Control selection options for genome-wide association studies in cohorts

Abstract

Investigators planning studies within cohorts have many options for choosing an efficient sampling design for genome-wide association and other molecular epidemiology studies. Consideration of person-year and proportional hazards analyses of full cohorts may add further insight into ramifications of different designs. Empirical evidence from genome-wide association studies can supplement intuition and simulations in comparing properties of various case-control designs within cohorts. Additional theoretical and empirical work, justification of sampling choice in publications, and consideration of context and scientific aims can improve designs and, thereby, increase the scientific value and cost effectiveness of future studies.

Figure 1. Empirical comparison of P-values from unadjusted (p_u) and age-adjusted (p_a) P-values for 530,000 SNPs from Cancer Genetic Markers of Susceptibility (CGEMS) genome-wide association studies of prostate (5) and breast (6) cancers

The figure displays the cumulative distribution of the ratio of the larger to the smaller P-value. The triangles denote the graphs for SNPs from the breast cancer analysis; dots denote the graphs for prostate cancer. Black display is used when p_u < p_a; grey display is used when p_a < p_u. Figure 1(a) displays the plot on a natural scale; figure 1(b) displays the same data on a logarithm scale in base 10. The P-value ratios tend to be higher for the breast cancer analysis than for the prostate cancer analysis. Data from builds 1 and 8 (http://cgems.cancer.gov/data/) for prostate cancer and breast cancer studies, respectively.