Dr. Kerry Naish1, Dr. Marine Brieuc1, Charles Waters1, Dr. Maureen Purcell2
1SAFS
2Western Fisheries Research Center, USGS
May 17, 2016 9:00 (PST): FSH 105
The genetic basis of pathogen resistance across outbred salmon populations: a comparative analysis using Random Forest
Key questions in both aquaculture and conservation of wild populations are whether genomic regions associated with specific traits are conserved across lines or populations, and to what extent trait-linked markers might be used to predict population responses to selection. In aquaculture, the ability to improve unrelated lines using common trait-linked markers provides the industry with tools necessary to maintain several diverse breeding populations. In conservation, predicting differential population responses to anthropogenic change provides a way to evaluate different remedial actions. Infectious hematopoietic necrosis virus (IHNV) is a rhabdovirus endemic to the northeast Pacific that can cause morbidity and mortality in the commercial rainbow trout and Atlantic salmon aquaculture industries, as well as in supplementation hatcheries. We have investigated the genetic basis of IHNV resistance across different populations of steelhead salmon Oncorhynchus mykiss, in a broader effort to characterize the role of differential host response in the epidemiology of the pathogen. Using the rainbow trout 50K SNP chip, we performed genome wide association analyses across five outbred populations challenged with the pathogen. Traits within these populations may be encoded by many segregating loci of small effect, whose interactions may be influenced by dominance and epistasis. Therefore, we used Random Forest because this non-parametric approach has the advantage of identifying suites of interacting loci that explain phenotypic variation collectively, but may not have large effect sizes on their own. Here, we report several genomic regions associated with resistance and evaluate their conservation across populations. We also review the key steps, analytical approaches, and lessons we have learned in the application of Random Forest to genome wide association studies in outbred salmon populations, with a view to improving genomic resources available to the community as a whole.