Genome-Wide Association Analysis With a 50K Transcribed Gene SNP-Chip Identifies QTL Affecting Muscle Yield in Rainbow Trout

Detection of coding/functional SNPs that change the biological function of a gene may lead to identification of putative causative alleles within QTL regions and discovery of genetic markers with large effects on phenotypes. This study has two-fold objectives, first to develop, and validate a 50K transcribed gene SNP-chip using RNA-Seq data. To achieve this objective, two bioinformatics pipelines, GATK and SAMtools, were used to identify ∼21K transcribed SNPs with allelic imbalances associated with important aquaculture production traits including body weight, muscle yield, muscle fat content, shear force, and whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs ere identified from pooled RNA-Seq data collected from ∼620 fish, representing 98 families from growth- and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ∼29K transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ∼5K non-synonymous SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples passing quality control (QC; 1,641 fish) was ≥ 98.5%. The second objective of this study was to test the feasibility of using the new SNP-chip in GWA (Genome-wide association) analysis to identify QTL explaining muscle yield variance. GWA study on 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71 and 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions were previously reported as important regulators of muscle development and cell signaling. No major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same fish population. These results indicate improved detection power of the transcribed gene SNP-chip in the target trait and population, allowing identification of large-effect QTLs for important traits in rainbow trout

Whole-body transcriptome of selectively bred, resistant-, control-, and susceptible-line rainbow trout following experimental challenge with Flavobacterium psychrophilum

Genetic improvement for enhanced disease resistance in fish is an increasingly utilized approach to mitigate endemic infectious disease in aquaculture. In domesticated salmonid populations, large phenotypic variation in disease resistance has been identified but the genetic basis for altered responsiveness remains unclear. We previously reported three generations of selection and phenotypic validation of a bacterial cold water disease (BCWD) resistant line of rainbow trout, designated ARS-Fp-R. This line has higher survival after infection by either standardized laboratory challenge or natural challenge as compared to two reference lines, designated ARS-Fp-C (control) and ARS-Fp-S (susceptible). In this study, we utilized 1.1 g fry from the three genetic lines and performed RNA-seq to measure transcript abundance from the whole body of naive and Flavobacterium psychrophilum infected fish at day 1 (early time-point) and at day 5 post-challenge (onset of mortality). Sequences from 24 libraries were mapped onto the rainbow trout genome reference transcriptome of 46,585 predicted protein coding mRNAs that included 2633 putative immune-relevant gene transcripts. A total of 1884 genes (4.0% genome) exhibited differential transcript abundance between infected and mock-challenged fish (FDR \u3c 0.05) that included chemokines, complement components, tnf receptor superfamily members, interleukins, nod-like receptor family members, and genes involved in metabolism and wound healing. The largest number of differentially expressed genes occurred on day 5 post-infection between naive and challenged ARS-Fp-S line fish correlating with high bacterial load. After excluding the effect of infection, we identified 21 differentially expressed genes between the three genetic lines. In summary, these data indicate global transcriptome differences between genetic lines of naive animals as well as differentially regulated transcriptional responses to infection

Genome-wide association analysis and accuracy of genome-enabled breeding value predictions for resistance to infectious hematopoietic necrosis virus in a commercial rainbow trout breeding population

International audienceAbstractBackgroundInfectious hematopoietic necrosis (IHN) is a disease of salmonid fish that is caused by the IHN virus (IHNV). Under intensive aquaculture conditions, IHNV can cause significant mortality and economic losses. Currently, there is no proven and cost-effective method for IHNV control. Clear Springs Foods, Inc. has been applying selective breeding to improve genetic resistance to IHNV in their rainbow trout breeding program. The goals of this study were to elucidate the genetic architecture of IHNV resistance in this commercial population by performing genome-wide association studies (GWAS) with multiple regression single-step methods and to assess if genomic selection can improve the accuracy of genetic merit predictions over conventional pedigree-based best linear unbiased prediction (PBLUP) using cross-validation analysis.ResultsTen moderate-effect quantitative trait loci (QTL) associated with resistance to IHNV that jointly explained up to 42% of the additive genetic variance were detected in our GWAS. Only three of the 10 QTL were detected by both single-step Bayesian multiple regression (ssBMR) and weighted single-step GBLUP (wssGBLUP) methods. The accuracy of breeding value predictions with wssGBLUP (0.33–0.39) was substantially better than with PBLUP (0.13–0.24).ConclusionsOur comprehensive genome-wide scan for QTL revealed that genetic resistance to IHNV is controlled by the oligogenic inheritance of up to 10 moderate-effect QTL and many small-effect loci in this commercial rainbow trout breeding population. Taken together, our results suggest that whole genome-enabled selection models will be more effective than the conventional pedigree-based method for breeding value estimation or the marker-assisted selection approach for improving the genetic resistance of rainbow trout to IHNV in this population

国内虹鳟代表性养殖群体的高通量SNP芯片检测及遗传分析

本研究旨在对国内虹鳟(Oncorhynchus mykiss)代表性养殖群体开展全基因组水平的遗传评估。利用57K单核苷酸多态性(single nucleotide polymorphism,SNP)芯片,检测了来自不同地域的6个虹鳟养殖群体样本共计48尾,包括黑龙江虹鳟、黑龙江金鳟、四川虹鳟、四川金鳟、北京虹鳟和北京金鳟,共获得有效SNP位点50201个,在中国虹鳟中的多态比例达到97.7%,表明该芯片虽然基于美国和挪威虹鳟群体设计,但对中国群体同样具有良好的适用性。各群体最小等位基因频率均值为0.240~0.267,与国外主流养殖群体相近,黑龙江虹鳟、四川虹鳟和北京虹鳟群体内遗传多样性丰富,多态位点比例为83.6%~84.9%,与国外主流养殖群体相近,而黑龙江金鳟、四川金鳟和北京金鳟,多态位点比例相对较低,在60.2%~76.9%范围内。应用6个中国虹鳟群体和2个美国虹鳟群体数据开展系统发育分析、主成分分析和群体遗传结构STRUCTURE分析,结果显示8个群体可分为3个祖源类群,其中3个金鳟群体为遗传联系较紧密的一个类群,黑龙江虹鳟和北京虹鳟为一个类群,而四川虹鳟与2个美国虹鳟群体为一个类群,部分中国养殖群体中有显著离群个体存在,表明群体遗传背景不均一。本研究表明,高密度SNP芯片在我国虹鳟养殖群体遗传分析中具有广泛的应用前景,能够为种质资源评估、本土化良种培育、制种和引种工作提供基因组水平的参考信息。国家科技支撑计划项目(2015BAD25B01);;中国水产科学研究院基本科研业务费专项(2015C007

Whole-genome mapping of quantitative trait loci and accuracy of genomic predictions for resistance to columnaris disease in two rainbow trout breeding populations

International audienceAbstractBackgroundColumnaris disease (CD) is an emerging problem for the rainbow trout aquaculture industry in the US. The objectives of this study were to: (1) identify common genomic regions that explain a large proportion of the additive genetic variance for resistance to CD in two rainbow trout (Oncorhynchus mykiss) populations; and (2) estimate the gains in prediction accuracy when genomic information is used to evaluate the genetic potential of survival to columnaris infection in each population.MethodsTwo aquaculture populations were investigated: the National Center for Cool and Cold Water Aquaculture (NCCCWA) odd-year line and the Troutlodge, Inc., May odd-year (TLUM) nucleus breeding population. Fish that survived to 21 days post-immersion challenge were recorded as resistant. Single nucleotide polymorphism (SNP) genotypes were available for 1185 and 1137 fish from NCCCWA and TLUM, respectively. SNP effects and variances were estimated using the weighted single-step genomic best linear unbiased prediction (BLUP) for genome-wide association. Genomic regions that explained more than 1% of the additive genetic variance were considered to be associated with resistance to CD. Predictive ability was calculated in a fivefold cross-validation scheme and using a linear regression method.ResultsValidation on adjusted phenotypes provided a prediction accuracy close to zero, due to the binary nature of the trait. Using breeding values computed from the complete data as benchmark improved prediction accuracy of genomic models by about 40% compared to the pedigree-based BLUP. Fourteen windows located on six chromosomes were associated with resistance to CD in the NCCCWA population, of which two windows on chromosome Omy 17 jointly explained more than 10% of the additive genetic variance. Twenty-six windows located on 13 chromosomes were associated with resistance to CD in the TLUM population. Only four associated genomic regions overlapped with quantitative trait loci (QTL) between both populations.ConclusionsOur results suggest that genome-wide selection for resistance to CD in rainbow trout has greater potential than selection for a few target genomic regions that were found to be associated to resistance to CD due to the polygenic architecture of this trait, and because the QTL associated with resistance to CD are not sufficiently informative for selection decisions across populations

A first generation integrated map of the rainbow trout genome

Background Rainbow trout (Oncorhynchus mykiss) are the most-widely cultivated cold freshwater fish in the world and an important model species for many research areas. Coupling great interest in this species as a research model with the need for genetic improvement of aquaculture production efficiency traits justifies the continued development of genomics research resources. Many quantitative trait loci (QTL) have been identified for production and life-history traits in rainbow trout. An integrated physical and genetic map is needed to facilitate fine mapping of QTL and the selection of positional candidate genes for incorporation in marker-assisted selection (MAS) programs for improving rainbow trout aquaculture production. Results The first generation integrated map of the rainbow trout genome is composed of 238 BAC contigs anchored to chromosomes of the genetic map. It covers more than 10% of the genome across segments from all 29 chromosomes. Anchoring of 203 contigs to chromosomes of the National Center for Cool and Cold Water Aquaculture (NCCCWA) genetic map was achieved through mapping of 288 genetic markers derived from BAC end sequences (BES), screening of the BAC library with previously mapped markers and matching of SNPs with BES reads. In addition, 35 contigs were anchored to linkage groups of the INRA (French National Institute of Agricultural Research) genetic map through markers that were not informative for linkage analysis in the NCCCWA mapping panel. The ratio of physical to genetic linkage distances varied substantially among chromosomes and BAC contigs with an average of 3,033 Kb/cM. Conclusions The integrated map described here provides a framework for a robust composite genome map for rainbow trout. This resource is needed for genomic analyses in this research model and economically important species and will facilitate comparative genome mapping with other salmonids and with model fish species. This resource will also facilitate efforts to assemble a whole-genome reference sequence for rainbow trout

Identification of High-Confidence Structural Variants in Domesticated Rainbow Trout Using Whole-Genome Sequencing

Genomic structural variants (SVs) are a major source of genetic and phenotypic variation but have not been investigated systematically in rainbow trout (Oncorhynchus mykiss), an important aquaculture species of cold freshwater. The objectives of this study were 1) to identify and validate high-confidence SVs in rainbow trout using whole-genome re-sequencing; and 2) to examine the contribution of transposable elements (TEs) to SVs in rainbow trout. A total of 96 rainbow trout, including 11 homozygous lines and 85 outbred fish from three breeding populations, were whole-genome sequenced with an average genome coverage of 17.2&times;. Putative SVs were identified using the program Smoove which integrates LUMPY and other associated tools into one package. After rigorous filtering, 13,863 high-confidence SVs were identified. Pacific Biosciences long-reads of Arlee, one of the homozygous lines used for SV detection, validated 98% (3,948 of 4,030) of the high-confidence SVs identified in the Arlee homozygous line. Based on principal component analysis, the 85 outbred fish clustered into three groups consistent with their populations of origin, further indicating that the high-confidence SVs identified in this study are robust. The repetitive DNA content of the high-confidence SV sequences was 86.5%, which is much higher than the 57.1% repetitive DNA content of the reference genome, and is also higher than the repetitive DNA content of Atlantic salmon SVs reported previously. TEs thus contribute substantially to SVs in rainbow trout as TEs make up the majority of repetitive sequences. Hundreds of the high-confidence SVs were annotated as exon-loss or gene-fusion variants, and may have phenotypic effects. The high-confidence SVs reported in this study provide a foundation for further rainbow trout SV studies. &nbsp;</p

Genome-Wide Association Analysis With a 50K Transcribed Gene SNP-Chip Identifies QTL Affecting Muscle Yield in Rainbow Trout

Detection of coding/functional SNPs that change the biological function of a gene may lead to identification of putative causative alleles within QTL regions and discovery of genetic markers with large effects on phenotypes. This study has two-fold objectives, first to develop, and validate a 50K transcribed gene SNP-chip using RNA-Seq data. To achieve this objective, two bioinformatics pipelines, GATK and SAMtools, were used to identify ~21K transcribed SNPs with allelic imbalances associated with important aquaculture production traits including body weight, muscle yield, muscle fat content, shear force, and whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs ere identified from pooled RNA-Seq data collected from ~620 fish, representing 98 families from growth- and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ~29K transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ~5K non-synonymous SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples passing quality control (QC; 1,641 fish) was ≥ 98.5%. The second objective of this study was to test the feasibility of using the new SNP-chip in GWA (Genome-wide association) analysis to identify QTL explaining muscle yield variance. GWA study on 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71 and 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions were previously reported as important regulators of muscle development and cell signaling. No major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same fish population. These results indicate improved detection power of the transcribed gene SNP-chip in the target trait and population, allowing identification of large-effect QTLs for important traits in rainbow trout

Evaluation of Genome-Enabled Selection for Bacterial Cold Water Disease Resistance Using Progeny Performance Data in Rainbow Trout: Insights on Genotyping Methods and Genomic Prediction Models

Bacterial cold water disease (BCWD) causes significant economic losses in salmonid aquaculture, and traditional family-based breeding programs aimed at improving BCWD resistance have been limited to exploiting only between-family variation. We used genomic selection (GS) models to predict genomic breeding values (GEBVs) for BCWD resistance in 10 families from the first generation of the NCCCWA BCWD resistance breeding line, compared the predictive ability (PA) of GEBVs to pedigree-based estimated breeding values (EBVs), and compared the impact of two SNP genotyping methods on the accuracy of GEBV predictions. The BCWD phenotypes survival days (DAYS) and survival status (STATUS) had been recorded in training fish (n = 583) subjected to experimental BCWD challenge. Training fish, and their full sibs without phenotypic data that were used as parents of the subsequent generation, were genotyped using two methods: restriction-site associated DNA (RAD) sequencing and the Rainbow Trout Axiom®57 K SNP array (Chip). Animal-specific GEBVs were estimated using four GS models: BayesB, BayesC, single-step GBLUP (ssGBLUP), and weighted ssGBLUP (wssGBLUP). Family-specific EBVs were estimated using pedigree and phenotype data in the training fish only. The PA of EBVs and GEBVs was assessed by correlating mean progeny phenotype (MPP) with mid-parent EBV (family-specific) or GEBV (animalspecific). The best GEBV predictions were similar to EBV with PA values of 0.49 and 0.46 vs. 0.50 and 0.41 for DAYS and STATUS, respectively. Among the GEBV prediction methods, ssGBLUP consistently had the highest PA. The RAD genotyping platform had GEBVs with similar PA to those of GEBVs from the Chip platform. The PA of ssGBLUP and wssGBLUP methods was higher with the Chip, but for BayesB and BayesC methods it was higher with the RAD platform. The overall GEBV accuracy in this study was low to moderate, likely due to the small training sample used. This study explored the potential of GS for improving resistance to BCWD in rainbow trout using, for the first time, progeny testing data to assess the accuracy of GEBVs, and it provides the basis for further investigation on the implementation of GS in commercial rainbow trout populations