Association mapping: methodologies, strategies, and issues

Abstract

Recent advances in molecular genetic technology allow for detailed characterization of genetic variation and easy cost-efficient accumulation of such data, even for large human samples. One such advance that presents incredible opportunities for identifying associations between genetic polymorphisms and disease-related phenotypes is the ability to quickly type a large number of single-nucleotide polymorphisms (SNPs). Contributors to Group 10 of Genetic Analysis Workshop 14 explored the potential of SNP genotypes for the association mapping of disease-related genes in family-based studies. Using both real data involving alcoholism susceptibility, made available by the Collaborative Study on the Genetics of Alcoholism (COGA), and simulated data involving personality-disorder susceptibility, group members investigated specific methodological issues involved in association mapping, such as multiple testing, single SNPs vs. combinations and haplotypes, and the effect of linkage disequilibrium on SNP-based linkage; evaluated existing methodologies for association mapping using SNPs, short-tandem repeats (STRs), or a combination of the two; and introduced new or modified association-mapping methods, including a gamma random effects (GRE) model and the quantitative trait linkage disequilibrium (QTLD) test. These papers are unified by the application of association-based methods to analyze SNPs, microsatellite markers, or both, to identify chromosomal regions harboring genes that contribute to quantitative endophenotype variation, and thus to disease risk. Their diversity attests to the breadth and flexibility of association-mapping approaches to the genetics of complex disease.