A principal components regression approach to multilocus genetic association studies

Abstract

With the rapid development of modern genotyping technology, it is becoming commonplace to genotype densely spaced genetic markers such as single nucleotide polymorphisms (SNPs) along the genome. This development has inspired a strong interest in using multiple markers located in the target region for the detection of association. We introduce a principal components (PCs) regression method for candidate gene association studies where multiple SNPs from the candidate region tend to be correlated. In this approach, the total variance in the original genotype scores is decomposed into parts that correspond to uncorrelated PCs. The PCs with the largest variances are then used as regressors in a multiple regression. Simulation studies suggest that this approach can have higher power than some popular methods. An application to CHI3L2 gene expression data confirms a significant association between CHI3L2 gene expression level and SNPs from this gene that has been previously reported by others.

Gene-based interaction analysis by incorporating external linkage disequilibrium information

“…9 Several methods have been developed to test whether a gene is associated with the trait of interest. [10][11][12][13] The central idea of these methods is to summarize marker genotypes into a few components so that the overall degrees of freedom are reduced while most information in the data is retained. Extensive simulations demonstrate that gene-based association analysis can increase the power of detecting genetic association compared with single-marker-based analysis.…”

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

“…[3][4][5][6][7][8][9][10][11] The second approach uses an unsupervised dimension reduction procedure, such as principal component (PC) analysis, to select a proportion of genetic variation (contained in either a subset of SNPs or selected PCs) without referring to their association with the outcome, and then relates the selected components to the outcome. [12][13][14][15] The third approach employs a supervised variable selection (SVS) procedure to identify a subset of variables that are most relevant to the outcome and then designs a test statistic based on the selected variables. 16,17 For the first and second approaches, it is possible to design a test statistic with a known asymptotic distribution.…”

Power analysis of principal components regression in genetic association studies

“…Wang and Elston (2007) provided a weighted Fourier transformation test to reduce the DF and improve the power. More recently, several studies applied principal components regression (PCR) to test for association of the set of SNPs with the phenotype (Gauderman et al, 2007;Wang and Abbott, 2008). This approach uses the first few principal components (PCs) directly to assess genetic association.…”

“…Although principal components (PCs) analysis is a popular and efficient statistical method for reducing high dimensionality, one of the crucial problems is to determine the number of PCs to retain for constructing the test statistic. Some authors (Wang and Abbott, 2008;Gauderman et al, 2007) have suggested choosing the first few PCs that account for 80%~90% of the total variation in the original SNPs. However, these first few PCs may be unrelated to the outcome.…”