Chunk #17 — Limitations and misunderstandings of clinical, translational, and research applications of PRS — Pleiotropy, confounding, and causal inference
Recent methods have been developed to enable a deeper understanding of pleiotropic effects, in which the same genetic variant is associated with multiple outcomes. A recent method for multi-trait analysis of summary statistics models sample overlap and genetic correlation between studies to improve effect size estimation for SNPs associated with each trait, thereby improving prediction accuracy (19; 23). Consequently, researchers have jointly analyzed genetically correlated traits including schizophrenia, bipolar disorder, and major depressive disorder to improve prediction accuracy for each disorder (35; 36). To better understand the biological pathways underlying polygenic signals, extensions of LD score regression methods have been developed that partition heritability from summary statistics into functional annotations that disproportionately contribute to the association signal, such as cell-type specific gene expression (33; 37). These statistical tools aid in the biological interpretation of polygenic signals and genetically correlated phenotypes.
