Person:

Hunter, David

Genetic variations, such as single nucleotide polymorphisms (SNPs) in microRNAs (miRNA) or in the miRNA binding sites may affect the miRNA dependent gene expression regulation, which has been implicated in various cancers, including breast cancer, and may alter individual susceptibility to cancer. We investigated associations between miRNA related SNPs and breast cancer risk. First we evaluated 2,196 SNPs in a case-control study combining nine genome wide association studies (GWAS). Second, we further investigated 42 SNPs with suggestive evidence for association using 41,785 cases and 41,880 controls from 41 studies included in the Breast Cancer Association Consortium (BCAC). Combining the GWAS and BCAC data within a meta-analysis, we estimated main effects on breast cancer risk as well as risks for estrogen receptor (ER) and age defined subgroups. Five miRNA binding site SNPs associated significantly with breast cancer risk: rs1045494 (odds ratio (OR) 0.92; 95% confidence interval (CI): 0.88–0.96), rs1052532 (OR 0.97; 95% CI: 0.95–0.99), rs10719 (OR 0.97; 95% CI: 0.94–0.99), rs4687554 (OR 0.97; 95% CI: 0.95–0.99, and rs3134615 (OR 1.03; 95% CI: 1.01–1.05) located in the 3′ UTR of CASP8, HDDC3, DROSHA, MUSTN1, and MYCL1, respectively. DROSHA belongs to miRNA machinery genes and has a central role in initial miRNA processing. The remaining genes are involved in different molecular functions, including apoptosis and gene expression regulation. Further studies are warranted to elucidate whether the miRNA binding site SNPs are the causative variants for the observed risk effects.

Approximately 15–30% of all breast cancer tumors are estrogen receptor negative (ER−). Compared with ER-positive (ER+) disease they have an earlier age at onset and worse prognosis. Despite the vast number of risk variants identified for numerous cancer types, only seven loci have been unambiguously identified for ER-negative breast cancer. With the aim of identifying new susceptibility SNPs for this disease we performed a pleiotropic genome-wide association study (GWAS). We selected 3079 SNPs associated with a human complex trait or disease at genome-wide significance level (P<5×10−8) to perform a secondary analysis of an ER-negative GWAS from the National Cancer Institute's Breast and Prostate Cancer Cohort Consortium (BPC3), including 1998 cases and 2305 controls from prospective studies. We then tested the top ten associations (i.e. with the lowest P-values) using three additional populations with a total sample size of 3509 ER+ cases, 2543 ER− cases and 7031 healthy controls. None of the 3079 selected variants in the BPC3 ER-GWAS were significant at the adjusted threshold. 186 variants were associated with ER− breast cancer risk at a conventional threshold of P<0.05, with P-values ranging from 0.049 to 2.3×10−4. None of the variants reached statistical significance in the replication phase. In conclusion, this study did not identify any novel susceptibility loci for ER-breast cancer using a “pleiotropic approach”.

Endometrial cancer (EC) contributes substantially to total burden of cancer morbidity and mortality in the United States. Family history is a known risk factor for EC, thus genetic factors may play a role in EC pathogenesis. Three previous genome-wide association studies (GWAS) have found only one locus associated with EC, suggesting that common variants with large effects may not contribute greatly to EC risk. Alternatively, we hypothesize that rare variants may contribute to EC risk. We conducted an exome-wide association study (EXWAS) of EC using the Infinium HumanExome BeadChip in order to identify rare variants associated with EC risk. We successfully genotyped 177,139 variants in a multiethnic population of 1,055 cases and 1,778 controls from four studies that were part of the Epidemiology of Endometrial Cancer Consortium (E2C2). No variants reached global significance in the study, suggesting that more power is needed to detect modest associations between rare genetic variants and risk of EC.

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×(10^{−9})). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.

We investigate 71 single nucleotide polymorphisms (SNPs) identified in meta-analytic studies of genome-wide association studies (GWAS) of breast cancer, the majority of which are located in intergenic or intronic regions. To explore regulatory impacts of these variants we conducted expression quantitative loci (eQTL) analyses on tissue samples from 376 invasive postmenopausal breast cancer cases in the Nurses’ Health Study (NHS) diagnosed from 1990–2004. Expression analysis was conducted on all formalin-fixed paraffin-embedded (FFPE) tissue samples (and on 264 adjacent normal samples) using the Affymetrix Human Transcriptome Array. Significance and ranking of associations between tumor receptor status and expression variation was preserved between NHS FFPE and TCGA fresh-frozen sample sets (Spearman r = 0.85, p<10^-10 for 17 of the 21 Oncotype DX recurrence signature genes). At an FDR threshold of 10%, we identified 27 trans-eQTLs associated with expression variation in 217 distinct genes. SNP-gene associations can be explored using an open-source interactive browser distributed in a Bioconductor package. Using a new a procedure for testing hypotheses relating SNP content to expression patterns in gene sets, defined as molecular function pathways, we find that loci on 6q14 and 6q25 affect various gene sets and molecular pathways (FDR < 10%). Although the ultimate biological interpretation of the GWAS-identified variants remains to be uncovered, this study validates the utility of expression analysis of this FFPE expression set for more detailed integrative analyses.

The Nurses’ Health Study (NHS), Nurses’ Health Study II (NHSII), Health Professionals Follow Up Study (HPFS) and the Physicians Health Study (PHS) have collected detailed longitudinal data on multiple exposures and traits for approximately 310,000 study participants over the last 35 years. Over 160,000 study participants across the cohorts have donated a DNA sample and to date, 20,691 subjects have been genotyped as part of genome-wide association studies (GWAS) of twelve primary outcomes. However, these studies utilized six different GWAS arrays making it difficult to conduct analyses of secondary phenotypes or share controls across studies. To allow for secondary analyses of these data, we have created three new datasets merged by platform family and performed imputation using a common reference panel, the 1,000 Genomes Phase I release. Here, we describe the methodology behind the data merging and imputation and present imputation quality statistics and association results from two GWAS of secondary phenotypes (body mass index (BMI) and venous thromboembolism (VTE)). We observed the strongest BMI association for the FTO SNP rs55872725 (β = 0.45, p = 3.48x10-22), and using a significance level of p = 0.05, we replicated 19 out of 32 known BMI SNPs. For VTE, we observed the strongest association for the rs2040445 SNP (OR = 2.17, 95% CI: 1.79–2.63, p = 2.70x10-15), located downstream of F5 and also observed significant associations for the known ABO and F11 regions. This pooled resource can be used to maximize power in GWAS of phenotypes collected across the cohorts and for studying gene-environment interactions as well as rare phenotypes and genotypes.

Background: Observational studies examining associations between adult height and risk of colorectal, prostate, and lung cancers have generated mixed results. We conducted meta-analyses using data from prospective cohort studies and further carried out Mendelian randomization analyses, using height-associated genetic variants identified in a genome-wide association study (GWAS), to evaluate the association of adult height with these cancers. Methods and Findings: A systematic review of prospective studies was conducted using the PubMed, Embase, and Web of Science databases. Using meta-analyses, results obtained from 62 studies were summarized for the association of a 10-cm increase in height with cancer risk. Mendelian randomization analyses were conducted using summary statistics obtained for 423 genetic variants identified from a recent GWAS of adult height and from a cancer genetics consortium study of multiple cancers that included 47,800 cases and 81,353 controls. For a 10-cm increase in height, the summary relative risks derived from the meta-analyses of prospective studies were 1.12 (95% CI 1.10, 1.15), 1.07 (95% CI 1.05, 1.10), and 1.06 (95% CI 1.02, 1.11) for colorectal, prostate, and lung cancers, respectively. Mendelian randomization analyses showed increased risks of colorectal (odds ratio [OR] = 1.58, 95% CI 1.14, 2.18) and lung cancer (OR = 1.10, 95% CI 1.00, 1.22) associated with each 10-cm increase in genetically predicted height. No association was observed for prostate cancer (OR = 1.03, 95% CI 0.92, 1.15). Our meta-analysis was limited to published studies. The sample size for the Mendelian randomization analysis of colorectal cancer was relatively small, thus affecting the precision of the point estimate. Conclusions: Our study provides evidence for a potential causal association of adult height with the risk of colorectal and lung cancers and suggests that certain genetic factors and biological pathways affecting adult height may also affect the risk of these cancers.

Background: Observational epidemiological studies have shown that high body mass index (BMI) is associated with a reduced risk of breast cancer in premenopausal women but an increased risk in postmenopausal women. It is unclear whether this association is mediated through shared genetic or environmental factors. Methods: We applied Mendelian randomization to evaluate the association between BMI and risk of breast cancer occurrence using data from two large breast cancer consortia. We created a weighted BMI genetic score comprising 84 BMI-associated genetic variants to predicted BMI. We evaluated genetically predicted BMI in association with breast cancer risk using individual-level data from the Breast Cancer Association Consortium (BCAC) (cases = 46,325, controls = 42,482). We further evaluated the association between genetically predicted BMI and breast cancer risk using summary statistics from 16,003 cases and 41,335 controls from the Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) Project. Because most studies measured BMI after cancer diagnosis, we could not conduct a parallel analysis to adequately evaluate the association of measured BMI with breast cancer risk prospectively. Results: In the BCAC data, genetically predicted BMI was found to be inversely associated with breast cancer risk (odds ratio [OR] = 0.65 per 5 kg/m2 increase, 95% confidence interval [CI]: 0.56–0.75, p = 3.32 × 10−10). The associations were similar for both premenopausal (OR = 0.44, 95% CI:0.31–0.62, p = 9.91 × 10−8) and postmenopausal breast cancer (OR = 0.57, 95% CI: 0.46–0.71, p = 1.88 × 10−8). This association was replicated in the data from the DRIVE consortium (OR = 0.72, 95% CI: 0.60–0.84, p = 1.64 × 10−7). Single marker analyses identified 17 of the 84 BMI-associated single nucleotide polymorphisms (SNPs) in association with breast cancer risk at p < 0.05; for 16 of them, the allele associated with elevated BMI was associated with reduced breast cancer risk. Conclusions: BMI predicted by genome-wide association studies (GWAS)-identified variants is inversely associated with the risk of both pre- and postmenopausal breast cancer. The reduced risk of postmenopausal breast cancer associated with genetically predicted BMI observed in this study differs from the positive association reported from studies using measured adult BMI. Understanding the reasons for this discrepancy may reveal insights into the complex relationship of genetic determinants of body weight in the etiology of breast cancer.