Dataset Information

Genome-wide identification of loci associated with growth in rainbow trout.

ABSTRACT: BACKGROUND:Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout. RESULTS:A previously developed 50?K gene-transcribed SNP chip, containing ~?21?K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping a total of 789 fish with available phenotypic data for bodyweight gain. Genotyped fish were obtained from two consecutive generations produced in the NCCCWA growth-selection breeding program. Weighted single-step GBLUP (WssGBLUP) was used to perform a genome-wide association (GWA) analysis to identify quantitative trait loci (QTL) associated with bodyweight gain. Using genomic sliding windows of 50 adjacent SNPs, 247 SNPs associated with bodyweight gain were identified. SNP-harboring genes were involved in cell growth, cell proliferation, cell cycle, lipid metabolism, proteolytic activities, chromatin modification, and developmental processes. Chromosome 14 harbored the highest number of SNPs (n?=?50). An SNP window explaining the highest additive genetic variance for bodyweight gain (~?6.4%) included a nonsynonymous SNP in a gene encoding inositol polyphosphate 5-phosphatase OCRL-1. Additionally, based on a single-marker GWA analysis, 33 SNPs were identified in association with bodyweight gain. The highest SNP explaining variation in bodyweight gain was identified in a gene coding for thrombospondin-1 (THBS1) (R2?=?0.09). CONCLUSION:The majority of SNP-harboring genes, including OCRL-1 and THBS1, were involved in developmental processes. Our results suggest that development-related genes are important determinants for growth and could be prioritized and used for genomic selection in breeding programs.

SUBMITTER: Ali A

PROVIDER: S-EPMC7059289 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide identification of loci associated with growth in rainbow trout.

Ali Ali A Al-Tobasei Rafet R Lourenco Daniela D Leeds Tim T Kenney Brett B Salem Mohamed M

BMC genomics 20200305 1

<h4>Background</h4>Growth is a major economic production trait in aquaculture. Improvements in growth performance will reduce time and cost for fish to reach market size. However, genes underlying growth have not been fully explored in rainbow trout.<h4>Results</h4>A previously developed 50 K gene-transcribed SNP chip, containing ~ 21 K SNPs showing allelic imbalances potentially associated with important aquaculture production traits including body weight, muscle yield, was used for genotyping ...[more]

PMID: 32138655

Similar Datasets

Project description:BackgroundSomatic growth is a complex process that involves the action and interaction of genes and environment. A number of quantitative trait loci (QTL) previously identified for body weight and condition factor in rainbow trout (Oncorhynchus mykiss), and two other salmonid species, were used to further investigate the genetic architecture of growth-influencing genes in this species. Relationships among previously mapped candidate genes for growth and their co-localization to identified QTL regions are reported. Furthermore, using a comparative genomic analysis of syntenic rainbow trout linkage group clusters to their homologous regions within model teleost species such as zebrafish, stickleback and medaka, inferences were made regarding additional possible candidate genes underlying identified QTL regions.ResultsBody weight (BW) QTL were detected on the majority of rainbow trout linkage groups across 10 parents from 3 strains. However, only 10 linkage groups (i.e., RT-3, -6, -8, -9, -10, -12, -13, -22, -24, -27) possessed QTL regions with chromosome-wide or genome-wide effects across multiple parents. Fewer QTL for condition factor (K) were identified and only six instances of co-localization across families were detected (i.e. RT-9, -15, -16, -23, -27, -31 and RT-2/9 homeologs). Of note, both BW and K QTL co-localize on RT-9 and RT-27. The incidence of epistatic interaction across genomic regions within different female backgrounds was also examined, and although evidence for interaction effects within certain QTL regions were evident, these interactions were few in number and statistically weak. Of interest, however, was the fact that these predominantly occurred within K QTL regions. Currently mapped growth candidate genes are largely congruent with the identified QTL regions. More QTL were detected in male, compared to female parents, with the greatest number evident in an F1 male parent derived from an intercross between domesticated and wild strain of rainbow trout which differed strongly in growth rate.ConclusionsStrain background influences the degree to which QTL effects are evident for growth-related genes. The process of domestication (which primarily selects faster growing fish) may largely reduce the genetic influences on growth-specific phenotypic variation. Although heritabilities have been reported to be relatively high for both BW and K growth traits, the genetic architecture of K phenotypic variation appears less defined (i.e., fewer major contributing QTL regions were identified compared with BW QTL regions).

Project description:Fillet yield (FY, %) is an economically-important trait in rainbow trout aquaculture that affects production efficiency. Despite that, FY has received little attention in breeding programs because it is difficult to measure on a large number of fish and cannot be directly measured on breeding candidates. The recent development of a high-density SNP array for rainbow trout has provided the needed tool for studying the underlying genetic architecture of this trait. A genome-wide association study (GWAS) was conducted for FY, body weight at 10 (BW10) and 13 (BW13) months post-hatching, head-off carcass weight (CAR), and fillet weight (FW) in a pedigreed rainbow trout population selectively bred for improved growth performance. The GWAS analysis was performed using the weighted single-step GBLUP method (wssGWAS). Phenotypic records of 1447 fish (1.5 kg at harvest) from 299 full-sib families in three successive generations, of which 875 fish from 196 full-sib families were genotyped, were used in the GWAS analysis. A total of 38,107 polymorphic SNPs were analyzed in a univariate model with hatch year and harvest group as fixed effects, harvest weight as a continuous covariate, and animal and common environment as random effects. A new linkage map was developed to create windows of 20 adjacent SNPs for use in the GWAS. The two windows with largest effect for FY and FW were located on chromosome Omy9 and explained only 1.0-1.5% of genetic variance, thus suggesting a polygenic architecture affected by multiple loci with small effects in this population. One window on Omy5 explained 1.4 and 1.0% of the genetic variance for BW10 and BW13, respectively. Three windows located on Omy27, Omy17, and Omy9 (same window detected for FY) explained 1.7, 1.7, and 1.0%, respectively, of genetic variance for CAR. Among the detected 100 SNPs, 55% were located directly in genes (intron and exons). Nucleotide sequences of intragenic SNPs were blasted to the Mus musculus genome to create a putative gene network. The network suggests that differences in the ability to maintain a proliferative and renewable population of myogenic precursor cells may affect variation in growth and fillet yield in rainbow trout.

Project description:Bacterial cold water disease (BCWD) is an important disease in rainbow trout aquaculture. Previously, we have identified and validated two major QTL (quantitative trait loci) for BCWD resistance, located on chromosomes Omy08 and Omy25, in the odd-year Troutlodge May spawning population. We also demonstrated that marker-assisted selection (MAS) for BCWD resistance using the favorable haplotypes associated with the two major QTL is feasible. However, each favorable haplotype spans a large genomic region of 1.3-1.6 Mb. Recombination events within the haplotype regions will result in new haplotypes associated with BCWD resistance, which will reduce the accuracy of MAS for BCWD resistance over time. The objectives of this study were 1) to identify additional SNPs (single nucleotide polymorphisms) associated with BCWD resistance using whole-genome sequencing (WGS); 2) to validate the SNPs associated with BCWD resistance using family-based association mapping; 3) to refine the haplotypes associated with BCWD resistance; and 4) to evaluate MAS for BCWD resistance using the refined QTL haplotypes. Four consecutive generations of the Troutlodge May spawning population were evaluated for BCWD resistance. Parents and offspring were sequenced as individuals and in pools based on their BCWD phenotypes. Over 12 million SNPs were identified by mapping the sequences from the individuals and pools to the reference genome. SNPs with significantly different allele frequencies between the two BCWD phenotype groups were selected to develop SNP assays for family-based association mapping in three consecutive generations of the Troutlodge May spawning population. Among the 78 SNPs derived from WGS, 77 SNPs were associated with BCWD resistance in at least one of the three consecutive generations. The additional SNPs associated with BCWD resistance allowed us to reduce the physical sizes of haplotypes associated with BCWD resistance to less than 0.5 Mb. We also demonstrated that the refined QTL haplotypes can be used for MAS in the Troutlodge May spawning population. Therefore, the SNPs and haplotypes reported in this study provide additional resources for improvement of BCWD resistance in rainbow trout.

Project description:BACKGROUND:Photosynthesis is able to convert solar energy into chemical energy in the form of biomass, but the efficiency of photosynthetic solar energy conversion is low. Chlorophyll fluorescence measurements are rapid, non-destructive, and can provide a wealth of information about the efficiencies of the photosynthetic light reaction processes. Efforts aimed at assessing genetic variation and/or mapping of genetic loci associated with chlorophyll fluorescence phenotypes have been rather limited. RESULTS:Evaluation of SoySNP50K iSelect SNP Beadchip data from the 189 genotypes phenotyped in this analysis identified 32,453 SNPs with a minor allele frequency (MAF)???5%. A total of 288 (non-unique) SNPs were significantly associated with one or more of the 21 chlorophyll fluorescence phenotypes. Of these, 155 were unique SNPs and 100 SNPs were only associated with a single fluorescence phenotype, while 28, 11, 2, and 14 SNPs, were associated with two, three, four and five or more fluorescence phenotypes, respectively. The 288 non-unique SNPs represent 155 unique SNPs that mark 53 loci. The 155 unique SNPs included 27 that were associated with three or more phenotypes, and thus were called multi-phenotype SNPs. These 27 multi-phenotype SNPs marked 13 multi-phenotype loci (MPL) identified by individual SNPs associated with multiple chlorophyll fluorescence phenotypes or by more than one SNP located within 0.5 MB of other multi-phenotype SNPs. CONCLUSION:A search in the genomic regions highlighted by these 13 MPL identified genes with annotations indicating involvement in photosynthetic light dependent reactions. These, as well as loci associated with only one or two chlorophyll fluorescence traits, should be useful to develop a better understanding of the genetic basis of photosynthetic light dependent reactions as a whole as well as of specific components of the electron transport chain in soybean. Accordingly, additional genetic and physiological analyses are necessary to determine the relevance and effectiveness of the identified loci for crop improvement efforts.

Dataset Information

Genome-wide identification of loci associated with growth in rainbow trout.

Publications

Genome-wide identification of loci associated with growth in rainbow trout.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets