Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down

Abstract

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential "outside-the-skin" environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

Keywords: GWAS; development; genetics; gene–environment correlation; polygenic scores.

Fig. 1.

Polygenic scoring. Polygenic scores aggregate information from up to millions of single-nucleotide polymorphisms (SNPs) across the genome into a single composite index summarizing genome-wide genetic influence on a target phenotype. The procedure of computing a polygenic score is conducted using whole-genome SNP data on a research participant and scoring information from some external database, typically a published genome-wide association study (GWAS). In the first step, the participant’s SNP genotypes are assigned weights that indicate the direction and magnitude of that SNP’s association with a target phenotype. Most often these weights are the effect sizes, ${\widehat{b}}_j$, estimated in a GWAS that did not include the research participant. Next, the number of phenotype-associated alleles (0, 1, or 2) at each SNP ($j$) is counted, and the count is multiplied by the weight, ${\widehat{b}}_j$. Finally, the weighted count is summed across SNPs to compute the participant’s polygenic score. The resulting distribution of polygenic scores across participants is normal. Polygenic-score analysis often involves two additional features. First, because polygenic scores draw information from many sites across the genome, they are sensitive to bias arising from allele-frequency differences between populations of different ancestry, called population stratification (Martin et al., 2017). Polygenic-score analysis is therefore typically conducted within populations that share genetic ancestry (e.g., Europeans), and the analysis usually includes covariate adjustment for principal components estimated from genome-wide SNP data to account for any residual population stratification (Price et al., 2006). Second, polygenic-score analysis is sometimes restricted to a subset of SNPs. This includes procedures to select SNPs that are statistically independent of one another and procedures to select SNPs that meet other criteria, such as having p values in the discovery GWAS that fall below a certain threshold. To date, evidence suggests that polygenic scores are best constructed using data from all available SNPs (Dudbridge & Newcombe, 2016; Ware et al., 2017).