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1.  Testing SNPs and sets of SNPs for importance in association studies 
A major goal of genetic association studies concerned with single nucleotide polymorphisms (SNPs) is the detection of SNPs exhibiting an impact on the risk of developing a disease. Typically, this problem is approached by testing each of the SNPs individually. This, however, can lead to an inaccurate measurement of the influence of the SNPs on the disease risk, in particular, if SNPs only show an effect when interacting with other SNPs, as the multivariate structure of the data is ignored. In this article, we propose a testing procedure based on logic regression that takes this structure into account and therefore enables a more appropriate quantification of importance and ranking of the SNPs than marginal testing. Since even SNP interactions often exhibit only a moderate effect on the disease risk, it can be helpful to also consider sets of SNPs (e.g. SNPs belonging to the same gene or pathway) to borrow strength across these SNP sets and to identify those genes or pathways comprising SNPs that are most consistently associated with the response. We show how the proposed procedure can be adapted for testing SNP sets, and how it can be applied to blocks of SNPs in linkage disequilibrium (LD) to overcome problems caused by LD.
doi:10.1093/biostatistics/kxq042
PMCID: PMC3006123  PMID: 20601626
Feature selection; GENICA; Importance measure; logicFS; Logic regression
2.  A multilevel model to address batch effects in copy number estimation using SNP arrays 
Submicroscopic changes in chromosomal DNA copy number dosage are common and have been implicated in many heritable diseases and cancers. Recent high-throughput technologies have a resolution that permits the detection of segmental changes in DNA copy number that span thousands of base pairs in the genome. Genomewide association studies (GWAS) may simultaneously screen for copy number phenotype and single nucleotide polymorphism (SNP) phenotype associations as part of the analytic strategy. However, genomewide array analyses are particularly susceptible to batch effects as the logistics of preparing DNA and processing thousands of arrays often involves multiple laboratories and technicians, or changes over calendar time to the reagents and laboratory equipment. Failure to adjust for batch effects can lead to incorrect inference and requires inefficient post hoc quality control procedures to exclude regions that are associated with batch. Our work extends previous model-based approaches for copy number estimation by explicitly modeling batch and using shrinkage to improve locus-specific estimates of copy number uncertainty. Key features of this approach include the use of biallelic genotype calls from experimental data to estimate batch-specific and locus-specific parameters of background and signal without the requirement of training data. We illustrate these ideas using a study of bipolar disease and a study of chromosome 21 trisomy. The former has batch effects that dominate much of the observed variation in the quantile-normalized intensities, while the latter illustrates the robustness of our approach to a data set in which approximately 27% of the samples have altered copy number. Locus-specific estimates of copy number can be plotted on the copy number scale to investigate mosaicism and guide the choice of appropriate downstream approaches for smoothing the copy number as a function of physical position. The software is open source and implemented in the R package crlmm at Bioconductor (http:www.bioconductor.org).
doi:10.1093/biostatistics/kxq043
PMCID: PMC3006124  PMID: 20625178
Bioinformatics; Hierarchical models; DNA copy number variations; Single nucleotide polymorphism array

Results 1-2 (2)