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

Abstract

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.

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

“…Fish population, marker density, LD, and size of adjacent SNP windows may, partially, explain the discrepancies in the results obtained from the different studies. In addition, SNPs used in the current study were identified from fish families of extreme phenotypes and thus, perhaps, are more informative for the current GWA analysis [19]. In agreement with previous GWA studies, growth is multifactorial in nature, and growth-related genes regulate development, cell proliferation, energy metabolism, and growth [89,104].…”

“…In this study, a 50K SNP chip was used to identify genomic regions associated with bodyweight gain, based on 50 SNP sliding windows and single-marker association analysis. Since the chip contains SNPs potentially associated with the bodyweight [16,19], the fish used in building the SNP-chip were not used for the current GWA analyses.…”

“…About 90% of the genetic variation comes from SNPs that are highly adaptable to large-scale genotyping and, therefore, most suitable for genomewide association studies [8]. The rainbow trout genome was successfully used for calling variants [18], and these variants have been used to build a 50K transcribed gene SNP chip suitable for association mapping [19]. Genome-wide association (GWA) studies have been employed to test the association between SNP markers spread throughout the genome and complex quantitative traits of interest [20].…”

“…Recently, a few GWA studies have been implemented in aquaculture species [20], including rainbow trout. These studies aimed to identify markers associated with bodyweight [22], fillet quality [19,22], and disease resistance [23]. Growth traits are controlled by small-effect variants in the farmed Atlantic salmon [24].…”

“…The SNP chip has been successfully used to identify QTL associated with muscle yield [19], and fillet firmness and protein content [25] in rainbow trout. The objective of this study was to use the 50K 5 SNP array to identify large-effect QTL associated with the growth rate that could be applied in genomic selection.…”