The identification of several hundred genomic regions affecting disease risk has proven the ability of genome-wide association studies have proven their ability to identify genetic contributors to disease. Currently, single-nucleotide polymorphism (SNP) association analysis is the most widely used method of genome-wide association data, but recent research shows that multi-marker tests of association may provide greater power, especially when more than one mutation is present within a gene and the mutations are in low linkage disequilibrium with each other. Here we use a multi-marker association test based on regression to SNPs located within known genes to obtain a gene-level score of association. We then perform pathway analysis using this score as a measure of gene importance. We use two tests of pathway enrichment - a binomial test and a random set method. By utilizing publicly available gene and pathway information, we identify B cell, cytokine and inflammation response, and antigen presentation pathways as being associated with rheumatoid arthritis. These results confirm known biological mechanisms for auto-immunity disorders, of which rheumatoid arthritis is one.

Objective To identify specific genetic pathways showing altered expression in peripheral blood of depressed subjects with bipolar disorder (BPD). Methods Illumina Sentrix BeadChip (Human-6v2)microarrays containing > 48,000 transcript probes were used to measure levels of gene expression in peripheral blood from 20 depressed subjects with BPD and in 15 healthy control subjects. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) was used to confirm a subset of these differences. Results A total of 1,180 genes were differentially expressed between subjects with BPD and healthy controls (fold-change > 1.3, false discovery rate-corrected p < 0.05, covaried for age and sex). Of these, 559 genes were up-regulated in BPD subjects and 621 were down-regulated. Surprisingly, there was no difference between medicated (n =11) and unmedicated (n =9) subjects with BPD for any of these genes. Pathway analysis using GeneGo MetaCore software showed that the most significantly affected pathway was the mitochondrial electron transport chain (ETC). Of the 85 objects (genes or proteins) in this pathway, 22 were up-regulated and 2 down-regulated in subjects with BPD. qRT-PCR confirmed up-regulation of nuclear encoded ETC genes in complexes I, III, IV, and V and, in addition, demonstrated up-regulation of mitochondrially encoded genes in each of these complexes. Conclusion These results suggest that increased expression of multiple components of the mitochondrial ETC may be a primary deficit in bipolar depression, rather than an effect of medication.

The interaction among multiple genes and environmental factors can affect an individual's susceptibility to disease. Some genes may not show strong marginal associations when they affect disease risk through interactions with other genes. As a result, these genes may not be identified by single-marker methods that are widely used in genome-wide association studies. To explore this possibility in real data, we carried out a two-stage model selection procedure of joint single-nucleotide polymorphism (SNP) analysis to detect genes associated with rheumatoid arthritis (RA) using Genetic Analysis Workshop 16 genome-wide association study data. In the first stage, the genetic markers were screened through an exhaustive two-dimensional search, through which promising SNP and SNP pairs were identified. Then, LASSO was used to choose putative SNPs from the candidates identified in the first stage. We then use the RA data collected by the Wellcome Trust Case Control Consortium to validate the putative genetic factors. Balancing computational load and statistical power, this method detects joint effects that may fail to emerge from single-marker analysis. Based on our proposed approach, we not only replicated the identification of important RA risk genes, but also found novel genes and their epistatic effects on RA. To our knowledge, this is the first two-dimensional scan based analysis for a real genome-wide association study.