Evaluating cost efficiency of SNP chips in genome-wide association studies.
Genome-wide association (GWA) studies have recently emerged as a major approach to gene discovery for many complex diseases. Since GWA scans are expensive, cost efficiency is an important factor to consider in study design. However, it often requires extensive and time-consuming computer simulations to compare cost efficiency across different single nucleotide polymorphism (SNP) chips. Here, we propose two simulation-free approaches to cost efficiency comparisons across SNP chips. In the first method, the overall power under a given disease model is calculated for each SNP chip and various sample sizes. Then SNP chips can be compared with respect to the sample sizes required to achieve the same level of power. In the second method, for a desired level of genomic coverage, the effective r(2) threshold values are calculated for each SNP chip. Since r(2) is inversely proportional to the sample size to achieve the same power, the required sample sizes can then be compared among SNP chips. These two methods are complementary to each other. The first approach provides direct power comparisons, but it requires information on disease model and may not be reliable for SNP chips that contain many non-HapMap SNPs. The second approach allows sample size comparisons based on the coverage of SNP chips, and it can be modified for SNP chips that contain non-HapMap SNPs. These methods are particularly relevant for large epidemiological studies in which enough subjects are available for GWA screening and follow-up stages. We illustrate these approaches using five currently available whole genome SNP chips.
Li
Chun
C
Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA. chun.li@vanderbilt.edu
Li
Mingyao
M
Long
Ji-Rong
JR
Cai
Qiuyin
Q
Zheng
Wei
W
D003198:Computer Simulation; D003362:Cost-Benefit Analysis; D015894:Genome, Human; D006801:Humans; D020641:Polymorphism, Single Nucleotide