Genome‐wide association reveals genetic basis for the propensity to migrate in wild populations of rainbow and steelhead trout

Abstract

Little is known of the genetic basis of migration despite the ecological benefits migratory species provide to their communities and their rapid global decline due to anthropogenic disturbances in recent years. Using next generation sequencing of restriction-site associated DNA (RAD) tags we genotyped thousands of SNPs in two wild populations of migratory steelhead and resident rainbow trout (Oncorhynchus mykiss) from the Pacific Northwest of the United States. One population maintains a connection to the sea, whereas the other population has been sequestered from its access to the ocean for more than fifty years by a hydropower dam. Here we performed a genome-wide association study (GWAS) to identify 504 RAD SNP markers from several genetic regions that were associated with the propensity to migrate both within and between the populations. Our results corroborate those in previous quantitative trait loci (QTL) studies and provide evidence for additional loci associated with this complex migratory life history. Our results suggest a complex multi-genic basis with several loci of small effect distributed throughout the genome contributing to migration in this species. We also determined that despite being sequestered for decades, the landlocked population continues to harbor genetic variation associated with a migratory life history and ATPase activity. Furthermore we demonstrate the utility of genotyping-by-sequencing and how RAD-tag SNP data can be readily compared between studies to investigate migration within this species.

Rapid parallel evolution of standing variation in a single, complex, genomic region is associated with life history in steelhead/rainbow trout

“…Our results provide data from natural populations in California and Oregon that support prior studies on diverse O. mykiss lineages using candidate genes, genome wide association and QTL mapping to explore the genetic architecture of traits associated with life history in O. mykiss, including migration [17,32,42,46,48,[65][66][67][68][69][70][71][72][73][74][75][76]. Many of these studies have identified QTL or significant associations between markers on Omy5 and life-history traits such as spawn timing [47,65,68], smoltification [42,46] and development rate or early maturation [17,66,67,70].…”

Genomic tools for new insights to variation, adaptation, and evolution in the salmonid fishes: a perspective for charr

“…Genome-wide association analysis suggested different genomic regions underlying different phenotypic components of the migratory phenotype (Hecht et al, 2012(Hecht et al, , 2013. Annotation of genome regions linked to significant SNPs indicated they were likely in regions associated with physiological processes important in migration (Hecht et al, 2013;Hale et al, 2013). The study identified a number of new loci associated with migratory traits and corroborated loci that had been identified in earlier QTL studies.…”

“…With a similar paired design, Hecht and colleagues sought the genetic loci associated with propensity to migrate using a genome-wide association approach using SNPs of known location from species-specific linkage maps (Hecht et al, 2012;Miller et al, 2012). Genome-wide association analysis suggested different genomic regions underlying different phenotypic components of the migratory phenotype (Hecht et al, 2012(Hecht et al, , 2013. Annotation of genome regions linked to significant SNPs indicated they were likely in regions associated with physiological processes important in migration (Hecht et al, 2013;Hale et al, 2013).…”

Adaptive genetic variation distinguishes Chilean blue mussels (Mytilus chilensis) from different marine environments

“…Prior to running “ustacks” for SNP discovery, all samples were standardized to 3.5 million reads to assure that each individual was contributing in same proportion of the overall variation in sequence (Hecht et al. 2013). Discovery of SNPs with the “ustacks” module included setting the “m” parameter to 10 (minimum depth of coverage required to create a stack) and using the “snp model type.” A catalog of SNPs was created with the “cstacks” module including two individuals from every location, and this catalog was used to genotype each individual with the “sstacks” module.…”