Comparison of tagging single-nucleotide polymorphism methods in association analyses

Development of a genotyping microarray for studying the role of gene-environment interactions in risk for lung cancer

A microarray (LungCaGxE), based on Illumina BeadChip technology, was developed for high-resolution genotyping of genes that are candidates for involvement in environmentally driven aspects of lung cancer oncogenesis and/or tumor growth. The iterative array design process illustrates techniques for managing large panels of candidate genes and optimizing marker selection, aided by a new bioinformatics pipeline component, Tagger Batch Assistant. The LungCaGxE platform targets 298 genes and the proximal genetic regions in which they are located, using ≈ 13,000 DNA single nucleotide polymorphisms (SNPs), which include haplotype linkage markers with a minimum allele frequency of 1% and additional specifically targeted SNPs, for which published reports have indicated functional consequences or associations with lung cancer or other smoking-related diseases. The overall assay conversion rate was 98.9%; 99.0% of markers with a minimum Illumina design score of 0.6 successfully generated allele calls using genomic DNA from a study population of 1873 lung-cancer patients and controls.

Comprehensive analysis of common genetic variation in 61 genes related to steroid hormone and insulin-like growth factor-I metabolism and breast cancer risk in the NCI breast and prostate cancer cohort consortium

There is extensive evidence that increases in blood and tissue concentrations of steroid hormones and of insulin-like growth factor I (IGF-I) are associated with breast cancer risk. However, studies of common variation in genes involved in steroid hormone and IGF-I metabolism have yet to provide convincing evidence that such variants predict breast cancer risk. The Breast and Prostate Cancer Cohort Consortium (BPC3) is a collaboration of large US and European cohorts. We genotyped 1416 tagging single nucleotide polymorphisms (SNPs) in 37 steroid hormone metabolism genes and 24 IGF-I pathway genes in 6292 cases of breast cancer and 8135 controls, mostly Caucasian, postmenopausal women from the BPC3. We also imputed 3921 additional SNPs in the regions of interest. None of the SNPs tested was significantly associated with breast cancer risk, after correction for multiple comparisons. The results remained null when cases and controls were stratified by age at diagnosis/recruitment, advanced or nonadvanced disease, body mass index, with or without in situ cases; or restricted to Caucasians. Among 770 estrogen receptor-negative cases, an SNP located 3' of growth hormone receptor (GHR) was marginally associated with increased risk after correction for multiple testing (P(trend) = 1.5 × 10(-4)). We found no significant overall associations between breast cancer and common germline variation in 61 genes involved in steroid hormone and IGF-I metabolism in this large, comprehensive study. Although previous studies have shown that variations in these genes can influence endogenous hormone levels, the magnitude of the effect of single SNPs does not appear to be sufficient to alter breast cancer risk.

A genome-wide linkage scan reveals CD53 as an important regulator of innate TNF-alpha levels

Cytokines are major immune system regulators. Previously, innate cytokine profiles determined by lipopolysaccharide stimulation were shown to be highly heritable. To identify regulating genes in innate immunity, we analyzed data from a genome-wide linkage scan using microsatellites in osteoarthritis (OA) patients (The GARP study) and their innate cytokine data on interleukin (IL)-1beta, IL-1Ra, IL-10 and tumor necrosis factor (TNF)alpha. A confirmation cohort consisted of the Leiden 85-Plus study. In this study, a linkage analysis was followed by manual selection of candidate genes in linkage regions showing LOD scores over 2.5. An single-nucleotide polymorphism (SNP) gene tagging method was applied to select SNPs on the basis of the highest level of gene tagging and possible functional effects. QTDT was used to identify the SNPs associated with innate cytokine production. Initial association signals were modeled by a linear mixed model. Through these analyses, we identified 10 putative genes involved in the regulation of TNFalpha. SNP rs6679497 in gene CD53 showed significant association with TNFalpha levels (P=0.001). No association of this SNP was observed with OA. A novel gene involved in the innate immune response of TNFalpha is identified. Genetic variation in this gene may have a role in diseases and disorders in which TNFalpha is closely involved.

Candidate gene analysis using imputed genotypes: cell cycle single-nucleotide polymorphisms and ovarian cancer risk

Polymorphisms in genes critical to cell cycle control are outstanding candidates for association with ovarian cancer risk; numerous genes have been interrogated by multiple research groups using differing tagging single-nucleotide polymorphism (SNP) sets. To maximize information gleaned from existing genotype data, we conducted a combined analysis of five independent studies of invasive epithelial ovarian cancer. Up to 2,120 cases and 3,382 controls were genotyped in the course of two collaborations at a variety of SNPs in 11 cell cycle genes (CDKN2C, CDKN1A, CCND3, CCND1, CCND2, CDKN1B, CDK2, CDK4, RB1, CDKN2D, and CCNE1) and one gene region (CDKN2A-CDKN2B). Because of the semi-overlapping nature of the 123 assayed tagging SNPs, we performed multiple imputation based on fastPHASE using data from White non-Hispanic study participants and participants in the international HapMap Consortium and National Institute of Environmental Health Sciences SNPs Program. Logistic regression assuming a log-additive model was done on combined and imputed data. We observed strengthened signals in imputation-based analyses at several SNPs, particularly CDKN2A-CDKN2B rs3731239; CCND1 rs602652, rs3212879, rs649392, and rs3212891; CDK2 rs2069391, rs2069414, and rs17528736; and CCNE1 rs3218036. These results exemplify the utility of imputation in candidate gene studies and lend evidence to a role of cell cycle genes in ovarian cancer etiology, suggest a reduced set of SNPs to target in additional cases and controls.

Genome-wide association analyses of expression phenotypes

A number of issues arise when analyzing the large amount of data from high-throughput genotype and expression microarray experiments, including design and interpretation of genome-wide association studies of expression phenotypes. These issues were considered by contributions submitted to Group 1 of the Genetic Analysis Workshop 15 (GAW15), which focused on the association of quantitative expression data. These contributions evaluated diverse hypotheses, including those relevant to cancer and obesity research, and used various analytic techniques, many of which were derived from information theory. Several observations from these reports stand out. First, one needs to consider the genetic model of the trait of interest and carefully select which single nucleotide polymorphisms and individuals are included early in the design stage of a study. Second, by targeting specific pathways when analyzing genome-wide data, one can generate more interpretable results than agnostic approaches. Finally, for datasets with small sample sizes but a large number of features like the Genetic Analysis Workshop 15 dataset, machine learning approaches may be more practical than traditional parametric approaches.