Mining elite loci and candidate genes for root morphology-related traits at the seedling stage by genome-wide association studies in upland cotton(Gossypium hirsutum L.)

Root system architecture plays an essential role in water and nutrient acquisition in plants,and it is significantly involved in plant adaptations to various environmental stresses.In this study,a panel of 242 cotton accessions was collected to investigate six root morphological traits at the seedling stage,including main root length(MRL),root fresh weight(RFW),total root length(TRL),root surface area(RSA),root volume(RV),and root average diameter(AvgD).The correlation analysis of the six root morphological traits revealed strong positive correlations of TRL with RSA,as well as RV with RSA and AvgD,whereas a significant negative correlation was found between TRL and AvgD.Subsequently,a genome-wide association study(GWAS)was performed using the root phenotypic and genotypic data reported previously for the 242 accessions using 56,010 single nucleotide polymorphisms(SNPs)from the CottonSNP80K array.A total of 41 quantitative trait loci(QTLs)were identified,including nine for MRL,six for RFW,nine for TRL,12 for RSA,12 for RV and two for AvgD.Among them,eight QTLs were repeatedly detected in two or more traits.Integrating these results with a transcriptome analysis,we identified 17 candidate genes with high transcript values of transcripts per million(TPM)≥30 in the roots.Furthermore,we functionally verified the candidate gene GH_D05G2106,which encodes a WPP domain protein 2in root development.A virus-induced gene silencing(VIGS)assay showed that knocking down GH_D05G2106significantly inhibited root development in cotton,indicating its positive role in root system architecture formation.Collectively,these results provide a theoretical basis and candidate genes for future studies on cotton root developmental biology and root-related cotton breeding.

Analyses and identifications of quantitative trait loci and candidate genes controlling mesocotyl elongation in rice

Rice direct seeding has the significant potential to save labor and water,conserve environmental resources,and reduce greenhouse gas emissions tremendously.Therefore,rice direct seeding is becoming the major cultivation technology applied to rice production in many countries.Identifying and utilizing genes controlling mesocotyl elongation is an effective approach to accelerate breeding procedures and meet the requirements for direct-seeded rice(DSR) production.This study used a permanent mapping population with 144 recombinant inbred lines(RILs) and 2 828 bin-markers to detect quantitative trait loci(QTLs) associated with mesocotyl length in 2019 and 2020.The mesocotyl lengths of the rice RILs and their parents,Lijiangxintuanheigu(LTH) and Shennong 265(SN265),were measured in a growth chamber at 30°C in a dark environment.A total of 16 QTLs for mesocotyl length were identified on chromosomes 1(2),2(4),3(2),4,5,6,7,9,11(2),and 12.Seven of these QTLs,including qML1a,qML1b,qML2d,qML3a,qML3b,qML5,and qML11b,were reproducibly detected in both years via the interval mapping method.The major QTL,qML3a,was reidentified in two years via the composite interval mapping method.A total of 10 to 413 annotated genes for each QTL were identified in their smallest genetic intervals of 37.69 kb to 2.78 Mb,respectively.Thirteen predicted genes within a relatively small genetic interval(88.18 kb) of the major mesocotyl elongation QTL,qML3a,were more thoroughly analyzed.Finally,the coding DNA sequence variations among SN265,LTH,and Nipponbare indicated that the LOC_Os03g50550 gene was the strongest candidate gene for the qML3a QTL controlling the mesocotyl elongation.This LOC_Os03g50550 gene encodes a mitogen-activated protein kinase.Relative gene expression analysis using qRT-RCR further revealed that the expression levels of the LOC_Os03g50550 gene in the mesocotyl of LTH were significantly lower than in the mesocotyl of SN265.In conclusion,these results further strengthen our knowledge about rice’s genetic mechanisms of mesocotyl elongation.This investigation’s discoveries will help to accelerate breeding programs for new DSR variety development.

Dissection of Genetic Effects of Quantitative Trait Loci(QTL) in Transgenic Cotton

When alien DNA inserts into cotton genome in multi-copy manner,several QTL in cotton genome are disrupted,which are called dQTL in this study.Transgenic mutant line is near-isogenic to its recipient which is divergent for the dQTL from remaining QTL.So,a set of data from a

Identification of Quantitative Trait Loci for Anthesis-Silking Interval and Yield Components Under Drought Stress in Maize

A genetic linkage map with 89 SSR marker loci was constructed based on a maize (Zea mays L.) population consisting of 184 F-2 individuals from the cross, Huangzao 4 X Ye 107. The 184 F-3 families were evaluated in the field under well-watered and drought-stressed regimes in Shanxi Province of China. The objectives of the study were to identify genetic segments responsible for the expression of anthesis-silking interval (ASI), ear setting and grain yield, and to examine if the quantitative trait loci (QTLs) for ASI or yield components can be used in marker-assisted selection (MAS) to improve grain yield under drought conditions. Results showed that under well-watered and drought-stressed regimes, three and two QTLs involved in the expression of ASI were detected on chromosomes 1, 2 and 3, and 2 and 5, respectively. Under well-watered regime, two QTLs for ear setting were detected on chromosomes 3 and 6, explaining about 19.9% of the phenotypic variance, and displayed additive and partial dominant effects, respectively. Under drought-stressed condition, four QTLs for ear setting were detected on chromosomes 3, 7 and 10, which were responsible for interpreting 60.4% of the phenotypic variance, and showed dominant or partial dominant effects. Under well-watered condition, four QTLs controlling grain yield were identified on chromosomes 3, 6 and 7, while five QTLs were identified under drought stress on chromosomes 1, 2, 4 and 8. The gene action was of additive or partial dominant effects, and each QTL could explain 7.3% to 22.0% of the phenotypic variance, respectively. Under drought conditions, ASI and ear setting percentage were highly correlated with grain yield, which can be used as secondary traits for grain yield selection. Based on linked markers detected and gene action analyzed, an MAS strategy for yield improvement under drought condition could be established, which consists of QTLs contributing to decreased ASI and to increased ear setting and grain yield, respectively.

Detection of Quantitative Trait Loci Associated with Several Internal Organ Traits and Teat Number Trait in a Pig Population

Quantitative trait loci （QTL） were detected for 8 internal organ traits, 3 carcass length traits, and teat number trait in 214 pigs in a resource population that included 180 F2 individuals. A total of 39 microsatellite markers were examined on SSC4, SSC6, SSC7, SSC8, and SSC13. The genetic traits included heart weight （HW）, lung weight （LW）, liver and gallbladder weight （LGW）, spleen weight （SPW）, stomach weight （STW）, small intestine weight （S1W）, large intestine weight （LIW）, kidney weight （KW）, carcass length to the first cervical vertebra （CL1）, carcass length to the first thoracic vertebra （CL2）, rib numbers （RNS）, and teat numbers （TNS）. Results indicated that, 3 highly significant QTL （P≤0.01 at chromosome-wise level） for HW （at 30 cM on SSC6）, RNS （at 115 cM on SSC7）, TNS （at 110 cM on SSC7）, and 6 significant QTL （P≤0.05 at chromosome-wise level） for LW （at 119 cM on SSC13）, LGW （at 94 cM on SSC6）, SPW （at 106 cM on SSC8）, SIW （0 cM on SSC4）, LIW （170 cM on SSC 4）, and TNS （at 95 cM on SSC6） were detected. The phenotypic variances for which these QTL were accounted ranged from 0.04 % to 14.06 %. Most of these QTL had not been previously reported.

Detection of the Quantitative Trait Loci with AFLP and RFLP Markers for Phosphorus Uptake and Use Efficiency in Rice

To identify genetic factors underlying phosphorus (P) uptake and use efficiency under low_P stress in rice (Oryza sativa L.), 84 selected genotypes (recombinant inbred lines) and their parents (which differed in tolerance for low_P stress) “IR20” and IR55178_3B_9_3, were cultured in liquid medium supplemented with adequate and low P levels in a greenhouse. Plants were sampled after 6 weeks in culture for measurements of plant dry weight, P concentration, P uptake and P use efficiency under both P sufficient and stress conditions. A total of 179 molecular markers, including 26 RFLPs and 153 AFLPs, mapped on all 12 chromosomes of rice based on the 84 selected genotypes were used to detect the quantitative trait loci (QTLs) underlying tolerance for low_P stress. Three QTLs were detected on chromosomes 6, 7 and 12, respectively, for relative plant dry weight (RPDW) and relative P uptake (RPUP). One of the QTLs flanked by RG9 and RG241 on chromosome 12 had a major effect which explained about 50% of the variations in the two parameters across the population. The results coincided with the QTLs for low_P stress based on relative tillering ability from the same population from a cross between Nipponbare and Kasalath under soil condition. The identical major QTL for P uptake and plant growth under low_P stress in both liquid medium and soil strongly suggests that the ability of P uptake mainly controls rice tolerance for low_P stress.

糯玉米茎秆穿刺强度QTL分析与基因组选择

茎秆穿刺强度是衡量玉米茎秆机械强度和抗倒伏能力的重要指标之一,本研究以衍生于糯玉米自交系衡白522和通系5的198个重组自交系为试验材料,对茎秆穿刺强度进行数量性状位点(QTL)分析和基因组选择研究。单个环境QTL分析共检测到4个控制糯玉米茎秆穿刺强度的QTL,每个QTL的表型变异贡献率均小于10.00%,且仅在单个环境中被检测到;多个环境QTL分析共检测到8个QTL与环境互作,其加性效应总共可解释24.64%的表型变异,加性效应与环境互作贡献率为17.51%;上位性QTL分析共检测到4对QTL与QTL互作,可解释8.25%的表型变异。基因组选择中,当训练群体占群体总数的80%,随机选择500个标记即可获得较高的预测准确性;但是根据单个环境QTL分析结果,选择机率常用对数值排名前200的标记,即可大幅度提高基因组选择预测准确性。

大豆遗传图谱构建及粗脂肪含量QTL定位

大豆是我国重要的粮油作物,提高其粗脂肪含量是我国大豆育种的重要目标.以长江春2号与渝蜀鲜2号杂交的F 2代186株单株为定位群体,在实验室前期构建的遗传图谱基础上进行标记加密,利用Joinmap 4.0软件进行遗传连锁分析,构建了包含480个标记的大豆遗传图谱,总长度为2469.3 cM,标记间平均距离为5.14 cM.结合大豆在2021年重庆、2022年重庆、2022年云南和2023年重庆4个环境下的粗脂肪含量表型数据,通过区间作图(Interval Mapping,IM)检测相应性状的数量性状位点(Quantitative Trait Locus,QTL).在4个环境中共检测到21个与粗脂肪含量相关的QTLs,分布在16个连锁群上,其中,有3个QTLs(qOIL01.1,qOIL04.1和qOIL14.2)在两个环境中均能检测到.

Identification of quantitative trait loci for the dead leaf rate and the seedling dead rate under alkaline stress in rice

The quantitative trait loci （QTLs） for the dead leaf rate （DLR） and the dead seedling rate （DSR） at the different rice growing periods after transplanting under alkaline stress were identified using an F2：3 population, which included 200 individuals and lines derived from a cross between two japonica rice cultivars Gaochan 106 and Changbai 9 with microsatellite markers. The DLR detected at 20 days to 62 days after transplanting under alkaline stress showed continuous normal or near normal distributions in F3 lines, which was the quantitative trait controlled by multiple genes. The DSR showed a continuous distribution with 3 or 4 peaks and was the quantitative trait controlled by main and multiple genes when rice was grown for 62 days after transplanting under alkaline stress. Thirteen QTLs associated with DLR were detected at 20 days to 62 days after transplanting under alkaline stress. Among these, qDLR9-2 located in RM5786-RM160 on chromosome 9 was detected at 34 days, 41 days, 48 days, 55 days, and 62 days, respectively; qDLR4 located in RM3524-RM3866 on chromosome 4 was detected at 34 days, 41 days, and 48 days, respectively; qDLR7-1 located in RM3859-RM320 on chromosome 7 was detected at 20 days and 27 days; and qDLR6-2 in RM1340-RM5957 on chromosome 6 was detected at 55 days and 62 days, respectively. The alleles of both qDLR9-2 and qDLR4 were derived from alkaline sensitive parent ＂Gaochanl06＂. The alleles of both qDLR7-1 and qDLR6-2 were from alkaline tolerant parent Changbai 9. These gene actions showed dominance and over dominance primarily. Six QTLs associated with DSR were detected at 62 days after transplanting under alkaline stress. Among these, qDSR6-2 and qDSR8 were located in RM1340-RM5957 on chromosome 6 and in RM3752-RM404 on chromosome 8, respectively, which were associated with DSR and accounted for 20.32% and 18.86% of the observed phenotypic variation, respectively; qDSR11-2 and qDSR11-3 were located in RM536-RM479 and RM2596-RM286 on chromosome 11, respectively, which were associated with DSR explaining 25.85% and 15.41% of the observed phenotypic variation, respectively. The marker flanking distances of these QTLs were quite far except that of qDSR6-2, which should be researched further.

Mapping quantitative trait loci associated with starch paste viscosity attributes by using double haploid populations of rice (Oryza sativa L.)

The paste viscosity attributes of starch,measured by rapid visco analyzer(RVA),are important factors for the evaluation of the cooking and eating qualities of rice in breeding programs.To determine the genetic roots of the paste viscosity attributes of rice grains,quantitative trait loci(QTLs)associated with the paste viscosity attributes were mapped,using a double haploid(DH)population derived from Zhongjiazao 17(YK17),a super rice variety,crossed with D50,a tropic japonica variety.Fifty-four QTLs,for seven parameters of the RVA profiles,were identified in three planting seasons.The 54 QTLs were located on all of the 12 chromosomes,with a single QTL explaining 5.99 to 47.11%of phenotypic variation.From the QTLs identified,four were repeatedly detected under three environmental conditions and the other four QTLs were repeated under two environments.Most of the QTLs detected for peak viscosity(PKV),trough viscosity(TV),cool paste viscosity(CPV),breakdown viscosity(BDV),setback viscosity(SBV),and peak time(PeT)were located in the interval of RM 6775-RM 3805 under all three environmental conditions,with the exception of pasting temperature(PaT).For digenic interactions,eight QTLs with six traits were identified for additivexenvironment interactions in all three planting environments.The epistatic interactions were estimated only for PKV,SBV and PaT.The present study will facilitate further understanding of the genetic architecture of eating and cooking quality(ECQ)in the rice quality improvement program.

Mapping Quantitative Trait Loci Associated with Photoperiod Sensitivity in Maize(Zea mays L.)

Photoperiod sensitivity in maize plays an essential role in utilizing tropic and sub-tropic germplasm to temperate areas. This study aims to identify and map the QTLs responsible for the characteristics measuring photoperiod sensitivity, days from planting to silking （SD）, photoperiod response coefficient of silking （PRC）, and anthesis-silking interval （ASI）. Using the population derived from Zheng 58, photoperiod-insensitive parent, and Ya 8701, photoperiod-sensitive parent, a linkage map was constructed with 93 single sequence repeat （SSR） markers. Phenotyping of 296 F2-3 families of the population in replicated-field test was conducted in both long-day （Beijing, China） and short-day （Sichuan, China） conditions. Ten QTLs were identified to be associated with the SD and ASI on chromosomes 3, 4, 6, 8, and 10 in the longday conditions, and 11 QTLs were detected to be related to the SD and ASI on chromosomes 2, 3, 4, 5, 6, 8, and 10 in the short-day conditions, respectively. A QTL associated with the PRC as a major effect in the long-day conditions located in the same position as the QTL related to the SD and ASI in the map, and was on chromosome 10 linked with marker bnlg1655. Using these QTLs in the marker-assisted selection, the photoperiod sensibility could be reduced by selection of the alleles responsible for the SD, PRC, and ASI in breeding programs.

Quantitative trait loci analysis for root traits in synthetic hexaploid wheat under drought stress conditions

Synthetic hexaploid wheat(SHW),possesses numerous genes for drought that can help breeding for drought-tolerant wheat varieties.We evaluated 10 root traits at seedling stage in 111 F9 recombinant inbred lines derived from a F2 population of a SHW line(SHW-L1)and a common wheat line,under normal(NC)and polyethylene glycol-simulated drought stress conditions(DC).We mapped quantitative trait loci(QTLs)for root traits using an enriched high-density genetic map containing 120370 single nucleotide polymorphisms(SNPs),733 diversity arrays technology markers(DArT)and 119 simple sequence repeats(SSRs).With four replicates per treatment,we identified 19 QTLs for root traits under NC and DC,and 12 of them could be consistently detected with three or four replicates.Two novel QTLs for root fresh weight and root diameter under NC explained 9 and 15.7%of the phenotypic variation respectively,and six novel QTLs for root fresh weight,the ratio of root water loss,total root surface area,number of root tips,and number of root forks under DC explained 8.5–14%of the phenotypic variation.Here seven of eight novel QTLs could be consistently detected with more than three replicates.Results provide essential information for fine-mapping QTLs related to drought tolerance that will facilitate breeding drought-tolerant wheat cultivars.

Mapping of quantitative trait loci using the skew-normal distribution

In standard interval mapping (IM) of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. When this assumption of normality is violated, the most commonly adopted strategy is to use the previous model after data transformation. However, an appropriate transformation may not exist or may be difficult to find. Also this approach can raise interpretation issues. An interesting alternative is to consider a skew-normal mixture model in standard IM, and the resulting method is here denoted as skew-normal IM. This flexible model that includes the usual symmetric normal distribution as a special case is important, allowing continuous variation from normality to non-normality. In this paper we briefly introduce the main peculiarities of the skew-normal distribution. The maximum likelihood estimates of parameters of the skew-normal distribution are obtained by the expectation-maximization (EM) algorithm. The proposed model is illustrated with real data from an intercross experiment that shows a significant departure from the normality assumption. The performance of the skew-normal IM is assessed via stochastic simulation. The results indicate that the skew-normal IM has higher power for QTL detection and better precision of QTL location as compared to standard IM and nonparametric IM.

基于全基因组关联分析的水稻穗长QTL定位及其候选基因分析

分别于2021年和2022年在湖南长沙种植254份水稻3K种质资源,在成熟期测定各种质的穗长。结合种质基因型进行全基因组关联分析,共检测到3个穗长QTL,分布于水稻第1、5、6号染色体,分别命名为qPL-1、qPL-5和qPL-6,相对贡献率为9.06%~28.27%;结合QTL区间内基因功能注释和基因不同单倍型的穗长差异显著性分析结果,最终获得qPL-1、qPL-5和qPL-6的候选基因,其中qPL-6与sped1-D共定位,Os01g0715600、Os05g0132700为调控穗长的新候选基因;对Os01g0715600、Os05g0132700和Os06g0597500的单倍型进行聚合分析,发现这3个基因存在累加效应。

高粱×苏丹草后代群体农艺及产量性状QTL初步定位

为进一步探究高粱籽粒和茎秆产量的遗传规律,运用粒用高粱忻粱52和苏丹草TS 185作为亲本进行杂交并得到F_(2)及F_(2)∶3群体,利用115对多态性引物对430份F_(2)群体使用区间作图法构建遗传连锁图谱,以LOD值等于3作为阈值。结果表明,分蘖数、叶片数、茎粗、穗长、株高、茎秆鲜质量、整株鲜质量、着壳率、穗质量、千粒质量、单穗粒质量、单穗粒数12个农艺性状共检测到86个QTL。在1号染色体sam17164—sam15397区间定位到茎秆鲜质量的QTL;在2号染色体Xcup64—Xcup26区间定位到分蘖数的QTL,Xtxp019—sam01138区间定位到叶片的QTL,Xcup26—Xtxp080区间定位到茎秆鲜质量的QTL;在3号染色体sam44791—sam33751区间定位到穗长的QTL;在7号染色体sam39622—sam43980区间定位到着壳率的QTL;在8号染色体sam10491—sam17740区间定位到整株鲜质量的QTL;在10号染色体sam710901b—sam59778区间定位到分蘖数的QTL,以上这些都是新检测到的位点。

Quantitative Trait Loci for Resistance to Stripe Disease in Rice(Oryza sativa)

In order to map the quantitative trait loci for rice stripe resistance, a molecular linkage map was constructed based on the F2 population derived from a cross between Zhaiyeqing 8 and Wuyujing 3. Reactions of the two parents, F1 individual and 129 F2：3 lines to, rice stripe were JnvestJgated by both artificial Jnoculation at laboratory and natural infection in the field, and the ratios of disease rating index were scored. The distribution of the ratios of disease rating index in Zhaiyeqing 8/Wuyujing 3 F2：3 population ranged from 0 to 134,08 and from 6.25 to 133.6 under artificial inoculation at laboratory and natural infection in the field, respectively, and showed a marked bias towards resistant parent （Zhaiyeqing 8）, indicating that the resistance to rice stripe was controlled by quantitative trait loci （QTL）. QTL analysis showed that the QTLs detected by the two inoculation methods were completely different. Only one QTL, qSTVT, was detected under artificial inoculation, at which the Zhaiyeqing 8 allele increased the resistance to rice stripe, while two QTLs, qSTV5 and qSTV1, were detected under natural infection, in which resistant alleles came from Zhaiyeqing 8 and Wuyujing 3, respectively. These results showed that resistant parent Zhaiyeqing 8 carried the alleles associated with the resistance to rice stripe virus and the small brown planthopper, and susceptible parent Wuyujing 3 also carried the resistant allele to rice stripe virus. In comparison with the results previously reported, QTLs detected in the study were new resistant genes to rice stripe disease. This will provide a new resistant resource for avoiding genetic vulnerability for single utilization of the resistant gene Stvb-i.

Mapping of Quantitative Trait Loci Affecting Eggshell Quality in an F_2 Population Derived from Strong and Weak Eggshell Lines of the White Leghorn Chicken Breed

Broken and cracked eggshells cause major economic losses to the egg production industry. An F2 population of 262 hens obtained by crossing a strong egg shell line with a weak egg shell line of the White Leghorn breed was used for detecting the quantitative trait loci （QTL） affecting eggshell quality. The 2 lines were developed from the same founder population by two-way selection for egg shell strength with nondestructive deformation. Of the 1 014 microsatellite markers tested, 35 were mapped on 10 autosomal linkage groups. There was no informative marker on chromosome Z. The QTLs associated with 7 traits, i.e., body weight, short length of egg, long length of egg, eggshell strength, eggshell thickness （EST）, eggshell weight （ESW）, and egg weight （EW）, were identified. Highly significant （P〈0.01） QTLs associated with EST and ESW and a significant （P〈 0.05） QTL associated with EW were mapped to a region flanking ABR0545 and ABR0362 on chromosome 9. These QTLs are good candidates to be employed in the development of strategies for reducing the number of broken and cracked eggs in commercial layer houses by employing marker assisted selection.

Mapping Quantitative Trait Loci Controlling Endosperm Traits with Molecular Marker

Based on the genetic models for triploid endosperm traits and on the methods for mapping diploid quantitative traits loci (QTLs), the genetic constitutions, components of means and genetic variances of QTL controlling endosperm traits under flanking marker genotypes of different generations were presented. From these results, a multiple linear regression method for mapping QTL underlying endosperm traits in cereals was proposed, which used the means of endosperm traits under flanking marker genotypes as a dependent variable, the coefficient of additive effect (d) and dominance effect (h1 and/or h2) of a putative QTL in a given interval as independent variables. This method can work at any position in a genome covered by markers and increase the estimation precision of QTL location and their effects by eliminating the interference of other relative QTLs. This method can also be easily used in other uneven data such as markers and quantitative traits detected or measured in plants and tissues different either in generations or at chromosomal ploidy levels, and in endosperm traits controlled by complicated genetic models considering the effects produced by genotypes of both maternal plants and seeds on them.

QTL analysis for some quantitative traits in bread wheat

Quantitative trait loci (QTL) analysis was conducted in bread wheat for 14 important traits utilizing data from four different mapping populations involving different approaches of QTL analysis. Analysis for grain protein content (GPC) sug- gested that the major part of genetic variation for this trait is due to environmental interactions. In contrast, pre-harvest sprouting tolerance (PHST) was controlled mainly by main effect QTL (M-QTL) with very little genetic variation due to environmental interactions; a major QTL for PHST was detected on chromosome arm 3AL. For grain weight, one QTL each was detected on chromosome arms 1AS, 2BS and 7AS. QTL for 4 growth related traits taken together detected by different methods ranged from 37 to 40; nine QTL that were detected by single-locus as well as two-locus analyses were all M-QTL. Similarly, single-locus and two-locus QTL analyses for seven yield and yield contributing traits in two populations respectively allowed detection of 25 and 50 QTL by composite interval mapping (CIM), 16 and 25 QTL by multiple-trait composite interval mapping (MCIM) and 38 and 37 QTL by two-locus analyses. These studies should prove useful in QTL cloning and wheat improvement through marker aided selection.

Quantitative trait loci detection of E dwardsiella tarda resistance in Japanese flounder Paralichthys olivaceus using bulked segregant analysis

In recent years, Edwardsiella tarda has become one of the most deadly pathogens of Japanese fl ounder( Paralichthys olivaceus), causing serious annual losses in commercial production. In contrast to the rapid advances in the aquaculture of P. o livaceus, the study of E. tarda resistance-related markers has lagged behind, hindering the development of a disease-resistant strain. Thus, a marker-trait association analysis was initiated, combining bulked segregant analysis(BSA) and quantitative trait loci(QTL) mapping. Based on 180 microsatellite loci across all chromosomes, 106 individuals from the F1333(♀: F0768 ×♂: F0915)(Nomenclature rule: F+year+family number) were used to detect simple sequence repeats(SSRs) and QTLs associated with E. tarda resistance. After a genomic scan, three markers(Scaffold 404-21589, Scaffold 404-21594 and Scaffold 270-13812) from the same linkage group(LG)-1 exhibited a signifi cant difference between DNA, pooled/bulked from the resistant and susceptible groups( P <0.001). Therefore, 106 individuals were genotyped using all the SSR markers in LG1 by single marker analysis. Two different analytical models were then employed to detect SSR markers with different levels of signifi cance in LG1, where 17 and 18 SSR markers were identifi ed, respectively. Each model found three resistance-related QTLs by composite interval mapping(CIM). These six QTLs, designated q E1–6, explained 16.0%–89.5% of the phenotypic variance. Two of the QTLs, q E-2 and q E-4, were located at the 66.7 c M region, which was considered a major candidate region for E. tarda resistance. This study will provide valuable data for further investigations of E. tarda resistance genes and facilitate the selective breeding of disease-resistant Japanese fl ounder in the future.