Location: Cool and Cold Water Aquaculture ResearchTitle: Identification of SNPs associated with muscle yield and quality traits using allelic-imbalance analysis analyses of pooled RNA-Seq samples in rainbow trout
|AL-TOBASEI, RAFET - Middle Tennessee State University|
|ALI, ALI - Middle Tennessee State University|
|Leeds, Timothy - Tim|
|KENNEY, BRETT - West Virginia University|
|SALEM, MOHAMED - Middle Tennessee State University|
Submitted to: Biomed Central (BMC) Genomics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/1/2017
Publication Date: 8/7/2017
Citation: Tobasei, R., Ali, A., Leeds, T.D., Liu, S., Palti, Y., Kenney, B., Salem, M. 2017. Identification of SNPs associated with muscle yield and quality traits using allelic-imbalance analysis analyses of pooled RNA-Seq samples in rainbow trout. Biomed Central (BMC) Genomics. 18:582. https://doi.org/10.1186/s12864-017-3992-z.
Interpretive Summary: Increasing the proportion of a whole fish that is edible fillet, or fillet/muscle yield, is a means to improve production efficiency, and thus is of interest to both fish farmers and consumers. Similarly other fillet attributes such as color, fat content and firmness are of interest to consumers and can increase the market value of the fish. However, fillet yield and other characteristics pose a challenge to animal breeders aiming to improve this trait through selective breeding because they cannot be directly measured in breeding candidates. The continued development of rainbow trout genome resources now allow breeders and scientists to accurately predict the genetic merit of fish lacking phenotypic data based on analysis of their DNA, and also to identify the genes or chromosomal loci that affect the trait of interest. In this study, we continue to develop genome research resources by identifying single point mutations in genes and other genome elements that could be differentially expressed between fish from families with divergent fillet yield and fillet attributes.
Technical Abstract: Coding/functional SNPs change the biological function of a gene and, therefore, could serve as “large-effect” genetic markers. In this study, we used two bioinformatics pipelines, GATK and SAMtools, for discovering coding/functional SNPs with allelic-imbalances associated with total body weight, muscle yield, muscle fat content, shear force, and whiteness. Phenotypic data were collected for approximately 500 fish, representing 98 families (5 fish/family), from a growth-selected line, and the muscle transcriptome was sequenced from 22 families with divergent phenotypes (4 low- versus 4 high-ranked families per trait). GATK detected 59,112 putative SNPs; of these SNPs, 4,798 showed allelic imbalances (greater than 2.0 as an amplification and less than 0.5 as loss of heterozygosity). SAMtools detected 87,066 putative SNPs; and of them, 4,962 had allelic imbalances between the low- and high-ranked families. Only 1,829 SNPs with allelic imbalances were common between the two datasets, indicating significant differences in algorithms. The two datasets contained 7,930 non-redundant SNPs of which 4,439 mapped to 1,498 protein-coding genes (with 6.4% non-synonymous SNPs) and 684 mapped to 295 lncRNAs. Validation of a subset of 92 SNPs revealed 1) an 86.7-93.8% success rate in calling polymorphic SNPs and 2) 95.4% consistent matching between DNA and cDNA genotypes indicating a high rate of identifying SNPs with allelic imbalances. In addition, 4.64% SNPs revealed random monoallelic expression. Genome distribution of the SNPs with allelic imbalances exhibited high density for all five traits in several chromosomes, especially chromosome 9, 20 and 28. Most of the SNP-harboring genes were assigned to important growth-related metabolic pathways. These results demonstrate utility of RNA-Seq in assessing phenotype-associated allelic imbalances in pooled RNA-Seq samples. The SNPs identified in this study were included in a new SNP array design for genomic and genetic analyses in rainbow trout.