Genome-wide association study on complex diseases: genetic statis-tical issues

Abstract

Since the first genome-wide association study on human age-related macular degeneration was reported by Science journal in 2005, a series of genome-wide association studies have been published on human complex diseases or traits, such as obesity, type 2 diabetes, coronary artery disease, Alzheimer's disease and so on. The study of human genetics has recently undergone a dramatic transition which is called "the first wave of genome-wide association study". Some issues in statistical analysis of genome-wide association studies were reviewed by this paper. First, statistical analysis guidelines, methods and examples for genome-wide association studies of different designs, including unrelated case-control studies, population-based studies, and family-based association studies; second, multiple testing correction of P values, including Bonferroni correction, step-down Bonferroni correction, permutation correction, and the correction based on false discovery rate; third, population stratification and its effect on inference of genotype-phenotype associations. The False Positive Re-port Probability has been successfully applied in a recent genome-wide association study on coronary artery disease to con-trol the population stratification. Although genetic statistical methodology has been greatly developed in control of false positive associations caused by multiple testing or population stratification, it is still not sufficient to achieve the goal. Replicating genotype-phenotype associations is the only way to identify true association between genetic markers and common disease traits. The first wave of genome-wide association studies is producing an impressive list of unexpected associations between genes or chromosomal regions and a broad range of diseases. Traditional statistical techniques are adequate for the analysis and interpretation of these results. However, much more sophisticated methods of statistical analysis are likely to be required as we delve further into the genome in the search for networks of interacting gene variants, or interactions be-tween gene-gene networks and environmental factors. Finally, some useful links about statistical software for genome-wide association studies were provided.