Identification of SNPs associated with muscle yield and quality traits using allelic-imbalance analyses of pooled RNA-Seq samples in rainbow trout

Abstract

BackgroundCoding/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).ResultsGATK detected 59,112 putative SNPs; of these SNPs, 4798 showed allelic imbalances (>2.0 as an amplification and <0.5 as loss of heterozygosity). SAMtools detected 87,066 putative SNPs; and of them, 4962 had allelic imbalances between the low- and high-ranked families. Only 1829 SNPs with allelic imbalances were common between the two datasets, indicating significant differences in algorithms. The two datasets contained 7930 non-redundant SNPs of which 4439 mapped to 1498 protein-coding genes (with 6.4% non-synonymous SNPs) and 684 mapped to 295 lncRNAs. Validation of a subset of 92 SNPs revealed 1) 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.ConclusionThese 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-Chip design (available from Affymetrix) for genomic and genetic analyses in rainbow trout.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3992-z) contains supplementary material, which is available to authorized users.

Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout

“…Recently, we used RNA-Seq and two bioinformatic pipelines, GATK and SAMtools, for discovering coding/functional SNPs from 98 rainbow trout fish families (5 fish each) showing variations in whole-body weight, muscle yield, muscle fat content, shear force, and whiteness [13]. GATK detected 59,112 putative SNPs and SAMtools detected 87,066 putative SNPs.…”

“…Validation of a subset of 92 SNPs revealed 1) 86.7-93.8% success rate in identifying polymorphic SNPs and 2) 95.4% consistent matching between DNA and cDNA genotypes, indicating a high rate of identifying SNPs using RNA-Seq. This SNP data set was recently published and is available through the NCBI dbSNP database (accession numbers ss#2711191806-2711287038 in addition to ss#2137497773) [13].…”

“…The Affymetrix SNPolisher software was used to calculate the chip SNP- and sample-metrics and assess QCs and filter samples/genotypes at the default setting [16]. Forty-seven SNPs previously genotyped by a Fluidigm PCR-based assay [13] were used to check quality of Affymetrix chip genotyping using 120 samples genotyped by both the chip and Fluidigm SNP assays. In addition, we confirmed the quality of the SNPs and the order of the samples included in the genotyping panel through pedigree check.…”

“…Single nucleotide polymorphisms (SNPs) are abundant and distributed genome-wide, therefore, they are most suitable for high-throughput association studies [9; 10]. SNPs located within or near coding sequences, cSNPs, are especially important because they have the potential to change protein function [11; 12; 13]. Therefore, cSNPs are particularly useful as genetic markers with large-effect on phenotypes, allowing MAS and improved accuracy of whole-genome selection.…”

“…Recently, we used an RNA-Seq approach to identify putative SNPs with allelic imbalances associated with total body weight, muscle yield, muscle fat content, shear force, and whiteness [12; 13]. Similarly, RNA-Seq data were used to identify SNPs with allelic imbalances in fish families showing variations in resistance to Flavobacterium psychrophilum , the etiological agent of BCWD in rainbow trout [14; 15].…”