Nicholas B. Larson

Clinical Characteristics of Ovarian Cancer Classified by BRCA1, BRCA2, and RAD51C Status

We evaluated homologous recombination deficient (HRD) phenotypes in epithelial ovarian cancer (EOC) considering BRCA1, BRCA2, and RAD51C in a large well-annotated patient set. We evaluated EOC patients for germline deleterious mutations (n = 899), somatic mutations (n = 279) and epigenetic alterations (n = 482) in these genes using NGS and genome-wide methylation arrays. Deleterious germline mutations were identified in 32 (3.6%) patients for BRCA1, in 28 (3.1%) for BRCA2 and in 26 (2.9%) for RAD51C. Ten somatically sequenced patients had deleterious alterations, six (2.1%) in BRCA1 and four (1.4%) in BRCA2. Fifty two patients (10.8%) had methylated BRCA1 or RAD51C. HRD patients with germline or somatic alterations in any gene were more likely to be high grade serous, have an earlier diagnosis age and have ovarian and/or breast cancer family history. The HRD phenotype was most common in high grade serous EOC. Identification of EOC patients with an HRD phenotype may help tailor specific therapies.

PurBayes: estimating tumor cellularity and subclonality in next-generation sequencing data

SUMMARY We have developed a novel Bayesian method, PurBayes, to estimate tumor purity and detect intratumor heterogeneity based on next-generation sequencing data of paired tumor-normal tissue samples, which uses finite mixture modeling methods. We demonstrate our approach using simulated data and discuss its performance under varying conditions. AVAILABILITY PurBayes is implemented as an R package, and source code is available for download through CRAN at http://cran.r-project.org/package=PurBayes. CONTACT larson.nicholas@mayo.edu SUPPLEMENTARY INFORMATION Supplementary data are available online at Bioinformatics online.

Kernel canonical correlation analysis for assessing gene–gene interactions and application to ovarian cancer

Although single-locus approaches have been widely applied to identify disease-associated single-nucleotide polymorphisms (SNPs), complex diseases are thought to be the product of multiple interactions between loci. This has led to the recent development of statistical methods for detecting statistical interactions between two loci. Canonical correlation analysis (CCA) has previously been proposed to detect gene–gene coassociation. However, this approach is limited to detecting linear relations and can only be applied when the number of observations exceeds the number of SNPs in a gene. This limitation is particularly important for next-generation sequencing, which could yield a large number of novel variants on a limited number of subjects. To overcome these limitations, we propose an approach to detect gene–gene interactions on the basis of a kernelized version of CCA (KCCA). Our simulation studies showed that KCCA controls the Type-I error, and is more powerful than leading gene-based approaches under a disease model with negligible marginal effects. To demonstrate the utility of our approach, we also applied KCCA to assess interactions between 200 genes in the NF-κB pathway in relation to ovarian cancer risk in 3869 cases and 3276 controls. We identified 13 significant gene pairs relevant to ovarian cancer risk (local false discovery rate <0.05). Finally, we discuss the advantages of KCCA in gene–gene interaction analysis and its future role in genetic association studies.

P-selectin and subclinical and clinical atherosclerosis: The Multi-Ethnic Study of Atherosclerosis (MESA)

OBJECTIVE P-selectin is a cellular adhesion molecule that has been shown to be crucial in development of coronary heart disease (CHD). We sought to determine the role of P-selectin on the risk of atherosclerosis in a large multi-ethnic population. METHODS Data from the Multi-Ethnic Study of Atherosclerosis (MESA), including 1628 African, 702 Chinese, 2393 non-Hispanic white, and 1302 Hispanic Americans, were used to investigate the association of plasma P-selectin with CHD risk factors, coronary artery calcium (CAC), intima-media thickness, and CHD. Regression models were used to investigate the association between P-selectin and risk factors, Tobit model for CAC, and Cox regression for CHD events. RESULTS Mean levels of P-selectin differed by ethnicity and were higher in men (P<0.001). For all ethnic groups, P-selectin was positively associated with measures of adiposity, blood pressure, current smoking, LDL, and triglycerides and inversely with HDL. A significant ethnic interaction was observed for the association of P-selectin and prevalent diabetes; however, P-selectin was positively associated with HbA1c in all groups. Higher P-selectin levels were associated with greater prevalence of CAC. Over 10.1 years of follow-up, there were 335 incident CHD events. There was a positive linear association between P-selectin levels and rate of incident CHD after adjustment for traditional risk factors. However, association was only significant in non-Hispanic white Americans (HR: 1.81, 95% CI 1.07 to 3.07, P=0.027). CONCLUSION We observed ethnic heterogeneity in the association of P-selectin and risk of CHD.

Comprehensively Evaluating cis-Regulatory Variation in the Human Prostate Transcriptome by Using Gene-Level Allele-Specific Expression

The identification of cis-acting regulatory variation in primary tissues has the potential to elucidate the genetic basis of complex traits and further our understanding of transcriptomic diversity across cell types. Expression quantitative trait locus (eQTL) association analysis using RNA sequencing (RNA-seq) data can improve upon the detection of cis-acting regulatory variation by leveraging allele-specific expression (ASE) patterns in association analysis. Here, we present a comprehensive evaluation of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genotypes from 471 samples of normal primary prostate tissue. Using statistical models that integrate ASE information, we identified extensive cis-eQTLs across the prostate transcriptome and found that approximately 70% of expressed genes corresponded to a significant eQTL at a gene-level false-discovery rate of 0.05. Overall, cis-eQTLs were heavily concentrated near the transcription start and stop sites of affected genes, and effects were negatively correlated with distance. We identified multiple instances of cis-acting co-regulation by using phased genotype data and discovered 233 SNPs as the most strongly associated eQTLs for more than one gene. We also noted significant enrichment (25/50, p = 2E-5) of previously reported prostate cancer risk SNPs in prostate eQTLs. Our results illustrate the benefit of assessing ASE data in cis-eQTL analyses by showing better reproducibility of prior eQTL findings than of eQTL mapping based on total expression alone. Altogether, our analysis provides extensive functional context of thousands of SNPs in prostate tissue, and these results will be of critical value in guiding studies examining disease of the human prostate.

A Kernel Regression Approach to Gene-Gene Interaction Detection for Case-Control Studies

Gene‐gene interactions are increasingly being addressed as a potentially important contributor to the variability of complex traits. Consequently, attentions have moved beyond single locus analysis of association to more complex genetic models. Although several single‐marker approaches toward interaction analysis have been developed, such methods suffer from very high testing dimensionality and do not take advantage of existing information, notably the definition of genes as functional units. Here, we propose a comprehensive family of gene‐level score tests for identifying genetic elements of disease risk, in particular pairwise gene‐gene interactions. Using kernel machine methods, we devise score‐based variance component tests under a generalized linear mixed model framework. We conducted simulations based upon coalescent genetic models to evaluate the performance of our approach under a variety of disease models. These simulations indicate that our methods are generally higher powered than alternative gene‐level approaches and at worst competitive with exhaustive SNP‐level (where SNP is single‐nucleotide polymorphism) analyses. Furthermore, we observe that simulated epistatic effects resulted in significant marginal testing results for the involved genes regardless of whether or not true main effects were present. We detail the benefits of our methods and discuss potential genome‐wide analysis strategies for gene‐gene interaction analysis in a case‐control study design.

Identification of candidate genes for prostate cancer-risk SNPs utilizing a normal prostate tissue eQTL data set.

Multiple studies have identified loci associated with the risk of developing prostate cancer but the associated genes are not well studied. Here we create a normal prostate tissue-specific eQTL data set and apply this data set to previously identified prostate cancer (PrCa)-risk SNPs in an effort to identify candidate target genes. The eQTL data set is constructed by the genotyping and RNA sequencing of 471 samples. We focus on 146 PrCa-risk SNPs, including all SNPs in linkage disequilibrium with each risk SNP, resulting in 100 unique risk intervals. We analyse cis-acting associations where the transcript is located within 2 Mb (±1 Mb) of the risk SNP interval. Of all SNP–gene combinations tested, 41.7% of SNPs demonstrate a significant eQTL signal after adjustment for sample histology and 14 expression principal component covariates. Of the 100 PrCa-risk intervals, 51 have a significant eQTL signal and these are associated with 88 genes. This study provides a rich resource to study biological mechanisms underlying genetic risk to PrCa.

Circulating level of hepatocyte growth factor predicts incidence of type 2 diabetes mellitus: The Multi-Ethnic Study of Atherosclerosis (MESA)

BACKGROUND Hepatocyte growth factor (HGF) is a pleotropic factor posited to have metabolic homeostatic properties. The purpose of this study is to examine whether level of HGF is associated with the development of type 2 diabetes. METHODS Data from the Multi-Ethnic Study of Atherosclerosis (MESA) were used to examine the prospective association between serum level of HGF and incident diabetes. Fasting HGF was measured at Exam 1 (2000-2002) in 5395 participants free from diabetes (61.5±10.2 years old) and incidence of diabetes was determined at four subsequent follow-up exams over 12 years. Hazard ratios (HR) for incident diabetes were estimated according to 1 standard deviation (SD) unit increment of HGF (1 SD=26 μg/l), before and after adjustment for age, sex, race/ethnicity, education, study center, smoking status, alcohol consumption, body mass index, waist circumference, fasting glucose and insulin, C-reactive protein, and interleukin-6 levels. RESULTS A 1 SD increment of baseline HGF was associated with a 46% (95% CI=1.37, 1.56) increased risk of diabetes before adjustment. After adjustment, diabetes risk per 1 SD increment of HGF was attenuated but remained significantly increased (HR=1.21; 95% CI=1.12, 1.32). Men had a significantly greater HR compared to women per equivalent increase of HGF (p-value for sex interaction=0.04). There was no evidence of effect modification by race/ethnicity. CONCLUSIONS This study advances understanding from cross-sectional studies and investigation of incident insulin resistance, demonstrating higher level of HGF is associated with incident diabetes and may reflect a unique type of impaired metabolism.

Circulating cellular adhesion molecules and risk of diabetes: the Multi-Ethnic Study of Atherosclerosis (MESA)

To test the hypothesis that soluble cellular adhesion molecules would be positively and independently associated with risk of diabetes.

Regularized rare variant enrichment analysis for case-control exome sequencing data.

Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next‐generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base‐level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here, we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene‐level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene‐based least absolute shrinkage and selection operator (LASSO) and exon‐based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene‐centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research.