RNA-Seq Identifies SNP Markers for Growth Traits in Rainbow Trout

Abstract

Fast growth is an important and highly desired trait, which affects the profitability of food animal production, with feed costs accounting for the largest proportion of production costs. Traditional phenotype-based selection is typically used to select for growth traits; however, genetic improvement is slow over generations. Single nucleotide polymorphisms (SNPs) explain 90% of the genetic differences between individuals; therefore, they are most suitable for genetic evaluation and strategies that employ molecular genetics for selective breeding. SNPs found within or near a coding sequence are of particular interest because they are more likely to alter the biological function of a protein. We aimed to use SNPs to identify markers and genes associated with genetic variation in growth. RNA-Seq whole-transcriptome analysis of pooled cDNA samples from a population of rainbow trout selected for improved growth versus unselected genetic cohorts (10 fish from 1 full-sib family each) identified SNP markers associated with growth-rate. The allelic imbalances (the ratio between the allele frequencies of the fast growing sample and that of the slow growing sample) were considered at scores >5.0 as an amplification and <0.2 as loss of heterozygosity. A subset of SNPs (n = 54) were validated and evaluated for association with growth traits in 778 individuals of a three-generation parent/offspring panel representing 40 families. Twenty-two SNP markers and one mitochondrial haplotype were significantly associated with growth traits. Polymorphism of 48 of the markers was confirmed in other commercially important aquaculture stocks. Many markers were clustered into genes of metabolic energy production pathways and are suitable candidates for genetic selection. The study demonstrates that RNA-Seq at low sequence coverage of divergent populations is a fast and effective means of identifying SNPs, with allelic imbalances between phenotypes. This technique is suitable for marker development in non-model species lacking complete and well-annotated genome reference sequences.

Co-localization of growth QTL with differentially expressed candidate genes in rainbow trout

“…Comparisons of GH-transgenic and domestic salmonids with slower growing wildtype controls suggest the somatrophic axis may influence growth via a variety of genes, including those related to carbohydrate, lipid, and energy metabolism, cell/tissue structure, development, and the response to stress (Rise et al 2006;Devlin et al 2009Devlin et al , 2013Tymchuk et al 2009;White et al 2013;Xu et al 2013). Similarly, RNAseq analysis and additional microarray studies using rainbow trout muscle have identified genes related to lipid and carbohydrate metabolism, ATP production (Salem et al 2012;Kocmarek et al 2014), muscle components (Salem et al 2012), and transcription factors (Kocmarek et al 2014) to be differentially expressed between large and small fish. Although these studies have been effective at identifying which genes are differentially expressed in fish with different growth phenotypes they are less able to link expression of the gene to particular QTL.…”

A framework for incorporating evolutionary genomics into biodiversity conservation and management

“…If coverage is deep enough, biases are addressed and appropriate experimental replicates are included; differences in gene expression can also be detected between samples [70]. Transcriptome sequencing is usually performed at the individual level, but it is also possible to estimate allele frequencies from sequencing of pooled samples [71] and to compare different lines and populations [72]. Variant identification from transcriptome data can suffer the same biases from low coverage sequences as Poolseq experiments and can also suffer from allelic dropout when only certain alleles are expressed in individual samples.…”

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“…Presently RNA-Seq is being widely used in functional genomics studies both in aquaculture and their related model species like Zebrafish (Hegedus et al, 2009;Aanes et al, 2011;Ordas et al, 2011) catfish , Atlantic cod (Johansen et al, 2011), rainbow trout (Lewis et al, 2010;Salem et al, 2010), European eel (Coppe et al, 2010) and spotted gar (Amores et al, 2011). Salem et al, (2012) using RNA sequencing identified about 22 SNP markers and 1 mtDNA haplotype with growth traits in rainbow trout. studies whole genome transcriptome in rainbow trout exposed to various stress conditions using RNA sequencing.…”