Understanding
the Genetic Architecture of Human Complex Traits
Most human traits, including common diseases (e.g., obesity, cardiovascular disease, cancer, and mental illness), are complex because they are often affected by many genetic and environmental factors. Understanding the genetic architecture of complex traits and mapping the relevant genes are pivotal for genetics and medical research.
Methods for
Genome-wide association (GWA) Analysis
Population stratification and relatedness are two major confounding factors that could cause inflation of test statistics in GWAS. Linear mixed model (LMM)-based association methods that fit many variants as random effects as background control when testing a variant in query for the association have been proved effective in capturing known and unknown confounding effects. We pointed out that the conventional LMM-based association methods are underpowered because of double fitting (i.e., fitting the variant in query twice, once as a fixed effect and again as a random) and proposed the leave-one-chromosome-out (LOCO) strategy as a remedy (Yang et al. 2014 Nat Genet).
From GWAS to Biology
The association signals identified from GWAS are informative in terms of the genomic locations of the loci conferring the traits. However, due to the complexity of LD between genetic variants, the causative variants underpinning the GWAS signals are often elusive, and the genes on which the causal variants act are also largely unknown. We have a long-standing interest in developing analytical methods, curating relevant data, and performing integrative analyses to identify genes and/or functional genomic elements responsible for the GWAS signals for a range of complex traits and disorders such as obesity and type-2 diabetes.
Developing
Bioinformatics Tools
We reason that any useful statistical method should be implemented in easy-to-use and resource-efficient software and have been doing so over the past 15 years by delivering multiple bioinformatics tools, including GCTA, SMR, and OSCA.
First, GCTA (genome-wide complex trait analysis) was initially developed to estimate the proportion of variance in a phenotype that can be explained by all genome-wide SNPs (Yang et al. 2011 AJHG). It has been extended substantially to include modules for genome-wide association analysis (e.g., fastGWA, fastGWA-GLMM, MLMA, MLMA-LOCO), fine-mapping (COJO), gene-based test (fastBAT, ACAT-V, and fastGWA-BB), and Mendelian randomization analysis (e.g., GSMR), etc.
