Key research themes
1. How can meta-analysis and specialized software improve the reliability and reproducibility of genetic association study results?
Genetic association studies often suffer from low reproducibility due to experimental design flaws, small sample sizes, population stratification, and other biases. Meta-analysis techniques synthesize results across studies to increase statistical power and resolve conflicting findings. Dedicated software tailored for genetic association studies can standardize quality control checks (e.g., Hardy-Weinberg equilibrium), model different genetic inheritance modes, and automate heterogeneity and publication bias assessments to reduce analytical errors and enhance reproducibility.
2. What are the methodological advancements and statistical models for improved detection of genetic associations in complex traits considering sample structure, polygenic effects, and multiple variants?
Genetic association analyses must account for complex sample relatedness (family, population stratification), polygenic architecture, and multi-variant effects to improve power and reduce false positives. Linear mixed models and Bayesian random effects frameworks have been developed to jointly model genotypes and phenotypes, allowing flexible priors (e.g., Gaussian, heavy-tailed) and variable selection. Also, multi-marker and multi-trait models facilitate detection of smaller signals and biological networks influencing traits, addressing limitations of single-variant tests. These advances improve inference precision, especially in admixed or family-based cohorts.
3. How can genetic ancestry and admixture mapping be integrated in association studies to improve detection power and control for population stratification in admixed populations?
Admixed populations pose challenges for GWAS because population structure and linkage disequilibrium patterns differ across ancestries, potentially masking or confounding associations. Incorporating global and local ancestry into association testing improves power and controls confounding. Methods have been developed to jointly model genotype and locus-specific ancestry, accounting for admixture linkage disequilibrium and complex correlation structures. These methods enable multi-locus modeling and interaction testing in admixed populations, facilitating the discovery of population-specific genetic effects and elucidating genetic contributions to trait variability across ancestries.