Combining multiple family-based association studies.
While high-throughput genotyping technologies are becoming readily available, the merit of using these technologies to perform genome-wide association studies has not been established. One major concern is that for studies of complex diseases and traits, the whole-genome approach requires such large sample sizes that both recruitment and genotyping pose considerable challenge. Here we propose a novel statistical method that boosts the effective sample size by combining data obtained from several studies. Specifically, we consider a situation in which various studies have genotyped non-overlapping subjects at largely non-overlapping sets of markers. Our approach, which exploits the local linkage disequilibrium structure without assuming an explicit population model, opens up the possibility of improving statistical power by incorporating existing data into future association studies.
Tang
Hua
H
Department of Genetics, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, USA.
Peng
Jie
J
Department of Statistics, University of California, Davis, California 95616, USA.
Wang
Pei
P
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109, USA.
Coram
Marc
M
Department of Genetics, Stanford University School of Medicine, 300 Pasteur Drive, Stanford, California 94305, USA.
Hsu
Li
L
Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington 98109, USA.