McDonnell, Shannon K.

AbstractGene‐set analyses have been widely used in gene expression studies, and some of the developed methods have been extended to genome wide association studies (GWAS). Yet, complications due to linkage disequilibrium (LD) among single nucleotide polymorphisms (SNPs), and variable numbers of SNPs per gene and genes per gene‐set, have plagued current approaches, often leading to ad hoc “fixes.” To overcome some of the current limitations, we developed a general approach to scan GWAS SNP data for both gene‐level and gene‐set analyses, building on score statistics for generalized linear models, and taking advantage of the directed acyclic graph structure of the gene ontology when creating gene‐sets. However, other types of gene‐set structures can be used, such as the popular Kyoto Encyclopedia of Genes and Genomes (KEGG). Our approach combines SNPs into genes, and genes into gene‐sets, but assures that positive and negative effects of genes on a trait do not cancel. To control for multiple testing of many gene‐sets, we use an efficient computational strategy that accounts for LD and provides accurate step‐down adjustedP‐values for each gene‐set. Application of our methods to two different GWAS provide guidance on the potential strengths and weaknesses of our proposed gene‐set analyses.

Abstract The mitochondrion, conventionally thought to be an organelle specific to energy metabolism, is in fact multifunctional and implicated in many diseases, including cancer. To evaluate whether mitochondria-related genes are associated with increased risk for prostate cancer, we genotyped 24 single-nucleotide polymorphisms (SNP) within the mitochondrial genome and 376 tagSNPs localized to 78 nuclear-encoded mitochondrial genes. The tagSNPs were selected to achieve ≥80% coverage based on linkage disequilibrium. We compared allele and haplotype frequencies in ∼1,000 prostate cancer cases with ∼500 population controls. An association with prostate cancer was not detected for any of the SNPs within the mitochondrial genome individually or for 10 mitochondrial common haplotypes when evaluated using a global score statistic. For the nuclear-encoded genes, none of the tagSNPs were significantly associated with prostate cancer after adjusting for multiple testing. Nonetheless, we evaluated unadjusted P values by comparing our results with those from the Cancer Genetic Markers of Susceptibility (CGEMS) phase I data set. Seven tagSNPs had unadjusted P ≤ 0.05 in both our data and in CGEMS (two SNPs were identical and five were in strong linkage disequilibrium with CGEMS SNPs). These seven SNPs (rs17184211, rs4147684, rs4233367, rs2070902, rs3829037, rs7830235, and rs1203213) are located in genes MTRR, NDUFA9, NDUFS2, NDUFB9, and COX7A2, respectively. Five of the seven SNPs were further included in the CGEMS phase II study; however, none of the findings for these were replicated. Overall, these results suggest that polymorphisms in the mitochondrial genome and those in the nuclear-encoded mitochondrial genes evaluated are not substantial risk factors for prostate cancer. (Cancer Epidemiol Biomarkers Prev 2008;17(12):3558–66)