Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population

Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population View ORCID Profile Ya-Ping Lin , Ehtisham Hussain , Yun-Che Hsu , Chen-Yu Lin , You-Syuan Chen , Lung-Hsin Hsu , View ORCID Profile Ching-Yuan Hung , Shu-Mei Huang , Jo-Yi Yen , View ORCID Profile Assaf Eybishitz , Peter Hanson , View ORCID Profile Seung Won Kang , View ORCID Profile Ken Hoshikawa doi: https://doi.org/10.1101/2025.01.23.634636 Ya-Ping Lin 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ya-Ping Lin For correspondence: ya-ping.lin{at}worldveg.org hoshikawak0339{at}jircas.go.jp Ehtisham Hussain 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan 2 Department of Plant Breeding and Genetics, College of Agriculture, University of Sargodha , Sargodha, Pakistan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yun-Che Hsu 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Chen-Yu Lin 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site You-Syuan Chen 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan 3 Department of Horticulture and Landscape Architecture, National Taiwan University , Taipei, Taiwan 4 Institute of Plant Biology, National Taiwan University , Taipei, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lung-Hsin Hsu 5 Tainan District Agricultural Research and Extension Station , No. 70, Muchang Rd., Hsinhua District, Tainan 712009, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ching-Yuan Hung 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ching-Yuan Hung Shu-Mei Huang 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jo-Yi Yen 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Assaf Eybishitz 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Assaf Eybishitz Peter Hanson 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan Find this author on Google Scholar Find this author on PubMed Search for this author on this site Seung Won Kang 6 Institute of Life and Environmental Sciences, University of Tsukuba , 1-1-1 Tennodai, Tsukuba, 305-8572, Japan 7 Tsukuba-Plant Innovation Research Center, University of Tsukuba , 1-1-1 Tennodai, Tsukuba, 305-8572, Japan 8 JST /JICA, SATREPS Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Seung Won Kang Ken Hoshikawa 1 The World Vegetable Center , Shanhua, Tainan, 74151, Taiwan 8 JST /JICA, SATREPS 9 Biological Resources and Post-harvest Division, Japan International Research Center for Agricultural Sciences , Ohwashi 1-1, Tsukuba, Ibaraki, 305-8686, Japan Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ken Hoshikawa For correspondence: ya-ping.lin{at}worldveg.org hoshikawak0339{at}jircas.go.jp Abstract Full Text Info/History Metrics Preview PDF Abstract Heat stress threatens tomato productivity by reducing pollen viability, fruit set, and consequently, overall yield. While heat-tolerant traits are predominantly found in wild tomatoes, the introgression of heat tolerance into elite cultivars remains challenging due to linkage drag. To address this issue, the World Vegetable Center developed a Multi-parent Advanced Generation Inter-Cross (MAGIC) population, derived from crosses between four heat-tolerant and disease-resistant cultivars. Phenotypic evaluations revealed that fruit number accounted for approximately 57% of reproductive output, making it a critical selection index for heat tolerance. A total of 16,350 SNPs were developed for the MAGIC population, and genome-wide association studies (GWAS) identified 50 QTLs linked to the evaluated traits. Notably, three QTLs on chromosomes 1, 3 and 11 emerged as hubs influencing multiple reproductive traits, underscoring their critical role in heat tolerance. SL4.0CH11_47205149 associated with fruit number was converted into a Kompetitive Allele-Specific PCR (KASP) marker and validated for its significant association with yield. The identification of key QTLs and the prioritization of fruit number as a primary determinant of reproductive success offer valuable insights for targeted breeding strategies. Highlight Number of fruits is crucial for selecting heat-tolerant tomatoes in open fields. A marker linked to high fruit production helps develop high-yielding tomatoes better suited to withstand climate change. Introduction Tomato ( Solanum lycopersicum ) is one of the most extensively cultivated and economically significant crops worldwide, serving both as a dietary staple and a vital raw material in the food industry. However, its production is increasingly threatened by environmental stressors, particularly heat stress exacerbated by climate change ( Hoshikawa et al., 2021 ). The optimal temperature range for tomato growth is 26-28°C during the day and 22°C at night, whereas temperatures exceeding 32°C during the day or 26°C at night trigger physiological disruptions, impeding normal plant development ( Sato et al., 2000 ). Global climate change has intensified the frequency and severity of heatwaves in key tomato-producing regions, further amplifying these challenges ( McCarl et al., 2016 ). Heat stress negatively affects photosynthesis by reducing the maximum photochemical quantum efficiency (Fv/Fm) of photosystem II, impairing chlorophyll function and overall photosynthetic capacity ( Ahammed et al., 2018 ; Wen et al., 2019 ). At the cellular level, it compromises membrane fluidity, protein stability, and enzymatic activities, while inducing oxidative stress through the accumulation of reactive oxygen species (ROS) ( Yu et al., 2019 ). The reproductive phase of tomatoes is particularly sensitive to heat stress, with substantial impacts on microsporogenesis, pollen viability, and fruit set ( Frank et al., 2009 ; Lin et al., 2010 ; Xu et al., 2017a ; Zhou et al., 2017 ; Iovane and Aronne, 2022 ; Elazazi et al., 2024 ). Prolonged exposure to temperatures above 26°C (day) and 20°C (night) disrupts fruit set and significantly reduces yield ( Lin et al., 2010 ). Quantitative analyses reveal severe reductions in reproductive traits under heat stress. For instance, long-term exposure to mild heat (32°C, 70% relative humidity, and a 14-hour light period) results in a reduction of pollen viability by 86%, pollen number by 56%, and female fertility by 39% ( Xu et al., 2017b ). These findings underscore the urgency of understanding the genetic basis of heat tolerance in tomatoes to develop climate-resilient cultivars. The genetic foundation of heat tolerance in cultivated tomatoes has been significantly constrained by domestication ( Aflitos et al., 2014 ; Lin et al., 2014 ; Blanca et al., 2015 ; Mata-Nicolás et al., 2020 ). Historically, breeding programs have prioritized traits such as fruit size, firmness, color, and high yields under optimal conditions, often at the cost of stress resilience ( Schouten et al., 2019 ). Consequently, modern tomato cultivars possess limited genetic diversity for heat-tolerant traits. In contrast, wild tomato species, such as Solanum pimpinellifolium , retain critical heat-tolerance characteristics, including high pollen viability, sustained chlorophyll content, and efficient photochemical performance under elevated temperatures ( Driedonks et al., 2018 ; Wen et al., 2019 ; Gonzalo et al., 2020 ). These traits are vital for ensuring reproductive success and maintaining photosynthetic capacity during heat stress, emphasizing the role of wild relatives as an essential genetic resource for adaptive breeding. However, integrating heat-tolerance traits from wild species into elite tomato lines presents significant challenges. One major issue is linkage drag, where undesirable traits are co-inherited with beneficial genes. Additionally, restoring agronomic traits such as large fruit size and desirable flavor requires extensive breeding efforts. The polygenic nature of heat tolerance further complicates these efforts, as it involves complex interactions between traits like days to flowering, pollen viability, and fruit set. Addressing these challenges necessitates integrated strategies, including quantitative trait locus (QTL) mapping and targeted selection of key traits. Over the past decade, significant advances in QTL mapping have enhanced our understanding of the genetic architecture of heat tolerance in tomatoes. Previous studies have implemented diverse mapping populations to identify genomic regions associated with heat tolerance traits. For example, Xu et al. (2017a) identified QTLs of qPN7 and qPV11 on chromosomes 7 and 11 that explained 18.6% and 36.3% of pollen number and viability, respectively. Gonzalo et al. (2020) identified QTL AB7.1_T2 on chromosome 7 that explained 6.42% of pollen viability under heat stress conditions, and QTLs fln4.1_T2_2E , fln4.1_T3_2E , and frn4.1_T2_2E were all close to solcap_snp_sl_41710 on chromosome 2 at 6.2 MB for flower number, fruit number, and fruit set, respectively, across multiple levels of heat stress conditions. Bineau et al. (2021) used a MAGIC population and a small-fruit tomato core collection to identify a QTL hub on chromosome 3, from 53MB to 65 MB, regulating reproductive traits including days to flowering, flower number, fruit number, and fruit set. These mapping studies indicated the QTL clusters on chromosomes 2 and 3 were the hub regulating reproductive traits under heat stress conditions. In addition, Wen et al. (2021) focused on the physiological indexes like relative electrical conductivity (REC), chlorophyll content (CC) to assess heat stress. A QTL on chromosome 1at 82 MB to 86 MB was identified to be associated with both REC and CC. Despite advancements in genetic studies, most mapping efforts for heat tolerance in tomatoes have relied on bi-parental crosses, which have limited applicability to diverse genetic materials. To overcome this limitation, multi-parent advanced generation intercross (MAGIC) populations have emerged as a powerful tool for increasing genetic diversity through the inclusion of multiple parental founders. In tomatoes, the first MAGIC population was developed by Pascual et al. (2014) using eight founder lines: four accessions each of Solanum lycopersicum var. cerasiforme and Solanum lycopersicum var. lycopersicum, selected to represent a broad spectrum of genetic diversity. This study demonstrated an 87% increase in recombination frequency compared to traditional bi-parental populations, enabling the identification of multiple QTLs associated with fruit weight across diverse environments. This innovative resource significantly enhanced the precision of QTL mapping and narrowed the number of candidate polymorphisms for causal variants, providing a strong foundation for tomato breeding and genetic research. Additionally, the population was later utilized to investigate the complex genetic architecture underlying phenotypic plasticity and genotype × environment (G × E) interactions in tomatoes, a critical factor for improving crop resilience under climate change. By identifying candidate genes associated with G × E, the research offers valuable insights for developing climate-adapted tomato cultivars, ultimately boosting agricultural productivity under variable environmental conditions ( Diouf et al., 2020 ). The second MAGIC population, ToMAGIC, was developed using interspecific crosses among eight founders: four accessions each of S. lycopersicum var. cerasiforme and S. pimpinellifolium , the latter being a wild ancestor of cultivated tomatoes ( Arrones et al., 2024 ). These founders were chosen to capture the genetic diversity and geographical distribution of the two taxa. ToMAGIC has proven to be a valuable resource for genetic studies, facilitating the discovery of novel genes associated with key traits such as fruit size, plant pigmentation, and leaf morphology through GWAS. Furthermore, it provides untapped genetic variation for integration into breeding pipelines, supporting the development of improved tomato varieties. Therefore, MAGIC populations enhance QTL mapping by incorporating diverse genetic backgrounds from multiple founders, leading to balanced allele frequencies and increased recombination rates. This approach not only facilitates the identification of causal variants but also reduces the number of candidate polymorphisms, significantly improving the efficiency and precision of genetic research and breeding programs. This study leverages a MAGIC population derived from heat-tolerant and disease-resistant Solanum lycopersicum parental lines to identify key genomic loci underlying heat tolerance in tomatoes. By combining phenotypic evaluations, SNP marker development, and GWAS analysis, this research aimed to elucidate the genetic architecture of heat tolerance and provide actionable insights for breeding strategies. The identification of key QTLs and trait correlations will not only advance our understanding of tomato adaptation to heat stress but also contribute to the development of resilient varieties capable of addressing the challenges posed by climate change. Materials and methods Development of the MAGIC population Design of the MAGIC population was based on two major purposes: 1) identification of genes underlying heat tolerance in tomato, and 2) generation of new, elite heat tolerant lines with potential for use in breeding new varieties for the tropics and subtropics. Consequently, other traits besides heat tolerance were considered in choice of parents to create the MAGIC population. Begomoviruses causing Tomato yellow leaf curl diseases (TYLCD) severely constrain tomato production in most tropical regions of Asia, Africa, and the Americas. High temperatures and high TYLCD pressure often occur together in the field, complicating evaluation of tomato lines for heat-related traits in field trials. Candidate MAGIC population foundation parents were assessed with markers for presence of the Ty 1/3 gene conferring tolerance to a wide range of begomoviruses. Parents were genotyped for other disease resistance genes including Bwr- 12 (tolerance to bacterial wilt disease caused by Ralstonia solanacearum, a common soilborne pathogen in the tropics) and Ph- 3 (a gene conferring resistance to some strains of Phytophthora infestans causing late blight). The marker information is listed in Supplementary Table 1. Candidate parents were also evaluated for horticultural (vigor, plant habit, maturity) and fruit (weight, color, cracking) traits. Evaluation and Selection of the MAGIC parental lines Ten breeding materials (eight S. lycopersicum and two S. pimpinellifolium ) were evaluated for pollen viability and high temperature fruit set in a greenhouse from August to October 2017 at the World Vegetable Center, Shanhua, Tainan. Mean day and night temperatures during this period were 35.4L, and 28.1L, respectively; the highest temperature was 43.4L and the lowest was 24.4L. The candidate heat-tolerant parents were selected from literature review and breeders’ experience ( Table 1 ). We used a RCBD with three blocks and four plants per plot. Pollen was collected from the most recently opened flowers on the second to sixth inflorescences of the main stem for each plant. The pollen viability was assessed following the protocol described by Abdul-Baki (1992) . Briefly, anther cones were removed from the flowers, opened, and squashed using tweezers. The released pollen was then stained by vortexing in 150 µL of 0.5% acetocarmine solution. A 10 µL aliquot of the stained pollen was placed on a glass slide, covered with coverslip, and observed under 40x magnification. Viable pollen grains (stained red) and non-viable pollen grains (transparent) were counted. Fruit set was determined as the ratio of the total number of fruits to the total number of flowers on the second to sixth inflorescences of the main stem. View this table: View inline View popup Download powerpoint Table 1 Cultivated and wild tomato candidate parents evaluated for pollen viability and fruit set in the greenhouse, WorldVeg Taiwan Eight cultivated tomato lines were selected as the foundation parents of the MAGIC population ( Table 1 ) based on the greenhouse trial. To develop F 1 hybrids, we initially crossed heat-tolerant lines with disease-resistant lines. The resulting four combinations were as follows: CLN3961D × Saladette = CLN4394F1; CLN4079C × CLN1621L = CLN4396F1; CLN4058A × Nagcarlang = CLN4395F1; and Siberia × Tstar D. (TstarD)= CLN4220F1. Subsequently, two F 1 hybrids were crossed to produce double hybrids: CLN4394F1 × CLN4396F1 = CLN4433 and CLN4395 × CLN4220 = CLN4430. To enhance recombinant events, crossing the double crosses was performed by growing 120 plants each of CLN4430 and CLN4433. Each plant in both populations was used once as a male and once as a female in crosses. This process resulted in a total of 240 intercrosses between the double hybrids, allowing for the mixing of all eight genomes. The progeny derived from these crosses was advanced through successive generations using the single-seed descent method. To mitigate the risk of losing progeny under open-field conditions, four plants per progeny were initially planted. To preserve genetic integrity and avoid the effects of artificial selection on gene flow and pool structure, the first plant from each progeny was retained and harvested for the next generation. Additionally, superior plants were identified, selected, and harvested. As a result, 262 progenies were ultimately maintained and advanced to the F 5:6 generation. A detailed workflow of the crossing process is presented in Fig. 1 . Download figure Open in new tab Figure 1. The overview of the MAGIC population. A) Development of the MAGIC population; B) PCA of the phenotypes in the open-field heat stress condition; C) PCoA of the MAGIC population. Download figure Open in new tab Figure 2. Manhattan plots of A) days to flowering, B) pollen viability, C) flower number, and D) fruit number. Download figure Open in new tab Figure 3. Phenotypic distributions of marker designed from SL4.0CH11_47205149. A, B, and C refer to days to flowering, fruit number, and yield, respectively. Phenotyping of the MAGIC population For phenotypic evaluation, a total of 262 F 5:6 lines were planted in two replications. Four plants per progeny per replication were evaluated in the field under natural heat stress conditions during the summer season in 2021 at the World Vegetable Center, Headquarters, Shanhua, Tainan. The seeds were sowed on the 15 th of July, and the seedlings were transplanted on the 12 th of August. The highest, lowest, and mean temperature was 37.2L, 19.7L, and 28.3L, respectively. We evaluated lines for multiple traits, including plant vigor, days to flowering (DF), stigma exsertion (SE), anther dehiscence (AD), pollen viability (PV), flower number (FLN), fruit number (FRN), fruit set (FS), and TYLCD scores. Plant vigor was assessed visually on a scale from 1 (weak) to 5 (strong). Days to flowering were recorded as the days until 50% of the plants had flowered. Stigma exertion and anther dehiscence were assessed through observations of six flowers of the main stem per plant, sampled from two to three plants per plot. Stigma exertion was calculated as the ratio of flowers with exerted stigmas to the total six flowers observed per plant, while anther dehiscence was determined as the ratio of flowers with dehiscent anthers to the same total. Pollen viability was measured using Ampha Z32 Impedance Flow Cytometer (Amphasys AG, Switzerland) to determine the number of viable pollen grains and total pollen count. Pollen viability tests were conducted on the second and fourth inflorescences, with two open flowers randomly collected from two plants within each plot. The anther cones were removed from the flowers, placed in a 2.0 mL Eppendorf tube containing 750 µL of AmphaFluid 6 (AF6) buffer (Amphasys AG, Switzerland), and vigorously scraped along the tube bottom to release pollen until the solution became turbid. The turbid solution was filtered, and an additional 750 µL of AF6 buffer was used to wash any remaining pollen on the filter membrane. After the measurement, pollen viability was quantified using AmphaSoft 2.1.6 software (Amphasys AG, Switzerland). Flower number, fruit number, and fruit set were counted and estimated on the second to sixth inflorescences of the main stem. TYLCD scores were evaluated by the experienced breeders using a scale from 1 (healthy, no symptoms) to 6 (most severe symptoms). The correlation and principal component analysis were conducted and visualized with the R program ( R core team, 2023 ). We used ridge and lasso regressions to evaluate the contribution of traits to the reproductive output. The response is fruit set, and the predictors are plant vigor, days to flowering, stigma exsertion, anther dehiscence, pollen viability, flowering number, fruit number and TYLCD scores. The evaluation was performed 100 times and we used mean values as the contribution of each trait. This was performed using R package glmnet ( Tay et al., 2023 ). Genotyping For the eight parents, young leaves of individual seedlings were collected for 10X coverage of the whole genome resequencing. The sequencing was outsourced using Illumina HiSeq 2500 platform of pair-end reads. After data was retrieved, we processed the raw reads following the GATK Best Practice to perform the reference genome-based SNP calling ( DePristo et al., 2011 ). In brief, we used SolexQA++ “dynamictrim” to clean the reads ( Cox et al., 2010 ). The cleaned reads were mapped to SL4.0 using BWA ( Li and Durbin, 2009 ; Hosmani et al., 2019 ). The individual SNPs were called by gatk functions “HaplotypeCaller” and “GenotypeGVCFs” and then merged by Picard MergeVcfs ( McKenna et al., 2010 ; https://broadinstitute.github.io/picard/ ). The SNP variants were filtered with the threshold of sequencing quality > 20 and mapping quality > 40. Then, we removed all the missing and heterozygous SNPs. Meanwhile, young leaves of the F 5:6 progenies were collected, and we divided the samples evenly into three groups to prepare the Ape KI-digested DNA libraries. The sequencing was outsourced using Illumina HiSeq 2500 platform of pair-end reads. After data retrieval, we processed the raw reads following the similar procedure with what we did for the WGS. In brief, we used Stacks function “process_radtags” to demultiplex the data and to check quality scores of the reads ( Catchen et al., 2013 ). The SNPs were developed following the GATK Best Practice and filtered with the threshold of sequencing quality > 20 and mapping quality > 40 ( DePristo et al., 2011 ). Samples with more than 50% missing sites and SNPs with more than 50 missing sites were removed from the dataset. We aligned the progeny SNP matrix with the parents SNP matrix, and the missing sites were imputed using Beagle 5.5 ( Browning et al., 2018 ). Mapping of the reproductive traits The genetic distance of the parental lines and the progeny was first estimated in TASSEL and visualized using R program ( R core team, 2023 ). Since this population was generated from multiple parents, we considered it germplasm and conducted association mapping using the Genome-wide Efficient Mixed Model Association software (GEMMA; Zhou and Stephens, 2012 ). The candidate SNPs were defined as P score < 0.001. The heritability was estimated using the Bayesian Sparse Linear Mixed Model (BSLMM) implemented in GEMMA software ( Zhou et al., 2013 ). We performed 1,000,000 burn-in steps followed by 1,000,000 sampling steps, recording 1 state every 10 steps. The median of the resulting 100,000 records was used to represent the heritability. The QTLs of reproductive traits in the four previous studies ( Xu et al., 2017a ; Gonzalo et al., 2020 ; Bineau et al., 2021 ; Elazazi et al., 2024 ) were browsed on the SL4.0 genome using Genome Browser hosted on the Sol Genomics Network ( https://solgenomics.sgn.cornell.edu/ ) to confirm the consistency with the QTL identified in this study. Marker validation Genomic DNA was extracted from leaf tissue using FavorPrep™ plant genomic DNA extraction mini kit. The DNA quality and concentration were assessed using Qubit® 2.0 Fluorometer (Invitrogen). DNA samples were diluted to a final concentration of approximately 5 ng/μL and stored at -20°C until further analysis. Allele-specific primers were designed using the online tool ( http://primerdigital.com/tools/kasp.html ). Two allele-specific forward primers and a common reverse primer were synthesized. Primers were ordered as part of a KASP assay mix from Genomics (Biosci. & Tech. Co., Ltd. Taiwan). The two-allele specific forward primers i.e., forward 1, forward 2 and the common reverse primer were mixed with the ratio of 1:1:5.2 respectively to make the KASP assay. The KASP assay reactions were conducted in a 96-well plate format. Each reaction was prepared in a total volume of 10 μL, containing 5 μL of KASP Master Mix (LGC Genomics, UK), 0.3 μL of KASP assay mix (containing allele-specific primers and the common reverse primer), 2.7 μL of nuclease-free water, 5 ng of DNA template. PCR was performed on a Bio-Rad CFX Connect Real-Time System Cycler with the following conditions: Initial temperature of 94°C for 15 minutes for hot-start activation. Touchdown cycling: 10 cycles of 94°C for 20 seconds, with the annealing temperature 61°C for 60 seconds. Amplification cycling: 31 cycles of 94°C for 20 seconds and 55°C for 60 seconds. Cooling: the 37°C for 3 minutes to cool down the reaction temperature so that the signal reading could be completed in the same real-time PCR machine. Fluorescent signals were detected using the same instrument with detection of FAM and HEX dyes to distinguish between alleles. Fluorescent intensity data were collected after amplification cycles. Samples were assigned genotypes as follows: FAM fluorescence only: Homozygous for allele 1. HEX fluorescence only: Homozygous for allele 2. Dual FAM and HEX/VIC fluorescence: Heterozygous. Data were analyzed with Bio-Rad CFX Manager 3.1 to determine allele frequency, genotype call rate, and other relevant metrics. Positive controls and negative controls (no DNA) were included in the wells after the samples in each PCR plate to validate assay accuracy and rule out contamination. Samples with ambiguous results were retested to confirm genotype calls. Phenotypic evaluation for marker validation Given that eight founder lines were utilized to develop the markers, we anticipated that these markers can be applied more broadly in our breeding programs, provided the minor allele of a given marker was present in more than one parental line. Therefore, 27 crosses generated as part of our routine breeding programs, comprising a total of 207 F 2:3 families, were planted during the 2024 spring season at the World Vegetable Center, Headquarters, Shanhua, Tainan. Three individual plants were evaluated for each F 2:3 family. We evaluated three traits: fruit number, fruit weight, and yield, which is the product of fruit number and fruit weight, given that our goal is to develop a marker for yield under heat stress conditions. ANOVA was used to confirm if the marker is associated with the phenotypes. Results Evaluation of candidate MAGIC parental lines To assess heat tolerance, ten tomato materials were evaluated for pollen viability and fruit set, revealing significant genotypic effects for both traits (p < 0.01). Among the S. lycopersicum accessions, the highest to lowest pollen viability was observed in the following order: CLN1621L, Tomato 337, Nagcarlang, Siberia, Divisoria-2, Saladette, NCHS-1, and TstarD ( Table 1 ). However, the rankings for fruit set differed, with Saladette, Siberia, CLN1621L, and Nagcarlang showing superior performance. As expected, the two small-fruited S. pimpinellifolium accessions exhibited higher pollen viability and fruit set than the cultivated S. lycopersicum . Given the goal of developing high-yielding tomato varieties without the linkage drag associated with wild tomatoes, fruit set among S. lycopersicum was prioritized as the key selection criterion for identifying heat-tolerant parental lines. Based on this criterion, Saladette, Siberia, CLN1621L, and Nagcarlang emerged as the most promising heat tolerant candidate although each is begomovirus and bacterial wilt susceptible. Consequently, four additional multiple disease resistant lines with superior fruit and horticultural traits were also included as parents. Field trials further elucidated the heat tolerance of the parental lines under open-field conditions realistic agricultural conditions. CLN1621L and Nagcarlang exhibited superior pollen viability in open-field environments ( Table 2 ). Additionally, Nagcarlang and Saladette demonstrated the highest fruit set and fruit number among the materials tested ( Table 2 ), underscoring Nagcarlang’s robustness across multiple experiments essential for reproductive success under heat stress. Interestingly, TstarD exhibited high fruit set and fruit number in field conditions despite its low pollen viability. This contrasted with its greenhouse performance, where it ranked among the lowest for both fruit set and pollen viability, indicating that TstarD is highly sensitive to environmental conditions. These findings highlight the importance of considering environmental adaptability when selecting heat-tolerant parental lines for breeding programs. View this table: View inline View popup Table 2 Horticultural characteristics, marker information, and phenotypes of the MAGIC parental lines Fruit number as a selection index for heat tolerance in open-field conditions The phenotypic distributions of the F 5:6 lines followed normal distributions across all traits (Supplementary Table 2 and Supplementary Fig. 1). The ranges of plant vigor spanned from 2 to 4.25, days to flowering ranged from 10.5 to 44.0, anther dehiscence and stigma exsertion both varied from 0 to 100%, pollen viability ranged from 1.35 to 69.40%, flower number ranged from 1.5 to 12.1, fruit number ranged from 0 to 45.75, fruit set ranged from 0 to 56.95%, and TYLCD scores ranged from 1 to 4. Analysis of the relationships among these reproductive traits under heat stress conditions revealed that days to flowering exhibited a negative correlation with all other reproductive traits, with Pearson’s correlation coefficients ( r ) ranging from -0.09 to -0.38, suggesting that early flowering is crucial for maintaining tomato productivity under high-temperature conditions (Supplementary Fig. 2). Among the reproductive traits, fruit set showed the strongest correlation with fruit number ( r = 0.86, p < 0.001), while the correlation between pollen viability and fruit set was relatively weak ( r = 0.22, p < 0.001). Interestingly, plant vigor was negatively correlated with fruit set ( r = -0.16, p = 0.01), indicating a potential trade-off between vegetative growth and reproductive development. Meanwhile, a positive correlation was observed between TYLCD scores and fruit set ( r = 0.12, p = 0.04), suggesting a likely trade-off between disease resistance and reproductive success. To establish a selection index for heat tolerance in open-field conditions, the relative contributions of various traits to reproductive output were quantified. Ridge and lasso regression analyses consistently identified fruit number as the most significant contributor to fruit set, explaining 57.1% of the observed variance. Days to flowering was the second most influential trait, contributing 8.6%, while other traits had minimal effects (Supplementary Fig. 4). These findings highlight the pivotal role of fruit number in determining reproductive success under heat stress, with fruit set primarily dependent on fruit number rather than pollen viability or other factors. Genetic diversity of this population To establish a robust breeding program, we first assessed the genetic diversity among eight parental lines to ensure a diverse genetic background. Whole-genome resequencing identified a total of 4,075,083 SNPs. Chromosomes 6 and 9 exhibited higher SNP density compared to other chromosomes (Supplementary Fig. 5A). Heat-tolerant parental lines contained 1,743,836 SNPs, whereas disease-resistant parental lines harbored 3,682,871 SNPs (Supplementary Fig. 5B and 5C). It is worth noting that SNPs within heat-tolerant parents were concentrated on chromosome 9, suggesting that the higher SNP density on chromosome 6 was attributable to disease-resistant QTLs. The PCoA revealed close genetic relationships among heat-tolerant parents, except for CLN1621L, while disease-resistant parents exhibited considerable genetic diversity ( Fig. 1C ). This diversity was consistently reflected in SNP density differences between the two groups (Supplementary Fig. 5). These results indicate that the genetic basis for heat tolerance in cultivated tomatoes may derive from a limited number of sources, underscoring the importance of expanding genetic diversity for this trait. In contrast, the disease-resistant parents, which were derived from WorldVeg breeding lines that incorporated resistance traits originally derived wild species, displayed broader genetic diversity as expected. The SNP alignment between parental lines and progeny resulted in 16,350 SNPs after filtering out those with fewer than 200 samples to ensure sufficient sample size for GWAS. The overall SNP density was approximately 48 Kb per SNP, with the highest density observed on chromosome 6 (17 Kb) and the lowest on chromosome 1 (195 Kb) ( Table 3 ). The PCoA of this population revealed two distinct genetic groups with evidence of admixture, indicating the influence of unexpected selection forces during generation advancement ( Fig. 1C ). Nonetheless, GWAS can proceed with the correction of kinship to account for the genetic structure. View this table: View inline View popup Download powerpoint Table 3 SNP density across the genome. QTL identification Given the observed genetic diversity among the parental lines, this MAGIC population was treated as a diverse panel for conducting a GWAS. As a result, we identified a total of 143 SNPs significantly associated with the evaluated phenotypes (Supplementary Table 3). Among them, three SNPs, SL4.0CH01_7511997, SL4.0CH01_24105448, and SL4.0CH01_62158112, were associated with three of the four reproductive traits: days to flowering, pollen viability, flower number, and fruit number. Considering the extensive heterochromatic regions and large linkage intervals in tomato, significant SNPs were clustered based on sharp signals observed in the Manhattan plots, leading to the identification of 50 QTLs associated with these traits (Supplementary Fig. 6). These included 4 QTLs associated with plant vigor, 8 QTLs linked to days to flowering, 2 QTLs each for anther dehiscence and stigma exsertion, and 8 QTLs related to pollen viability ( Table 4 ). Additionally, 15, 8, and 1 QTLs were identified for flower number, fruit number, and fruit set, respectively, along with 2 QTLs associated with TYLCD resistance scores ( Table 4 ). Among these QTLs, three intervals emerged as key hubs for reproductive traits: SL4.0CH01_62158112:64367878, which was associated with days to flowering, flower number, and fruit number; SL4.0CH03_58367447:61284134, which was linked to days to flowering and pollen viability; and SL4.0CH11_47205149:47255006, which was associated with days to flowering and fruit number. These findings highlight the critical role of these three QTL intervals in influencing reproductive traits under heat stress conditions and their potential utility in tomato breeding programs. View this table: View inline View popup Table 4 QTL identified in this study The heritability of the ten evaluated traits ranged from 0.1 to 0.56 (Supplementary Table 4), indicating varying degrees of genetic control. Pollen viability showed the highest heritability, consistent with our result that it was less influenced by the temperature. Fruit number, the most important yield component under heat stress conditions, showed 0.47 heritability, aligning with the observation that fruit number serves as a more reliable selection index of yield in open-field conditions. In contrast, fruit set displayed low heritability, highlighting its high sensitivity to environmental factors. Given these findings, it is evident that while heat tolerance is a complex trait influenced by multiple factors, selection indices for breeding programs targeting open-field conditions should prioritize fruit number over pollen viability or fruit set. This approach would enhance the effectiveness of selecting for heat-tolerant and high-yielding cultivars under realistic agricultural conditions. Candidate genes linked to the significant SNPs Our analysis identified 56 candidate genes associated with significant SNPs ( Table 4 ). A detailed investigation focused on five candidate genes with potential relevance to fruit number, a critical determinant of reproductive output. Among these, SL4.0CH01_6664246 was linked to the gene Solyc01g010970.3 ( ARGONAUTE7 , SlAGO7 ), a key regulator of the trans-acting small interfering RNAs (ta-siRNA) pathway ( Table 4 ). This gene plays a conserved role in controlling leaf pattern across species. Previous studies have demonstrated that SlAGO7 enhances tomato yield by modifying inflorescence structure and increasing fruit number thorough auxin signaling ( Lin et al., 2016 ), reinforcing the confidence in our findings. Additionally, SlARF4 , a member of the tomato ( Solanum lycopersicum ) auxin response factor (ARF) gene family, is repressed by the tasi-ARF pathway, which is positively regulated by SlAGO7 ( Liu et al., 2023 ). Notably, higher starch content in developing fruits of SlARF4 down-regulated lines correlates with the up-regulation of genes and enzyme activities involved in starch biosynthesis, suggesting that SlARF4 negatively regulates these processes. Taken together, these findings reveal the involvement of ARFs in controlling sugar content—an essential feature of fruit quality—and provide insight into the connection between auxin signaling, chloroplast activity, and sugar metabolism in developing fruits ( Sagar et al., 2013 ). Furthermore, SNPs located at SL4.0CH11_46715869:47307248 were directly associated with Solyc11g062310.2 and Solyc11g062370.1 . Solyc11g062310.2 , annotated as proteasome-associated protein ECM29, plays a pivotal role in proteasomal disassembly in response to oxidative stress ( Wang et al., 2017 ). This function facilitates the removal of damaged proteins, ensuring cellular homeostasis during environmental stressors. Meanwhile, Solyc11g062370.1 , annotated as Stomatal Closure-Related Actin-Binding Protein 1, highlights the importance of stomatal regulation in plant heat tolerance. Stomatal closure supports water retention and balances heat dissipation under elevated COL conditions, mitigating heat stress effects in tomato seedlings ( Zhang et al., 2018 ). A candidate marker to improve yield under heat stress conditions To enhance yield under heat stress conditions, we focused on SNPs linked to fruit number. Among the eight identified QTLs, SSL4.0CH11_47205149 on chromosome 11 was the most significant; this SNP was also associated with days to flowering ( Table 4 ). To enable practical selection for yield under heat stress conditions, we converted this SNP into a KASP marker and evaluated the significance under heat stress conditions using the breeding materials from other crosses (Supplementary Table 5). Although the marker was not significantly associated with fruit number ( P = 0.20) or days to flowering ( P = 0.25), it demonstrated a significant association with yield ( P = 0.02). This suggests that the QTL contributes to yield through its regulatory effects on days to flowering and fruit number, though covariation between these traits may obscure statistical significance. When we conducted a multivariate ANOVA incorporating both traits, the association remained non-significant ( P = 0.20). This result may be attributed either to dominant effects within the F 2 populations or to other genotype-by-genotype interactions that influenced the marker’s application. Notably, the highest-yielding plants under heat stress were heterozygous for this marker, highlighting the critical role of hybrid vigor (heterosis) in enhancing heat tolerance and ensuring yield stability (Figure 5). Discussion Key yield components under greenhouse and open-field conditions The World Vegetable Center has devoted over half a century to developing heat-tolerant tomatoes for tropical regions where open-field cultivation is the predominant agricultural practice. Our findings reveal significant differences in yield components when comparing greenhouse and open-field conditions, necessitating adjustments to the selection index for heat tolerance. In our experiments, selecting heat-tolerant parental lines in greenhouse conditions yielded strikingly different results than open-field evaluations. Specifically, only two heat-tolerant parental lines identified in controlled greenhouse environments maintained high fruit set when tested under open-field conditions. This discrepancy underscores the influence of environmental factors. While previous studies focused primarily on greenhouse environments, the present research highlights the complexities of open-field conditions, where factors such as flower and fruit drop may obscure the direct relationship between pollen viability and yield. This finding emphasizes the importance of conducting heat-stress evaluations in realistic field settings to capture the multifaceted environmental interactions affecting yield components. Previous studies have identified pollen viability and fruit set as critical selection indices for maintaining tomato yields under heat stress. However, our research suggests that fruit number is a more reliable key index for evaluating heat tolerance in open-field conditions. This conclusion is supported by two observations: (1) the correlation between pollen viability and fruit set is weaker than the correlation between fruit number and fruit set, and (2) fruit number accounts for over 50% of the variability in fruit set. Furthermore, fruit set exhibited low heritability under open-field conditions, likely due to the cumulative impact of flower and fruit drop, and other environmentally sensitive factors that are challenging to control in such settings. These findings can guide the development of effective breeding programs. By prioritizing fruit number as a selection index, breeders can better address the environmental variability inherent in open-field conditions, ultimately improving heat tolerance and yield stability in tomato cultivation under tropical climates. Consistency of identified QTLs with previous research The identification of QTLs in this study aligns closely with findings reported in previous research. For pollen viability, Xu et al. (2017a) identified the QTL qPV11 associated with pollen viability, located near solcap_snp_sl_36066 at approximately 2.5 Mb on chromosome 11. In the current study, a QTL associated with pollen viability was identified in the interval SL4.0CH11_5089525:5293346. Given the use of Nagcarlang as the heat-tolerant parent in both studies and the presence of long linkage disequilibrium in tomatoes, it is likely that these represent the same QTL. In addition, solcap_snp_sl_58584 on chromosome 3 at 56 Mb was identified as a QTL for pollen viability under control conditions ( Gonzalo et al., 2020 ). This QTL is in close proximity with our pollen viability QTL at SL4.0CH03_59376133:61284134, suggesting this region may regulate pollen viability under various temperatures. For days to flowering, Bineau et al. (2021) used a tomato small-fruit core collection and a MAGIC population to identify flw3.3 and flw11.5 , QTLs for days to flowering. The former is located near SL4.0CH03_58367447, a QTL identified in this study. For flower number, Xu et al. (2017a) identified qFPI1 and qIN1 , QTLs associated with flowers per inflorescence and inflorescence number, respectively, on chromosome 1 near solcap_snp_sl_13762 at 68 Mb. Our study identified a QTL for flower number in the interval SL4.0CH01_62158112:68773937, which is in the same interval. This region should represent the same underlying QTL. In addition, nflw2.3 and nflw3.2 at approximately 45 Mb on chromosome 2 and 64 Mb on chromosome 3, respectively were identified ( Bineau et al. 2021 ). The former co-locates with the flower number QTLs identified in this study SL4.0CH02_45225081:45486119 and the later is close to SL4.0CH03_59803867. Further, Gonzalo et al. (2020) used an interspecific cross, S. lycopersicum x S. pimpinellifolium , to identify fln2.1_T3_2E , a QTL for flower number under heat stress conditions near solcap_snp_sl_49669 at 44 Mb on chromosome 2. This QTL is close to our identified flower number QTL at SL4.0CH02_45225081:45486119, providing additional evidence for the robustness of this locus across species. For fruit number, Bineau et al. (2021) reported a QTL for fruit number nfr3.2 near 54 Mb on chromosome 3, which overlaps with our fruit number QTL SL4.0CH03_54894805. Moreover, Elazazi et al. (2024) identified a QTL hub on chromosome 6, especially on SL4.0CH06_32950646 for fruit set and yield. This QTL is next to our QTLs of fruit set (SL4.0CH06_31977354), days to flowering (SL4.0CH06_31613992), and flower number (SL4.0CH06_32204322), underscoring the consistency of key reproductive trait QTLs under heat stress conditions. Collectively, this study and prior research identify consistent QTLs on chromosome 2 (45 Mb), chromosome 3 (59–62 Mb), and chromosome 6 (31-32 Mb), associated with flower number, fruit number, pollen viability, and fruit set. The QTL interval from 59 MB to 62 MB on chromosome 3 is particularly noteworthy given that the fruit number and pollen viability are highly heritable. Nevertheless, the QTL on 45 Mb of chromosome 2 is also a promising candidate QTL for flower number, as it has been detected in multiple studies involving an interspecific cross ( S. lycopersicum x S. pimpinellifolium ), a diverse small-fruit tomato panel, and our MAGIC population, indicating its stability and major-effect nature. This region represents a promising target for candidate gene research and further genetic analysis. Review of the candidate genes Although linkage disequilibrium in tomatoes is typically uneven and tends to span long distances, which poses challenges for pinpointing candidate genes, our analysis effectively identified potential genes within significant signals ( Sim et al., 2012 ; Bineau et al., 2021 ). Among these, a notable candidate gene on chromosome 3 within the 59–62 Mb region is Solyc03g115810.3 , annotated as the vacuolar fusion protein MON1. MON1 is crucial for membrane trafficking through the secretory pathway, forming a functional complex with CALCIUM CAFFEINE ZINC SENSITIVITY1 (CCZ1) to act as a guanine nucleotide exchange factor (GEF) for Rab7 GTPases. This complex facilitates proper vacuolar trafficking and protein sorting ( Cui et al., 2014 ). Recent findings underscore the importance of the MON1/CCZ1 complex, particularly SchMON1 and SchCCZ1 from Solanum chilense , in enhancing salt stress tolerance ( Madrid-Espinoza et al., 2024 ). Under saline conditions, these genes improve vesicular trafficking, enabling the sequestration of sodium ions into vacuoles and preventing their toxic accumulation in the cytoplasm. Moreover, the co-expression of SchMON1 and SchCCZ1 has been shown to rescue the dwarf phenotype of mon1-1 and ccz1a/b mutants, enhance endocytic activity, and reduce reactive oxygen species (ROS) levels in transgenic plants. The suppression of ROS is particularly noteworthy, as it protects cells from oxidative stress—a common consequence of salinity stress. RT-qPCR analyses further revealed dynamic expression changes of these genes under salt stress, supporting their physiological relevance. These results suggest that pre-vacuolar vesicular trafficking mediated by the MON1/CCZ1 complex represents a promising mechanism for improving salt tolerance in crops, which is critical for sustaining agricultural productivity in saline environments. This study also reveals the critical role of stomatal regulation in heat stress tolerance, particularly the function of the gene Solyc11g062370.1 . Solyc11g062370.1 is annotated as stomatal closure-related actin-binding protein 1, which may regulate stomatal closure, a key adaptive mechanism that conserves water and enables efficient heat dissipation under stress conditions ( Zhang et al., 2018 ). Stomatal movement, mediated by abscisic acid (ABA) signaling, is central to managing water loss during stress. ABA triggers stomatal closure, thereby reducing transpiration and improving water-use efficiency ( Leshem et al., 2010 ). Previous studies have revealed that vesicle-associated membrane proteins (VAMPs), particularly AtVAMP71 proteins, are essential for vesicle trafficking, which underpins membrane movement necessary for stomatal dynamics during stress ( Leshem et al., 2010 ). Reduced AtVAMP71 expression is associated with diminished ROS signaling and impaired drought resilience in plants. In addition, VAMPs also demonstrate significant adaptive functions in tomato microspores under heat stress. Heat stress upregulates the expression of VAMP homologues, such as AtVAMP725 , which are implicated in vesicle trafficking and pollen-specific thermotolerance ( Frank et al., 2009 ). This underscores the dual importance of VAMPs in both cellular maintenance and stress resilience, contributing to improved crop yield under adverse environmental conditions ( Frank et al., 2009 ). Moreover, Xue et al. (2018) also identified VAMP711 as a key regulator in drought stress responses. VAMP711 modulates plasma membrane HL-ATPase activity during ABA-induced stomatal closure. It interacts with Arabidopsis plasma membrane HL-ATPases AHA1 and AHA2 to inhibit their activity, enabling effective stomatal closure. Loss of VAMP711 function increases HL-ATPase activity, delaying ABA-induced stomatal closure and exacerbating drought sensitivity. Conversely, overexpression of VAMP711 mitigates drought stress in mutants with constitutively active HL-ATPase ( Xue et al., 2018 ). These findings emphasize the importance of vesicle trafficking in stomatal regulation, which is critical for drought and heat stress tolerance in plants. Exploring the responses of floral development genes to heat stress Solyc03g117230.1 , known as EXCESSIVE NUMBER OF FLORAL ORGANS ( ENO ), was identified within the QTL region of SL4.0CH03_59376133:61284134. ENO is a key regulator of flower number in tomato plants. Mutants of ENO exhibit slightly increased inflorescence branching and flower count compared to wild-type plants, indicating its role in controlling floral meristem size and reproductive output ( Yuste-Lisbona et al., 2020 ). Another significant gene is Solyc11g071810.1 , or FASCIATED ( fas ), located on chromosome 11 at 53 Mb, near the QTL for fruit number identified in this study (SL4.0CH11_46715869:47307248). Mutations in fas result in enlarged floral meristems and fasciated inflorescences, extending meristem expansion periods and increasing locule numbers in fruits. The mutation exhibits a nearly complete recessive effect relative to the wild type, emphasizing its importance in determining locule number and overall fruit morphology ( Chu et al., 2019 ). In addition, the LOCULE NUMBER ( lc ) gene ( Solyc02g083950.2 ), located at 45 Mb on chromosome 2 near the QTL for flower number (SL4.0CH02_45225081:45486119), significantly affects tomato fruit development by increasing locule numbers, typically from two to three or four. This gene also exhibits pleiotropic effects on floral organ development, such as an increase in petal numbers. The combined roles of ENO , fas , and lc are essential in regulating tomato fruit size through their interactions in the floral meristem development network. The fas gene, encoding SlCLV3, and lc , encoding SlWUS , are central components of the CLAVATA-WUSCHEL (CLV-WUS) signaling pathway that balances stem-cell proliferation and differentiation. Mutations in ENO expand SlWUS expression domains, maintaining stem-cell homeostasis in the floral meristem. These interactions, along with synergistic mutations, result in increased locule number and fruit size, reflecting their evolutionary adaptation for enhancing fruit development in cultivated tomatoes ( Yuste-Lisbona et al., 2020 ). Given their pivotal roles in regulating floral meristem activity, ENO , fas , and l c likely influence reproductive traits under heat stress as well. This is particularly relevant in the context of our QTL findings, as these genes and their regulatory networks may contribute to stable reproductive development across fluctuating environmental conditions. Future research should focus on exploring the functional interactions among these genes under heat stress conditions. Such studies could provide valuable insights into the genetic basis of abiotic stress tolerance and help develop strategies to enhance productivity and resilience in tomato crops. Heterosis Heterosis has been widely observed in cultivated tomatoes, particularly for reproductive traits such as seed number and fruit number. Hybrids frequently outperform their best inbred parents, demonstrating a strong correlation between heterozygosity and yield-related phenotypes ( Semel et al., 2006 ). One example is the heterozygosity of the SINGLE FLOWER TRUSS ( SFT ; Solyc03g063100.1 ) gene on chromosome 3, which has been shown to increase tomato yield through over-dominance mechanism ( Krieger et al., 2010 ). The findings from this study suggest the potential discovery of another QTL SL4.0CH11_46715869:47307248 that enhances yield under heat stress via over-dominance effects. These results underscore the importance of exploiting hybrid vigor in breeding programs to improve heat tolerance and yield, particularly under challenging environmental conditions. Applicability and limitations The genetic diversity of the parental lines used in this study suggests that the marker developed can be effectively applied to other breeding populations. The MAGIC population offers greater genetic diversity compared to traditional bi-parental populations, thereby enhancing mapping resolution. Furthermore, the MAGIC population has a reduced issue of population structure bias, as recombination events effectively break down structured linkage disequilibrium ( Pascual et al., 2014 ). In this study, we successfully applied the marker developed from the MAGIC population to other breeding materials, yielding promising results. The consistency between the QTL identified in our study and those reported in previous research further supports the robustness and transferability of the marker. This highlights the potential of the MAGIC-derived markers for broader use in breeding programs targeting complex traits. Despite these promising findings, this research has some limitations, primarily related to SNP density. While previous studies using the commonly employed SolCAP tomato SNP array reported an average SNP density of approximately 120 Kb, ranging from 50 Kb to 170 Kb ( Sim et al., 2012 ), our study achieved a higher SNP density than SolCAP. However, even with this improvement, the resolution remains insufficient for pinpointing candidate genes regulating key reproductive traits. To overcome this limitation, future studies could incorporate RNA sequencing experiments to capture transcriptomic variation associated with reproductive traits. Additionally, fine mapping studies would be invaluable for narrowing down candidate genes, enabling more precise identification of functional loci. These approaches would enhance our understanding of the genetic basis of reproductive traits and facilitate the development of molecular tools for crop improvement. Author contributions YPL and PH: conceptualization and funding acquisition; YPL, KH, and EH: formal analysis; YPL, YCH, EH, CYL, YSC, CYL, LHH, JYY, PH, and KH: investigation; YPL, YCH, E H, SMH, and JYY: data curation; YPL, EH and KH: writing - original draft; YPL, EH, AE, PH, and KH: writing - review & editing; YPL, EH, and CYH: visualization; YPL, AE, PH, SWK, and KH: supervision. Conflict of interest No conflict of interest declared. Data availability The sequencing data underlying this article are available in NCBI at http://www.ncbi.nlm.nih.gov/bioproject/1210015 , and can be accessed with BioProject PRJNA1210015. Supplementary data Fig. S1. Phenotypic distributions of the MAGIC population. Fig. S2. Correlation plot of the traits. Fig. S3. Contribution of the traits to tomato reproductive output under the open-field heat stress condition estimated by ridge and lasso regressions. Fig. S4. SNP distributions of the MAGIC parents developed from the whole genome resequencing. Fig. S5. Manhattan plots of the traits. Table S1. Marker information used in disease-resistant parents. Table S2. Mean values of the phenotype assessment for the MAGIC population in the open-field condition. Table S3. Significant SNPs of the reproductive traits detected using the MAGIC population. Table S4. Heritability of the traits. Table S5. Mean values of the phenotype assessment for the F2 populations in the open-field condition. Acknowledgements We extend our heartfelt gratitude to Peter Hanson for his invaluable contributions during his nearly three decades of leadership in the tomato breeding team at the World Vegetable Center. His dedication and expertise were instrumental in the development of this MAGIC population. We thank T. Komatsuda for technical assistance. This work was supported by Ministry of Agriculture, Taiwan (107AS-4.5.1-ST-a2), the National Science and Technology Center, Taiwan (MOST 110-2313-B-125-001-MY3 to Y.-P. Lin), Science and Technology Research Partnership for Sustainable Development (SATREPS; Grant No. 22578475), and Japan Science and Technology Agency (JST) / Japan International Cooperation Agency (JICA). We also thank the long-term strategic donors to the World Vegetable Center, including the governments of Taiwan, Germany, Thailand, the Philippines, South Korea, Japan, UK, USAID, and ACIAR. Footnotes Author information: Ya-Ping Lin, ya-ping.lin{at}worldveg.org , Ehtisham Hussain, ehtishampbg{at}outlook.com , Yun-Che Hsu, yun-che.hus{at}worldveg.org , Chen-Yu Lin, jean.lin{at}worldveg.org , You-Syuan Chen, r11628111{at}ntu.edu.tw , Lung-Hsin Hsu, lshsu{at}mail.tndais.gov.tw , Ching-Yuan Hung, joy.hung{at}worldveg.org , Shu-Mei Huang, shu-mei.huang{at}worldveg.org , Jo-Yi Yen, joyce.yen{at}worldveg.org , Assaf Eybishitz, assaf.eybishitz{at}worldveg.org , Peter Hanson, peterhansonmn{at}gmail.com , Seung Won Kang, kang.seungwon.ga{at}u.tsukuba.ac.jp , Ken Hoshikawa, hoshikawak0339{at}jircas.go.jp References ↵ Abdul-Baki AA . 1992 . Determination of Pollen Viability in Tomatoes . Journal of the American Society for Horticultural Science 117 , 473 – 476 . OpenUrl Abstract / FREE Full Text ↵ Aflitos S , Schijlen E , De Jong H , et al. 2014 . Exploring genetic variation in the tomato ( Solanum lycopersicon ) clade by whole-genome sequencing . The Plant Journal 80 , 136 – 148 . 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Genome-wide efficient mixed-model analysis for association studies . Nature Genetics 44 , 821 – 824 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 26, 2025. Download PDF Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population Ya-Ping Lin , Ehtisham Hussain , Yun-Che Hsu , Chen-Yu Lin , You-Syuan Chen , Lung-Hsin Hsu , Ching-Yuan Hung , Shu-Mei Huang , Jo-Yi Yen , Assaf Eybishitz , Peter Hanson , Seung Won Kang , Ken Hoshikawa bioRxiv 2025.01.23.634636; doi: https://doi.org/10.1101/2025.01.23.634636 Share This Article: Copy Citation Tools Identification of QTL for reproductive success under heat stress conditions through a tomato MAGIC population Ya-Ping Lin , Ehtisham Hussain , Yun-Che Hsu , Chen-Yu Lin , You-Syuan Chen , Lung-Hsin Hsu , Ching-Yuan Hung , Shu-Mei Huang , Jo-Yi Yen , Assaf Eybishitz , Peter Hanson , Seung Won Kang , Ken Hoshikawa bioRxiv 2025.01.23.634636; doi: https://doi.org/10.1101/2025.01.23.634636 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Genomics Subject Areas All Articles Animal Behavior and Cognition (7624) Biochemistry (17651) Bioengineering (13873) Bioinformatics (41887) Biophysics (21424) Cancer Biology (18566) Cell Biology (25465) Clinical Trials (138) Developmental Biology (13365) Ecology (19871) Epidemiology (2067) Evolutionary Biology (24293) Genetics (15591) Genomics (22478) Immunology (17715) Microbiology (40331) Molecular Biology (17150) Neuroscience (88492) Paleontology (666) Pathology (2828) Pharmacology and Toxicology (4817) Physiology (7635) Plant Biology (15114) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9817) Zoology (2268)