MicroRNA processing and binding site polymorphisms are not replicated in the Ovarian Cancer Association Consortium.

Background: Single nucleotide polymorphisms (SNP) in microRNA-related genes have been associated with epithelial ovarian cancer (EOC) risk in two reports, yet associated alleles may be inconsistent across studies.

Methods: We conducted a pooled analysis of previously identified SNPs by combining genotype data from 3,973 invasive EOC cases and 3,276 controls from the Ovarian Cancer Association Consortium. We also conducted imputation to obtain dense coverage of genes and comparable genotype data for all studies.

Gene set analysis of SNP data: benefits, challenges, and future directions.

The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each single-nucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs.

LIN28B polymorphisms influence susceptibility to epithelial ovarian cancer.

Defective microRNA (miRNA) biogenesis contributes to the development and progression of epithelial ovarian cancer (EOC). In this study, we examined the hypothesis that single nucleotide polymorphisms (SNP) in miRNA biogenesis genes may influence EOC risk. In an initial investigation, 318 SNPs in 18 genes were evaluated among 1,815 EOC cases and 1,900 controls, followed up by a replicative joint meta-analysis of data from an additional 2,172 cases and 3,052 controls.

Xenobiotic-Metabolizing gene polymorphisms and ovarian cancer risk.

Because selected xenobiotic-metabolizing enzymes process pro-carcinogens that could initiate ovarian carcinogenesis, we hypothesized that single nucleotide polymorphisms (SNPs) in the genes encoding xenobiotic-metabolizing enzymes are associated with risk of ovarian cancer. Cases with invasive epithelial ovarian cancer (N = 1571 including 956 of serous sub-type) and controls (N = 2046) from three studies were genotyped at 11 SNPs in EPHX1, ADH4, ADH1A, NQO2, NAT2, GSTP1, CYP1A1, and NQO1, following an initial SNP screen in a subset of participants. Logistic regression analysis of genotypes obtained via Illumina GoldenGate and Sequenom iPlex technologies revealed the following age- and study-adjusted associations: EPHX1 rs1051740 with increased serous ovarian cancer risk [per-allele odds ratio (OR) 1.

Inflammation-related gene variants as risk factors for pancreatic cancer.

Background: Recent reports support an association between chronic inflammation and progression to pancreatic cancer (PC).

Methods: This case-control, candidate gene association study evaluated 1,354 Caucasian patients with pancreatic ductal adenocarcinoma and 1,189 healthy Caucasian controls. We genotyped 1,538 single nucleotide polymorphism (SNP) in 102 genes from inflammatory pathways involving NF-κB.

Inherited variants in mitochondrial biogenesis genes may influence epithelial ovarian cancer risk.

Background: Mitochondria contribute to oxidative stress, a phenomenon implicated in ovarian carcinogenesis. We hypothesized that inherited variants in mitochondrial-related genes influence epithelial ovarian cancer (EOC) susceptibility.

Methods: Through a multicenter study of 1,815 Caucasian EOC cases and 1,900 controls, we investigated associations between EOC risk and 128 single nucleotide polymorphisms (SNPs) from 22 genes/regions within the mitochondrial genome (mtDNA) and 2,839 nuclear-encoded SNPs localized to 138 genes involved in mitochondrial biogenesis (BIO, n = 35), steroid hormone metabolism (HOR, n = 13), and oxidative phosphorylation (OXP, n = 90) pathways.

Prostate cancer susceptibility polymorphism rs2660753 is not associated with invasive ovarian cancer.

Background: We previously reported an association between rs2660753, a prostate cancer susceptibility polymorphism, and invasive epithelial ovarian cancer (EOC; OR = 1.2, 95% CI=1.0-1.

Mycophenolic acid response biomarkers: a cell line model system-based genome-wide screen.

Mycophenolic acid (MPA) is commonly used to treat patients with solid organ transplants during maintenance immunosuppressive therapy. Response to MPA varies widely, both for efficacy and drug-induced toxicity. A portion of this variation can be explained by pharmacokinetic and pharmacodynamic factors, including genetic variation in MPA-metabolizing UDP-glucuronyltransferase isoforms and the MPA targets, inosine monophosphate dehydrogenase 1 and 2.

The role of KRAS rs61764370 in invasive epithelial ovarian cancer: implications for clinical testing.

Purpose: An assay for the single-nucleotide polymorphism (SNP), rs61764370, has recently been commercially marketed as a clinical test to aid ovarian cancer risk evaluation in women with family histories of the disease. rs67164370 is in a 3'-UTR miRNA binding site of the KRAS oncogene and is a candidate for epithelial ovarian cancer (EOC) susceptibility. However, only one published article, analyzing fewer than 1,000 subjects in total, has examined this association.

Distinct germ line polymorphisms underlie glioma morphologic heterogeneity.

Two recent genome-wide association studies reported that single nucleotide polymorphisms (SNPs) in (or near) TERT (5p15), CCDC26 (8q24), CDKN2A/B (9p21), PHLDB1 (11q23), and RTEL1 (20q13) are associated with infiltrating glioma. From these reports, it was not clear whether the single nucleotide polymorphism associations predispose to glioma in general or whether they are specific to certain glioma grades or morphologic subtypes. To identify hypothesized associations between susceptibility loci and tumor subtype, we genotyped two case-control groups composed of the spectrum of infiltrating glioma subtypes and stratified the analyses by type.

Genome-wide association analyses of genetic, phenotypic, and environmental risks in the age-related eye disease study.

Purpose: To present genome-wide association analyses of genotypic and environmental risks on age-related macular degeneration (AMD) using 593 subjects from the age-related eye disease study (AREDS), after adjusting for population stratification and including questionable controls.

Methods: Single nucleotide polymorphism (SNP) associations with AMD for the non-Hispanic white population were investigated using a log-additive model after adjusting for population stratification. Replication of possible SNP-disease association was performed by genotyping an independent group of 444 AMD case and 300 control subjects.

Acetaminophen-NAPQI hepatotoxicity: a cell line model system genome-wide association study.

Acetaminophen is the leading cause of acute hepatic failure in many developed nations. Acetaminophen hepatotoxicity is mediated by the reactive metabolite N-acetyl-p-benzoquinonimine (NAPQI). We performed a "discovery" genome-wide association study using a cell line-based model system to study the possible contribution of genomics to NAPQI-induced cytotoxicity.

Betaine-homocysteine methyltransferase: human liver genotype-phenotype correlation.

Betaine-homocysteine methyltransferase (BHMT) catalyzes the remethylation of homocysteine. BHMT is highly expressed in the human liver. In the liver, BHMT catalyzes up to 50% of homocysteine metabolism.

Radiation pharmacogenomics: a genome-wide association approach to identify radiation response biomarkers using human lymphoblastoid cell lines.

Radiation therapy is used to treat half of all cancer patients. Response to radiation therapy varies widely among patients. Therefore, we performed a genome-wide association study (GWAS) to identify biomarkers to help predict radiation response using 277 ethnically defined human lymphoblastoid cell lines (LCLs).

Self-contained gene-set analysis of expression data: an evaluation of existing and novel methods.

Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained.

Ecto-5'-nucleotidase and thiopurine cellular circulation: association with cytotoxicity.

Thiopurine drugs such as 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG) are used to treat acute lymphoblastic leukemia of childhood. To test the hypothesis that variation in the expression of genes within the "thiopurine pathway" might influence 6-MP and 6-TG sensitivity, we generated basal gene expression profiles and IC(50) values for both of these thiopurine drugs using a model system consisting of 194 Human Variation Panel lymphoblastoid cell lines. Association analysis showed that thiopurine S-methyltransferase, ecto-5'-nucleotidase (NT5E), and multidrug resistance protein 4 (ABCC4) expression were correlated with thiopurine cytotoxicity.

Common variants at 19p13 are associated with susceptibility to ovarian cancer.

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecological malignancy in the developed world, accounting for 4% of the deaths from cancer in women. We performed a three-phase genome-wide association study of EOC survival in 8,951 individuals with EOC (cases) with available survival time data and a parallel association analysis of EOC susceptibility. Two SNPs at 19p13.

A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24.

Ovarian cancer accounts for more deaths than all other gynecological cancers combined. To identify common low-penetrance ovarian cancer susceptibility genes, we conducted a genome-wide association study of 507,094 SNPs in 1,768 individuals with ovarian cancer (cases) and 2,354 controls, with follow up of 21,955 SNPs in 4,162 cases and 4,810 controls, leading to the identification of a confirmed susceptibility locus at 9p22 (in BNC2). Here, we report on nine additional candidate loci (defined as having P ≤ 10⁻⁴) identified after stratifying cases by histology, which we genotyped in an additional 4,353 cases and 6,021 controls.

Progesterone receptor gene polymorphisms and risk of endometriosis: results from an international collaborative effort.

Objective: To investigate the association between self-reported endometriosis and the putative functional promoter +331C/T single nucleotide polymorphism and the PROGINS allele.

Design: Control subjects from ovarian cancer case-control studies participating in the international Ovarian Cancer Association Consortium. The majority of controls are drawn from population-based studies.

Bayesian mixture models for the incorporation of prior knowledge to inform genetic association studies.

In the last decade, numerous genome-wide linkage and association studies of complex diseases have been completed. The critical question remains of how to best use this potentially valuable information to improve study design and statistical analysis in current and future genetic association studies. With genetic effect size for complex diseases being relatively small, the use of all available information is essential to untangle the genetic architecture of complex diseases.

Genetic variation in TYMS in the one-carbon transfer pathway is associated with ovarian carcinoma types in the Ovarian Cancer Association Consortium.

Background: We previously reported the risks of ovarian carcinoma for common polymorphisms in one-carbon transfer genes. We sought to replicate associations for DPYD rs1801265, DNMT3A rs13420827, MTHFD1 rs1950902, MTHFS rs17284990, and TYMS rs495139 with risk of ovarian carcinoma overall and to use the large sample of assembled cases to investigate associations by histologic type.

Methods: Associations were evaluated in the Ovarian Cancer Association Consortium, including 16 studies of 5,593 epithelial ovarian carcinoma cases and 9,962 controls of white non-Hispanic origin.

Localizing putative markers in genetic association studies by incorporating linkage disequilibrium into bayesian hierarchical models.

Objective: Numerous methods have been proposed to model the association between multiple single nucleotide polymorphisms (SNPs) and a phenotype. Often these methods do not explicitly model the information regarding the linkage disequilibrium (LD) between SNPs. Furthermore, many methods shrink the SNP effects towards zero, rather than to an unknown latent gene-level effect.

Confirmation of linkage to and localization of familial colon cancer risk haplotype on chromosome 9q22.

Genetic risk factors are important contributors to the development of colorectal cancer. Following the definition of a linkage signal at 9q22-31, we fine mapped this region in an independent collection of colon cancer families. We used a custom array of single-nucleotide polymorphisms (SNP) densely spaced across the candidate region, performing both single-SNP and moving-window association analyses to identify a colon neoplasia risk haplotype.

Utilizing genotype imputation for the augmentation of sequence data.

Background: In recent years, capabilities for genotyping large sets of single nucleotide polymorphisms (SNPs) has increased considerably with the ability to genotype over 1 million SNP markers across the genome. This advancement in technology has led to an increase in the number of genome-wide association studies (GWAS) for various complex traits. These GWAS have resulted in the implication of over 1500 SNPs associated with disease traits.

Conference Scene: Lessons learned from the 5th Statistical Analysis Workshop of the Pharmacogenetics Research Network.

The Pharmacogenomics Research Network holds a statistical analysis workshop every other year to share novel statistical methods and study designs for pharmacogenomics research, as well as insightful analyses of substantive ongoing studies. The 5th workshop was held 15 April 2009, in Rochester (MN, USA), in conjunction with the general Pharmacogenomics Research Network meeting. This summary of the ten contributed talks highlights a variety of timely topics, including identification of functional variants, how to maximize power using various study designs, and pathway analysis approaches.