and PI3K-Akt [ 122 ]. Thus, the authors propose using these ARTP-induced mutations as potential markers for selective breeding to enhance hypoxia tolerance in blunt snout bream [ 122 ]. CONCLUSIONS Hypoxia tolerance, broadly, appears to be an inherited polygenic trait characterized by a complex genetic architecture, in which variation is governed by multiple polymorphisms distributed throughout the genome, each of which exerts small to moderate effects. The wide phenotypic variability observed in hypoxia tolerance among farmed fish is largely attributed to the high genetic diversity within these populations. These genetic variations, mainly in the form of SNPs, arise independently in different populations, strains, and species over time. Furthermore, laboratory-based genetic modifications, such as gynogenesis, transgenesis, and potentially gene editing (Fig. 2C), offer promising avenues to develop desired phenotypic traits. The identification of species-specific QTLs, SNPs, and candidate genes is essential, as each species possesses distinct gene regulatory mechanisms governing hypoxia responses. Integrating GWAS with transcriptome analyses enhances detection power, while the use of large sample sizes and medium-to-high density SNP microarrays significantly increases the likelihood of identifying statistically significant markers and candidate genes. These advancements will facilitate the development of genetically improved fish strains with enhanced resilience to hypoxic stress. Finally, the selection of an optimal breeding strategy—whether genomic selection (GS) or marker-assisted selection (MAS)—depends on the proportion of phenotypic variance explained by significant SNPs. In species such as rainbow trout and yellow croaker, where hypoxia tolerance is highly polygenic and SNPs exhibit small individual effects, genomic selection is the preferred approach. Conversely, in species like channel catfish and golden pompano, where specific SNPs explain a higher proportion of phenotypic variance, marker-assisted selection may be a viable strategy. These findings underscore the necessity of understanding the genetic architecture of hypoxia tolerance in each species to implement the most effective selective breeding programs. Abbreviations ddRAD-Seq: Double-digest restriction-site associated DNA sequencing LOE: Loss of Equilibrium ILOS: incipient lethal oxygen saturation PRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses GWAS: Genome-wide Association Study HIF: Hypoxia-Inducible Factor HRE: Hypoxia-Response Element QTL: Quantitative Trait loci SNP: Single-nucleotide polymorphism t LOE : time to LOE (Loss of equilibrium) ASR: Aquatic Surface Respiration VEGF: Vascular Endothelial Growth Factor MAPK: Mitogen-Activated Protein Kinase PI3K/AKT/mTOR: phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR) Declarations Availability of data and materials Not applicable. Ethics declarations Not applicable Ethics approval and consent to participate. Not applicable Consent for publication Not applicable Competing interests The authors declare that they have no competing interests. Funding S.B. was supported by the Maintenance Scholarship for Foreigners - PhD Program – Pontificia Universidad Católica de Valparaíso. N.S. was supported by ANID-Chile FONDECYT POSTDOCTORADO No. 3240697. J.G-M was supported by ANID-Chile Fondecyt regular Nº 1231206 and PUCV Interdisciplinary Associative Research (AIE)-University/Company N°39.365/2023. Authors’ contributions JG-M and SB designed and wrote the manuscript. N.S. and R.P. critically edited and finalized the manuscript. The authors read and approved the final manuscript. Acknowledgements Not applicable. References Díaz R, Rabalais NN, Breitburg DL. Agriculture’s impact on aquaculture: Hypoxia and eutrophication in marine waters. OECD; 2012. Diaz RJ, Breitburg DL. The hypoxic environment. In: Richards JG, Farrell AP, Brauner CJ, editors. Fish Physiology. Elsevier; 2009. p. 1–23. IPCC. Climate Change: the scientific basis. 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Front Genet. 2021;12:811685. https://doi.org/10.3389/fgene.2021.811685 Li HL, Gu XH, Li BJ, Chen X, Lin HR, Xia JH. Characterization and functional analysis of hypoxia-inducible factor HIF1α and its inhibitor HIF1αn in tilapia. PLoS One. 2017;12:e0173478. https://doi.org/10.1371/journal.pone.0173478 Yang Z, Wang L, Wong SM, Yue GH. The HIF1αn gene and its association with hypoxia tolerance in the Asian seabass. Gene. 2020;731:144341. https://doi.org/10.1016/j.gene.2020.144341 Schwander T, Libbrecht R, Keller L. Supergenes and complex phenotypes. Curr Biol. 2014;24:R288-94. https://doi.org/10.1016/j.cub.2014.01.056 Zhang G, Li J, Zhang J, Liang X, Wang T, Yin S. A high-density SNP-based genetic map and several economic traits-related loci in Pelteobagrus vachelli . BMC Genomics. 2020;21:700. https://doi.org/10.1186/s12864-020-07115-7 Ye M, Kong L, Jian Z, Qiu Z, Lin X, Zhang Y, et al. Genetic insights into hypoxia tolerance in silver sillago ( Sillago sihama ) through QTL mapping and SNP association analysis. Aquaculture. 2024;592:741174. https://doi.org/10.1016/j.aquaculture.2024.741174 Li F-G, Chen J, Jiang X-Y, Zou S-M. Transcriptome analysis of blunt snout bream ( Megalobrama amblycephala ) reveals putative differential expression genes related to growth and hypoxia. PLoS One. 2015;10:e0142801. https://doi.org/10.1371/journal.pone.0142801 Zhao S-S, Su X-L, Yang H-Q, Zheng G-D, Zou S-M. Functional exploration of SNP mutations in HIF2αb gene correlated with hypoxia tolerance in blunt snout bream ( Megalobrama amblycephala ). Fish Physiol Biochem. 2023;49:239–51. https://doi.org/10.1007/s10695-023-01173-w Su X-L, Zheng G-D, Zou S-M. Knockout of EPO gene in blunt snout bream ( Megalobrama amblycephala ) by CRISPR/Cas9 reveals its roles in hypoxia-tolerance. Aquaculture. 2024;592:741227. https://doi.org/10.1016/j.aquaculture.2024.741227 Su X-L, Zhao S-S, Xu W-J, Shuang L, Zheng G-D, Zou S-M. Efficiently whole-genomic mutagenesis approach by ARTP in blunt snout bream ( Megalobrama amblycephala ). Aquaculture. 2022;555:738241. https://doi.org/10.1016/j.aquaculture.2022.738241 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6010748","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":414481453,"identity":"5201e14d-a79c-48c7-97da-964ee784fc17","order_by":0,"name":"Sergio P. Barahona","email":"","orcid":"https://orcid.org/0000-0002-0136-7205","institution":"Pontificia Universidad Católica de Valparaíso","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"P.","lastName":"Barahona","suffix":""},{"id":414481454,"identity":"3feb3004-dd7a-4b9c-aad7-9f5b0036e479","order_by":1,"name":"Nicolas Salinas-Parra","email":"","orcid":"https://orcid.org/0000-0001-5278-5928","institution":"Pontificia Universidad Católica de Valparaíso","correspondingAuthor":false,"prefix":"","firstName":"Nicolas","middleName":"","lastName":"Salinas-Parra","suffix":""},{"id":414481455,"identity":"82f1309d-f309-4b51-9fd8-97f32086e616","order_by":2,"name":"Rodrigo Pulgar","email":"","orcid":"https://orcid.org/0000-0003-3394-406X","institution":"Universidad de Chile","correspondingAuthor":false,"prefix":"","firstName":"Rodrigo","middleName":"","lastName":"Pulgar","suffix":""},{"id":414481456,"identity":"55d0abd3-b761-4a51-be59-11decb62f2ed","order_by":3,"name":"José Gallardo-Matus","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACxgYILcMHJCQYKqDCD4jQwsMG1nIGKpxAhG0QLYxtRGhhbm9/+IDhlx0PG/vZhzc+zruT2D8jgfEDPi2MPWeMDRj7knnYeNKNLWdue5Y440YCswReLTNy2CQYe5iBDktjk+bddjhxA88BNrwOY5yR/vwHY089Dxv/M6CWOURpSTBjYPhxmIdNAmRLA1ALewMBLUC/SCQ2HAdqecZsOePYM+MZxxub8frFEBhiHz78qZbj509jvPGh5o5sfzPzwQ8f8GlpABKJbXD+AQZE9OIA8mDyD4qWUTAKRsEoGAWoAACPV0s8r3/MCgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-7717-8726","institution":"Pontificia Universidad Católica de Valparaíso","correspondingAuthor":true,"prefix":"","firstName":"José","middleName":"","lastName":"Gallardo-Matus","suffix":""}],"badges":[],"createdAt":"2025-02-12 00:39:03","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6010748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6010748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76191728,"identity":"1f20009c-a58b-4042-9c5e-6c1574262568","added_by":"auto","created_at":"2025-02-13 09:45:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":5939544,"visible":true,"origin":"","legend":"\u003cp\u003eEnvironmental and production factors associated with dissolved oxygen concentration in both (A) tank and (B) sea cage culture conditions. Hypoxia-generating and oxygen-generating factors are indicated in red and green backgrounds, respectively.\u003c/p\u003e","description":"","filename":"FIGURE1.png","url":"https://assets-eu.researchsquare.com/files/rs-6010748/v1/ad071eca674f103fd879f707.png"},{"id":76191733,"identity":"8f84eef8-87f0-4422-9485-37eb4addbea5","added_by":"auto","created_at":"2025-02-13 09:45:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":793867,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Association between phenotypes and SNP genotypes, (B), selective breeding targeting hypoxia tolerance, (C) gene editing targeting hypoxia tolerance.\u003c/p\u003e","description":"","filename":"FIGURE2.png","url":"https://assets-eu.researchsquare.com/files/rs-6010748/v1/7cbccb042430a5026f6ef26d.png"},{"id":76191732,"identity":"257457a2-258c-4b25-abc2-0d836ba9b1dc","added_by":"auto","created_at":"2025-02-13 09:45:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":139775,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram showing the process of filtering and selecting articles for this systematic review.\u003c/p\u003e","description":"","filename":"FIGURE3.png","url":"https://assets-eu.researchsquare.com/files/rs-6010748/v1/582d2a88bac7f590fadd483f.png"},{"id":76192553,"identity":"f2751576-42c6-492c-9168-efacd386f1d0","added_by":"auto","created_at":"2025-02-13 09:53:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8164992,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6010748/v1/bb809515-e965-4cb4-b355-b0a25a1e9f30.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenetic variation of hypoxia tolerance in farmed fish: a systematic review for selective breeding purposes\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHypoxia is a complex environmental stressor defined by reduced levels of dissolved oxygen in water [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Rising temperatures associated with climate change are expected to increase frequency and intensity of hypoxic events worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a result, many coastal regions are experiencing an expansion of hypoxic zones [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Since dissolved oxygen is the primary limiting factor of the aerobic metabolism of aquatic organisms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], its depletion critically reduces the yield, quality, and survival of farmed fish [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], thereby threatening the sustainability of the aquaculture industry.\u003c/p\u003e \u003cp\u003eHypoxia arises from multiple factors within aquaculture production systems, prompting the implementation of mitigation measures at the cage or culture pond level (Fig.\u0026nbsp;1). Environmental drivers include climate change [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], thermal stratification due to diurnal and seasonal temperature fluctuations [\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], upwelling of oxygen-depleted deep waters [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], coastal eutrophication [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and microbial decomposition of harmful algal blooms [\u003cspan additionalcitationids=\"CR20 CR21 CR22\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Additionally, gill damage caused by harmful algal blooms [\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], jellyfish [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] or amoebae [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] can further impair the ability of fish to tolerate hypoxia. Production-related factors contributing to hypoxia events include high stocking densities in ponds or culture cages [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], cage size, the accumulation of organic matter from excreta and uneaten food, biofouling that clogs cage nets and obstructs proper water flow [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], nocturnal algal respiration, system failures, and human errors [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These factors are often transient and difficult to predict, causing sharp and unexpected fluctuations in dissolved oxygen levels across time and space [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Since farmed fish, particularly in marine environments, cannot escape hypoxic conditions, those unable to tolerate low oxygen levels often experience high mortality rates. Indeed, mass mortality events triggered by hypoxia have been reported in various production systems worldwide [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], highlighting the severity of this stressor [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. To mitigate hypoxia, aquaculture facilities commonly employ aeration technologies such as nanobubble diffusion systems, water jets, paddles, air injection [\u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] or artificial upwelling [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] (Fig.\u0026nbsp;1). However, these technologies can be costly to implement and maintain on a large scale [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and remain susceptible to human error [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHypoxic events do not always lead to fish mortality. Their impact depends on the nature of hypoxia, whether it is acute (short-term and extreme) or chronic (prolonged exposure). In some cases, hypoxia induces sublethal effects, altering physiological, tissue, and molecular processes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. At the physiological level, hypoxia reduces metabolic rate, growth rate, feed conversion efficiency, and immune response [\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These adverse effects increase fish susceptibility to pathogens and reduce their ability to adapt to environmental changes, ultimately compromising survival [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. At the tissue-level, hypoxia can cause gill damage [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], while chronic exposure may lead to cell cycle arrest or apoptosis in response to extensive genetic damage [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. However, fish have evolved both physiological and metabolic mechanisms to tolerate hypoxia [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. For instance, they can enhance O₂ uptake and transport, upregulate anaerobic ATP production pathways such as glycolysis, and suppress high-ATP-demanding molecular processes such as protein synthesis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Most of these hypoxia tolerance mechanisms are mediated by hypoxia-responsive genes activated by specific pathways. The HIF-1 pathway, regulated by the heterodimeric transcription factor HIF-1α/HIF-1β, controls the expression of multiple hypoxia-sensitive genes, such as \u003cem\u003eVEGF\u003c/em\u003e, \u003cem\u003eEPO\u003c/em\u003e, \u003cem\u003eLDH\u003c/em\u003e and \u003cem\u003eGLUT\u003c/em\u003e [\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Additional signaling pathways, such as MAPK and PI3K/AKT/mTOR, also contribute to hypoxia tolerance in fish [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Since fish exhibit varying degrees of hypoxia tolerance [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e], even within the same species [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], understanding the genetic architecture of this trait could inform artificial selection strategies to enhance hypoxia resilience in aquaculture [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA simple and cost-effective method for assessing hypoxia tolerance in fish is the loss-of-equilibrium (LOE) test [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. This protocol consists of gradually reducing oxygen levels by injecting nitrogen, recording the time at which the fish loses its ability to maintain dorsoventral balance. A short time-to-LOE (t\u003csub\u003eLOE\u003c/sub\u003e) indicates a hypoxia-sensitive individual, whereas a longer t\u003csub\u003eLOE\u003c/sub\u003e denotes greater hypoxia tolerance [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Additional LOE-based metrics include LOE\u003csub\u003ecrit\u003c/sub\u003e (O\u003csub\u003e2\u003c/sub\u003e LOE), which quantifies the oxygen concentration (mg/L), saturation (%) or partial pressure (KPa or torr) at which equilibrium loss occurs [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], and LOE\u003csub\u003e50\u003c/sub\u003e, which represents the time or oxygen concentration (mg/L) at which 50% of fish in a group lose equilibrium [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Beyond LOE-based assessments, hypoxia tolerance can be evaluated using critical oxygen tension (P\u003csub\u003ecrit\u003c/sub\u003e), which reflects a fish\u0026rsquo;s ability to uptake dissolved oxygen from water [\u003cspan additionalcitationids=\"CR77 CR78\" citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], incipient lethal oxygen saturation (ILOS) [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], survival time [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], and binary survival/mortality traits [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additional physiological and molecular indicator of hypoxia tolerance include cardiac function parameters [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], gill remodeling [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e], metabolic activity [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e], enzymatic activities [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e], differential gene expression [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and transcriptomic analysis [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHeritability (h\u0026sup2;) estimates for hypoxia tolerance have been reported in several commercially important fish species, ranging from moderate in rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e, h\u0026sup2; = 0.28) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], to medium in common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e, h\u0026sup2; = 0.50) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e] and high in large yellow croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e, h\u0026sup2; = 0.61\u0026ndash;0.65) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. This reported genetic variability in hypoxia tolerance suggests an adaptive potential of fish populations facing expanding hypoxic environments. Understanding the genetic basis of this variation is key to designing artificial selection strategies that improve the resilience of farmed species (Fig.\u0026nbsp;2B).\u003c/p\u003e \u003cp\u003eAt the molecular level, genetic variation associated with hypoxia tolerance has been documented at the single nucleotide polymorphism (SNP) marker level, with specific SNPs and candidate genes identified in rainbow trout [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] and channel catfish [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. SNPs located in regulatory regions\u0026mdash;such as hypoxia-responsive elements (HREs) within promoters of HIF-target genes, as well as in introns, enhancers, UTRs, or exons\u0026mdash;can directly or indirectly influence gene transcription or expression efficiency, thereby modulating the hypoxia tolerance phenotype (Fig.\u0026nbsp;2A) [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the critical role of hypoxia tolerance in aquaculture sustainability, this systematic review consolidates and critically evaluates current knowledge on the genetic basis of this trait in farmed fish. It synthesizes advances ranging from phenotypic assessments to the identification of molecular markers, highlighting their potential applications in selective breeding programs to enhance resilience to low-oxygen environments.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis systematic review adhered to the guidelines of the PRISMA 2020 statement [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Articles indexed in databases and focusing on hypoxia tolerance in farmed fish and containing data on (i) heritability (h\u003csup\u003e2\u003c/sup\u003e), (ii) QTLs, (iii) SNPs, (iv) G\u0026times;E interaction analysis, (v) genome-wide association studies (GWAS), (vi) candidate genes, (vii) phenotypic variability among families, strains, populations, or species, or (viii) genetic correlations were included. Articles focusing on traits unrelated to hypoxia tolerance (e.g. thermal tolerance) were excluded. The following types of publications were also excluded: (i) graduate theses, editorials, letters to the editor, reviews, book chapters, conference proceedings and abstracts, (ii) non-English language manuscripts, (iii) articles without full-text availability, (iv) studies focusing exclusively on transcriptomics, gene expression, biomarkers and/or epigenetic markers, (v) studies on non-fish species or on non-farmed fish, (vi) studies that did not compare tolerance to hypoxia across groups (e.g., gynogenetic lines or strains), (vii) studies analyzing growth rate, immunity, biochemical parameters, and/or physiological responses under varying hypoxic or anoxic conditions or hypoxia-induced stress or (viii) studies examining the effects of diets or pharmaceuticals on hypoxia tolerance. A systematic search for relevant articles was conducted in the Web of Science (WoS) and Scopus databases in September 2024, using the following strategy:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"1\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(\u0026ldquo;genetic variation\u0026rdquo; or \u0026ldquo;genetic variant\u0026rdquo; or \u0026ldquo;genetic variability\u0026rdquo; or \u0026ldquo;genetic architecture\u0026rdquo; or \u0026ldquo;geographic variation\u0026rdquo; or genotype* or allele* or polymorphism or \u0026ldquo;genomic variant*\u0026rdquo; or \u0026ldquo;phenotypic plasticity\u0026rdquo; or \u0026ldquo;phenotypic variation\u0026rdquo; or strain* or inter-strain* or inter-population* or \u0026ldquo;population differences\u0026rdquo; or line* or family* or inter-family* or correlation or \u0026ldquo;genetic correlation\u0026rdquo; or GWAS or \u0026ldquo;genome-wide association study\u0026rdquo; or \u0026ldquo;genome-wide association analysis\u0026rdquo; or QTL* or \u0026ldquo;genomic prediction\u0026rdquo; or \u0026ldquo;single nucleotide polymorphism\u0026rdquo; or \u0026ldquo;candidate SNPs\u0026rdquo; or \u0026ldquo;novel SNP*\u0026rdquo; or \u0026ldquo;SNP chip\u0026rdquo; or SNP* or \u0026ldquo;SNP array\u0026rdquo; or \u0026ldquo;SNP panel*\u0026rdquo; or marker* or \u0026ldquo;genomic selection\u0026rdquo; or GS or \u0026ldquo;candidate genes\u0026rdquo; or \u0026ldquo;annotated candidate genes\u0026rdquo; or imput* or heritability* or \u0026ldquo;putative candidate genes\u0026rdquo; or \u0026ldquo;genetic improvement\u0026rdquo; or \u0026ldquo;artificial selection\u0026rdquo; or \u0026ldquo;selective breeding\u0026rdquo;)\u003c/p\u003e \u003cp\u003eAND\u003c/p\u003e \u003cp\u003e(\u0026ldquo;hypoxia tolerance\u0026rdquo; or \u0026ldquo;tolerance to hypoxia\u0026rdquo; or \u0026ldquo;oxygen stress\u0026rdquo; or \u0026ldquo;oxygen tolerance\u0026rdquo; or \u0026ldquo;oxygen variability\u0026rdquo; or \u0026ldquo;hypoxic stress\u0026rdquo; or \u0026ldquo;hypoxic conditions\u0026rdquo; or \u0026ldquo;low oxygen tolerance\u0026rdquo; or \u0026ldquo;hypoxia-resilient\u0026rdquo; or \u0026ldquo;oxygen metabolism\u0026rdquo; or \u0026ldquo;hypoxia tolerance trait*\u0026rdquo; or LOE or \u0026ldquo;loss of equilibrium\u0026rdquo;)\u003c/p\u003e \u003cp\u003eAND\u003c/p\u003e \u003cp\u003e(fish* or finfish* or \u0026ldquo;teleost fish*\u0026rdquo; farmed or \u0026ldquo;farmed fish\u0026rdquo; or aquaculture or fisher* or inland or marine or \u0026ldquo;grass carp\u0026rdquo; or \u0026ldquo;silver carp\u0026rdquo; or \u0026ldquo;Nile tilapia\u0026rdquo; or \u0026ldquo;common carp\u0026rdquo; or catla or \u0026ldquo;bighead carp\u0026rdquo; or Carassius or \u0026ldquo;striped catfish\u0026rdquo; or \u0026ldquo;roho labeo\u0026rdquo; or \u0026ldquo;clarias catfish*\u0026rdquo; or \u0026ldquo;tilapias nei\u0026rdquo; or \u0026ldquo;Wuchang bream\u0026rdquo; or \u0026ldquo;rainbow trout\u0026rdquo; or \u0026ldquo;black carp\u0026rdquo; or \u0026ldquo;largemouth black bass\u0026rdquo; or \u0026ldquo;Atlantic salmon\u0026rdquo; or \u0026ldquo;milkfish\u0026rdquo; or \u0026ldquo;mullets nei\u0026rdquo; or \u0026ldquo;githead seabream\u0026rdquo; or \u0026ldquo;large yellow croaker\u0026rdquo; or \u0026ldquo;European seabass\u0026rdquo; or \u0026ldquo;groupers nei\u0026rdquo; or \u0026ldquo;coho salmon\u0026rdquo; or \u0026ldquo;Japanese seabass\u0026rdquo; or \u0026ldquo;giant seaperch\u0026rdquo; or \u0026ldquo;red drum\u0026rdquo;)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe third block of the search strategy includes the common names of the top 15 inland and top 15 marine/coastal fish species with the highest global production according to FAO 2022\u0026rsquo;s report [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. However, all farmed fish species identified through the search strategy were included, regardless of their explicit mention in the FAO 2022\u0026rsquo;s report. The Endnote software package was used to manage retrieved references, automatically remove duplicates, and organize sources by theme. Study selection was conducted manually based on titles and abstracts, following predefined inclusion/exclusion criteria. Articles that met these criteria were subjected to a full-text review. One reviewer conducted the initial screening, and a second reviewer verified the selection. Any discrepancies in inclusion or exclusion were resolved through consensus.\u003c/p\u003e"},{"header":"RESULTS AND DISCUSSION","content":"\u003cp\u003eThis systematic review synthesizes current knowledge on genetic variation underlying hypoxia tolerance in farmed fish. A comprehensive search across two databases retrieved 963 articles. After removing 143 duplicates and excluding 780 articles that did not meet the inclusion criteria, 40 studies were selected for analysis (Fig.\u0026nbsp;3). The selected articles were categorized into two groups: (a) traditional genetics and selective breeding studies, which assessed hypoxia tolerance at the phenotypic level by comparing strains, genetic lines, families, or hybrids (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), or (b) modern genomics and biotechnology studies, which investigated hypoxia tolerance using advanced genomic approaches such as genome-wide association studies (GWAS), genomic selection or mutagenesis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In total, the review identified 26 farmed fish and three catfish hybrid lines, including blunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e), rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e), common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e), Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e), bighead carp (\u003cem\u003eAristichthys nobilis\u003c/em\u003e), grass carp (\u003cem\u003eCtenopharyngodon idellus\u003c/em\u003e), Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e), channel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e), among others. These findings highlight the growing interest in understanding the genetic basis of hypoxia tolerance and its potential applications in selective breeding for aquaculture sustainability\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of the phenotypic studies of hypoxia tolerance identified in this review (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod for measuring hypoxia tolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrait(s) evaluated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of groups compared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTypes of groups compared\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenetically identical lines with different growth rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBorey \u003cem\u003eet al\u003c/em\u003e. (2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrains with different growth rates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRoze \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncipient lethal oxygen saturation ILOS (% O\u003csub\u003e2\u003c/sub\u003e Saturation), critical oxygen level (O\u003csub\u003e2crit\u003c/sub\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhang \u003cem\u003eet al\u003c/em\u003e. (2018)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASR (mgO\u003csub\u003e2\u003c/sub\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eObirikorang \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eASR (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAkrokoh \u003cem\u003eet al\u003c/em\u003e. (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtlantic salmon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmo salar\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFamilies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAnttila \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtlantic salmon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmo salar\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival of embryos (Number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWild genetically different populations from different rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eC\u0026ocirc;te \u003cem\u003eet al\u003c/em\u003e. (2012)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiant seapearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLates calcarifer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (% air sat)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTwo populations geographically separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCollins \u003cem\u003eet al\u003c/em\u003e. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel catfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIctalurus punctatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWang \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups (control strain vs genetically improved strain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWu \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups (control strain vs genetically improved strain)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZhao \u003cem\u003eet al\u003c/em\u003e. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean O\u003csub\u003e2\u003c/sub\u003e LOE (mg/L) at fish began to lose equilibrium, Average number of fish that lost equilibrium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups (control vs hybrid)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGong \u003cem\u003eet al\u003c/em\u003e. (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQing bo / Hiina astelparrak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSpinibarbus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen \u003cem\u003eet al\u003c/em\u003e. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParabramis pekinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen \u003cem\u003eet al\u003c/em\u003e. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrass carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCtenopharyngodon idellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen \u003cem\u003eet al\u003c/em\u003e. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBig head carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAristichthys nobilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHypophthalmichthys molitrix\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyprinus carpio var Jian\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRespirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (torr)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eChen et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQing bo / Hiina astelparrak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSpinibarbus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyprinus carpio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrucian carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarassius carassius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThick-jawed bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama pellegrini\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHypophthalmichthys molitrix\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBighead carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAristichthys nobilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrass carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCtenopharyngodon idellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMylopharyngodon piceus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (KPa), P\u003csub\u003ecrit\u003c/sub\u003e (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDhillon \u003cem\u003eet al\u003c/em\u003e. (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSablefish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAnoplopoma fimbria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecrit\u003c/sub\u003e (% air sat), O\u003csub\u003e2\u003c/sub\u003e LOE (% air sat), t\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGroups (juvenile vs. adult)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLeeuwis \u003cem\u003eet al\u003c/em\u003e. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMountain carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSchizothorax prenanti\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSharp-jaw barbell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOnychostoma sima\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQing bo / Hiina astelparrak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSpinibarbus sinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrucian carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarassius carassius\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyprinus carpio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBighead carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAristichthys nobilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHypophthalmichthys molitrix\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eParabramis pekinensis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrass carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCtenopharyngodon idellus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMylopharyngodon piceus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChinese hook\u003c/p\u003e \u003cp\u003esnout carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZacco platypus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Respirometry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003e50\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P\u003csub\u003ecrit\u003c/sub\u003e (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), ASR50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTriploid and diploid strains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eScott et al. (2015)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrook trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalvelinus fontinalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min), PO\u003csub\u003e2\u003c/sub\u003e LOE (KPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTriploid and diploid strains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJensen \u0026amp; Benfey (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e LOE (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGynogenetic lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGong et al. (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e LOE (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGynogenetic lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFu et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon carp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCyprinus carpio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min), Survival status (alive/dead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFamilies (Growth hormone (GH)-transgenic common carp v/s Control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDunham et al. (2002)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test, Mortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitial loss of equilibrium\u003c/p\u003e \u003cp\u003e(ILOE), final loss of equilibrium (FLOE), and mortality. All traits measured as (mgO\u003csub\u003e2\u003c/sub\u003eL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParasite resistant strains\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFetherman et al. (2016)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid catfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePelteobagrus fulvidraco \u0026times; Leiocassis longirostris\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFloating head 50 (mgO\u003csub\u003e2\u003c/sub\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHybrid types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWang et al. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid catfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIctalurus punctatus \u0026times; Ictalurus furcatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrains from different rivers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDunham et al. (2014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral information concerning hypoxia tolerance from genetic articles identified in this review (n\u0026thinsp;=\u0026thinsp;17).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMethod for measuring hypoxia tolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrait(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eType of genetics study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenotyping method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of SNPs evaluated\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNumber of genotyped fish\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNumber of significant (a) or suggestive (b) marker associated with hypoxia tolerance trait(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePhenotypic variance explained (PVE) of the most significant marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHeritability (h\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE (sec)\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP association study (Wald test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSanger sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLi \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiant seapearch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLates calcarifer\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival status (alive/dead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNP association study (Chi-squared test)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSanger sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYang \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel catfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eIctalurus punctatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250K SNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAcross strains: 1\u003csup\u003ea\u003c/sup\u003e and 16\u003csup\u003eb ;\u003c/sup\u003e within strains: Kansas (26\u003csup\u003ea\u003c/sup\u003e), Kmix (4\u003csup\u003ea\u003c/sup\u003e), Thompson (1\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAcross strains: 5.71%, Within strains: Kansas (25.32%), Kmix (23.04%), Thompson (32.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWang \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatfish hybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eI. punctatus \u0026times; I. furcatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e250K SNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e208,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003csup\u003ea\u003c/sup\u003e y 31\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eZhong \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge yellow croaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLarimichthys crocea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min), Survival status (alive/dead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eddRAD-Seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e54,224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSurvival time: 2\u003csup\u003ea*\u003c/sup\u003e, Survival status: 4\u003csup\u003ea*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSurvival time: 18.04%, Survival status: 8.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSurvival time: 0.65, Binary trait: 0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDing \u003cem\u003eet al\u003c/em\u003e. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge yellow croaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLarimichthys crocea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min), Survival status (alive/dead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55K SNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120,815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSurvival time: 5\u003csup\u003eb\u003c/sup\u003e, Survival status: 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSurvival time: 4.98%, Survival status: 5.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDing \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57K SNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e418,925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7\u003csup\u003ea\u003c/sup\u003e y 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePrchal \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOreochromis niloticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQTL mapping analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eddRAD-Seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLi \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePompano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrachinotus ovatus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenome-wide Association Study (GWAS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWhole-genome resequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e706,991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSan \u003cem\u003eet al\u003c/em\u003e. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatfish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePelteobagrus vachelli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQTL mapping analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eddRAD-Seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 QTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eZhang \u003cem\u003eet al\u003c/em\u003e. (2020)]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver sillago\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSillago sihama\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTime until gasping (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQTL mapping analysis and SNP association study (Linear regression)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenotyping-by-sequencing (GBS) method and Sanger Sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGBS: 4,735 SNP, Sanger: 13 SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6 QTL, 5 SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eQTL: 7.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eYe \u003cem\u003eet al\u003c/em\u003e. (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHaplotype analysis (Chi-squared tes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSanger Sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 haplotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eWang \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssociation analysis of diplotypes (Anova)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSanger Sequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1 haplotype\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eZhao \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGolden pompano\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTrachinotus blochii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e (min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBulked Segregant RNA-Seq (BSR-seq)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSNP calling from RNA-seq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e821,398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16 QTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eLiu \u003cem\u003eet al\u003c/em\u003e. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypoxia challenge test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLOE\u003csub\u003ecrit\u003c/sub\u003e (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWhole-genomic mutagenesis to increase hypoxia tolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWhole-genome resequencing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,195,434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSu \u003cem\u003eet al\u003c/em\u003e. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge yellow croaker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLarimichthys crocea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMortality hypoxia test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSurvival time (min), Survival status (alive/dead)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenomic selection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 K SNP array\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38, 472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNot provided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSurvival time: 0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05, Survival status: 0.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eDing \u003cem\u003eet al\u003c/em\u003e. (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eTraditional genetics and selective breeding studies of Hypoxia Tolerance\u003c/h3\u003e\n\u003cp\u003eSince accurate and cost-effective measurement of hypoxia tolerance is crucial for implementing selective breeding programs in aquaculture, it is relevant to first discuss the detected methods. In the traditional genetics and selective breeding studies retrieved by our search strategy (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), hypoxia tolerance was primarily assessed using three key traits: (1) time to loss of equilibrium (t\u003csub\u003eLOE\u003c/sub\u003e) measured by hypoxia challenge test, (2) survival time or survival status (alive/dead) determined by mortality hypoxia test, and (3) critical oxygen partial pressure (P\u003csub\u003ecrit\u003c/sub\u003e) measured via respirometry. Less commonly used indicators included aquatic surface respiration (ASR) and aerial emergence, sometimes referred to as \u0026ldquo;floating head\u0026rdquo;. ASR describes the selective uptake of oxygen-rich water from the surface layer, while aerial emergence involves leaving the water to breathe air directly. The loss-of-equilibrium (LOE) test is widely used due to its simplicity, speed, and minimal equipment requirements (e.g., nitrogen tanks or standard farm equipment). This method is also scalable for high-throughput screening in a short time [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. However, observer bias can influence results, requiring standardized training for personnel conducting the assessments. In contrast, the survival time or survival status (alive/dead) measured by the mortality hypoxia test provides an unambiguous outcome and enables the selection of individuals with extreme hypoxia tolerance, which is relevant to real-world survival scenarios. However, ethical concerns arise due to the use of lethal hypoxia levels, particularly in large-scale trials involving hundreds or thousands of fish. A common limitation of the aforementioned methods is that they assess acute hypoxia responses rather than long-term adaptation to low oxygen environments. By contrast, the critical oxygen partial pressure (P\u003csub\u003ecrit\u003c/sub\u003e) measured via respirometry provides detailed physiological insight into oxygen uptake efficiency under hypoxic conditions. Additionally, it is non-lethal, allowing for repeated measurements on the same individuals [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. However, despite these advantages, respirometry is complex, expensive, and time-consuming, restricting its feasibility in large-scale breeding programs.\u003c/p\u003e \u003cp\u003eThe efficient implementation of selection breeding programs for hypoxia tolerance requires understanding its genetic correlations with other economically important traits, such as growth and pathogen resistance. These relationships can be either favorable or antagonistic, influencing the feasibility of breeding strategies. For instance, the genetically improved farmed Tilapia (GIFT) strain, known for its fast growth, was compared with the native, slower-growing Akosombo strain to evaluate differences in hypoxia tolerance [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. The GIFT strain exhibited a significantly higher oxygen consumption rate (MO₂), engaged in aquatic surface respiration (ASR) at higher oxygen levels, and showed an increased ventilatory frequency (fᵥ) compared to the Akosombo strain. Based on these physiological responses, the authors concluded that the GIFT strain had lower hypoxia tolerance [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. However, this negative correlation between growth rate and hypoxia tolerance is not always observed. In a study on common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e), researchers compared survival under low dissolved oxygen between a normal strain and a transgenic F₂ strain carrying a trout growth hormone (rtGH) transgene [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Interestingly, the transgenic carp exhibited a longer mean survival time under hypoxic conditions, suggesting a beneficial pleiotropic effect of the inserted transgene on hypoxia tolerance. Similarly, a study in rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) compared two strains with differing growth rates for their time to loss of equilibrium (t\u003csub\u003eLOE\u003c/sub\u003e) under hypoxia [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Significant inter-individual variation was observed, but overall, the fast-growing strain demonstrated greater hypoxia tolerance (t\u003csub\u003eLOE\u003c/sub\u003e: 180\u0026ndash;410 min) compared to the slow-growing strain (t\u003csub\u003eLOE\u003c/sub\u003e: 130\u0026ndash;280 min). In contrast, the relationship between hypoxia tolerance and pathogen resistance remains poorly studied [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. One of the few available studies, conducted by Fetherman et al., compared hypoxia tolerance between a strain resistant to \u003cem\u003eMyxobolus cerebralis\u003c/em\u003e, the causative agent of whirling disease, and a non-resistant strain. No significant differences in hypoxia tolerance were detected between the two groups [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. This highlights the need for further research to determine whether genetic selection for hypoxia tolerance affects disease resistance in aquaculture species.\u003c/p\u003e \u003cp\u003eSeveral studies identified in our search have highlighted the importance of evaluating hypoxia tolerance across different strains and populations. This is expected, as understanding inter-strain and inter-population variation is essential for optimizing breeding programs that enhance resilience to low-oxygen environments. For instance, significant differences of hypoxia tolerance have been documented among strains of rainbow trout [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Similarly, a study on Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e) assessed embryo survival under hypoxia across four genetically distinct populations from different rivers in France, revealing significant differences among them [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. In channel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e), a broad range of hypoxia tolerance has also been reported, with time to loss of equilibrium (t\u003csub\u003eLOE\u003c/sub\u003e) ranging from 8 to 104 minutes across six different strains [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. These findings suggest a strong genetic component underlying hypoxia tolerance, potentially influenced by local adaptation or historical selection pressures. However, not all species exhibit clear interpopulation differences. A study on giant seaperch (\u003cem\u003eLates calcarifer\u003c/em\u003e), a farmed catadromous species, compared hypoxia tolerance between two geographically distinct populations: one from a tropical river with severe oxygen fluctuations and another from a subtropical river. No significant differences in critical oxygen partial pressure (P\u003csub\u003ecrit\u003c/sub\u003e) were found between populations [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. The authors suggested that in this species, physiological plasticity may play a more dominant role than local genetic adaptation in determining hypoxia tolerance [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Overall, these findings highlight the significant genetic contribution to hypoxia tolerance and emphasize the importance of strain selection in breeding programs aimed at improving fish survival and performance under low-oxygen conditions.\u003c/p\u003e \u003cp\u003eOther studies detected by our search highlighted the potential of triploid and gynogenetic strains for improving hypoxia tolerance in fish. For example, a study in rainbow trout found that triploid (3n) strains exhibited lower hypoxia tolerance than diploid (2n) strains [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], primarily due to reduced gill surface area. Similarly, another study in brook trout (\u003cem\u003eSalvelinus fontinalis\u003c/em\u003e) found the triploids (3n) less tolerant to hypoxia than diploids (2n) although this difference was very small [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. In contrast, gynogenetic strains have shown enhanced hypoxia tolerance. In blunt-snouted bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e), two gynogenetic lines were developed by activating sperm-activated eggs using UV-C irradiation to induce chromosome duplication [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. Both lines exhibited greater hypoxia tolerance than the normal strain, though the specific mechanisms underlying this improvement remain unclear. Likewise, a separate study generated a gynogenetic strain by activating eggs with UV-inactivated red crucian carp spermatozoa, producing individuals with superior hypoxia tolerance compared to the control group [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. These findings highlight the potential of gynogenesis as a tool for enhancing hypoxia tolerance in aquaculture. However, further research is needed to elucidate the genetic and physiological mechanisms driving these improvements and to assess their practical applications in selective breeding programs.\u003c/p\u003e \u003cp\u003eHybridization has long been recognized as a powerful tool for enhancing desirable traits in aquaculture, including hypoxia tolerance. By crossing strains within the same species or closely related species, researchers have explored whether hybrid vigor (heterosis) can improve resilience to low-oxygen conditions. For instance, a study comparing the hypoxia tolerance of blunt snout bream (\u003cem\u003eMegalobrama ampblycephala\u003c/em\u003e, BSB) and its hybrid (M. \u003cem\u003eamblycephala\u003c/em\u003e ♀ \u0026times; \u003cem\u003eCulter alburnus\u003c/em\u003e ♂, BTBB) found that the hybrid exhibited significantly greater tolerance. The BTBB hybrid had a lower mean oxygen loss of equilibrium (LOE) threshold (0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01 mg/L) and fewer individuals losing equilibrium (5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58 fish) compared to BSB (0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03 mg/L, 24.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 fish), suggesting a strong heterotic effect [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Similarly, artificial hybridization between \u003cem\u003ePelteobagrus fulvidraco\u003c/em\u003e and \u003cem\u003eLeiocassis longirostris\u003c/em\u003e produced two hybrid lines: PL (\u003cem\u003eP. fulvidraco\u003c/em\u003e ♀ \u003cem\u003e\u0026times; L. longirostris\u003c/em\u003e ♂) and LP (\u003cem\u003eL. longirostris\u003c/em\u003e ♀ \u0026times; \u003cem\u003eP. fulvidraco\u003c/em\u003e ♂). The PL hybrid exhibited a higher hatching rate, expected morphological traits, and greater hypoxia tolerance, as evidenced by increased enzyme activity and upregulation of HIF-related genes [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. In catfish, hybrids of \u003cem\u003eIctalurus punctatus\u003c/em\u003e \u0026times; \u003cem\u003eIctalurus furcatus\u003c/em\u003e displayed varying degrees of hypoxia tolerance depending on the geographical origin of the \u003cem\u003eI. furcatus\u003c/em\u003e parent strain, suggesting that local thermal regimes influence this trait [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. A separate study by Chen et al. [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e] reinforced these findings, reporting that hybrids of \u003cem\u003eM. amblycephala\u003c/em\u003e \u0026times; \u003cem\u003eCulter alburnus\u003c/em\u003e inherited broad hypoxia tolerance from \u003cem\u003eC. alburnus\u003c/em\u003e. Lastly, hybridization has also been explored in disease-resistant strains. A cross between resistant and non-resistant \u003cem\u003eOncorhynchus mykiss\u003c/em\u003e strains for \u003cem\u003eMyxobolus cerebralis\u003c/em\u003e exhibited greater hypoxia tolerance than one of the pure lines, further supporting the potential of hybridization in selective breeding programs [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. Overall, these studies underscore the potential of hybridization as a viable strategy to enhance hypoxia tolerance in aquaculture species, leveraging both hybrid vigor and genetic contributions from specific parental lineages to develop more resilient fish strains.\u003c/p\u003e \u003cp\u003eHypoxia tolerance varies not only within species but also across different aquaculture-relevant species, influencing their suitability for specific culture conditions. Several studies have explored these interspecific differences, providing valuable insights into aquaculture management. In China, a study evaluating six commercially important cyprinid species from the Yangtze River found significant variation in their critical oxygen partial pressure (P\u003csub\u003ecrit\u003c/sub\u003e), indicating that each species has different oxygen demands and culture requirements [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Similarly, another study [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] assessed hypoxia tolerance in 10 cyprinid species, including two strains of \u003cem\u003eCyprinus carpio\u003c/em\u003e, widely used in Chinese aquaculture. The results showed a broad range of hypoxia tolerances: \u003cem\u003eCarassius carassius\u003c/em\u003e (crucian carp) and \u003cem\u003eCarassius auratus\u003c/em\u003e (goldfish), both adapted to slow-moving water bodies, displayed extreme tolerance (LOE\u003csub\u003ecrit\u003c/sub\u003e ~ 0 KPa). Six species exhibited moderate tolerance (LOE\u003csub\u003ecrit\u003c/sub\u003e: 0.1\u0026ndash;0.3 KPa), whereas \u003cem\u003eMegalobrama pellegrini\u003c/em\u003e (thick-jawed bream) and \u003cem\u003eSpinibarbus sinensis\u003c/em\u003e (qingbo), which typically inhabit fast-flowing rivers, were the most sensitive (LOE\u003csub\u003ecrit\u003c/sub\u003e ~ 0.6 KPa). Interestingly, hypoxia tolerance (LOE\u003csub\u003ecrit\u003c/sub\u003e) did not correlate with oxygen uptake capacity (P\u003csub\u003ecrit\u003c/sub\u003e, Pearson\u0026rsquo;s correlation\u0026thinsp;=\u0026thinsp;0.004, P\u0026thinsp;=\u0026thinsp;0.60), reinforcing that P\u003csub\u003ecrit\u003c/sub\u003e alone is not a reliable indicator of hypoxia tolerance. The authors concluded that, at least in cyprinids, hypoxia tolerance is independent of phylogenetic relationships [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. A follow-up study by Fu et al. [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] expanded this analysis to 12 cyprinid species from habitats with different flow regimes (rapid, slow, and intermediate). Consistent with previous findings, species from fast-flowing environments exhibited lower hypoxia tolerance than those from slow-moving waters, further supporting the idea that hypoxia tolerance is more strongly influenced by habitat than by phylogenetic lineage [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Beyond cyprinids, hypoxia tolerance has also been studied in \u003cem\u003eAnoplopoma fimbria\u003c/em\u003e, a species gaining interest in aquaculture. A study comparing juveniles and adults found that adults, which naturally inhabit deep oxygen minimum zones (~\u0026thinsp;1500 m), exhibited significantly higher hypoxia tolerance (O₂ LOE: ~5.4% oxygen saturation) than juveniles (O₂ LOE: ~8.3% oxygen saturation) [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. These findings underscore the complexity of hypoxia tolerance across species, highlighting the importance of considering ecological adaptations and life stage differences when developing aquaculture strategies.\u003c/p\u003e\n\u003ch3\u003eSelective Breeding Results\u003c/h3\u003e\n\u003cp\u003eDespite the growing interest in improving hypoxia tolerance through selective breeding, only one study identified in this review successfully implemented a multi-generational breeding program [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. This initiative was launched in April 2007 by the Bream Genetics and Breeding Center (BGBC) at Shanghai Ocean University, China. The base population (F\u003csub\u003e0\u003c/sub\u003e) was sourced from wild fish in Poyang Lake, and subsequent generations were selectively bred for increased hypoxia tolerance. By 2015, the F₄ generation demonstrated a significantly lower loss of equilibrium threshold (LOE\u003csub\u003ecrit\u003c/sub\u003e: 0.54 mg/L at 10\u0026deg;C) compared to the control group, \u0026lsquo;Pujiang No. 1\u0026rsquo; (LOE\u003csub\u003ecrit\u003c/sub\u003e: 0.72 mg/L at 10\u0026deg;C), confirming that selective breeding had successfully enhanced hypoxia tolerance in this lineage [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. More recently, in 2022, a study evaluated the \u0026lsquo;Pujiang No. 2\u0026rsquo; line, which was developed in 2020 by the same institution. This new strain exhibited a 27% greater tolerance to hypoxia than its predecessor, further validating the effectiveness of selective breeding in improving this trait [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. These findings highlight the potential of long-term breeding programs to enhance hypoxia tolerance in aquaculture species, paving the way for the development of more resilient fish strain\u003c/p\u003e\n\u003ch3\u003eModern genetics and selective breeding studies of Hypoxia Tolerance\u003c/h3\u003e\n\u003cp\u003eRecent advances in genetics have significantly contributed to the understanding of hypoxia tolerance in farmed fish. In this systematic review, a total of 17 modern genetic studies were identified. They have employed diverse methodologies, including genetic association studies (n\u0026thinsp;=\u0026thinsp;2), genome-wide association studies (GWAS) (n\u0026thinsp;=\u0026thinsp;7), quantitative trait locus (QTL) mapping (n\u0026thinsp;=\u0026thinsp;3), SNP screening (n\u0026thinsp;=\u0026thinsp;2), Bulked Segregant Analysis coupled with RNA sequencing (BSR-Seq) (n\u0026thinsp;=\u0026thinsp;1), transgenic approaches (n\u0026thinsp;=\u0026thinsp;1), and whole-genome mutagenesis (n\u0026thinsp;=\u0026thinsp;1). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In most cases, hypoxia tolerance was assessed through loss-of-equilibrium (LOE) experiments. However, the classification criteria for hypoxia-sensitive (HS) and hypoxia-tolerant (HT) individuals varied widely among studies. For instance, some studies defined HS individuals as the first 10% to lose equilibrium and HT individuals as the last 10% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], while others used broader thresholds, such as the first and last 35% [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], the first and last 5% [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e], or the first and last 6% [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. In certain cases, researchers selected a fixed number of individuals instead of percentages, such as the first and last 48 [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] or the first and last 14 [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e] to lose equilibrium, to avoid challenges in identifying intermediate phenotypes. Despite these methodological differences, all genotypic studies reviewed identified QTLs, SNPs, potential candidate genes, and the biological pathways in which these genes might be involved (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings underscore the genetic basis of hypoxia tolerance and provide valuable insights for selective breeding programs aimed at improving resilience to low-oxygen environments in aquaculture.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of candidate genes proposed (n) and signaling pathways and cellular processes involved for hypoxia tolerance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSome candidate genes identified\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignaling pathways and Cellular Processes involved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIF1αn (also known as FIH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLi \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGiant seapearch (\u003cem\u003eLates calcarifer\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHIF1αn (also known as FIH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYang \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003elrrcl\u003c/em\u003e, \u003cem\u003etceb3\u003c/em\u003e, \u003cem\u003emlip\u003c/em\u003e, \u003cem\u003efam83b\u003c/em\u003e, \u003cem\u003egclc\u003c/em\u003e, \u003cem\u003efgfr2\u003c/em\u003e, \u003cem\u003eplpp4\u003c/em\u003e, \u003cem\u003efbxo9\u003c/em\u003e, \u003cem\u003ebmp5\u003c/em\u003e, \u003cem\u003ebag2, nf1\u003c/em\u003e, \u003cem\u003elgals9\u003c/em\u003e, \u003cem\u003eucp2\u003c/em\u003e, \u003cem\u003egdnf\u003c/em\u003e, \u003cem\u003edhrs13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMitogen-Activated Protein Kinase (MAPK) pathway, PI3K/AKT/mTOR (PAM) signaling pathway, hypoxia-mediated angiogenesis, cellular proliferation, apoptosis, survival\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWang \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e♂ Hybrid F \u0026times; ♀ Channel catfish\u003c/p\u003e \u003cp\u003eHybrid F1: ♀ Channel catfish \u0026times; ♂ Blue catfish (\u003cem\u003eI. punctatus\u003c/em\u003e \u0026times; \u003cem\u003eI. furcatus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003edmbx1a\u003c/em\u003e, \u003cem\u003epif1\u003c/em\u003e, \u003cem\u003eptger4\u003c/em\u003e, \u003cem\u003eartn\u003c/em\u003e, \u003cem\u003est3gal3a\u003c/em\u003e, \u003cem\u003ekdm4a\u003c/em\u003e, \u003cem\u003eptprf\u003c/em\u003e, \u003cem\u003epkib, cyp1a1\u003c/em\u003e, \u003cem\u003eccbe1\u003c/em\u003e, \u003cem\u003esema7a\u003c/em\u003e, \u003cem\u003earid3a\u003c/em\u003e, \u003cem\u003earid3b\u003c/em\u003e, \u003cem\u003efam219b\u003c/em\u003e, \u003cem\u003ep2ry1\u003c/em\u003e, \u003cem\u003erap2b\u003c/em\u003e, \u003cem\u003eklhl5\u003c/em\u003e, \u003cem\u003efam114a1\u003c/em\u003e, \u003cem\u003eklf3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVascular endothelial growth factor (VEGF) pathway, Mitogen-Activated Protein Kinase (MAPK) pathway, PI3K/AKT/mTOR (PAM) signaling pathway, p53-mediated apoptosis, damage checkpoint\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhong \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge yellow croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003emybpc1\u003c/em\u003e, \u003cem\u003eatp1a1\u003c/em\u003e, \u003cem\u003eprmt5\u003c/em\u003e, \u003cem\u003epsmb5\u003c/em\u003e, \u003cem\u003emybpc1\u003c/em\u003e, \u003cem\u003eatp1a1\u003c/em\u003e, \u003cem\u003eegln2\u003c/em\u003e, \u003cem\u003epygm\u003c/em\u003e, \u003cem\u003ecamk2d\u003c/em\u003e, \u003cem\u003earsj\u003c/em\u003e, \u003cem\u003etrmt10a\u003c/em\u003e, \u003cem\u003eaco1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF signaling pathway, oxidative stress, energy metabolism, ion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDing \u003cem\u003eet al\u003c/em\u003e. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge yellow croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003epds5a\u003c/em\u003e, \u003cem\u003esmin14\u003c/em\u003e, \u003cem\u003eugdh\u003c/em\u003e, \u003cem\u003elias\u003c/em\u003e, \u003cem\u003erfc1\u003c/em\u003e, \u003cem\u003eklf3\u003c/em\u003e, \u003cem\u003etbc1d1\u003c/em\u003e, \u003cem\u003epgm2\u003c/em\u003e, \u003cem\u003emelk\u003c/em\u003e, \u003cem\u003ethsd4\u003c/em\u003e, \u003cem\u003epolq\u003c/em\u003e, \u003cem\u003ecamk2d2\u003c/em\u003e, \u003cem\u003eankb\u003c/em\u003e, \u003cem\u003epgd\u003c/em\u003e, \u003cem\u003ewfs1a\u003c/em\u003e, \u003cem\u003egpi\u003c/em\u003e, \u003cem\u003estim2a\u003c/em\u003e, \u003cem\u003eslc34a2a\u003c/em\u003e, \u003cem\u003eho1\u003c/em\u003e, \u003cem\u003enfix\u003c/em\u003e, \u003cem\u003eccna2\u003c/em\u003e, \u003cem\u003egrik4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDNA replication and repair, glucose metabolism, erythropoiesis, glucose transport, iron metabolism, ion regulation, pentose phosphate pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDing \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eids\u003c/em\u003e, \u003cem\u003efmr1\u003c/em\u003e, \u003cem\u003earx\u003c/em\u003e, \u003cem\u003elonrf3\u003c/em\u003e, \u003cem\u003ecommd5\u003c/em\u003e, \u003cem\u003emap4k4\u003c/em\u003e, \u003cem\u003esmu1\u003c/em\u003e, \u003cem\u003eb4galt1\u003c/em\u003e, \u003cem\u003ere1\u003c/em\u003e, \u003cem\u003eabca1\u003c/em\u003e, \u003cem\u003enoa1\u003c/em\u003e, \u003cem\u003eigfbp7, noxo1, bcl2a, mylk3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGlycogen metabolism, glucose metabolism, cation regulation, DNA repair, HIF-1α regulation, mitochondrial metabolism, hematopoiesis, angiogenesis, oxidative response pathways, apoptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePrchal \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003egpr132\u003c/em\u003e, \u003cem\u003eabcg4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLactate sensing and signaling, oxidative stress protection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLi \u003cem\u003eet al\u003c/em\u003e. (2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePompano (\u003cem\u003eTrachinotus ovatus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003elonrf3\u003c/em\u003e, \u003cem\u003ecommd5\u003c/em\u003e, \u003cem\u003efam199x\u003c/em\u003e, \u003cem\u003egpr137\u003c/em\u003e, \u003cem\u003epld7\u003c/em\u003e, \u003cem\u003esyvn1\u003c/em\u003e, \u003cem\u003esmad5\u003c/em\u003e, \u003cem\u003emdga1\u003c/em\u003e, \u003cem\u003egabra4\u003c/em\u003e, \u003cem\u003eap1ar\u003c/em\u003e, \u003cem\u003ecfap100\u003c/em\u003e, \u003cem\u003epold1\u003c/em\u003e, \u003cem\u003ezgc:55558\u003c/em\u003e, \u003cem\u003etrit1\u003c/em\u003e, \u003cem\u003ekif18a\u003c/em\u003e, \u003cem\u003eNEK3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eglycolysis, DNA repair, acid balance, apoptosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSan \u003cem\u003eet al\u003c/em\u003e. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGolden pompano (\u003cem\u003eTrachinotus blochii\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePTGS2, CYLD, Ifih1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnaerobic metabolism, stress response, immune response, waste discharge, cell death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLiu \u003cem\u003eet al\u003c/em\u003e. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePelteobagrus vachelli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003esema7a\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImmune processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhang \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSilver sillago (\u003cem\u003eSillago sihama\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ecyp20a1\u003c/em\u003e, \u003cem\u003emgst3b\u003c/em\u003e, \u003cem\u003ekcnh2\u003c/em\u003e, \u003cem\u003ecluh\u003c/em\u003e, \u003cem\u003eadk\u003c/em\u003e, \u003cem\u003exdh\u003c/em\u003e, and \u003cem\u003eslc19a2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eXenobiotic biodegradation, defense against oxidative stress, membrane potential equilibrium, mitochondrial integrity, ribose metabolism, nucleotide metabolism, ion transport and caption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYe \u003cem\u003eet al\u003c/em\u003e. (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eegln2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWang \u003cem\u003eet al\u003c/em\u003e. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ehif2αb\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF-1 signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZhao \u003cem\u003eet al\u003c/em\u003e. (2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEpo X1\u003c/em\u003e, \u003cem\u003eVEGFR1\u003c/em\u003e, \u003cem\u003eHO-1a\u003c/em\u003e, \u003cem\u003eLPAR6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHIF-1 signaling pathway, Vascular endothelial growth factor (VEGF) pathway, Forkhead box O (FOXO) signaling pathway, Janus kinases (JAKs), signal transducer and activator of transcription proteins (STATs), Mitogen-Activated Protein Kinase (MAPK) Pathway, PI3K/AKT/mTOR (PAM) signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSu \u003cem\u003eet al\u003c/em\u003e. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAnalysis of association between single genes and hypoxia tolerance\u003c/h3\u003e\n\u003cp\u003eTwo studies identified in our search explored the genetic basis of hypoxia tolerance by analyzing associations between single nucleotide polymorphisms (SNPs) in key genes and hypoxia-related traits. Li et al. (2017) identified a significant association between a SNP (8892953 G\u0026thinsp;\u0026gt;\u0026thinsp;A), located in the fourth intron of the hypoxia-inducible factor inhibitor gene (\u003cem\u003eHIF1αn\u003c/em\u003e, also known as \u003cem\u003eFIH1\u003c/em\u003e), and hypoxia tolerance in Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Out of 248 fish phenotyped for time to loss of equilibrium (t\u003csub\u003eLOE\u003c/sub\u003e), only the 192 individuals with extreme phenotypes (most sensitive and most tolerant) were genotyped via Sanger sequencing to avoid challenges in categorizing intermediate phenotypes. The SNP showed a significant genotype-phenotype association (\u003cem\u003eF\u003c/em\u003e-test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with further confirmation using PLINK analysis (\u003cem\u003eT\u003c/em\u003e = -1.856, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.07). Fish with the A/A genotype (n\u0026thinsp;=\u0026thinsp;77) exhibited the longest t\u003csub\u003eLOE\u003c/sub\u003e under hypoxic stress (15,140 seconds), followed by A/G heterozygotes (11,655 seconds, n\u0026thinsp;=\u0026thinsp;87) and G/G homozygotes (11,495 seconds, n\u0026thinsp;=\u0026thinsp;17) [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Similarly, a study on giant seaperch (\u003cem\u003eLates calcarifer\u003c/em\u003e) identified three SNPs in the third and fourth introns of \u003cem\u003eHIF1αn\u003c/em\u003e, with SNP 9331841 (C/T) in the fourth intron being significantly associated with hypoxia tolerance [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Sanger sequencing of this region in 140 hypoxia-dead and 140 surviving fish revealed a significant difference in allele frequencies (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In hypoxia-dead fish, the T allele frequency was 91.19%, compared to 89.79% in tolerant fish, whereas the C allele frequency was 8.91% in hypoxia-dead fish and 18.21% in tolerant fish. The other two SNPs (9331841 and 9331890) showed no significant association with hypoxia tolerance. Haplotype analysis of the three SNPs identified six haplotypes and nine genotype combinations. Notably, the GGT/GAT genotype was the most prevalent among hypoxia-tolerant fish. Based on these findings, the authors suggest that SNP 9332241 (C/T) and the GGT/GAT genotype could serve as markers for genetic improvement programs in giant seaperch [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGWAS and QTL Mapping\u003c/h2\u003e \u003cp\u003eGenome-wide association studies (GWAS) and quantitative trait locus (QTL) mapping are powerful tools for identifying genetic variants associated with hypoxia tolerance. These approaches have been applied in multiple fish species, revealing a complex genetic architecture underlying this trait. A GWAS study on six strains of channel catfish (\u003cem\u003eIctalurus punctatus\u003c/em\u003e) employed both \u0026ldquo;within-strain\u0026rdquo; and \u0026ldquo;between-strain\u0026rdquo; approaches to identify significant QTLs and SNPs associated with hypoxia tolerance [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Several of these SNPs were found in haplotypic blocks, suggesting that hypoxia tolerance in this species may be linked to the co-segregation of genomic segments [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. In the within-strain analysis, the Kansas strain showed 26 significant SNPs, while the Kmix and Thompson strains exhibited four and one significant SNP, respectively. The most significant SNPs in each strain accounted for high percentages of phenotypic variation (25.32%, 23.04%, and 32.04%, respectively). In the remaining three strains (103KS, Marion, and MarionS), no SNPs were significantly associated with hypoxia tolerance, although some were suggestively associated. In the across-strain analysis, only one significant SNP was detected, which explained 5.71% of phenotypic variance. According to the authors, these findings emphasize the complex genetic architecture of hypoxia tolerance. Since the SNPs analyzed between strains are not necessarily the same as those analyzed within individual strains, it appears that each strain has accumulated its own set of mutations, leading to strong genetic differentiation, as previously reflected in other phenotypic studies [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e]. Given that the SNPs identified in the strain-specific analyses appeared more important and numerous than those detected in the combined analysis, the authors suggest that strain-by-strain GWAS analysis is the preferred approach for breeding purposes [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. Another GWAS study examining hypoxia tolerance in the hybrid of channel catfish \u0026times; blue catfish [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] detected significant SNPs; however, these SNPs differed from those identified in the six channel catfish strains [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. The authors attributed this discrepancy to the complex genetic architecture of the trait, the contribution to phenotypic variation, and the influence of sample size and the number of families used in the analysis [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA recent genomic selection study in large yellow croaker, phenotyped 753 individuals using time survival (hours) and survival status (binary trait: hypoxia-sensitive HS, hypoxia-tolerant HT), and genotyped then using 38,472 high-quality SNPs [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. A great phenotype variation was detected in this population (time survival: 20.13\u0026ndash;38.6 h) and 200 individuals HS and 200 individuals HT. This study does not provide information concerning genome-wide significant SNPs or QTLs, but provides heritability estimates for both hypoxia-tolerance related traits (h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003esurvival status\u003c/sub\u003e = 0.65, h\u003csup\u003e2\u003c/sup\u003e\u003csub\u003esurvival time\u003c/sub\u003e= 0.62) [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn rainbow trout, a GWAS study on hypoxia tolerance identified three significant QTLs: two on chromosome 31 (Omy31) and one on chromosome 20 (Omy20) [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Additionally, two putative QTLs with suggestively associated SNPs were found, one located on chromosome 15 (Omy15) and the other on chromosome 28 (Omy28), each with a single SNP. The most significant SNPs from each QTL explained between 0.17% and 0.78% of the genetic variance. The authors note that, unlike other species where the most significant (\u0026ldquo;peak\u0026rdquo;) SNPs explained a large percentage of phenotypic variance (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), in rainbow trout these SNPs had minimal effects, reinforcing the idea that hypoxia tolerance in this species has a highly polygenic nature, governed by multiple loci with small individual effects [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. An intriguing hypothesis proposed by the authors is that one of the QTLs on chromosome 31 (Omy31_1) could be a \u0026ldquo;supergene,\u0026rdquo; given its high linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45). This term refers to clusters of neighboring genes that segregate together and contribute to a complex trait [\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn large yellow croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e), a study used \u003cem\u003ede novo\u003c/em\u003e SNP genotyping by ddRAD-Seq to analyze hypoxia tolerance through GWAS [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This study, which focused on two traits related to hypoxia tolerance (survival time and binary trait), identified two significant SNPs for survival time and four for the binary trait. Another study on the same species, combining GWAS and transcriptomic analysis, detected suggestively significant SNPs and 22 candidate genes identified by both approaches [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. In Nile tilapia, a 2017 study using ddRAD-Seq for genotyping identified four highly significant QTLs related to hypoxia tolerance and two candidate genes [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Golden pompano (\u003cem\u003eT. ovatus\u003c/em\u003e), a whole-genome sequencing study conducted to genotype and perform a GWAS for hypoxia tolerance [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] did not detect significant SNPs, which the authors attributed to the small sample size (n\u0026thinsp;=\u0026thinsp;100) and the high number of quality SNPs used in the analysis. However, four suggestively significant SNPs were identified. In \u003cem\u003ePelteobagrus vachelli\u003c/em\u003e, ddRAD-Seq was used to develop a high-resolution genetic linkage map, which was then used to analyze three traits of interest: growth, sex determination, and hypoxia tolerance [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e]. The authors identified a single significant QTL, and a candidate gene associated with hypoxia tolerance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The potential candidate genes identified in the reviewed studies are often downstream effectors of the HIF pathway or are involved in signaling pathways such as VEGF and mTOR, which are critical for angiogenesis and energy conservation, respectively. Genes related to erythropoiesis, ion regulation, glucose metabolism, DNA repair, and iron metabolism - key processes in the hypoxia response - were also identified (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Some candidate genes, including \u003cem\u003eklf3\u003c/em\u003e, \u003cem\u003eegln2\u003c/em\u003e, \u003cem\u003elonrf3\u003c/em\u003e, \u003cem\u003ecommd5\u003c/em\u003e, \u003cem\u003egpr132\u003c/em\u003e, and \u003cem\u003egpr137\u003c/em\u003e may be conserved across species.\u003c/p\u003e \u003cp\u003eA recent study investigated the genetic basis of hypoxia tolerance in silver sillago (\u003cem\u003eSillago sihama\u003c/em\u003e) using QTL mapping [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], in which 162 individuals were phenotyped based on the time from the start of the experiment to the onset of gasping (average: 398.73\u0026thinsp;\u0026plusmn;\u0026thinsp;94.68 min). Six QTLs associated with hypoxia tolerance were identified across five linkage groups (chromosomes: LG8, LG12, LG15, LG20, and LG23). Of the 567 genes detected, only seven were considered potential candidates after GO and KEGG enrichment analyses: \u003cem\u003ecyp20a1\u003c/em\u003e, \u003cem\u003emgst3b\u003c/em\u003e, \u003cem\u003ekcnh2\u003c/em\u003e, \u003cem\u003ecluh\u003c/em\u003e, \u003cem\u003eadk\u003c/em\u003e, \u003cem\u003exdh\u003c/em\u003e, and \u003cem\u003eslc19a2\u003c/em\u003e. The \u003cem\u003emgst3b\u003c/em\u003e gene, part of the glutathione-S-transferase family, was highlighted as particularly important based on prior research. A SNP association analysis of the \u003cem\u003emgst3b\u003c/em\u003e gene revealed five intronic SNPs (g.583 T\u0026thinsp;\u0026gt;\u0026thinsp;C, g.611 A\u0026thinsp;\u0026gt;\u0026thinsp;G, g.629 T\u0026thinsp;\u0026gt;\u0026thinsp;A, g.633 T\u0026thinsp;\u0026gt;\u0026thinsp;A, g.937 A\u0026thinsp;\u0026gt;\u0026thinsp;G), with heterozygous genotypes of four SNPs showing higher hypoxia tolerance compared to homozygous genotypes. Haplotype analysis indicated that the TATT haplotype was predominant. The authors suggest that these markers in \u003cem\u003emgst3b\u003c/em\u003e gene could serve as molecular markers for selective breeding in silver sillago [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a F5 strain of a genetically improved strain for hypoxia tolerance of \u003cem\u003eMegalobrama amblycephala\u003c/em\u003e, a study [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] based on a previous transcriptomic analysis [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e], investigated two SNPs located at positions 397 and 715 of the \u003cem\u003eegln2\u003c/em\u003e gene\u0026rsquo;s cDNA. These SNPs produced haplotypes with distinct phenotypes. Diplotype II (T\u003csup\u003e397\u003c/sup\u003eT\u003csup\u003e397\u003c/sup\u003eT\u003csup\u003e715\u003c/sup\u003eT\u003csup\u003e715\u003c/sup\u003e) exhibited higher hypoxia tolerance compared to other diplotype combinations. This was reflected in its increased erythrocyte and hemoglobin production under hypoxic conditions, higher catalase and superoxide dismutase enzyme activity, a lower LOE\u003csub\u003ecrit\u003c/sub\u003e, and reduced need for increased gill lamellar surface area [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. The authors suggested that haplotype II (T\u003csup\u003e397\u003c/sup\u003eT\u003csup\u003e715\u003c/sup\u003e) could serve as a marker for selecting hypoxia-tolerant individuals. Similarly, in the same species, another study explored two SNPs located at positions 203 and 752 of the \u003cem\u003ehif2αb\u003c/em\u003e gene cDNA, which is involved in regulating the hypoxia response [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. The study found that haplotype II (A\u003csup\u003e203\u003c/sup\u003eA\u003csup\u003e752\u003c/sup\u003e), forming diplotype II (A\u003csup\u003e203\u003c/sup\u003eA\u003csup\u003e203\u003c/sup\u003eA\u003csup\u003e752\u003c/sup\u003eA\u003csup\u003e752\u003c/sup\u003e), conferred greater hypoxia resistance than diplotypes I and III, as evidenced by a lower LOE\u003csub\u003ecrit\u003c/sub\u003e, higher erythrocyte count, and elevated catalase and superoxide dismutase activity. The authors proposed this haplotype II as another marker for hypoxia tolerance selection [\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBulked Segregant Analysis - RNA Seq (BSR-Seq)\u003c/h3\u003e\n\u003cp\u003eA technique known as BSR-Seq analysis, which combines RNA-Seq with Bulked Segregant Analysis (BSA) to correlate global gene expression patterns with SNPs [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e], was applied in golden pompano (\u003cem\u003eTrachinotus blochii\u003c/em\u003e). Authors found that hypoxia tolerance-related SNPs in this fish are determined by multiple linkage groups, mainly the linkage groups (LG) 18 and 22. Numerous candidate genes were proposed (768 and 348, for brain and liver respectively), which are involved in anaerobic energy metabolism, stress response, immune response, waste discharge, and cell death. These findings highlight the utility of BSR-Seq in identifying genetic variants and molecular mechanisms underlying hypoxia tolerance, providing potential markers for genetic selection in aquaculture.\u003c/p\u003e\n\u003ch3\u003eGene editing and hypoxia tolerance mutant lines\u003c/h3\u003e\n\u003cp\u003ePrecise genomic modification using gene editing techniques and others mutagenesis techniques presents new opportunities for developing hypoxia tolerance fish (Fig.\u0026nbsp;2C). For example, gene editing has been applied to blunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e) to create knockout mutants of the erythropoietin (EPO) gene [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. EPO is a glycoprotein hormone that plays a key role in regulating erythropoiesis and is a classic hypoxia-responsive gene in the HIF-1 pathway. Interestingly, EPO\u003csup\u003e\u0026minus;/\u0026minus;\u003c/sup\u003e mutants exhibited reduced red blood cell counts, lower hemoglobin levels, and increased oxygen tension thresholds under hypoxia, highlighting the crucial role of EPO in hypoxia tolerance in this species [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e]. Also, there is evidence that it is feasible to develop hypoxia-tolerant fish using techniques such as Atmospheric and Room Temperature Plasma (ARTP) mutagenesis. For example, in blunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e), atmospheric and room temperature plasma (ARTP) mutagenesis was used to generate complete genomic mutants [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. By applying ARTP to semen from a gynogenetic male, researchers produced 2,026 mutant offspring, of which 384 showed improved critical oxygen levels (LOE\u003csub\u003ecrit\u003c/sub\u003e: 0.45 mg/L) compared to the control group (LOE\u003csub\u003ecrit\u003c/sub\u003e: 0.86 mg/L) after three months of culture. Genome resequencing revealed 3,651 nonsynonymous mutations across 1,223 genes in ARTP mutants, with four genes (\u003cem\u003eEpo X1\u003c/em\u003e, \u003cem\u003eVEGFR1\u003c/em\u003e, \u003cem\u003eHO-1a\u003c/em\u003e, \u003cem\u003eLPAR6\u003c/em\u003e) showing differential expression under hypoxia. These four genes are involved in key pathways such as HIF-1, VEGF, FoxO, JAK-STAT, MAPK, mTOR, and PI3K-Akt [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. Thus, the authors propose using these ARTP-induced mutations as potential markers for selective breeding to enhance hypoxia tolerance in blunt snout bream [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e].\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eHypoxia tolerance, broadly, appears to be an inherited polygenic trait characterized by a complex genetic architecture, in which variation is governed by multiple polymorphisms distributed throughout the genome, each of which exerts small to moderate effects. The wide phenotypic variability observed in hypoxia tolerance among farmed fish is largely attributed to the high genetic diversity within these populations. These genetic variations, mainly in the form of SNPs, arise independently in different populations, strains, and species over time. Furthermore, laboratory-based genetic modifications, such as gynogenesis, transgenesis, and potentially gene editing (Fig.\u0026nbsp;2C), offer promising avenues to develop desired phenotypic traits.\u003c/p\u003e \u003cp\u003eThe identification of species-specific QTLs, SNPs, and candidate genes is essential, as each species possesses distinct gene regulatory mechanisms governing hypoxia responses. Integrating GWAS with transcriptome analyses enhances detection power, while the use of large sample sizes and medium-to-high density SNP microarrays significantly increases the likelihood of identifying statistically significant markers and candidate genes. These advancements will facilitate the development of genetically improved fish strains with enhanced resilience to hypoxic stress.\u003c/p\u003e \u003cp\u003eFinally, the selection of an optimal breeding strategy\u0026mdash;whether genomic selection (GS) or marker-assisted selection (MAS)\u0026mdash;depends on the proportion of phenotypic variance explained by significant SNPs. In species such as rainbow trout and yellow croaker, where hypoxia tolerance is highly polygenic and SNPs exhibit small individual effects, genomic selection is the preferred approach. Conversely, in species like channel catfish and golden pompano, where specific SNPs explain a higher proportion of phenotypic variance, marker-assisted selection may be a viable strategy. These findings underscore the necessity of understanding the genetic architecture of hypoxia tolerance in each species to implement the most effective selective breeding programs.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eddRAD-Seq: Double-digest restriction-site associated DNA sequencing\u003c/p\u003e\n\u003cp\u003eLOE: Loss of Equilibrium\u003c/p\u003e\n\u003cp\u003eILOS: incipient lethal oxygen saturation\u003c/p\u003e\n\u003cp\u003ePRISMA: Preferred Reporting Items for Systematic reviews and Meta-Analyses\u003c/p\u003e\n\u003cp\u003eGWAS: Genome-wide Association Study\u003c/p\u003e\n\u003cp\u003eHIF: Hypoxia-Inducible Factor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHRE: Hypoxia-Response Element\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eQTL: Quantitative Trait loci\u003c/p\u003e\n\u003cp\u003eSNP: Single-nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003et\u003csub\u003eLOE\u003c/sub\u003e: time to LOE (Loss of equilibrium)\u003c/p\u003e\n\u003cp\u003eASR: Aquatic Surface Respiration\u003c/p\u003e\n\u003cp\u003eVEGF: Vascular Endothelial Growth Factor\u003c/p\u003e\n\u003cp\u003eMAPK: Mitogen-Activated Protein Kinase\u003c/p\u003e\n\u003cp\u003ePI3K/AKT/mTOR: phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/mammalian target of rapamycin (mTOR)\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.B. was supported by the Maintenance Scholarship for Foreigners - PhD Program – Pontificia Universidad Católica de Valparaíso. N.S. was supported by ANID-Chile FONDECYT POSTDOCTORADO No. 3240697. J.G-M was supported by ANID-Chile Fondecyt regular Nº 1231206 and PUCV Interdisciplinary Associative Research (AIE)-University/Company N°39.365/2023.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJG-M and SB designed and wrote the manuscript. N.S. and R.P. critically edited and finalized the manuscript. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eD\u0026iacute;az R, Rabalais NN, Breitburg DL. Agriculture\u0026rsquo;s impact on aquaculture: Hypoxia and eutrophication in marine waters. OECD; 2012. \u003c/li\u003e\n\u003cli\u003eDiaz RJ, Breitburg DL. The hypoxic environment. In: Richards JG, Farrell AP, Brauner CJ, editors. Fish Physiology. 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Aquaculture. 2022;555:738241. https://doi.org/10.1016/j.aquaculture.2022.738241 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Pontificia Universidad Católica de Valparaíso","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypoxia tolerance, Selective breeding, Aquaculture, Genomic selection, GWAS.","lastPublishedDoi":"10.21203/rs.3.rs-6010748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6010748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe acceleration of climate change and increasing water pollution have contributed to a global increase in hypoxic events in the oceans. As a result, this environmental stressor has had significant economic repercussions for the marine aquaculture sector. Consequently, selective breeding for hypoxia-tolerant fish is being explored as a promising strategy to mitigate climate change effects. In this context, the present systematic review synthesizes and critically evaluates current knowledge regarding the genetic variation associated with hypoxia tolerance in farmed fish species. A literature search was conducted in Scopus and Web of Science, following the PRISMA 2020 guidelines. In total, 963 articles were identified, of which 40 met the inclusion criteria, encompassing 29 species and three hybrid lines. Among the farmed fish, the blunt snout bream (\u003cem\u003eMegalobrama amblycephala\u003c/em\u003e), rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e), common carp (\u003cem\u003eCyprinus carpio\u003c/em\u003e) and Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e) were the most extensively studied. The most commonly used traits to measure hypoxia tolerance included: 1) time of loss of equilibrium (t\u003csub\u003eLOE\u003c/sub\u003e), 2) survival time or status (alive/dead) and 3) critical oxygen partial pressure (P\u003csub\u003ecrit\u003c/sub\u003e), measured via respirometry. Notably, 22 studies reported substantial variability in hypoxia tolerance across families, strains, gynogenetic lines, growth-transgenic lines, hybrids, and species. Moreover, 15 studies identified SNP markers significantly associated with hypoxia tolerance; however, heritability estimates, reported in only two studies, ranged from 0.28 to 0.65. Furthermore, candidate genes were frequently identified as downstream effectors of the HIF pathway or as components of signaling pathways such as VEGF and mTOR, which are critical for angiogenesis and energy conservation, respectively. Additionally, genes involved in erythropoiesis, ion regulation, glucose metabolism, DNA repair, and iron metabolism, key processes in the hypoxia response, were identified. Given that aquatic environments are becoming increasingly hypoxic, these findings underscore the potential of the inherent genetic diversity present in farmed fish populations. In this context, genomic selection and gene editing emerge as promising tools for developing hypoxia-tolerant fish lines. Nevertheless, further research is warranted to implement such lines under field conditions, particularly because the correlations between hypoxia tolerance and other economically important traits, such as growth and pathogen resistance, remain largely unknown.\u003c/p\u003e","manuscriptTitle":"Genetic variation of hypoxia tolerance in farmed fish: a systematic review for selective breeding purposes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-13 09:45:43","doi":"10.21203/rs.3.rs-6010748/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90815567-5f29-4925-9100-320d230be748","owner":[],"postedDate":"February 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44199562,"name":"Aquaculture and Mariculture"},{"id":44199563,"name":"Epigenetics \u0026 Genomics"}],"tags":[],"updatedAt":"2025-02-13T09:45:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-13 09:45:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6010748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6010748","identity":"rs-6010748","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}