Abstract
Population-based case-control association is a promising approach for unravelling the genetic basis of complex diseases. One potential problem of this approach is the presence of population structure in the samples. Using the Collaborative Study on the Genetics of Alcoholism (COGA) single-nucleotide polymorphism (SNP) datasets, we addressed three questions: How can the degree of population structure be quantified, and how does the population structure affect association studies? How accurate and efficient is the genomic control method in correcting for population structure? The amount of population structure in the COGA SNP data was found to inflate the p-value in association tests. Genomic control was found to be effective only when the appropriate number of markers was used in the control group in order to correctly calibrate the test. The approach presented in this paper could be used to select the appropriate number of markers for use in the genomic control method of correcting population structure.
Highlights
Unraveling the genetic basis of psychiatric diseases such as alcoholism is becoming the major challenge and focus of genetic studies, and large-scale case-control association studies at the genomic level are a promising approach
The mean of FST in the Illumina data set was 0.070 with a variance of 0.006. These results indicate that there is a substantial amount of population structure in our samples
The results from the Illumina dataset were quite similar, and are not shown. These results indicate that genomic control can be an effective approach to correct for population structure
Summary
Unraveling the genetic basis of psychiatric diseases such as alcoholism is becoming the major challenge and focus of genetic studies, and large-scale case-control association studies at the genomic level are a promising approach. One potential problem for association studies is the presence of population structure in the samples, which raises the potential for confounding and spurious results. There has been much debate over how much population structure exists and how serious a problem it poses to association studies. There have been few studies of the effects of population structure on association studies using such data. We used the Collaborative Study on the Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to assess the effects of population structure on large-scale association studies. How can the degree of population structure be quantified, and how does the population structure affect association studies? How accurate and efficient is the genomic control method for correcting for population structure?
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