Successful design and conduct of genome-wide association studies

Abstract

Genome-wide association studies are becoming an increasingly effective tool for identifying genetic factors contributing to complex diseases. In this review, I discuss two sets of genome-wide association studies that identified novel genetic factors for age-related macular degeneration and genetic factors for type II diabetes. In reviewing these sets of studies, my goal is to identify factors that contributed to the success of these studies. Design-related factors include the selection of traits that show strong familiarity, the selection of clinically homogeneous populations and the selection of cases that have a family history. Ethnic stratification within the study sample can lead to biases, and methods to control for stratification are briefly reviewed. Finally, the impact of single nucleotide polymorphism selection on the power of a study and procedures for improving power by inferring genotypes, by combining data across studies and by performing multistage analyses are discussed. The continuing success of genome-wide association studies depends on careful selection of populations for study and on collaborative analytical approaches.

Figure 1

Maximal values of R² between two loci. Lines correspond to minor allele frequencies at one locus (with alleles A and a). The X-axis indicates allele frequencies at the second locus.

Figure 2

Power to detect a 4-fold increased relative risk for a dominant disease with 400 cases and 400 controls; 14% prevalence, 1% significance and 20% allele frequency of the causal allele. The X-axis indicates the SNP marker allele frequency, the Y-axis denotes D' between the marker and the causal allele and the Z-axis denotes power. The left panel indicates the power for cases sampled without reference to family history, whereas the right panel indicates power for cases sampled because they have an affected sibling.