Statistical Strategies for Next Generation Large-Scale Genetic Studies

Abstract

In the past 10 years, Genome-wide association studies (GWAS) have successfully identified thousands of loci that are associated with complex diseases and human traits. By aggregating samples from multiple populations across the world, a new wave of GWA meta-analyses have increased the statistical power to identify novel findings with smaller effect sizes. However, the amount of phenotypic variation explained by GWAS is much less than the total heritability estimated by twin and family studies. The missing heritability is believed to be caused by the following three reasons: i) classical approaches for meta-analysis are hampered by the presence of effect size and allelic heterogeneity; ii) the causal variants that fundamentally affect the diseases and traits are yet to be discovered; iii) the unexplored genetic impact of low-frequency and rare causal variants. To address these problems, we conducted four studies of trans-ethnic meta-analyses and fine-mapping. We began with a systematic review to identify the most powerful statistical approach to accommodate the issue of effect size heterogeneity. To address the problem of allelic heterogeneity, we designed a novel strategy to assess regional association evidence which successfully captures the additional phenotypic variation explained by multiple causal variants. In order to locate the causal variants with more accuracy, we evaluated the merit of trans-ethnic fine-mapping and accessed the impact of population-specific reference panel in identifying the functional variants that biologically affecting the phenotypes of interest. Last but not least, we extent to explore the feasibility of trans-ethnic fine-mapping for rare causal variants by evaluating whether the conditions that have made the process successful for common variants are also hold for rare variants.