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Work 1-8 of 8

Sorted by relevance

Article

Detection of de novo copy number deletions from targeted sequencing of trios

by Jack M Fu; Elizabeth Leslie; Alan F Scott; Jeffrey C Murray; Mary L Marazita; Terri H Beaty; Robert B Scharpf; Ingo Ruczinski

2019

Subjects
  • Biology, Genetics
  • Biology, Biostatistics
  • Health Sciences, Epidemiology
  • File Download
  • View Abstract

Abstract:Close

Motivation: De novo copy number deletions have been implicated in many diseases, but there is no formal method to date that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms. Results: We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability.

Article

Prioritizing individual genetic variants after kernel machine testing using variable selection

by Qianchuan He; Tianxi Cai; Yan Liu; Ni Zhao; Quaker E. Harmon; Lynn Almli; Elisabeth Binder; Stephanie M. Engel; Kerry Ressler; Karen Conneely; Xihong Lin; Michael C. Wu

2016

Subjects
  • Biology, Genetics
  • Health Sciences, Public Health
  • Health Sciences, Epidemiology
  • File Download
  • View Abstract

Abstract:Close

Kernel machine learning methods, such as the SNP-set kernel association test (SKAT), have been widely used to test associations between traits and genetic polymorphisms. In contrast to traditional single-SNP analysis methods, these methods are designed to examine the joint effect of a set of related SNPs (such as a group of SNPs within a gene or a pathway) and are able to identify sets of SNPs that are associated with the trait of interest. However, as with many multi-SNP testing approaches, kernel machine testing can draw conclusion only at the SNP-set level, and does not directly inform on which one(s) of the identified SNP set is actually driving the associations. A recently proposed procedure, KerNel Iterative Feature Extraction (KNIFE), provides a general framework for incorporating variable selection into kernel machine methods. In this article, we focus on quantitative traits and relatively common SNPs, and adapt the KNIFE procedure to genetic association studies and propose an approach to identify driver SNPs after the application of SKAT to gene set analysis. Our approach accommodates several kernels that are widely used in SNP analysis, such as the linear kernel and the Identity by State (IBS) kernel. The proposed approach provides practically useful utilities to prioritize SNPs, and fills the gap between SNP set analysis and biological functional studies. Both simulation studies and real data application are used to demonstrate the proposed approach.

Article

Testing cross-phenotype effects of rare variants in longitudinal studies of complex traits

by Prataydipta Rudra; K. Alaine Broadaway; Erin B. Ware; Min A. Jhun; Lawrence F. Bielak; Wei Zhao; Jennifer A. Smith; Patricia A. Peyser; Sharon L. R. Kardia; Michael Epstein; Debashis Ghosh

2018

Subjects
  • Health Sciences, Epidemiology
  • Biology, Biostatistics
  • Biology, Genetics
  • File Download
  • View Abstract

Abstract:Close

Many gene mapping studies of complex traits have identified genes or variants that influence multiple phenotypes. With the advent of next-generation sequencing technology, there has been substantial interest in identifying rare variants in genes that possess cross-phenotype effects. In the presence of such effects, modeling both the phenotypes and rare variants collectively using multivariate models can achieve higher statistical power compared to univariate methods that either model each phenotype separately or perform separate tests for each variant. Several studies collect phenotypic data over time and using such longitudinal data can further increase the power to detect genetic associations. Although rare-variant approaches exist for testing cross-phenotype effects at a single time point, there is no analogous method for performing such analyses using longitudinal outcomes. In order to fill this important gap, we propose an extension of Gene Association with Multiple Traits (GAMuT) test, a method for cross-phenotype analysis of rare variants using a framework based on the distance covariance. The approach allows for both binary and continuous phenotypes and can also adjust for covariates. Our simple adjustment to the GAMuT test allows it to handle longitudinal data and to gain power by exploiting temporal correlation. The approach is computationally efficient and applicable on a genome-wide scale due to the use of a closed-form test whose significance can be evaluated analytically. We use simulated data to demonstrate that our method has favorable power over competing approaches and also apply our approach to exome chip data from the Genetic Epidemiology Network of Arteriopathy.

Article

Powerful and robust cross-phenotype association test for case-parent trios

by S. Taylor Fischer; Yunxuan Jiang; K. Alaine Broadaway; Karen Conneely; Michael Epstein

2018

Subjects
  • Biology, Genetics
  • Biology, Biostatistics
  • File Download
  • View Abstract

Abstract:Close

There has been increasing interest in identifying genes within the human genome that influence multiple diverse phenotypes. In the presence of pleiotropy, joint testing of these phenotypes is not only biologically meaningful but also statistically more powerful than univariate analysis of each separate phenotype accounting for multiple testing. Although many cross-phenotype association tests exist, the majority of such methods assume samples composed of unrelated subjects and therefore are not applicable to family-based designs, including the valuable case-parent trio design. In this paper, we describe a robust gene-based association test of multiple phenotypes collected in a case-parent trio study. Our method is based on the kernel distance covariance (KDC) method, where we first construct a similarity matrix for multiple phenotypes and a similarity matrix for genetic variants in a gene; we then test the dependency between the two similarity matrices. The method is applicable to either common variants or rare variants in a gene, and resulting tests from the method are by design robust to confounding due to population stratification. We evaluated our method through simulation studies and observed that the method is substantially more powerful than standard univariate testing of each separate phenotype. We also applied our method to phenotypic and genotypic data collected in case-parent trios as part of the Genetics of Kidneys in Diabetes (GoKinD) study and identified a genome-wide significant gene demonstrating cross-phenotype effects that was not identified using standard univariate approaches.

Article

SNP Set Association Analysis for Familial Data

by Elizabeth D. Schifano; Michael Epstein; Lawrence F. Bielak; Min A. Jhun; Sharon L. R. Kardia; Patricia A. Peyser; Xihong Lin

2012

Subjects
  • Biology, Biostatistics
  • Biology, Genetics
  • Health Sciences, Epidemiology
  • File Download
  • View Abstract

Abstract:Close

Genome-wide association studies (GWAS) are a popular approach for identifying common genetic variants and epistatic effects associated with a disease phenotype. The traditional statistical analysis of such GWAS attempts to assess the association between each individual single-nucleotide polymorphism (SNP) and the observed phenotype. Recently, kernel machine-based tests for association between a SNP set (e.g., SNPs in a gene) and the disease phenotype have been proposed as a useful alternative to the traditional individual-SNP approach, and allow for flexible modeling of the potentially complicated joint SNP effects in a SNP set while adjusting for covariates. We extend the kernel machine framework to accommodate related subjects from multiple independent families, and provide a score-based variance component test for assessing the association of a given SNP set with a continuous phenotype, while adjusting for additional covariates and accounting for within-family correlation. We illustrate the proposed method using simulation studies and an application to genetic data from the Genetic Epidemiology Network of Arteriopathy (GENOA) study.

Article

Accounting for Population Stratification in DNA Methylation Studies

by Richard T. Barfield; Lynn Almli; Varun Kilaru; Alicia Smith; KristinaB. Mercer; Richard Duncan; Torsten Klengel; Divya Mehta; Elisabeth B. Binder; Karen N Conneely; Michael Epstein; Kerry Ressler

2014

Subjects
  • Biology, Genetics
  • Biology, Biostatistics
  • Psychology, Behavioral
  • File Download
  • View Abstract

Abstract:Close

DNA methylation is an important epigenetic mechanism that has been linked to complex diseases and is of great interest to researchers as a potential link between genome, environment, and disease. As the scale of DNA methylation association studies approaches that of genome-wide association studies, issues such as population stratification will need to be addressed. It is well-documented that failure to adjust for population stratification can lead to false positives in genetic association studies, but population stratification is often unaccounted for in DNA methylation studies. Here, we propose several approaches to correct for population stratification using principal components (PCs) from different subsets of genome-wide methylation data. We first illustrate the potential for confounding due to population stratification by demonstrating widespread associations between DNA methylation and race in 388 individuals (365 African American and 23 Caucasian). We subsequently evaluate the performance of our PC-based approaches and other methods in adjusting for confounding due to population stratification. Our simulations show that (1) all of the methods considered are effective at removing inflation due to population stratification, and (2) maximum power can be obtained with single-nucleotide polymorphism (SNP)-based PCs, followed by methylation-based PCs, which outperform both surrogate variable analysis and genomic control. Among our different approaches to computing methylation-based PCs, we find that PCs based on CpG sites chosen for their potential to proxy nearby SNPs can provide a powerful and computationally efficient approach to adjust for population stratification in DNA methylation studies when genome-wide SNP data are unavailable.

Article

Identification of 16q21 as a modifier of nonsyndromic orofacial cleft phenotypes

by Jenna C. Carlson; Jennifer Standley; Aline Petrin; John R Shaffer; Azeez Butali; Carmen J. Buxo; Eduardo Castilla; Kaare Christensen; Frederic W-D Deleyiannis; Jacqueline T. Hecht; L. Leigh Field; Ariuntuul Garidkhuu; Lina M. Moreno Uribe; Natsume Nagato; Ieda M. Orioli; Carmencita Padilla; Fernando Poletta; Satoshi Suzuki; Alexandre R. Vieira; George L. Wehby; Seth M. Weinberg; Terri H. Beaty; Eleanor Feingold; Jeffrey C. Murray; Mary L. Marazita; Elizabeth Leslie

2017

Subjects
  • Health Sciences, Medicine and Surgery
  • Health Sciences, Epidemiology
  • File Download
  • View Abstract

Abstract:Close

Orofacial clefts (OFCs) are common, complex birth defects with extremely heterogeneous phenotypic presentations. Two common subtypes—cleft lip alone (CL) and CL plus cleft palate (CLP)—are typically grouped into a single phenotype for genetic analysis (i.e., CL with or without cleft palate, CL/P). However, mounting evidence suggests there may be unique underlying pathophysiology and/or genetic modifiers influencing expression of these two phenotypes. To this end, we performed a genome-wide scan for genetic modifiers by directly comparing 450 CL cases with 1,692 CLP cases from 18 recruitment sites across 13 countries from North America, Central or South America, Asia, Europe, and Africa. We identified a region on 16q21 that is strongly associated with different cleft type (P = 5.611 × 10−8). We also identified significant evidence of gene–gene interactions between this modifier locus and two recognized CL/P risk loci: 8q21 and 9q22 (FOXE1) (P = 0.012 and 0.023, respectively). Single nucleotide polymorphism (SNPs) in the 16q21 modifier locus demonstrated significant association with CL over CLP. The marker alleles on 16q21 that increased risk for CL were found at highest frequencies among individuals with a family history of CL (P = 0.003). Our results demonstrate the existence of modifiers for which type of OFC develops and suggest plausible elements responsible for phenotypic heterogeneity, further elucidating the complex genetic architecture of OFCs.

Article

Sparse Principal Component Analysis for Identifying Ancestry-Informative Markers in Genome Wide Association Studies

by Seokho Lee; Michael Epstein; Richard Duncan; Xihong Lin

2012

Subjects
  • Biology, Genetics
  • Biology, Biostatistics
  • Statistics
  • File Download
  • View Abstract

Abstract:Close

Genome-Wide association studies (GWAS) routinely apply principal component analysis (PCA) to infer population structure within a sample to correct for confounding due to ancestry. GWAS implementation of PCA uses tens of thousands of single-nucleotide polymorphisms (SNPs) to infer structure, despite the fact that only a small fraction of such SNPs provides useful information on ancestry. The identification of this reduced set of Ancestry-Informative markers (AIMs) from a GWAS has practical value; for example, researchers can genotype the AIM set to correct for potential confounding due to ancestry in follow-up studies that utilize custom SNP or sequencing technology. We propose a novel technique to identify AIMs from Genome-Wide SNP data using sparse PCA. The procedure uses penalized regression methods to identify those SNPs in a Genome-Wide panel that significantly contribute to the principal components while encouraging SNPs that provide negligible loadings to vanish from the analysis. We found that sparse PCA leads to negligible loss of ancestry information compared to traditional PCA analysis of Genome-Wide SNP data. We further demonstrate the value of sparse PCA for AIM selection using real data from the International HapMap Project and a Genome-Wide study of inflammatory bowel disease. We have implemented our approach in open-source R software for public use.
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