Genome assisted gene-flow rescued genetic diversity without hindering growth performance of inbred coho salmon (Oncorhynchus kisutch) population selected for high growth phenotype

Genome assisted gene-flow rescued genetic diversity without hindering growth performance of inbred coho salmon (Oncorhynchus kisutch) population selected for high growth phenotype | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome assisted gene-flow rescued genetic diversity without hindering growth performance of inbred coho salmon (Oncorhynchus kisutch) population selected for high growth phenotype Junya Kobayashi, Ryo Honda, Sho Hosoya, Yuki Nochiri, Keisuke Matsuzaki, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5444805/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Marine Biotechnology → Version 1 posted 10 You are reading this latest preprint version Abstract Selective breeding is a powerful tool for improving aquaculture production. A well-managed breeding program is essential, as populations can otherwise lose genetic diversity, leading to reduced selection response and inbreeding excesses. In such cases, genetic diversity in broodstock must be restored by introducing individuals from external populations. However, this can reduce the accumulated genetic gains from selective breeding. On the other hand, selectively introduction of individuals with superior phenotypes will allow restoration of genetic diversity without sacrificing these gains. In this study, we demonstrated this possibility using a selectively bred (SB) and a randomly bred (RB) population of coho salmon ( Oncorhynchus kisutch ). Forty males with superior growth were selected from RB population using genomic selection and crossed with 127 randomly collected females from SB to produce a newly bred (NB) population. Genetic diversity, assessed from population statistics such as effective number of alleles, allele richness, and observed heterozygosity of 11 microsatellite markers, was higher in NB than in SB and RB. Additionally, folk length and body weight were compared among the three populations after a 12-month communal culture from fertilization. The least-squares means of folk length and body weight were similar between NB (164.9 mm and 57.9 g) and SB (161.1 mm and 53.7 g), while both were significantly larger than those of RB (150.4 mm and 43.0 g). Our results highlight the effectiveness of the genome-assisted gene flow in restoring the genetic diversity of SB populations without compromising their accumulated genetic gain in growth. Coho salmon Genetic rescue Genomic selection Sustainable aquaculture Figures Figure 1 Figure 2 Figure 2 Figure 3 Figure 4 Figure 4 Figure 6 Figure 9 Introduction Aquaculture is an important industry that plays an important role in securing food in today's world, where the demand for food is increasing due to global population growth and changes in the global environment (FAO. The state of world fisheries and aquaculture 2018. Meeting the sustainable development goals. Rome: FAO and UNEP; 2018). While there are various approaches to improve production efficiency, scientific selective breeding is one of the powerful tools that improve aquaculture production (Gjerdem 2012). For example, a large amount of production of major aquaculture species such as farmed Atlantic salmon ( Salmo salar ), rainbow trout ( Oncorhynchus mykiss ) and Genetically Improved Farmed Tilapia (GIFT), are derived from selective breeding populations. To gain the merits from selective breeding a well-managed breeding program is required (Houston et al., 2022 ; Boudry et al., 2021 ), otherwise misconducted program, i.e., the use of small number broodfish and/or the use of close relatives only, will result in losses in genetic diversity leading poor selection response, inbreeding excess necessitating termination or revival of the program (Gjedrem and Baranski, 2009; Gjedrem and Kolstad, 2012 ; Huang and Liao, 1990 ; Hulata et al, 1986 ; Kincaid, 1983 ; Knibb et al., 2014 ; Telchert-Coddington and Smitterman, 1988 ). This is especially true for breeding programs with small population, and special cares for genetic diversity should be paid for such programs (Taberlet et al, 2007 ). When a loss of genetic diversity exceeded the level required for sustainable management, the broodstock may require restoration of genetic variation. Restoration of genetic diversity can be partly achieved by increasing the number of broodfish. However, this approach cannot refill the alleles which have lost due to genetic drift. On the other hand, introduction of genetic material from external population can increase the number of alleles, allele richness, and gene diversity (Gjøen and Bentsen 1997 ; Knibb et al., 2020 ). This is analogous to the genetic rescue, or assisted gene flow, applied in conservation study (Kristensen et al. 2015 ; Ghildiyal et al., 2023 ; Pregler et al., 2023 ). Genetic rescue is the attempt to increase genetic diversity and mitigate detrimental effects from inbreeding by introducing genetic resources from external population. While introduction of individuals from outside can increase genetic diversity, it can also reduce the fitness of the wild population (i.e., outbreeding depression) (Allendorf et al., 2001 ; Frankham et al., 2011 ) or the accumulated genetic merits of the selected population (Gjøen and Bentsen 1997 ). Recently, genetic rescue with the aid of genomics has been imprimented (Allendorf et al., 2010 ; Carlson et al, 2014 ; Supple and Shapiro, 2018 ; Theissinger et al, 2023 ; Schmidt et al., 2024). One of such approaches is genomic prediction of adaptation and fitness related phenotypes, or genomic selection (Meuwissen et al, 2001), that enable to select individuals more adaptive to the introduced environments (Hagedorn et al., 2019; Guhlin et al., 2023 ). It is expected that this approach is also effective to increase genetic diversity of small aquaculture population without diminishing the genetic gain accumulated through selective breeding. Coho salmon ( Oncorhynchus kisutch ) is one of the important aquaculture species ranked 4th in production in Japan (Statistical Survey on Marine Fishery Production, Ministry of Agriculture, Forestry and Fisheries, Japan, 2023: https://www.e-stat.go.jp/stat-search/files/data?sinfid=000040181768&ext=xls , EXCEL file in Japanese, accessed September, 2024). This species is an exogeneous species in Japan, and the domestic stock is maintained using a population imported from north America in 1970’s. Since then, broodstocks were maintained at small scale. In 2011, the main production area of Japanese cultured coho salmon, i.e., Tohoku district including Miyagi Prefecture, was severely damaged by the tsunami disaster of the Great East Japan Earthquake (Sasaki et al., 2017 ). Immediately after the disaster, a national project was initiated to reconstruct the industry (“A scheme to revitalize agriculture and fisheries in disaster area through deploying highly advanced technology” funded by the Agriculture, Forestry, and Fisheries Research Council and Reconstruction Agency, Japan), including social implementation of the coho salmon population with high growth phenotype produced by phenotypic selection at the Inland Fisheries Experimental Station, Miyagi Prefecture Fisheries Technology Center (Miyagi, Japan). Meanwhile, the genetic health of the selectively bred (SB) population has been diagnosed using genome wide single nucleotide polymorphisms (SNPs) obtained by ddRAD-seq (Hosoya et al, 2018). The genetic analysis revealed reduced additive genetic variation and high genetic relatedness among individuals, suggesting the necessity of restoration of genetic diversity for SB population. In this study, we tested the possibility of a genome assisted gene flow to restore the genetic diversity of the SB population without deteriorating its growth performance. Individuals with high growth performance were selected from the randomly bred (RB) population, the source population of the SB, using genomic prediction. These selected individuals were crossed with randomly collected SB individuals to create a new bred (NB). The genetic diversity and growth performance were compared among the three populations. Materials and methods Ethical disclosure The phenotype recording and sample collection was carried out at the Freshwater Fisheries Experimental Station, Miyagi Prefecture Fisheries Technology Institute (Miyagi, Japan). The experiment was approved by Miyagi Prefectural Evaluation Committee for Research and Development Institutes. Selection of broodfish from the randomly breeding (RB) population Considering the maturation size of RB and SB populations, we decided to cross RB males and SB females to produce NB. At 33 months old (September 2016), we picked up RB individuals ( n = 768) with male-like morphology and measured folk length and body weight of each fish. The average (± standard deviation) folk length and body weight were 26.4 (± 3.0) cm and 222.5 (± 76.5) g, respectively (Supplementary Table S1 ). A fin clip was collected from the caudal fin for each specimen, and genomic DNA was extracted from the clip using Gentra Puregene Tissue Kit (QIAGEN, Venlo, Netherland) following manufacture’s instruction. The DNA concentration was measured using QuantiFluor dsDNA kit (Promega) and adjusted for 70 ng/uL using milli-Q water. The genetic sex was examined for each individual using male specific primer (OTY2-WSU) following Brunelli and Thogaard (2004). The PCR amplification was done using a T100 thermal cycler (Bio-Rad). Fragment analysis was done using Applied Biosystems 3130 Genetic Analyzer (Thermo Fisher). Male specific amplicon (435 bp) was detected from 515 out of 768 individuals. The biased sex ratio was observed because we collected individuals that showed male-like morph at sampling. Among them, 300 fish were randomly picked up for subsequent ddRAD-seq. We set this sample number based on the throughput of single lane of TruSeq v3 chemistry (PE100) on a Illumina HiSeq 2000 platform used for the subsequent sequencing. The library construction was done following Sakaguchi et al. ( 2015 ) where BglII and EcoRI (TOYOBO, Osaka, Japan) were used for DNA digestion. An average of 829,797 paired reads were obtained with the range of 40,049 to 2,992,012 (Supplementary Table S2). All sequence data is registered with the DDBJ (BioSample Submission ID: SSUB031092, available after publication). Sequence data are available at DDBJ DRA (accession ID: DRA019425). Adapter sequences were trimmed using Trimmomatic-0.39 (Bolger et al., 2014 ) with following threshold: allowing 2 mismatch, palindrome clip threshold of 30, and simple clip threshold of 10 (i.e., ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10); remove reads with leading quality less than 20 (LEADING:20); remove reads with trailing quality less than 20 (TRAILING:20), minimum length of 50 bp (MINLEN:50). The read pairs remaining at both sides were mapped onto the Coho salmon reference genome (Okis_V1; GenBank assembly accession: GCF_002021735.1) using BWA-mem (BWA v0.7.15) (Li 2013 ). Subsequently, reads with flag of 4 (unmapped), 256 (not primary alignment), or 2048 (supplementary alignment) and those of which mapping quality less than 10 were excluded using Samtools v 1.3.1 (Li et al. 2009 ) (i.e., -F 2308 -q 10 ). Genotype calling was performed using FreeBayes (v1.0.2) (Garrison and Marth, 2012 ). Subsequently, only biallelic SNPs with minor allele frequency larger than 0.02 were extracted and SNPs with high missing rate (> 40%) were excluded using VCFtools (v0.1.14) (Danecek et al., 2011 ). These per-chromosome genotype files were concatenated using BCFtools concat (v1.3.1) (Li 2009; Danecek et al., 2021 ). Additionally, genotypes with low ( 300) depth of coverage were masked and missing rate filter was applied again (i.e., retained SNPs with maximum missing rate of 40%). Finally, individuals of which genotype call rate less than 85% were excluded. The final number of individuals was 198 and the number of SNPs was 5,929. While the SNP size is enough for accurate estimation of heritability and breeding value for aquaculture species (Kariaridou et al, 2020; Hosoya et al, 2021 ), the sample size is insufficient for this purpose. However, precise estimation of these parameters is not the focus of this study. In addition, we selected broodstock candidates based on both breeding value and maturity status. Therefore, limited sample size does not pose a problem in this study. Genomic estimated breeding value (GEBV) were estimated for folk length and body weights at 33 months old using genomic best linear unbiased prediction (GBLUP) model per phenotype: y = µ + Za + e , where y , µ , and e are the vector of observed phenotype, the phenotypic means, and the residuals, respectively; Z is the corresponding incidence matrices for the additive effects a , which follows a normal distribution ~ N (0, Gσ a 2 ), where G is the genomic relationship matrix and σ a 2 is the genetic variance. We assume the residuals follows a normal distribution ~ N (0, Iσ e 2 ), where I is an identity matrix and σ e 2 is the error variance. The G matrix was obtained with the A.mat function and the GBLUP model was solved using the kin.blup of the R package rrBLUP (4.4) (Endelman, 2011 ). Narrow sense heritability was determined as h 2 = σ a 2 /( σ a 2 + σ e 2 ). We selected 40 individuals based on the rank of GEBVs, i.e., sum of the GEBVs divided by the average for each phenotype, and crossed with 127 females randomly sampled from SB to create a new breed (NB) in December 2016. Note that these 40 sires are not necessarily the top 40 individuals, as some of the top individuals did not reach maturity at mating and some others have died before the breeding season (Supplementary Table S3). A new generation of SB (223 dam x 110 sire) and RB (37 dam x 40 sire) were created at the same timing. The number of broodfish for RB was smaller than that for SB because the number of matured individuals was smaller in RB than SB due to their smaller body size. Assessments of genetic diversity and differences among three populations To assess the genetic differences among SB ( n = 95), RB ( n = 96), and NB ( n = 100), 11 microsatellite markers designed in previous studies (Beacham et al., 2011 ; Khoo et al., 2000 ; Nelson et al., 1998 ; Palti et al., 2002 ; Rexroad et al., 2002 ; Rodriguez et al., 2003 ; Sakamoto et al., 1996 ; Scribner et al., 1996 ; Smith et al. 1998 ; Williamson et al. 2002 ) were genotyped for 96, 95, and 100 individuals, respectively (Supplementary Table S4). These three groups were kept separately from fertilization, and thus their genetic origins were known. Genomic DNA was collected from adipose fin clip using Gentra Puregene Tissue Kit (QIAGEN). The 11 loci were amplified using tailed primer method (Schuelke 2000 ; Sekino 2006) where 19-bp of M13 sequence (CACGACGTTGTAAAACGAC) was added to the 5' end of the forward primer to label PCR amplicon with fluorescent dye (i.e., FAM, VIC, NED, PED). Polymorphisms were analyzed on ABI3130 DNA Analyzer (Applied Biosystems, MA, USA). The PCR conditions for each markers are described in Supplementary Information S5 The allele richness ( A R ) was obtained using allel.rich function of R package PopGenReport ver3.0.0 (Adamack and Gruber, 2014 ) and the number of effective allele ( A E ) was calculated as 1/ \(\:\sum\:_{i=1}^{n}{p}_{i}\) were p i is the frequency of each allele and n is the number of allele at the locus. Observed heterozygosity ( H O ), mean gene diversities within population ( H S ), and fixation index ( F IS ) were obtained using basic.stats function of R package hierfstat (ver0.5-11) (Goudet 2005 ). In addition, pair-wise F ST (Weir and Cockerham, 1984 ) and its 95% confidential interval were calculated using genet.dist and boot.ppfst (the number of bootstraps = 100) functions of hierfstat, respectively. Discriminant analysis of principal components (DAPC) was conducted to assess the population structure using dapc function of the R package adegenet (ver2.1.10) (Jombart, 2008 ); the number of axes retained in the Principal Component Analysis (PCA) step was set 60 ( n.pca = 60 ) and the number of axes retained in the Discriminant Analysis step as 2 ( n.da = 2 ). Scatter plot was drawn using scatter function of adegenet. We also conducted STRUCTURE analysis (Hubisz et al., 2009 ) assuming the number of max populations as 2 ( MAXPOPS 2 ) and no admixture. Progeny test We compared the growth performance among the three populations reared communally. Siblings of individuals used for the genetic comparison were involved. At the swim-up stage, individuals from the three populations were mixed in a communal holding tank. Thus, the genetic origin of each individual was unknown. After nine months from fertilization (August, 2017), a total of 737 individuals were transferred into five 1-kL circular tanks (148, 149, 143, 148, 149 fish per tank) each individual was tagged with a passive integrated transponder (PIT) tag and dissected adipose fin for genomic DNA extraction. Meanwhile, fork length (FL) and body weight (BW) were recorded. Then these fish were returned back into their original tank. They were reared another three months (until November, 2017) and measured FL and BW. At this sampling point, 15 fish were lost due to mortality or tag drop-off, resulting in 722 specimens used for the assignment test. Population assignments As the genetic origin of the 722 individuals were unknown, their population ID were inferred from the 11 microsatellite markers. Genomic DNA extraction and genotyping of the 11 loci were done as described above. We used NewHybrid software (Anderson, 2002 ) with initial 20,000 burn-in and subsequent 50,000 post burn-in sweeps. The genotype data collected from the genetic diversity analysis was included to utilize them as the training set. The accuracy of population assignments was evaluated from the proportion of the individuals accurately assigned to the original population. In addition, correlations in allele frequencies between inferred population (inferred RB, inferred SB, and inferred NB) and their sibling groups (RB, SB, and NB, respectively) were assessed per locus using Spearman’s correlation coefficient (ρ). Furthermore, pairwise Weir and Cockerham F ST was calculated among the six groups. Statistical analysis for growth performance comparison Population difference in growth performance was examined using general linear mixed model (LMM). The model was: y = Xb + Za + e , where y , b , and a are vectors of phenotype, fixed population effects, and random tank effects, respectively; X and Z are the design matrix for b and a ; e is a vector of residuals. The equation was solved using glm function of R/stats. Model comparison was done among models (i.e., with and without the population effects) based on Akaike’s information criterion (AIC) (Akaike 1973 ). Least squares mean for each population was compared using emmeans function of R package emmeans (ver. 1.7.2) (Lenth, 2023). The significant threshold was set as p < 0.05 and p -value were adjusted using Bonferroni method. Results Genomic selection from the RB population produced in 2013 Individual selection from the RB population produced in 2013 was done using GBLUP method (198 males and 5,929 SNPs). The average FL and BW (± standard deviation) of these individuals were 26.7 (± 29.3) cm and 230.5 (± 76.4) g, respectively. The distribution of these phenotype was apart from normal distribution (Shapiro-Wilk test: p = 0.034 and 0.005, respectively) (Fig. 1 a and 1 b) partly because the initial sampling of the 768 individuals was not random but chose individuals with male-like morphology. Thus, smaller individuals were not involved. Reflecting this, at least partially, the estimated heritability was low for both FL and BW ( h 2 = 0.119 and 0.103, respectively) and the correlation efficient between observed phenotype and GEBV were moderate ( r = 0.731 and 0.727, respectively) (Fig. 1 c and 1 d). We ranked the 198 males based on the sum of GEBVs divided by the average for FL and BW and selected 40 matured individuals from top rank (Supplementary Table S1 ). Restoration of genetic diversity in NB To assess the restoration of genetic diversity in NB, we genotyped 11 microsatellite markers and compared genetic parameters among RB, SB, and NB. Genotype information of each individual is available in Supplementary Table S6. The mean values of the number of allele ( A N ), allele richness ( A R ), the number of effective alleles ( A E ), observed heterozygosity ( H O ), mean gene diversities within population ( H S ), and fixation index ( F IS ) are listed in Table 1 (detailed information is available in Supplementary Table S2). As expected, mean values of SB were smaller than RB, but increased in NB in each statistics. This result clearly shows a restoration of genetic variation by introducing individuals from external populations. Only the exception was F IS , where the value was smaller in SB than in RB. It is expected that the higher the F IS , the higher the genetic relatedness of the parental individuals. However, F IS does not necessarily represent the genetic diversity of population level. Indeed, the value of RB is close to zero while H o was smallest in SB. The negative F IS of NB indicates heterozygote excess. This was in line with the expectation as NB was produced between the two genetically isolated populations. Extent of genetic differentiation among the three populations We evaluated the extent of genetic differentiation among populations using pair-wise Wier Cockerham F ST (Table 2). Significant genetic differentiation was observed in each pair, but the extent of differentiation was mitigated between NB and RB (0.046) or SB (0.041), compared to RB–NB pair (0.135). Genetic differentiation was also confirmed from DAPC analysis (Fig. 2 ). While RB and SB populations were clearly separated, NB population was plotted between the two populations and some extent of overlap at individual level was observed. The STRUCTURE analysis also revealed genetic differentiation at population level (Fig. 3 ). On the other hand, some individuals of RB and SB showed higher similarity between NB, while some of NB were closer to RB or SB. Population assignments Within the individuals with known population information, 1 and 3 individuals from RB were miss-assigned to SB and NB, respectively, while 3 individuals of SB were miss-assigned to NB (Table 3). On the other hand, 6 and 4 individuals of NB were miss-placed in RB and SB, respectively. The overall accuracy of population assignments for the 291 individuals with population records were 94.2% (274 out of 291): RB = 95.8%, SB = 96.8%, NB = 90.0%. It should be noted that these miss-assignments were not due to technical error, but reflected the genetic variation within each population. The 722 individuals without population information were evenly assigned to the three populations (infRB = 243, infSB = 248, infNB = 231) (Supplementary Table S7). The results of population assignments were assessed using allele frequency. The Spearman’s correlation coefficient ( ρ ), calculated for allele frequencies between each congenic population pair (i.e., infRB–RB, infSB–SB, and infNB–NB), ranged 0.762 to 1.000 and mean values were 0.938, 0.950, and 0.936, respectively (Table 4). We also examined the degree of population differentiation among the six groups (RB, SB, NB, infRB, infSB, and infNB). Between the two sibling pairs, F ST values were nearly zero, although the 95% CI was larger than zero between infRB–RB pair (0.001–0.009). When F ST values between non-sibling pairs were compared, the inferred population and their sibling group showed similar values (e.g., RB–SB = 0.135 and RB–infSB = 0.130). These results indicate that inferred populations and their sibling groups had highly similar allele frequency. Thus, it can be said the accuracy of the population assignments was high. Progeny test The effect of selective introgression from RB into SB was evaluated by comparing growth performance among the three inferred population. At first, significance of population effect was tested using AIC. The AIC values of the full model was smaller than that of the model without population effect, indicating the significance of population effect (Table 6). The least square mean of infSB (FL = 161.1 mm, P = 0.381; BW = 53.7 g, P = 0.355) was significantly larger than that of infRB (FL = 150.4 mm, BW = 43.0 g), confirming significant genetic improvements in the infSB lines (Fig. 4 ). The mean values of infNB (FL = 164.9 mm, BW = 57.9 g) was significantly larger than that of infRB while slightly, but not significantly, larger than that of infSB, suggesting that the selective introgression did not deteriorate the growth performance of the target population (i.e., SB). Discussion Gene flow from an external population may not effectively restore the genetic diversity of the target population if the introduced individuals share many alleles. This is particularly the case when small number of individuals were selected through genomic selection, as this approach tends to select individuals with similar genotypes across the genome (Sonesson et al, 2012 ). Therefore, the number of individuals selected (i.e., selection intensity) must be carefully considered. In this study, we selected 40 individuals from a pool of 198 based on two criteria: GEBV rank and availability for breeding. This relaxed selection resulted in selection of a genetically diverse group of individuals. While the number of introduced individuals (40) was chosen arbitrarily, it is advisable to determine the appropriate number in advance. For genetic rescue in the wild, this number depends on the level of inbreeding and the effective population size of the source and the target populations, leading to fewer established guidelines (Frankham, 2015; Shannon et al, 2023). When highly polymorphic markers, such as microsatellites, are available, the optimal number can be estimated based on allele frequency. When genome-wide SNPs are used, genetic distance or relatedness among selection candidates can guide decision-making. Alternatively, a two-dimensional site frequency spectrum (SFS) drawn from selection candidates and entire populations can provide a clear visual assessment of rare allele drop off. The SNP data used for genomic selection is directly available to calculate such statistics. The growth performance of NB population was comparable to that of SB population, indicating that hybridization between selected RB individuals and SB did not adversely affect growth performance. Along with the increased genetic diversity observed sin NB, our results indicate the effectiveness of the genome-assisted gene flow in restoring genetic diversity in a selectively bred population without compromising the growth performance in the recipient population (i.e., SB). One possible factor contributing to the sustained high growth performance in NB is outcross enhancement, or hybrid vigor, where offspring exhibit phenotypes superior to those of the parental populations. Indeed, genetic statistics such as the effective number of alleles and observed heterozygosity indicated a loss of genetic diversity in the SB population. However, these values were not markedly lower than those in the other two populations, and F IS value was less than zero. In addition, we did not observe abnormal phenotypes in the SB population. These results suggest that the population is not experiencing severe inbreeding depression, and that hybrid vigor is likely not the primary driver behind enhanced growth performance in the NB. A limitation of this study is that growth performance was only assessed in the first generation, and there is no guarantee that this performance will be maintained at the same level in the future generations. This is especially true if outcross enhancement had a positive effect on the growth of NB. To sustain growth performance for generations, relaxed truncation selection may be needed to eliminate smaller individuals from broodstock. In this case, care must be taken to prevent loss of genetic variation. Another limitation is the uncertainty surrounding whether similar results would be achieved if the growth performance disparity between the selected and source population was much greater than what we observed in this study. The success of genome-assisted gene flow depends on many factors and may not always restore genetic diversity without sacrificing accumulated genetic merit. To balance recovery of genetic diversity with the maintenance of an improved phenotype, a well-managed breeding plan is essential. More importantly, regardless of the application of gene flow, careful genetic management should be always prioritized in aquaculture breeding. Although such limitations needed to be addressed in future studies, our study demonstrated the feasibility of gene flow with genomic selection for genetic management of small populations. This will help sustainable managements of Japanese aquaculture populations coho salmon. As small-scale aquaculture production is crucial for global food security and local economies (Garlock et al, 2022 ; Dam Lam et al, 2022 ), the technologies that allow improvements in genetic merits and managements of genetic health of such populations simultaneously will significantly enhance the sustainability and resilience of aquaculture, contributing to the global sustainable developmental goals (SDGs) (Sonesson et al., 2023 ). Genome-assisted gene flow, as utilized in this study, represents one of such promising technologies. Declarations Competing Interests The authors declare no competing interests. Contributions Conceptualization: Sho Hosoya, Tadahide Kurokawa, Kiyoshi Kikuchi; Methodology: Junya Kobayashi, Ryo Honda, Sho Hosoya, Atsushi J. Nagano,, Tadahide Kurokawa, Kiyoshi Kikuchi; Formal analysis and investigation: Junya Kobayashi, Ryo Honda, Sho Hosoya, Masaki Yasugi, Atsushi J. Nagano; Writing - original draft preparation: Junya Kobayashi, Ryo Honda, Sho Hosoya; Writing - review and editing: Sho Hosoya; Tadahide Kurokawa, Kiyoshi Kikuchi; Funding acquisition: Tadahide Kurokawa, Sho Hosoya; Resources: Ryo Honda, Yuki Nochiri, Keisuke Matsuzaki, Koichi Sugimoto, Akira Kumagaya; Supervision: Tadahide Kurokawa, Sho Hosoya. Funding information This work was partially granted by the Agriculture, Forestry and Fisheries Research Council (AFFRC), Japan granted to T.K., and Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) (21H02279) granted to S.H.. Author Contribution S.H., K.K., and T.K. conceptualize the research; T.K., A.K., K.K., and S.H. contributed funding acquisition; R.H. conducted rearing experiments; R.H., Y.N., K.M., K.S., and A.K. contributed fish maintenance and data collection; J.K. conducted laboratory experiments; A.J.N. did ddRAD-seq; J.K. and S.H. did statistical analysis, data curation, and wrote the main manuscript text; K.K., and T.K. contributed revising the manuscript; all authors reviewed the manuscript. Data Availability Sequence data have been deposited in DNA Data Bank of Japan (DDBJ) with the BioSample Submission ID: SSUB031092. Other data is provided within supplementary information file. References Adamack AT, Gruber B (2014) PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384–387. https://doi.org/10.1111/2041-210X.12158 Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In B. N. Petrov F. Caski (Eds.), Proceedings of the 2nd International Symposium on Information Theory (pp. 267–281). Akadémiai Kiado Allendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. Nat Rev Genet 11:697–709. https://doi.org/10.1038/nrg2844 Allendorf FW, Leary RF, Spruell P, Wenburg JK (2001) The problems with hybrids: Setting conservation guidelines. Trends Ecol Evol 16:613–622 https://doi.org/10.1016/S0169-5347(01)02290-X Anderson EC, Thompson EA (2002) A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160:1217–1229 Beacham TD, Wetklo M, Deng L, MacConnachie C (2011) Coho salmon population structure in North America determined from microsatellites. Trans Am Fish Soc 140:253–270 Bolger AM, Lohse M, Usade B (2014) Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120 Boudry P, Allal F, Aslam ML, Bargelloni L, Bean TP, Brard-Fudulea S, Brieuc MS, Calboli FC, Gilbey J, Haffray P, Lamy J, Morvezen R, Purcell C, Prodöhl PA, Vandeputte M, Waldbieser GC, Sonesson AK, Houston RD (2021) Current status and potential of genomic selection to improve selective breeding in the main aquaculture species of International Council for the Exploration of the Sea (ICES) member countries. Aquac Rep 20:100700. https://doi.org/10.1016/j.aqrep.2021.100700 Brunelli JP, Thorgaard GH (2004) A new Y-chromosome-specific marker for Pacific salmon. Trans Am Fish Soc 133:1247–1253 Carlson SM, Cunningham CJ, Westley PA (2014) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521–530. https://doi.org/10.1016/j.tree.2014.06.005 Dam Lam R, Barman BK, Lozano Lazo DP et al (2022) Sustainability impacts of ecosystem approaches to small-scale aquaculture in Bangladesh. Sustain Sci 17:295–313. https://doi.org/10.1007/s11625-021-01076-w Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, 1000 Genomes Project Analysis Group (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158 Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve years of SAMtools and BCFtools. GigaScience 10. https://doi.org/10.1093/gigascience/giab008 Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250–255 Frankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465–475. https://doi.org/10.1111/j.1523-1739.2011.01662.x Garlock T, Asche F, Anderson J, Ceballos-Concha A, Love DC, Osmundsen TC, Pincinato RBM (2022) Aquaculture: The missing contributor in the food security agenda. Glob Food Secur 32:100620. https://doi.org/10.1016/j.gfs.2022.100620 Garrison E, Marth G (2012) Haplotype-based variant detection from short-read sequencing. ArXiv 1207.3907 Ghildiyal K, Nayak SS, Rajawat D, Sharma A, Chhotaray S, Bhushan B, Dutt T, Panigrahi M (2023) Genomic insights into the conservation of wild and domestic animal diversity: A review. Gene 886:147719. https://doi.org/10.1016/j.gene.2023.147719 Gjedrem T, Kolstad K (2012) Development of breeding programs for aquatic species should be given high priority. World Aquac 43:10–13 Gjøen HM, Bentsen HB (1997) Past, present, and future of genetic improvement in salmon aquaculture. ICES J Mar Sci 54:1009–1014 Goudet J (2005) Hierfstat, a package for R to compute and test variance components and F-statistics. Mol Ecol Notes 5:184–186 Guhlin J, Le Lec MF, Wold J, Koot E, Winter D, Biggs PJ, Galla SJ, Urban L, Foster Y, Cox MP, Digby A, Uddstrom LR, Eason D, Vercoe D, Davis T, Howard JT, Jarvis ED, Robertson FE, Robertson BC, Dearden PK (2023) Species-wide genomics of kākāpō provides tools to accelerate recovery. Nat Ecol Evol 7:1693–1705. https://doi.org/10.1038/s41559-023-02165-y Hagedorn M, Page CA, Flores LK, Tichy DM, Conn L, Chamberland T, Lager VF, Zuchowicz C, Lohr N, Blackburn K, Vardi H, Moore T, Moore J, Baums T, Vermeij IB, Marhaver MJ KL (2021) Assisted gene flow using cryopreserved sperm in critically endangered coral. Proc Natl Acad Sci USA 118 https://doi.org/10.1073/pnas.2110559118 Hosoya S, Yoshikawa S, Sato M, Kikuchi K (2021) Genomic prediction for testes weight of the tiger pufferfish, Takifugu rubripes , using medium to low density SNPs. Sci Rep 11:20372. https://doi.org/10.1038/s41598-021-99829-1 Houston RD, Kriaridou C, Robledo D (2022) Animal board invited review: Widespread adoption of genetic technologies is key to sustainable expansion of global aquaculture. Anim 16:100642. https://doi.org/10.1016/j.animal.2022.100642 Huang SSO, Liao IC (1990) Response to mass selection for growth rate in Oreochromis niloticus. Aquaculture 85:199–205 Hubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322–1332. https://doi.org/10.1111/j.1755-0998.2009.02591.x Hulata G, Wohlfarth GW, Halevy A (1986) Mass selection for growth rate in the Nile tilapia ( Oreochromis niloticus ). Aquaculture 57:177–184 Jombart T (2008) adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405 Kriaridou C, Tsairidou S, Houston RD, Robledo D (2020) Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms. Fron Genet 11:515310. https://doi.org/10.3389/fgene.2020.00124 Khoo SK, Ozaki A, Sakamoto T, Okamoto N (2000) Four highly polymorphic dinucleotide microsatellites in rainbow trout ( Oncorhynchus mykiss ). Anim Genet 31:73–74 Kincaid HL (1983) Inbreeding in fish populations used for aquaculture. Aquaculture 33:215–227 Knibb W, Giang C, Premachandra H, Ninh N, Domínguez B (2020) Feasible options to restore genetic variation in hatchery stocks of the globally important farmed shrimp species, Litopenaeus vannamei . Aquaculture 518:734823 https://doi.org/10.1016/j.aquaculture.2019.734823 Knibb W, Whatmore P, Lamont R, Quinn J, Powell D, Elizur A, Anderson T, Remilton C, Nguyen NH (2014) Can genetic diversity be maintained in long term mass selected populations without pedigree information?—A case study using banana shrimp Fenneropenaeus merguiensis . Aquaculture 428:71–78 Kristensen TN, Hoffmann AA, Pertoldi C, Stronen AV (2015) What can livestock breeders learn from conservation genetics and vice versa? Front Genet 6:38. https://doi.org/10.3389/fgene.2015.00038 Lenth RV (2022) emmeans: Estimated marginal means, aka least-squares means (R package version 1.7.2). https://CRAN.R-project.org/package=emmeans Li H (2013) Aligning sequence reads, clone sequences, and assembly contigs with BWA-MEM. ArXiv 1303.3997 Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25:2078–2079 Nelson RJ, Beacham TD, Small MP (1998) Microsatellite analysis of the population structure of a Vancouver Island sockeye salmon ( Oncorhynchus nerka ) stock complex using nondenaturing gel electrophoresis. Mol Mar Biol Biotechnol 7:312–319 Palti Y, Fincham MR, Rexroad CE III (2002) Characterization of 38 polymorphic microsatellite markers for rainbow trout ( Oncorhynchus mykiss ). Mol Ecol Resour 2:449–452 Pregler KC, Obedzinski M, Gilbert-Horvath EA, White B, Carlson SM, Garza JC (2023) Assisted gene flow from outcrossing shows the potential for genetic rescue in an endangered salmon population. Conserv Lett 16. https://doi.org/10.1111/conl.12934 Rexroad CE, Coleman RL, Hershberger WK, Killefer J (2002) Rapid communication: Thirty-eight polymorphic microsatellite markers for mapping in rainbow trout. J Anim Sci 80:541–542 Rodriguez F, Rexroad CE, Palti Y (2003) Characterization of twenty-four microsatellite markers for rainbow trout ( Oncorhynchus mykiss ). Mol Ecol Notes 3:619–622 Sakaguchi S, Sugino T, Tsumura Y (2015) High-throughput DNA marker system for molecular breeding of conifers: Development of a linkage map and identification of candidate markers for wood properties in Cryptomeria japonica. Tree Genet Genomes 11:103 Sakamoto T, Okamoto N, Ikeda Y (1996) Application of PCR primer pairs from rainbow trout to detect polymorphisms of CA repeat DNA loci in five confamilial species. Fish Sci 62:552–555 Sasaki K, Kurokawa T, Nikaido H, Muraoka D, Okuda Y (2017) Did farmed coho Salmon Oncorhynchus kisutch that escaped during the earthquake and tsunami disaster of 2011 interbreed with native masu salmon Oncorhynchus masou ? Bull Jpn Fish Res Edu Agen 45:63–67 Sekino M, Kobayashi T, Hara M (2006) Segregation and linkage analysis of 75 novel microsatellite DNA markers in pair crosses of Japanese abalone ( Haliotis discus hannai ) using the 5′-tailed primer method. Mar Biotechnol 8:453–466 https://doi.org/10.1007/s10126-005-6179-6 Schmidt TL, Thia JA, Hoffmann AA (2022) How can genomics help or hinder wildlife conservation? Annu Rev Anim Biosci 12:45–68. https://doi.org/10.1146/annurev-animal-021022-051810 Schuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233–234. https://doi.org/10.1038/72708 Scribner KT, Gust JR, Fields RL (1996) Isolation and characterization of novel salmon microsatellite loci: Cross-species amplification and population genetic applications. Can J Fish Aquat Sci 53:833–841 Smith CT, Koop BF, Nelson RJ (1998) Isolation and characterization of coho salmon ( Oncorhynchus kisutch ) microsatellites and their use in other salmonids. Mol Ecol 7:1614–1616 Sonesson AK, Hallerman E, Humphries F, Hilsdorf AWS, Leskien D, Rosendal K, Bartley D, Hu X, Gomez RG, Mair GC (2023) Sustainable management and improvement of genetic resources for aquaculture. J World Aquac Soc 54:364–396 https://doi.org/10.1111/jwas.12968 Sonesson AK, Woolliams JA, Meuwissen TH (2012) Genomic selection requires genomic control of inbreeding. Genet Sel Evol 44:27. https://doi.org/10.1186/1297-9686-44-27 Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:131. https://doi.org/10.1186/s13059-018-1520-3 Taberlet P, Valentini A, Rezaei HR, Naderi S, Pompanon F, Negrini R, Ajmone-Marsan P (2007) Are cattle, sheep, and goats endangered species? Mol Ecol 17:275–284. https://doi.org/10.1111/j.1365-294X.2007.03475.x Telchert-Coddington R, Smitterman RO (1988) Lack of response by Tilapia nilotica to mass selection for rapid early growth. Trans Am Fish Soc 117:290–300 Theissinger K, Fernandes C, Formenti G, Bista I, Berg PR, Bleidorn C, Bombarely A, Crottini A, Gallo GR, Godoy JA, Jentoft S, Malukiewicz J, Mouton A, Oomen RA, Paez S, Palsbøll PJ, Pampoulie C, Ruiz-López MJ, Secomandi S, Svardal H, Theofanopoulou C, Vries JD, Waldvogel AM, Zhang G, Jarvis ED, Bálint M, Ciofi C, Waterhouse RM, Mazzoni CJ, Höglund J, The European Reference Genome Atlas Consortium (2023) How genomics can help biodiversity conservation. Trends Genet 39:545–559. https://doi.org/10.1016/j.tig.2023.01.005 Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370 Williamson KS, Cordes JF, May B (2002) Characterization of microsatellite loci in chinook salmon ( Oncorhynchus tshawytscha ) and cross-species amplification in other salmonids. Mol Ecol Notes 2:17–19 White SL, Rash JM, Kazyak DC (2023) Is now the time? Review of genetic rescue as a conservation tool for brook trout. Ecol Evol 13. https://doi.org/10.1002/ece3.10142 Tables Table 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.xlsx Table2.xlsx Table3.xlsx Table4.xlsx Table5.xlsx Table6.xlsx SuppleFile.xlsx Cite Share Download PDF Status: Published Journal Publication published 01 Feb, 2025 Read the published version in Marine Biotechnology → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 09 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 15 Nov, 2024 Reviewers agreed at journal 15 Nov, 2024 Reviewers invited by journal 15 Nov, 2024 Editor assigned by journal 13 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 13 Nov, 2024 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. 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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-5444805","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":388506899,"identity":"e9aa5072-425c-4640-83f5-f03a4422bd18","order_by":0,"name":"Junya Kobayashi","email":"","orcid":"","institution":"University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Junya","middleName":"","lastName":"Kobayashi","suffix":""},{"id":388506900,"identity":"10ac6e62-236c-402f-8d88-576abd0ecadb","order_by":1,"name":"Ryo Honda","email":"","orcid":"","institution":"Miyagi Prefecture Fisheries Technology Institute, Freshwater Fisheries Experimental Station","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Honda","suffix":""},{"id":388506901,"identity":"1c361bf1-b32c-4b8a-bb04-1fdfee8716a7","order_by":2,"name":"Sho Hosoya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABM0lEQVRIie2QsUrEMBjHv3BwXXp0TRHaV2gpFETos+QotEsPNxEUzVGIr9DDZ3AtjoVAXYq3ZnBoEZwcUlxFzF3v8KQVHAX7g5CPJL/vnwRgZOQvgrvJAkDL4mu5+L49pHhKoftziP5GmdOD1gfKAMZtymXLgvjuhquU88AGTLRaVtwCjddwfN8PeSojc8XCRV7NlVKFLsUELTPBPdAjB8yqHyMSfzJjk0VeKAWxggA+bVNdcnXVBMBkPcMWifc2Y9exv26U8rFRCOoU43VQcUTiHM0YJ77YpNC9IpSCh1NcEUVm9vjg5kKlkDJ0mV6jVVbF3hS/OMXAWywRcinPLmx/HTe1vAxsQyMgZXliGUbYPJv9H9uCpruCqLGr023BTTqswHt/6apr1v6kjIyMjPwjPgGmpnQR+lDz8AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Sho","middleName":"","lastName":"Hosoya","suffix":""},{"id":388506902,"identity":"e135ada0-6063-4ddd-bc25-fbe5561c48be","order_by":3,"name":"Yuki Nochiri","email":"","orcid":"","institution":"Miyagi Prefecture Fisheries Technology Institute, Freshwater Fisheries Experimental Station","correspondingAuthor":false,"prefix":"","firstName":"Yuki","middleName":"","lastName":"Nochiri","suffix":""},{"id":388506903,"identity":"1c70e691-18bc-4d2f-ab54-8703a0937d59","order_by":4,"name":"Keisuke Matsuzaki","email":"","orcid":"","institution":"Miyagi Prefecture Fisheries Technology Institute, Freshwater Fisheries Experimental Station","correspondingAuthor":false,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"Matsuzaki","suffix":""},{"id":388506904,"identity":"8cd99fc1-9bc2-4853-88e9-ffef957268d6","order_by":5,"name":"Koichi Sugimoto","email":"","orcid":"","institution":"Miyagi Prefecture Fisheries Technology Institute, Freshwater Fisheries Experimental Station","correspondingAuthor":false,"prefix":"","firstName":"Koichi","middleName":"","lastName":"Sugimoto","suffix":""},{"id":388506905,"identity":"e10d0eab-52f9-47c0-8ec5-41b1eb384f13","order_by":6,"name":"Atsushi J. 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The X- and Y- axes are the 1st and 2nd principal component of DAPC, respectively. Each point represents individuals of RB (red), SB (green), and NB (blue) populations. The insets on top-right and top-left shows scree plots of PCA and DA eigenvalues, respectively.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/c0e07d8fbea814b3b2bc05bb.png"},{"id":71683924,"identity":"265e50e5-e638-4aed-82ba-c0893ca26c0b","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"eps","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224037,"visible":true,"origin":"","legend":"Histogram of folk length (a) and body weight (b), and correlation between observed and predicted values for folk length (c) and body weight (d).","description":"","filename":"Fig1.eps","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/ac533a64f58d4c9312f0fd6d.eps"},{"id":71684599,"identity":"2dbf52dc-10d6-41f7-a640-353903d2ef90","added_by":"auto","created_at":"2024-12-17 17:07:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13180,"visible":true,"origin":"","legend":"\u003cp\u003eSTRUCTURE assignment plots. Each vertical bar indicates cumulative assignment probability for RB (red), SB (green), and NB (blue) populations for each individual (RB: \u003cem\u003en\u003c/em\u003e = 95, SB: \u003cem\u003en\u003c/em\u003e = 96, NB: \u003cem\u003en\u003c/em\u003e= 100)\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/e2e814e53eca38a209770237.png"},{"id":71685488,"identity":"67cdd5d8-6a38-409a-afe1-24d2b9888078","added_by":"auto","created_at":"2024-12-17 17:15:41","extension":"eps","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":648498,"visible":true,"origin":"","legend":"DAPC scatter plot. The X- and Y- axes are the 1st and 2nd principal component of DAPC, respectively. Each point represents individuals of RB (red), SB (green), and NB (blue) populations. The insets on top-right and top-left shows scree plots of PCA and DA eigenvalues, respectively.","description":"","filename":"Fig2.eps","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/4421338f2adbf774deefa826.eps"},{"id":71684594,"identity":"1fdbea87-295f-4155-8fa6-f8fc47b2562f","added_by":"auto","created_at":"2024-12-17 17:07:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31768,"visible":true,"origin":"","legend":"\u003cp\u003eLeast square mean of folk length (left) and body weight (right) of each inderred population. Vertical segments represent 95% confidential interval. The values on the horizontal lines show adjusted \u003cem\u003eP\u003c/em\u003e values for each comparison.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/f2444bbf3ab470d7afe6d6f0.png"},{"id":71684604,"identity":"84864202-e706-46a5-8a1e-e9d51102b37d","added_by":"auto","created_at":"2024-12-17 17:07:41","extension":"eps","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":112999,"visible":true,"origin":"","legend":"STRUCTURE assignment plots. Each vertical bar indicates cumulative assignment probability for RB (red), SB (green), and NB (blue) populations for each individual (RB: \u0026thinsp;=\u0026thinsp;95, SB: \u0026thinsp;=\u0026thinsp;96, NB: \u0026thinsp;=\u0026thinsp;100)","description":"","filename":"Fig3.eps","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/c649645bd6f2eb3ba1bb11aa.eps"},{"id":71683942,"identity":"ad51735a-8a80-4fa3-912a-d663a9f9b300","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"eps","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":45197,"visible":true,"origin":"","legend":"Least square mean of folk length (left) and body weight (right) of each inderred population. Vertical segments represent 95% confidential interval. The values on the horizontal lines show adjusted values for each comparison.","description":"","filename":"Fig4.eps","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/f849ea7b844c593617c47903.eps"},{"id":75351971,"identity":"8374621a-ac8e-4874-8519-ba0880797844","added_by":"auto","created_at":"2025-02-03 16:12:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197738,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/194d9c05-bbbc-4c0d-ba7c-ea207d55d9a1.pdf"},{"id":71683925,"identity":"79e652aa-e39b-44ee-a81d-e9919450ab17","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10056,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/3f1513a08edd463fbe6e986c.xlsx"},{"id":71683920,"identity":"345f64ae-8ead-4ce2-899e-b6c8e582fad4","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":9688,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/746613002d1f670743ed00ae.xlsx"},{"id":71685487,"identity":"41416b24-96e5-4e5c-9f19-a559550a1c03","added_by":"auto","created_at":"2024-12-17 17:15:41","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9737,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/5b161cfcf34e7c8523307042.xlsx"},{"id":71683928,"identity":"2e64c3c9-10ed-4bc2-be6b-e1bc3ad03c24","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10107,"visible":true,"origin":"","legend":"","description":"","filename":"Table4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/ecacd86de14cb3328f447538.xlsx"},{"id":71684596,"identity":"cefe1ce7-227c-4231-a2e2-5ee47bd50146","added_by":"auto","created_at":"2024-12-17 17:07:41","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":10058,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/277b416c16ef191d427a42d0.xlsx"},{"id":71683933,"identity":"16497dd6-8e1b-4544-8986-ccb94963c2dd","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":9545,"visible":true,"origin":"","legend":"","description":"","filename":"Table6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/46218f2b8d4758d644583771.xlsx"},{"id":71683943,"identity":"f9cd2450-236e-4f4c-8664-a28090430dd1","added_by":"auto","created_at":"2024-12-17 16:59:41","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":219896,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5444805/v1/32b9430655313998e78197ef.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome assisted gene-flow rescued genetic diversity without hindering growth performance of inbred coho salmon (Oncorhynchus kisutch) population selected for high growth phenotype","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAquaculture is an important industry that plays an important role in securing food in today's world, where the demand for food is increasing due to global population growth and changes in the global environment (FAO. The state of world fisheries and aquaculture 2018. Meeting the sustainable development goals. Rome: FAO and UNEP; 2018). While there are various approaches to improve production efficiency, scientific selective breeding is one of the powerful tools that improve aquaculture production (Gjerdem 2012). For example, a large amount of production of major aquaculture species such as farmed Atlantic salmon (\u003cem\u003eSalmo salar\u003c/em\u003e), rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) and Genetically Improved Farmed Tilapia (GIFT), are derived from selective breeding populations. To gain the merits from selective breeding a well-managed breeding program is required (Houston et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boudry et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), otherwise misconducted program, i.e., the use of small number broodfish and/or the use of close relatives only, will result in losses in genetic diversity leading poor selection response, inbreeding excess necessitating termination or revival of the program (Gjedrem and Baranski, 2009; Gjedrem and Kolstad, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Huang and Liao, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hulata et al, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Kincaid, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1983\u003c/span\u003e; Knibb et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Telchert-Coddington and Smitterman, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). This is especially true for breeding programs with small population, and special cares for genetic diversity should be paid for such programs (Taberlet et al, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen a loss of genetic diversity exceeded the level required for sustainable management, the broodstock may require restoration of genetic variation. Restoration of genetic diversity can be partly achieved by increasing the number of broodfish. However, this approach cannot refill the alleles which have lost due to genetic drift. On the other hand, introduction of genetic material from external population can increase the number of alleles, allele richness, and gene diversity (Gj\u0026oslash;en and Bentsen \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Knibb et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This is analogous to the genetic rescue, or assisted gene flow, applied in conservation study (Kristensen et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ghildiyal et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pregler et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Genetic rescue is the attempt to increase genetic diversity and mitigate detrimental effects from inbreeding by introducing genetic resources from external population. While introduction of individuals from outside can increase genetic diversity, it can also reduce the fitness of the wild population (i.e., outbreeding depression) (Allendorf et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Frankham et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) or the accumulated genetic merits of the selected population (Gj\u0026oslash;en and Bentsen \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Recently, genetic rescue with the aid of genomics has been imprimented (Allendorf et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Carlson et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Supple and Shapiro, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Theissinger et al, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Schmidt et al., 2024). One of such approaches is genomic prediction of adaptation and fitness related phenotypes, or genomic selection (Meuwissen et al, 2001), that enable to select individuals more adaptive to the introduced environments (Hagedorn et al., 2019; Guhlin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is expected that this approach is also effective to increase genetic diversity of small aquaculture population without diminishing the genetic gain accumulated through selective breeding.\u003c/p\u003e \u003cp\u003eCoho salmon (\u003cem\u003eOncorhynchus kisutch\u003c/em\u003e) is one of the important aquaculture species ranked 4th in production in Japan (Statistical Survey on Marine Fishery Production, Ministry of Agriculture, Forestry and Fisheries, Japan, 2023: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.e-stat.go.jp/stat-search/files/data?sinfid=000040181768\u0026amp;ext=xls\u003c/span\u003e\u003cspan address=\"https://www.e-stat.go.jp/stat-search/files/data?sinfid=000040181768\u0026amp;ext=xls\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, EXCEL file in Japanese, accessed September, 2024). This species is an exogeneous species in Japan, and the domestic stock is maintained using a population imported from north America in 1970\u0026rsquo;s. Since then, broodstocks were maintained at small scale. In 2011, the main production area of Japanese cultured coho salmon, i.e., Tohoku district including Miyagi Prefecture, was severely damaged by the tsunami disaster of the Great East Japan Earthquake (Sasaki et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Immediately after the disaster, a national project was initiated to reconstruct the industry (\u0026ldquo;A scheme to revitalize agriculture and fisheries in disaster area through deploying highly advanced technology\u0026rdquo; funded by the Agriculture, Forestry, and Fisheries Research Council and Reconstruction Agency, Japan), including social implementation of the coho salmon population with high growth phenotype produced by phenotypic selection at the Inland Fisheries Experimental Station, Miyagi Prefecture Fisheries Technology Center (Miyagi, Japan). Meanwhile, the genetic health of the selectively bred (SB) population has been diagnosed using genome wide single nucleotide polymorphisms (SNPs) obtained by ddRAD-seq (Hosoya et al, 2018). The genetic analysis revealed reduced additive genetic variation and high genetic relatedness among individuals, suggesting the necessity of restoration of genetic diversity for SB population.\u003c/p\u003e \u003cp\u003eIn this study, we tested the possibility of a genome assisted gene flow to restore the genetic diversity of the SB population without deteriorating its growth performance. Individuals with high growth performance were selected from the randomly bred (RB) population, the source population of the SB, using genomic prediction. These selected individuals were crossed with randomly collected SB individuals to create a new bred (NB). The genetic diversity and growth performance were compared among the three populations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEthical disclosure\u003c/h2\u003e \u003cp\u003eThe phenotype recording and sample collection was carried out at the Freshwater Fisheries Experimental Station, Miyagi Prefecture Fisheries Technology Institute (Miyagi, Japan). The experiment was approved by Miyagi Prefectural Evaluation Committee for Research and Development Institutes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSelection of broodfish from the randomly breeding (RB) population\u003c/h3\u003e\n\u003cp\u003eConsidering the maturation size of RB and SB populations, we decided to cross RB males and SB females to produce NB. At 33 months old (September 2016), we picked up RB individuals (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;768) with male-like morphology and measured folk length and body weight of each fish. The average (\u0026plusmn;\u0026thinsp;standard deviation) folk length and body weight were 26.4 (\u0026plusmn;\u0026thinsp;3.0) cm and 222.5 (\u0026plusmn;\u0026thinsp;76.5) g, respectively (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A fin clip was collected from the caudal fin for each specimen, and genomic DNA was extracted from the clip using Gentra Puregene Tissue Kit (QIAGEN, Venlo, Netherland) following manufacture\u0026rsquo;s instruction. The DNA concentration was measured using QuantiFluor dsDNA kit (Promega) and adjusted for 70 ng/uL using milli-Q water. The genetic sex was examined for each individual using male specific primer (OTY2-WSU) following Brunelli and Thogaard (2004). The PCR amplification was done using a T100 thermal cycler (Bio-Rad). Fragment analysis was done using Applied Biosystems 3130 Genetic Analyzer (Thermo Fisher). Male specific amplicon (435 bp) was detected from 515 out of 768 individuals. The biased sex ratio was observed because we collected individuals that showed male-like morph at sampling. Among them, 300 fish were randomly picked up for subsequent ddRAD-seq.\u0026nbsp;We set this sample number based on the throughput of single lane of TruSeq v3 chemistry (PE100) on a Illumina HiSeq 2000 platform used for the subsequent sequencing. The library construction was done following Sakaguchi et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) where BglII and EcoRI (TOYOBO, Osaka, Japan) were used for DNA digestion.\u003c/p\u003e \u003cp\u003eAn average of 829,797 paired reads were obtained with the range of 40,049 to 2,992,012 (Supplementary Table S2). All sequence data is registered with the DDBJ (BioSample Submission ID: SSUB031092, available after publication). Sequence data are available at DDBJ DRA (accession ID: DRA019425). Adapter sequences were trimmed using Trimmomatic-0.39 (Bolger et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) with following threshold: allowing 2 mismatch, palindrome clip threshold of 30, and simple clip threshold of 10 (i.e., ILLUMINACLIP:TruSeq3-PE-2.fa:2:30:10); remove reads with leading quality less than 20 (LEADING:20); remove reads with trailing quality less than 20 (TRAILING:20), minimum length of 50 bp (MINLEN:50). The read pairs remaining at both sides were mapped onto the Coho salmon reference genome (Okis_V1; GenBank assembly accession: GCF_002021735.1) using BWA-mem (BWA v0.7.15) (Li \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Subsequently, reads with flag of 4 (unmapped), 256 (not primary alignment), or 2048 (supplementary alignment) and those of which mapping quality less than 10 were excluded using Samtools v 1.3.1 (Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) (i.e., \u003cem\u003e-F 2308 -q 10\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eGenotype calling was performed using FreeBayes (v1.0.2) (Garrison and Marth, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Subsequently, only biallelic SNPs with minor allele frequency larger than 0.02 were extracted and SNPs with high missing rate (\u0026gt;\u0026thinsp;40%) were excluded using VCFtools (v0.1.14) (Danecek et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These per-chromosome genotype files were concatenated using BCFtools concat (v1.3.1) (Li 2009; Danecek et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, genotypes with low (\u0026lt;\u0026thinsp;8) and high (\u0026gt;\u0026thinsp;300) depth of coverage were masked and missing rate filter was applied again (i.e., retained SNPs with maximum missing rate of 40%). Finally, individuals of which genotype call rate less than 85% were excluded. The final number of individuals was 198 and the number of SNPs was 5,929. While the SNP size is enough for accurate estimation of heritability and breeding value for aquaculture species (Kariaridou et al, 2020; Hosoya et al, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the sample size is insufficient for this purpose. However, precise estimation of these parameters is not the focus of this study. In addition, we selected broodstock candidates based on both breeding value and maturity status. Therefore, limited sample size does not pose a problem in this study.\u003c/p\u003e \u003cp\u003eGenomic estimated breeding value (GEBV) were estimated for folk length and body weights at 33 months old using genomic best linear unbiased prediction (GBLUP) model per phenotype:\u003c/p\u003e \u003cp\u003e \u003cb\u003ey\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003e\u0026micro;\u003c/b\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003eZa\u003c/b\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003ee\u003c/b\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cb\u003ey\u003c/b\u003e, \u003cb\u003e\u0026micro;\u003c/b\u003e, and \u003cb\u003ee\u003c/b\u003e are the vector of observed phenotype, the phenotypic means, and the residuals, respectively; \u003cb\u003eZ\u003c/b\u003e is the corresponding incidence matrices for the additive effects \u003cb\u003ea\u003c/b\u003e, which follows a normal distribution\u0026thinsp;~\u0026thinsp;\u003cb\u003eN\u003c/b\u003e(0, \u003cb\u003eGσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e), where \u003cb\u003eG\u003c/b\u003e is the genomic relationship matrix and \u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e is the genetic variance. We assume the residuals follows a normal distribution\u0026thinsp;~\u0026thinsp;\u003cb\u003eN\u003c/b\u003e(0, \u003cb\u003eIσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e), where \u003cb\u003eI\u003c/b\u003e is an identity matrix and \u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e is the error variance. The \u003cb\u003eG\u003c/b\u003e matrix was obtained with the \u003cem\u003eA.mat\u003c/em\u003e function and the GBLUP model was solved using the \u003cem\u003ekin.blup\u003c/em\u003e of the R package rrBLUP (4.4) (Endelman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Narrow sense heritability was determined as \u003cb\u003eh\u003c/b\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e /(\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003eσ\u003c/b\u003e\u003csub\u003e\u003cb\u003ee\u003c/b\u003e\u003c/sub\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e). We selected 40 individuals based on the rank of GEBVs, i.e., sum of the GEBVs divided by the average for each phenotype, and crossed with 127 females randomly sampled from SB to create a new breed (NB) in December 2016. Note that these 40 sires are not necessarily the top 40 individuals, as some of the top individuals did not reach maturity at mating and some others have died before the breeding season (Supplementary Table S3). A new generation of SB (223 dam x 110 sire) and RB (37 dam x 40 sire) were created at the same timing. The number of broodfish for RB was smaller than that for SB because the number of matured individuals was smaller in RB than SB due to their smaller body size.\u003c/p\u003e\n\u003ch3\u003eAssessments of genetic diversity and differences among three populations\u003c/h3\u003e\n\u003cp\u003eTo assess the genetic differences among SB (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;95), RB (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96), and NB (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;100), 11 microsatellite markers designed in previous studies (Beacham et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Khoo et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nelson et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Palti et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rexroad et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rodriguez et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Sakamoto et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Scribner et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Smith et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Williamson et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) were genotyped for 96, 95, and 100 individuals, respectively (Supplementary Table S4). These three groups were kept separately from fertilization, and thus their genetic origins were known. Genomic DNA was collected from adipose fin clip using Gentra Puregene Tissue Kit (QIAGEN). The 11 loci were amplified using tailed primer method (Schuelke \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Sekino 2006) where 19-bp of M13 sequence (CACGACGTTGTAAAACGAC) was added to the 5' end of the forward primer to label PCR amplicon with fluorescent dye (i.e., FAM, VIC, NED, PED). Polymorphisms were analyzed on ABI3130 DNA Analyzer (Applied Biosystems, MA, USA). The PCR conditions for each markers are described in Supplementary Information S5\u003c/p\u003e \u003cp\u003eThe allele richness (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eR\u003c/sub\u003e) was obtained using \u003cem\u003eallel.rich\u003c/em\u003e function of R package PopGenReport ver3.0.0 (Adamack and Gruber, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and the number of effective allele (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e) was calculated as 1/\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}{p}_{i}\\)\u003c/span\u003e\u003c/span\u003e were \u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the frequency of each allele and \u003cem\u003en\u003c/em\u003e is the number of allele at the locus. Observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), mean gene diversities within population (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e), and fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e) were obtained using \u003cem\u003ebasic.stats\u003c/em\u003e function of R package hierfstat (ver0.5-11) (Goudet \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In addition, pair-wise \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e (Weir and Cockerham, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) and its 95% confidential interval were calculated using \u003cem\u003egenet.dist\u003c/em\u003e and \u003cem\u003eboot.ppfst\u003c/em\u003e (the number of bootstraps\u0026thinsp;=\u0026thinsp;100) functions of hierfstat, respectively.\u003c/p\u003e \u003cp\u003eDiscriminant analysis of principal components (DAPC) was conducted to assess the population structure using \u003cem\u003edapc\u003c/em\u003e function of the R package adegenet (ver2.1.10) (Jombart, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); the number of axes retained in the Principal Component Analysis (PCA) step was set 60 (\u003cem\u003en.pca\u0026thinsp;=\u0026thinsp;60\u003c/em\u003e) and the number of axes retained in the Discriminant Analysis step as 2 (\u003cem\u003en.da\u0026thinsp;=\u0026thinsp;2\u003c/em\u003e). Scatter plot was drawn using \u003cem\u003escatter\u003c/em\u003e function of adegenet. We also conducted STRUCTURE analysis (Hubisz et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) assuming the number of max populations as 2 (\u003cem\u003eMAXPOPS 2\u003c/em\u003e) and no admixture.\u003c/p\u003e\n\u003ch3\u003eProgeny test\u003c/h3\u003e\n\u003cp\u003eWe compared the growth performance among the three populations reared communally. Siblings of individuals used for the genetic comparison were involved. At the swim-up stage, individuals from the three populations were mixed in a communal holding tank. Thus, the genetic origin of each individual was unknown. After nine months from fertilization (August, 2017), a total of 737 individuals were transferred into five 1-kL circular tanks (148, 149, 143, 148, 149 fish per tank) each individual was tagged with a passive integrated transponder (PIT) tag and dissected adipose fin for genomic DNA extraction. Meanwhile, fork length (FL) and body weight (BW) were recorded. Then these fish were returned back into their original tank. They were reared another three months (until November, 2017) and measured FL and BW. At this sampling point, 15 fish were lost due to mortality or tag drop-off, resulting in 722 specimens used for the assignment test.\u003c/p\u003e\n\u003ch3\u003ePopulation assignments\u003c/h3\u003e\n\u003cp\u003eAs the genetic origin of the 722 individuals were unknown, their population ID were inferred from the 11 microsatellite markers. Genomic DNA extraction and genotyping of the 11 loci were done as described above. We used NewHybrid software (Anderson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) with initial 20,000 burn-in and subsequent 50,000 post burn-in sweeps. The genotype data collected from the genetic diversity analysis was included to utilize them as the training set. The accuracy of population assignments was evaluated from the proportion of the individuals accurately assigned to the original population. In addition, correlations in allele frequencies between inferred population (inferred RB, inferred SB, and inferred NB) and their sibling groups (RB, SB, and NB, respectively) were assessed per locus using Spearman\u0026rsquo;s correlation coefficient (ρ). Furthermore, pairwise Weir and Cockerham \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e was calculated among the six groups.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis for growth performance comparison\u003c/h2\u003e \u003cp\u003ePopulation difference in growth performance was examined using general linear mixed model (LMM). The model was:\u003c/p\u003e \u003cp\u003e \u003cb\u003ey\u003c/b\u003e\u0026thinsp;=\u0026thinsp;\u003cb\u003eXb\u003c/b\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003eZa\u003c/b\u003e\u0026thinsp;+\u0026thinsp;\u003cb\u003ee\u003c/b\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cb\u003ey\u003c/b\u003e, \u003cb\u003eb\u003c/b\u003e, and \u003cb\u003ea\u003c/b\u003e are vectors of phenotype, fixed population effects, and random tank effects, respectively; \u003cb\u003eX\u003c/b\u003e and \u003cb\u003eZ\u003c/b\u003e are the design matrix for \u003cb\u003eb\u003c/b\u003e and \u003cb\u003ea\u003c/b\u003e; \u003cb\u003ee\u003c/b\u003e is a vector of residuals. The equation was solved using \u003cem\u003eglm\u003c/em\u003e function of R/stats. Model comparison was done among models (i.e., with and without the population effects) based on Akaike\u0026rsquo;s information criterion (AIC) (Akaike \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Least squares mean for each population was compared using \u003cem\u003eemmeans\u003c/em\u003e function of R package emmeans (ver. 1.7.2) (Lenth, 2023). The significant threshold was set as \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u003cem\u003ep\u003c/em\u003e-value were adjusted using Bonferroni method.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenomic selection from the RB population produced in 2013\u003c/h2\u003e \u003cp\u003eIndividual selection from the RB population produced in 2013 was done using GBLUP method (198 males and 5,929 SNPs). The average FL and BW (\u0026plusmn;\u0026thinsp;standard deviation) of these individuals were 26.7 (\u0026plusmn;\u0026thinsp;29.3) cm and 230.5 (\u0026plusmn;\u0026thinsp;76.4) g, respectively. The distribution of these phenotype was apart from normal distribution (Shapiro-Wilk test: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034 and 0.005, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) partly because the initial sampling of the 768 individuals was not random but chose individuals with male-like morphology. Thus, smaller individuals were not involved. Reflecting this, at least partially, the estimated heritability was low for both FL and BW (\u003cem\u003eh\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.119 and 0.103, respectively) and the correlation efficient between observed phenotype and GEBV were moderate (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.731 and 0.727, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). We ranked the 198 males based on the sum of GEBVs divided by the average for FL and BW and selected 40 matured individuals from top rank (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRestoration of genetic diversity in NB\u003c/h2\u003e \u003cp\u003eTo assess the restoration of genetic diversity in NB, we genotyped 11 microsatellite markers and compared genetic parameters among RB, SB, and NB. Genotype information of each individual is available in Supplementary Table S6. The mean values of the number of allele (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eN\u003c/sub\u003e), allele richness (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eR\u003c/sub\u003e), the number of effective alleles (\u003cem\u003eA\u003c/em\u003e\u003csub\u003eE\u003c/sub\u003e), observed heterozygosity (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eO\u003c/sub\u003e), mean gene diversities within population (\u003cem\u003eH\u003c/em\u003e\u003csub\u003eS\u003c/sub\u003e), and fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e) are listed in Table\u0026nbsp;1 (detailed information is available in Supplementary Table S2). As expected, mean values of SB were smaller than RB, but increased in NB in each statistics. This result clearly shows a restoration of genetic variation by introducing individuals from external populations. Only the exception was \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e, where the value was smaller in SB than in RB. It is expected that the higher the \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e, the higher the genetic relatedness of the parental individuals. However, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e does not necessarily represent the genetic diversity of population level. Indeed, the value of RB is close to zero while \u003cem\u003eH\u003c/em\u003eo was smallest in SB. The negative \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e of NB indicates heterozygote excess. This was in line with the expectation as NB was produced between the two genetically isolated populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eExtent of genetic differentiation among the three populations\u003c/h2\u003e \u003cp\u003eWe evaluated the extent of genetic differentiation among populations using pair-wise Wier Cockerham \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e (Table\u0026nbsp;2). Significant genetic differentiation was observed in each pair, but the extent of differentiation was mitigated between NB and RB (0.046) or SB (0.041), compared to RB\u0026ndash;NB pair (0.135).\u003c/p\u003e \u003cp\u003eGenetic differentiation was also confirmed from DAPC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While RB and SB populations were clearly separated, NB population was plotted between the two populations and some extent of overlap at individual level was observed. The STRUCTURE analysis also revealed genetic differentiation at population level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). On the other hand, some individuals of RB and SB showed higher similarity between NB, while some of NB were closer to RB or SB.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation assignments\u003c/h2\u003e \u003cp\u003eWithin the individuals with known population information, 1 and 3 individuals from RB were miss-assigned to SB and NB, respectively, while 3 individuals of SB were miss-assigned to NB (Table\u0026nbsp;3). On the other hand, 6 and 4 individuals of NB were miss-placed in RB and SB, respectively. The overall accuracy of population assignments for the 291 individuals with population records were 94.2% (274 out of 291): RB\u0026thinsp;=\u0026thinsp;95.8%, SB\u0026thinsp;=\u0026thinsp;96.8%, NB\u0026thinsp;=\u0026thinsp;90.0%. It should be noted that these miss-assignments were not due to technical error, but reflected the genetic variation within each population.\u003c/p\u003e \u003cp\u003eThe 722 individuals without population information were evenly assigned to the three populations (infRB\u0026thinsp;=\u0026thinsp;243, infSB\u0026thinsp;=\u0026thinsp;248, infNB\u0026thinsp;=\u0026thinsp;231) (Supplementary Table S7). The results of population assignments were assessed using allele frequency. The Spearman\u0026rsquo;s correlation coefficient (\u003cem\u003eρ\u003c/em\u003e), calculated for allele frequencies between each congenic population pair (i.e., infRB\u0026ndash;RB, infSB\u0026ndash;SB, and infNB\u0026ndash;NB), ranged 0.762 to 1.000 and mean values were 0.938, 0.950, and 0.936, respectively (Table\u0026nbsp;4). We also examined the degree of population differentiation among the six groups (RB, SB, NB, infRB, infSB, and infNB). Between the two sibling pairs, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values were nearly zero, although the 95% CI was larger than zero between infRB\u0026ndash;RB pair (0.001\u0026ndash;0.009). When \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e values between non-sibling pairs were compared, the inferred population and their sibling group showed similar values (e.g., RB\u0026ndash;SB\u0026thinsp;=\u0026thinsp;0.135 and RB\u0026ndash;infSB\u0026thinsp;=\u0026thinsp;0.130). These results indicate that inferred populations and their sibling groups had highly similar allele frequency. Thus, it can be said the accuracy of the population assignments was high.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eProgeny test\u003c/h2\u003e \u003cp\u003eThe effect of selective introgression from RB into SB was evaluated by comparing growth performance among the three inferred population. At first, significance of population effect was tested using AIC. The AIC values of the full model was smaller than that of the model without population effect, indicating the significance of population effect (Table\u0026nbsp;6). The least square mean of infSB (FL\u0026thinsp;=\u0026thinsp;161.1 mm, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.381; BW\u0026thinsp;=\u0026thinsp;53.7 g, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.355) was significantly larger than that of infRB (FL\u0026thinsp;=\u0026thinsp;150.4 mm, BW\u0026thinsp;=\u0026thinsp;43.0 g), confirming significant genetic improvements in the infSB lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The mean values of infNB (FL\u0026thinsp;=\u0026thinsp;164.9 mm, BW\u0026thinsp;=\u0026thinsp;57.9 g) was significantly larger than that of infRB while slightly, but not significantly, larger than that of infSB, suggesting that the selective introgression did not deteriorate the growth performance of the target population (i.e., SB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGene flow from an external population may not effectively restore the genetic diversity of the target population if the introduced individuals share many alleles. This is particularly the case when small number of individuals were selected through genomic selection, as this approach tends to select individuals with similar genotypes across the genome (Sonesson et al, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the number of individuals selected (i.e., selection intensity) must be carefully considered. In this study, we selected 40 individuals from a pool of 198 based on two criteria: GEBV rank and availability for breeding. This relaxed selection resulted in selection of a genetically diverse group of individuals. While the number of introduced individuals (40) was chosen arbitrarily, it is advisable to determine the appropriate number in advance. For genetic rescue in the wild, this number depends on the level of inbreeding and the effective population size of the source and the target populations, leading to fewer established guidelines (Frankham, 2015; Shannon et al, 2023). When highly polymorphic markers, such as microsatellites, are available, the optimal number can be estimated based on allele frequency. When genome-wide SNPs are used, genetic distance or relatedness among selection candidates can guide decision-making. Alternatively, a two-dimensional site frequency spectrum (SFS) drawn from selection candidates and entire populations can provide a clear visual assessment of rare allele drop off. The SNP data used for genomic selection is directly available to calculate such statistics.\u003c/p\u003e \u003cp\u003eThe growth performance of NB population was comparable to that of SB population, indicating that hybridization between selected RB individuals and SB did not adversely affect growth performance. Along with the increased genetic diversity observed sin NB, our results indicate the effectiveness of the genome-assisted gene flow in restoring genetic diversity in a selectively bred population without compromising the growth performance in the recipient population (i.e., SB). One possible factor contributing to the sustained high growth performance in NB is outcross enhancement, or hybrid vigor, where offspring exhibit phenotypes superior to those of the parental populations. Indeed, genetic statistics such as the effective number of alleles and observed heterozygosity indicated a loss of genetic diversity in the SB population. However, these values were not markedly lower than those in the other two populations, and \u003cem\u003eF\u003c/em\u003e\u003csub\u003eIS\u003c/sub\u003e value was less than zero. In addition, we did not observe abnormal phenotypes in the SB population. These results suggest that the population is not experiencing severe inbreeding depression, and that hybrid vigor is likely not the primary driver behind enhanced growth performance in the NB.\u003c/p\u003e \u003cp\u003eA limitation of this study is that growth performance was only assessed in the first generation, and there is no guarantee that this performance will be maintained at the same level in the future generations. This is especially true if outcross enhancement had a positive effect on the growth of NB. To sustain growth performance for generations, relaxed truncation selection may be needed to eliminate smaller individuals from broodstock. In this case, care must be taken to prevent loss of genetic variation. Another limitation is the uncertainty surrounding whether similar results would be achieved if the growth performance disparity between the selected and source population was much greater than what we observed in this study. The success of genome-assisted gene flow depends on many factors and may not always restore genetic diversity without sacrificing accumulated genetic merit. To balance recovery of genetic diversity with the maintenance of an improved phenotype, a well-managed breeding plan is essential. More importantly, regardless of the application of gene flow, careful genetic management should be always prioritized in aquaculture breeding.\u003c/p\u003e \u003cp\u003eAlthough such limitations needed to be addressed in future studies, our study demonstrated the feasibility of gene flow with genomic selection for genetic management of small populations. This will help sustainable managements of Japanese aquaculture populations coho salmon. As small-scale aquaculture production is crucial for global food security and local economies (Garlock et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Dam Lam et al, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the technologies that allow improvements in genetic merits and managements of genetic health of such populations simultaneously will significantly enhance the sustainability and resilience of aquaculture, contributing to the global sustainable developmental goals (SDGs) (Sonesson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Genome-assisted gene flow, as utilized in this study, represents one of such promising technologies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eContributions\u003c/h2\u003e\n\u003cp\u003eConceptualization: Sho Hosoya, Tadahide Kurokawa, Kiyoshi Kikuchi; Methodology: Junya Kobayashi, Ryo Honda, Sho Hosoya, Atsushi J. Nagano,, Tadahide Kurokawa, Kiyoshi Kikuchi; Formal analysis and investigation: Junya Kobayashi, Ryo Honda, Sho Hosoya, Masaki Yasugi, Atsushi J. Nagano; Writing - original draft preparation: Junya Kobayashi, Ryo Honda, Sho Hosoya; Writing - review and editing: Sho Hosoya; Tadahide Kurokawa, Kiyoshi Kikuchi; Funding acquisition: Tadahide Kurokawa, Sho Hosoya; Resources: Ryo Honda, Yuki Nochiri, Keisuke Matsuzaki, Koichi Sugimoto, Akira Kumagaya; Supervision: Tadahide Kurokawa, Sho Hosoya.\u003c/p\u003e\n\u003ch2\u003eFunding information\u003c/h2\u003e\n\u003cp\u003eThis work was partially granted by the Agriculture, Forestry and Fisheries Research Council (AFFRC), Japan granted to T.K., and Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) (21H02279) granted to S.H..\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eS.H., K.K., and T.K. conceptualize the research; T.K., A.K., K.K., and S.H. contributed funding acquisition; R.H. conducted rearing experiments; R.H., Y.N., K.M., K.S., and A.K. contributed fish maintenance and data collection; J.K. conducted laboratory experiments; A.J.N. did ddRAD-seq; J.K. and S.H. did statistical analysis, data curation, and wrote the main manuscript text; K.K., and T.K. contributed revising the manuscript; all authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eSequence data have been deposited in DNA Data Bank of Japan (DDBJ) with the BioSample Submission ID: SSUB031092. Other data is provided within supplementary information file.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamack AT, Gruber B (2014) PopGenReport: Simplifying basic population genetic analyses in R. Methods Ecol Evol 5:384\u0026ndash;387. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.12158\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.12158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkaike H (1973) Information theory and an extension of the maximum likelihood principle. In B. N. Petrov F. Caski (Eds.), Proceedings of the 2nd International Symposium on Information Theory (pp. 267\u0026ndash;281). Akad\u0026eacute;miai Kiado\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllendorf FW, Hohenlohe PA, Luikart G (2010) Genomics and the future of conservation genetics. Nat Rev Genet 11:697\u0026ndash;709. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nrg2844\u003c/span\u003e\u003cspan address=\"10.1038/nrg2844\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllendorf FW, Leary RF, Spruell P, Wenburg JK (2001) The problems with hybrids: Setting conservation guidelines. Trends Ecol Evol 16:613\u0026ndash;622\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0169-5347(01)02290-X\u003c/span\u003e\u003cspan address=\"10.1016/S0169-5347(01)02290-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson EC, Thompson EA (2002) A model-based method for identifying species hybrids using multilocus genetic data. Genetics 160:1217\u0026ndash;1229\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeacham TD, Wetklo M, Deng L, MacConnachie C (2011) Coho salmon population structure in North America determined from microsatellites. Trans Am Fish Soc 140:253\u0026ndash;270\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBolger AM, Lohse M, Usade B (2014) Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30:2114\u0026ndash;2120\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoudry P, Allal F, Aslam ML, Bargelloni L, Bean TP, Brard-Fudulea S, Brieuc MS, Calboli FC, Gilbey J, Haffray P, Lamy J, Morvezen R, Purcell C, Prod\u0026ouml;hl PA, Vandeputte M, Waldbieser GC, Sonesson AK, Houston RD (2021) Current status and potential of genomic selection to improve selective breeding in the main aquaculture species of International Council for the Exploration of the Sea (ICES) member countries. Aquac Rep 20:100700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aqrep.2021.100700\u003c/span\u003e\u003cspan address=\"10.1016/j.aqrep.2021.100700\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunelli JP, Thorgaard GH (2004) A new Y-chromosome-specific marker for Pacific salmon. Trans Am Fish Soc 133:1247\u0026ndash;1253\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarlson SM, Cunningham CJ, Westley PA (2014) Evolutionary rescue in a changing world. Trends Ecol Evol 29:521\u0026ndash;530. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tree.2014.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tree.2014.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDam Lam R, Barman BK, Lozano Lazo DP et al (2022) Sustainability impacts of ecosystem approaches to small-scale aquaculture in Bangladesh. Sustain Sci 17:295\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11625-021-01076-w\u003c/span\u003e\u003cspan address=\"10.1007/s11625-021-01076-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, 1000 Genomes Project Analysis Group (2011) The variant call format and VCFtools. Bioinformatics 27:2156\u0026ndash;2158\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve years of SAMtools and BCFtools. GigaScience 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/gigascience/giab008\u003c/span\u003e\u003cspan address=\"10.1093/gigascience/giab008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEndelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. Plant Genome 4:250\u0026ndash;255\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrankham R, Ballou JD, Eldridge MDB, Lacy RC, Ralls K, Dudash MR, Fenster CB (2011) Predicting the probability of outbreeding depression. Conserv Biol 25:465\u0026ndash;475. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1523-1739.2011.01662.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1523-1739.2011.01662.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarlock T, Asche F, Anderson J, Ceballos-Concha A, Love DC, Osmundsen TC, Pincinato RBM (2022) Aquaculture: The missing contributor in the food security agenda. Glob Food Secur 32:100620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gfs.2022.100620\u003c/span\u003e\u003cspan address=\"10.1016/j.gfs.2022.100620\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrison E, Marth G (2012) Haplotype-based variant detection from short-read sequencing. ArXiv 1207.3907\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhildiyal K, Nayak SS, Rajawat D, Sharma A, Chhotaray S, Bhushan B, Dutt T, Panigrahi M (2023) Genomic insights into the conservation of wild and domestic animal diversity: A review. Gene 886:147719. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gene.2023.147719\u003c/span\u003e\u003cspan address=\"10.1016/j.gene.2023.147719\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGjedrem T, Kolstad K (2012) Development of breeding programs for aquatic species should be given high priority. World Aquac 43:10\u0026ndash;13\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGj\u0026oslash;en HM, Bentsen HB (1997) Past, present, and future of genetic improvement in salmon aquaculture. ICES J Mar Sci 54:1009\u0026ndash;1014\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoudet J (2005) Hierfstat, a package for R to compute and test variance components and F-statistics. Mol Ecol Notes 5:184\u0026ndash;186\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuhlin J, Le Lec MF, Wold J, Koot E, Winter D, Biggs PJ, Galla SJ, Urban L, Foster Y, Cox MP, Digby A, Uddstrom LR, Eason D, Vercoe D, Davis T, Howard JT, Jarvis ED, Robertson FE, Robertson BC, Dearden PK (2023) Species-wide genomics of kākāpō provides tools to accelerate recovery. Nat Ecol Evol 7:1693\u0026ndash;1705. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41559-023-02165-y\u003c/span\u003e\u003cspan address=\"10.1038/s41559-023-02165-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagedorn M, Page CA, Flores LK, Tichy DM, Conn L, Chamberland T, Lager VF, Zuchowicz C, Lohr N, Blackburn K, Vardi H, Moore T, Moore J, Baums T, Vermeij IB, Marhaver MJ KL (2021) Assisted gene flow using cryopreserved sperm in critically endangered coral. Proc Natl Acad Sci USA 118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2110559118\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2110559118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHosoya S, Yoshikawa S, Sato M, Kikuchi K (2021) Genomic prediction for testes weight of the tiger pufferfish, \u003cem\u003eTakifugu rubripes\u003c/em\u003e, using medium to low density SNPs. Sci Rep 11:20372. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-99829-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-99829-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouston RD, Kriaridou C, Robledo D (2022) Animal board invited review: Widespread adoption of genetic technologies is key to sustainable expansion of global aquaculture. Anim 16:100642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.animal.2022.100642\u003c/span\u003e\u003cspan address=\"10.1016/j.animal.2022.100642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang SSO, Liao IC (1990) Response to mass selection for growth rate in Oreochromis niloticus. Aquaculture 85:199\u0026ndash;205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHubisz MJ, Falush D, Stephens M, Pritchard JK (2009) Inferring weak population structure with the assistance of sample group information. Mol Ecol Resour 9:1322\u0026ndash;1332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1755-0998.2009.02591.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1755-0998.2009.02591.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHulata G, Wohlfarth GW, Halevy A (1986) Mass selection for growth rate in the Nile tilapia (\u003cem\u003eOreochromis niloticus\u003c/em\u003e). Aquaculture 57:177\u0026ndash;184\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJombart T (2008) adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403\u0026ndash;1405\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKriaridou C, Tsairidou S, Houston RD, Robledo D (2020) Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms. Fron Genet 11:515310. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2020.00124\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2020.00124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhoo SK, Ozaki A, Sakamoto T, Okamoto N (2000) Four highly polymorphic dinucleotide microsatellites in rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e). Anim Genet 31:73\u0026ndash;74\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKincaid HL (1983) Inbreeding in fish populations used for aquaculture. Aquaculture 33:215\u0026ndash;227\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnibb W, Giang C, Premachandra H, Ninh N, Dom\u0026iacute;nguez B (2020) Feasible options to restore genetic variation in hatchery stocks of the globally important farmed shrimp species, \u003cem\u003eLitopenaeus vannamei\u003c/em\u003e. Aquaculture 518:734823\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.aquaculture.2019.734823\u003c/span\u003e\u003cspan address=\"10.1016/j.aquaculture.2019.734823\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnibb W, Whatmore P, Lamont R, Quinn J, Powell D, Elizur A, Anderson T, Remilton C, Nguyen NH (2014) Can genetic diversity be maintained in long term mass selected populations without pedigree information?\u0026mdash;A case study using banana shrimp \u003cem\u003eFenneropenaeus merguiensis\u003c/em\u003e. Aquaculture 428:71\u0026ndash;78\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKristensen TN, Hoffmann AA, Pertoldi C, Stronen AV (2015) What can livestock breeders learn from conservation genetics and vice versa? Front Genet 6:38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2015.00038\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2015.00038\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLenth RV (2022) emmeans: Estimated marginal means, aka least-squares means (R package version 1.7.2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=emmeans\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=emmeans\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H (2013) Aligning sequence reads, clone sequences, and assembly contigs with BWA-MEM. ArXiv 1303.3997\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R, 1000 Genome Project Data Processing Subgroup (2009) The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25:2078\u0026ndash;2079\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNelson RJ, Beacham TD, Small MP (1998) Microsatellite analysis of the population structure of a Vancouver Island sockeye salmon (\u003cem\u003eOncorhynchus nerka\u003c/em\u003e) stock complex using nondenaturing gel electrophoresis. Mol Mar Biol Biotechnol 7:312\u0026ndash;319\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalti Y, Fincham MR, Rexroad CE III (2002) Characterization of 38 polymorphic microsatellite markers for rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e). Mol Ecol Resour 2:449\u0026ndash;452\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePregler KC, Obedzinski M, Gilbert-Horvath EA, White B, Carlson SM, Garza JC (2023) Assisted gene flow from outcrossing shows the potential for genetic rescue in an endangered salmon population. Conserv Lett 16. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/conl.12934\u003c/span\u003e\u003cspan address=\"10.1111/conl.12934\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRexroad CE, Coleman RL, Hershberger WK, Killefer J (2002) Rapid communication: Thirty-eight polymorphic microsatellite markers for mapping in rainbow trout. J Anim Sci 80:541\u0026ndash;542\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez F, Rexroad CE, Palti Y (2003) Characterization of twenty-four microsatellite markers for rainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e). Mol Ecol Notes 3:619\u0026ndash;622\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakaguchi S, Sugino T, Tsumura Y (2015) High-throughput DNA marker system for molecular breeding of conifers: Development of a linkage map and identification of candidate markers for wood properties in Cryptomeria japonica. Tree Genet Genomes 11:103\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSakamoto T, Okamoto N, Ikeda Y (1996) Application of PCR primer pairs from rainbow trout to detect polymorphisms of CA repeat DNA loci in five confamilial species. Fish Sci 62:552\u0026ndash;555\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSasaki K, Kurokawa T, Nikaido H, Muraoka D, Okuda Y (2017) Did farmed coho Salmon \u003cem\u003eOncorhynchus kisutch\u003c/em\u003e that escaped during the earthquake and tsunami disaster of 2011 interbreed with native masu salmon \u003cem\u003eOncorhynchus masou\u003c/em\u003e? Bull Jpn Fish Res Edu Agen 45:63\u0026ndash;67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekino M, Kobayashi T, Hara M (2006) Segregation and linkage analysis of 75 novel microsatellite DNA markers in pair crosses of Japanese abalone (\u003cem\u003eHaliotis discus hannai\u003c/em\u003e) using the 5\u0026prime;-tailed primer method. Mar Biotechnol 8:453\u0026ndash;466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10126-005-6179-6\u003c/span\u003e\u003cspan address=\"10.1007/s10126-005-6179-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidt TL, Thia JA, Hoffmann AA (2022) How can genomics help or hinder wildlife conservation? Annu Rev Anim Biosci 12:45\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-animal-021022-051810\u003c/span\u003e\u003cspan address=\"10.1146/annurev-animal-021022-051810\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuelke M (2000) An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol 18:233\u0026ndash;234. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/72708\u003c/span\u003e\u003cspan address=\"10.1038/72708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScribner KT, Gust JR, Fields RL (1996) Isolation and characterization of novel salmon microsatellite loci: Cross-species amplification and population genetic applications. Can J Fish Aquat Sci 53:833\u0026ndash;841\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith CT, Koop BF, Nelson RJ (1998) Isolation and characterization of coho salmon (\u003cem\u003eOncorhynchus kisutch\u003c/em\u003e) microsatellites and their use in other salmonids. Mol Ecol 7:1614\u0026ndash;1616\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonesson AK, Hallerman E, Humphries F, Hilsdorf AWS, Leskien D, Rosendal K, Bartley D, Hu X, Gomez RG, Mair GC (2023) Sustainable management and improvement of genetic resources for aquaculture. J World Aquac Soc 54:364\u0026ndash;396\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jwas.12968\u003c/span\u003e\u003cspan address=\"10.1111/jwas.12968\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSonesson AK, Woolliams JA, Meuwissen TH (2012) Genomic selection requires genomic control of inbreeding. Genet Sel Evol 44:27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1297-9686-44-27\u003c/span\u003e\u003cspan address=\"10.1186/1297-9686-44-27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSupple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:131. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13059-018-1520-3\u003c/span\u003e\u003cspan address=\"10.1186/s13059-018-1520-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaberlet P, Valentini A, Rezaei HR, Naderi S, Pompanon F, Negrini R, Ajmone-Marsan P (2007) Are cattle, sheep, and goats endangered species? Mol Ecol 17:275\u0026ndash;284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-294X.2007.03475.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-294X.2007.03475.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTelchert-Coddington R, Smitterman RO (1988) Lack of response by \u003cem\u003eTilapia nilotica\u003c/em\u003e to mass selection for rapid early growth. Trans Am Fish Soc 117:290\u0026ndash;300\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTheissinger K, Fernandes C, Formenti G, Bista I, Berg PR, Bleidorn C, Bombarely A, Crottini A, Gallo GR, Godoy JA, Jentoft S, Malukiewicz J, Mouton A, Oomen RA, Paez S, Palsb\u0026oslash;ll PJ, Pampoulie C, Ruiz-L\u0026oacute;pez MJ, Secomandi S, Svardal H, Theofanopoulou C, Vries JD, Waldvogel AM, Zhang G, Jarvis ED, B\u0026aacute;lint M, Ciofi C, Waterhouse RM, Mazzoni CJ, H\u0026ouml;glund J, The European Reference Genome Atlas Consortium (2023) How genomics can help biodiversity conservation. Trends Genet 39:545\u0026ndash;559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tig.2023.01.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tig.2023.01.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358\u0026ndash;1370\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson KS, Cordes JF, May B (2002) Characterization of microsatellite loci in chinook salmon (\u003cem\u003eOncorhynchus tshawytscha\u003c/em\u003e) and cross-species amplification in other salmonids. Mol Ecol Notes 2:17\u0026ndash;19\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite SL, Rash JM, Kazyak DC (2023) Is now the time? Review of genetic rescue as a conservation tool for brook trout. Ecol Evol 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/ece3.10142\u003c/span\u003e\u003cspan address=\"10.1002/ece3.10142\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 6 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"marine-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbte","sideBox":"Learn more about [Marine Biotechnology](http://link.springer.com/journal/10126)","snPcode":"10126","submissionUrl":"https://submission.nature.com/new-submission/10126/3","title":"Marine Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Coho salmon, Genetic rescue, Genomic selection, Sustainable aquaculture","lastPublishedDoi":"10.21203/rs.3.rs-5444805/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5444805/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelective breeding is a powerful tool for improving aquaculture production. A well-managed breeding program is essential, as populations can otherwise lose genetic diversity, leading to reduced selection response and inbreeding excesses. In such cases, genetic diversity in broodstock must be restored by introducing individuals from external populations. However, this can reduce the accumulated genetic gains from selective breeding. On the other hand, selectively introduction of individuals with superior phenotypes will allow restoration of genetic diversity without sacrificing these gains.\u003c/p\u003e \u003cp\u003eIn this study, we demonstrated this possibility using a selectively bred (SB) and a randomly bred (RB) population of coho salmon (\u003cem\u003eOncorhynchus kisutch\u003c/em\u003e). Forty males with superior growth were selected from RB population using genomic selection and crossed with 127 randomly collected females from SB to produce a newly bred (NB) population. Genetic diversity, assessed from population statistics such as effective number of alleles, allele richness, and observed heterozygosity of 11 microsatellite markers, was higher in NB than in SB and RB. Additionally, folk length and body weight were compared among the three populations after a 12-month communal culture from fertilization. The least-squares means of folk length and body weight were similar between NB (164.9 mm and 57.9 g) and SB (161.1 mm and 53.7 g), while both were significantly larger than those of RB (150.4 mm and 43.0 g). Our results highlight the effectiveness of the genome-assisted gene flow in restoring the genetic diversity of SB populations without compromising their accumulated genetic gain in growth.\u003c/p\u003e","manuscriptTitle":"Genome assisted gene-flow rescued genetic diversity without hindering growth performance of inbred coho salmon (Oncorhynchus kisutch) population selected for high growth phenotype","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 16:59:36","doi":"10.21203/rs.3.rs-5444805/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T10:45:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-10T02:09:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T12:28:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248900632036686728430729192240485715034","date":"2024-11-18T19:09:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179424982431350791716612418119136906234","date":"2024-11-15T14:08:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205506561726600646749800245958487507446","date":"2024-11-15T14:02:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-15T13:57:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-13T09:04:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-13T09:03:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Marine Biotechnology","date":"2024-11-13T07:27:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"marine-biotechnology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mbte","sideBox":"Learn more about [Marine Biotechnology](http://link.springer.com/journal/10126)","snPcode":"10126","submissionUrl":"https://submission.nature.com/new-submission/10126/3","title":"Marine Biotechnology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b36f02a0-d7b1-4bd7-b4a5-674c20dbe4cc","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:09:54+00:00","versionOfRecord":{"articleIdentity":"rs-5444805","link":"https://doi.org/10.1007/s10126-025-10416-1","journal":{"identity":"marine-biotechnology","isVorOnly":false,"title":"Marine Biotechnology"},"publishedOn":"2025-02-01 15:58:07","publishedOnDateReadable":"February 1st, 2025"},"versionCreatedAt":"2024-12-17 16:59:36","video":"","vorDoi":"10.1007/s10126-025-10416-1","vorDoiUrl":"https://doi.org/10.1007/s10126-025-10416-1","workflowStages":[]},"version":"v1","identity":"rs-5444805","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5444805","identity":"rs-5444805","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}