Schaid, Daniel J.

Abstract Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10−8 seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type–specific expression quantitative trait locus and enhancer–gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10−5 in the three-cancer meta-analysis. Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052–67. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 932

ABSTRACTRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single‐marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden‐type approaches attempt to identify aggregation of RVs across case‐control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large‐scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway‐level RV analysis results from a prostate cancer (PC) risk case‐control sequencing study. Finally, we discuss potential extensions and future directions of this work.

Abstract African American Hereditary Prostate Cancer Study (AAHPC) was developed as a national collaboration to explore the role of genetics in the causation of hereditary prostate cancer (HPC) in African American (AA) men. AAHPC is in partnership with the International Consortium for Prostate Cancer Genetics (ICPCG), which conducts collaborative studies of HPC genetics in multiplex families. As part of an ICPCG sequencing study of 539 affected individuals from 366 HPC pedigrees, we performed whole exome sequencing in 21 ICPCG AA families, of which there were 14 AAHPC affected men from 11 pedigrees. The combined ICPCG AA cohort consisted of N=26 affected members. Post-variant calling quality control (QC) was implemented using Golden Helix SVS 8 software with filters set for removal of variants with Read Depth < 10, Quality Score < 20, Quality Score: Read Depth Ratio < 0.5, Call Rate < 0.75. Variants were additionally filtered by MAF based on the NHLBI ESP650051-V2 exomes variant frequencies for the AA population using a MAF threshold of 5%. Following QC, 176/299 SNVs and 20/39 INDELs remained for further analysis. In these analyses, we focused on 13 known cancer genes (MSR1, AR, BRCA1, BRCA2, BTNL2, EPHB2, CDH1, RNASEL, ELAC2, HOXB13, CHEK2, TP53 and NBN). Three sequenced families had > 1 affected members sequenced (2 or 3 per family) and the remaining 18 families had one member sequenced. Under the dominant model, our preliminary results show that no rare variants in the 13 candidate genes were found in 3/3 affecteds in two families. Rare SNVs in seven candidate genes (AR, CDH1, ELAC2, HOXB13, RNASEL, BRCA2 and EPHB2) were found in 2/3 affecteds for two families and 2/2 affecteds in one family. Several of the remaining 18 affected men (1 sequenced per pedigree) shared the same rare SNV in these candidate genes. For INDELs, rare variants in three candidate genes were found in pedigrees with ≥ 2 affecteds. Several of the remaining 18 affected men (one sequenced per pedigree) shared the same rare INDEL. Additional QC is underway to validate these variants and bioinformatic analyses are being used to predict effects of the variants in an effort to unravel the complex genetic heterogeneity of HPC in AA. Citation Format: Cheryl D. Cropp, Shannon K. McDonnell, Sumit Middha, Melissa DeRycke, Danielle M. Karyadi, Daniel Schaid, Stephen N. Thibodeau, William B. Isaacs, Elaine A. Ostrander, Janet Stanford, Kathleen A. Cooney, Joan E. Bailey-Wilson, John D. Carpten. Rare variant discovery in known cancer genes from whole-exome sequencingof African American hereditary prostate cancer families. [abstract]. In: Proceedings of the Eighth AACR Conference on The Science of Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; Nov 13-16, 2015; Atlanta, GA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2016;25(3 Suppl):Abstract nr B40.

Background— Whether knowledge of genetic risk for coronary heart disease (CHD) affects health-related outcomes is unknown. We investigated whether incorporating a genetic risk score (GRS) in CHD risk estimates lowers low-density lipoprotein cholesterol (LDL-C) levels. Methods and Results— Participants (n=203, 45–65 years of age, at intermediate risk for CHD, and not on statins) were randomly assigned to receive their 10-year probability of CHD based either on a conventional risk score (CRS) or CRS + GRS ( + GRS). Participants in the + GRS group were stratified as having high or average/low GRS. Risk was disclosed by a genetic counselor followed by shared decision making regarding statin therapy with a physician. We compared the primary end point of LDL-C levels at 6 months and assessed whether any differences were attributable to changes in dietary fat intake, physical activity levels, or statin use. Participants (mean age, 59.4±5 years; 48% men; mean 10-year CHD risk, 8.5±4.1%) were allocated to receive either CRS (n=100) or + GRS (n=103). At the end of the study period, the + GRS group had a lower LDL-C than the CRS group (96.5±32.7 versus 105.9±33.3 mg/dL; P =0.04). Participants with high GRS had lower LDL-C levels (92.3±32.9 mg/dL) than CRS participants ( P =0.02) but not participants with low GRS (100.9±32.2 mg/dL; P =0.18). Statins were initiated more often in the + GRS group than in the CRS group (39% versus 22%, P <0.01). No significant differences in dietary fat intake and physical activity levels were noted. Conclusions— Disclosure of CHD risk estimates that incorporated genetic risk information led to lower LDL-C levels than disclosure of CHD risk based on conventional risk factors alone. Clinical Trial Registration— URL: http://www.clinicaltrials.gov . Unique identifier: NCT01936675.

AbstractKernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant‐ and microbiome‐based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

Abstract Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR). Results: We observed no significant association between genetic variants and prostate cancer survival. Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study. Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes. Cancer Epidemiol Biomarkers Prev; 24(11); 1796–800. ©2015 AACR.

Abstract A genome-wide association study (GWAS) of prostate cancer in Kaiser Permanente health plan members (7,783 cases, 38,595 controls; 80.3% non-Hispanic white, 4.9% African-American, 7.0% East Asian, and 7.8% Latino) revealed a new independent risk indel rs4646284 at the previously identified locus 6q25.3 that replicated in PEGASUS (N = 7,539) and the Multiethnic Cohort (N = 4,679) with an overall P = 1.0 × 10−19 (OR, 1.18). Across the 6q25.3 locus, rs4646284 exhibited the strongest association with expression of SLC22A1 (P = 1.3 × 10−23) and SLC22A3 (P = 3.2 × 10−52). At the known 19q13.33 locus, rs2659124 (P = 1.3 × 10−13; OR, 1.18) nominally replicated in PEGASUS. A risk score of 105 known risk SNPs was strongly associated with prostate cancer (P &lt; 1.0 × 10−8). Comparing the highest to lowest risk score deciles, the OR was 6.22 for non-Hispanic whites, 5.82 for Latinos, 3.77 for African-Americans, and 3.38 for East Asians. In non-Hispanic whites, the 105 risk SNPs explained approximately 7.6% of disease heritability. The entire GWAS array explained approximately 33.4% of heritability, with a 4.3-fold enrichment within DNaseI hypersensitivity sites (P = 0.004). Significance: Taken together, our findings of independent risk variants, ethnic variation in existing SNP replication, and remaining unexplained heritability have important implications for further clarifying the genetic risk of prostate cancer. Our findings also suggest that there may be much promise in evaluating understudied variation, such as indels and ethnically diverse populations. Cancer Discov; 5(8); 878–91. ©2015 AACR. This article is highlighted in the In This Issue feature, p. 783

AbstractTwo recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.