Abstract
The North American Rheumatoid Arthritis Consortium case-control study collected case participants across the United States and control participants from New York. More than 500,000 single-nucleotide polymorphisms (SNPs) were genotyped in the sample of 2000 cases and controls. Careful adjustment for the confounding effect of population stratification must be conducted when analyzing these data; the variance inflation factor (VIF) without adjustment is 1.44. In the primary analyses of these data, a clustering algorithm in the program PLINK was used to reduce the VIF to 1.14, after which genomic control was used to control residual confounding. Here we use stratification scores to achieve a unified and coherent control for confounding. We used the first 10 principal components, calculated genome-wide using a set of 81,500 loci that had been selected to have low pair-wise linkage disequilibrium, as risk factors in a logistic model to calculate the stratification score. We then divided the data into five strata based on quantiles of the stratification score. The VIF of these stratified data is 1.04, indicating substantial control of stratification. However, after control for stratification, we find that there are no significant loci associated with rheumatoid arthritis outside of the HLA region. In particular, we find no evidence for association of TRAF1-C5 with rheumatoid arthritis.
Highlights
Population stratification occurs when a population is composed of subpopulations that have varying allele frequencies
It is worth noting that the identity-by-state (IBS) clustering approach to controlling for confounding by population stratification that is implemented in PLINK, and that was used by Plenge et al [11], only attained a variance inflation factor (VIF) of 1.14
Aside from single-nucleotide polymorphisms (SNPs) in the HLA region on chromosome 6, genome-wide we found no SNPs that were significantly associated with rheumatoid arthritis (RA) at the a = 0.05 level (Figure 1)
Summary
Population stratification occurs when a population is composed of subpopulations that have varying allele frequencies. To control for confounding by population stratification in case-control studies, statistical methods have been developed that use genetic markers to provide information on population structure. A new statistical approach for controlling for population stratification in case-control studies was recently proposed by Epstein et al [7] This method involves modeling the odds of disease, given data on substructure-informative loci. The association between genotypes and the trait is ascertained using a stratified test This approach is similar in spirit to the use of the propensity score to control for confounding in an observational study [8,9]. Epstein et al showed that testing using the stratification score could control for confounding by population stratification in some situations where other methods fail [7]
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