Genetic association studies.

Abstract

AbstractBased on the "common disease/common variant" hypothesis, genetic epidemiologists undertake genetic association studies to search for susceptibility genes for complex diseases, using either candidate gene or genome-wide approaches. Both approaches can use case-control, cohort, and case-parent trio designs, and can employ a variety of genetic markers. In the candidate gene approach, genes whose functions are known or suspected to be involved in disease predisposition, and specific polymorphisms within those genes, are studied. Genome-wide association studies (GWAS) examine genetic markers that span the entire genome in order to identify new genes or confirm previously known susceptibility genes. They use high-throughput technologies for genotyping millions of single nucleotide polymorphisms (SNPs). In order to have sufficient statistical power to detect the small effect sizes found in most GWAS, very large samples are used, often based on large-scale collaborations. Interpretation of genetic association results is complex and can be attributable to: a direct, causal relationship; an indirect relationship due to linkage disequilibrium; false-positive associations due to multiple comparisons, departure from Hardy-Weinberg equilibrium, and/or population stratification; false negatives due to inadequate statistical power, small effect sizes, and/or insufficient genomic coverage. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium is an example of an effective and productive GWAS collaboration that takes advantage of the hundreds of millions of dollars invested in National Institute of Health (NIH)-funded cohort studies. It represents an innovative model of investigator-initiated collaborative research to identify genetic loci associated with a variety of cardiovascular and aging phenotypes. Although more than 2,000 SNPs have been associated with complex traits and diseases to date based on GWAS, these associations do not explain most of the heritability of these traits. Using height as an example, possible reasons for this "missing heritability" are considered. Advances in DNA sequencing, especially exome sequencing, are being used to detect associations between rare genetic variants and common diseases based on new study designs and statistical analysis approaches that allow more direct identification of causal variants.