It is important for PGRN to have a set of analytic tools to evaluate available GWAS data for the contribution of “polygenic models” of pharmacological traits. Recently, two methods have been developed to detect the contribution of common SNPs in genome-wide association study (GWAS) data: polygenic analysis1 and mixed linear modeling2. Both methods test a polygenetic model in which many common SNPs in aggregate have a collective effect on phenotype.
In the first method, polygenic analysis, an additive polygenic risk score based on SNPs below a p-value threshold (PGWAS) in a discovery set of samples is then tested in an independent set of samples. Using this approach, polygenic effects have been demonstrated in schizophrenia1, multiple sclerosis3, height4, and body mass index (BMI)5. The second method, mixed linear modeling, estimates additive genetic variance under a mixed linear model with a random effect representing the polygenic component of trait variation. Applied to height2 and endometriosis6, this method demonstrated that common SNPs contribute to phenotypic variance.'
We hypothesize that there is a polygenic architecture underlying pharmacogenetic traits, and that many common variants in aggregate will predict response to therapy. Both of these methods will thus be applied to several of the unique datasets of the PGRN. First, we will use mixed linear modeling to determine the variance explained by common SNPs, within each phenotypic collection. Second, we will apply polygenic analysis as a complementary statistical approach. Both positive and negative results from these analyses are of interest, as results will inform us about the underlying genetic architecture of each trait and thus future discovery genetic studies. To help interpret results we will also conduct simulations based on GWAS sample size, trait definition, and different polygenic models of inheritance. We will also perform “power” and “prediction” analyses applying polygenic modeling results and systems biology “pathway” analyses, for phenotypes with evidence of a polygenic architecture.
A network resource for coordination of statistical analysis and methods development in the PGRN.