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Genetic Epidemiology

Anthony Francis Herzig, Teresa Nutile, Marie-Claude Babron, Marina Ciullo, Céline Bellenguez, Anne-Louise Leutenegger
In the search for genetic associations with complex traits, population isolates offer the advantage of reduced genetic and environmental heterogeneity. In addition, cost-efficient next-generation association approaches have been proposed in these populations where only a subsample of representative individuals is sequenced and then genotypes are imputed into the rest of the population. Gene mapping in such populations thus requires high-quality genetic imputation and preliminary phasing. To identify an effective study design, we compare by simulation a range of phasing and imputation software and strategies...
January 10, 2018: Genetic Epidemiology
Arnab Maity, Jing Zhao, Patrick F Sullivan, Jung-Ying Tzeng
We consider the problem of assessing the joint effect of a set of genetic markers on multiple, possibly correlated phenotypes of interest. We develop a kernel machine based multivariate regression framework, where the joint effect of the marker set on each of the phenotypes is modeled using prespecified kernel functions with unknown variance components. Unlike most existing methods that mainly focus on the global association between the marker set and the phenotype set, we develop estimation and testing procedures to study phenotype-specific associations...
January 3, 2018: Genetic Epidemiology
Eden R Martin, Ilker Tunc, Zhi Liu, Susan H Slifer, Ashley H Beecham, Gary W Beecham
Population substructure can lead to confounding in tests for genetic association, and failure to adjust properly can result in spurious findings. Here we address this issue of confounding by considering the impact of global ancestry (average ancestry across the genome) and local ancestry (ancestry at a specific chromosomal location) on regression parameters and relative power in ancestry-adjusted and -unadjusted models. We examine theoretical expectations under different scenarios for population substructure; applying different regression models, verifying and generalizing using simulations, and exploring the findings in real-world admixed populations...
December 30, 2017: Genetic Epidemiology
Ni Zhao, Xiang Zhan, Yen-Tsung Huang, Lynn M Almli, Alicia Smith, Michael P Epstein, Karen Conneely, Michael C Wu
Many large GWAS consortia are expanding to simultaneously examine the joint role of DNA methylation in addition to genotype in the same subjects. However, integrating information from both data types is challenging. In this paper, we propose a composite kernel machine regression model to test the joint epigenetic and genetic effect. Our approach works at the gene level, which allows for a common unit of analysis across different data types. The model compares the pairwise similarities in the phenotype to the pairwise similarities in the genotype and methylation values; and high correspondence is suggestive of association...
December 29, 2017: Genetic Epidemiology
Yiwen Luo, Arnab Maity, Michael C Wu, Chris Smith, Qing Duan, Yun Li, Jung-Ying Tzeng
Recent studies showed that population substructure (PS) can have more complex impact on rare variant tests and that similarity-based collapsing tests (e.g., SKAT) may suffer more severely by PS than burden-based tests. In this work, we evaluate the performance of SKAT coupling with principal components (PC) or variance components (VC) based PS correction methods. We consider confounding effects caused by PS including stratified populations, admixed populations, and spatially distributed nongenetic risk; we investigate which types of variants (e...
December 26, 2017: Genetic Epidemiology
Awen He, Wenyu Wang, N Tejo Prakash, Alexey A Tinkov, Anatoly V Skaln, Yan Wen, Jingcan Hao, Xiong Guo, Feng Zhang
Chemical elements are closely related to human health. Extensive genomic profile data of complex diseases offer us a good opportunity to systemically investigate the relationships between elements and complex diseases/traits. In this study, we applied gene set enrichment analysis (GSEA) approach to detect the associations between elements and complex diseases/traits though integrating element-gene interaction datasets and genome-wide association study (GWAS) data of complex diseases/traits. To illustrate the performance of GSEA, the element-gene interaction datasets of 24 elements were extracted from the comparative toxicogenomics database (CTD)...
December 18, 2017: Genetic Epidemiology
Jan Mielniczuk, Paweł Teisseyre
Detection of gene-gene interactions is one of the most important challenges in genome-wide case-control studies. Besides traditional logistic regression analysis, recently the entropy-based methods attracted a significant attention. Among entropy-based methods, interaction information is one of the most promising measures having many desirable properties. Although both logistic regression and interaction information have been used in several genome-wide association studies, the relationship between them has not been thoroughly investigated theoretically...
December 18, 2017: Genetic Epidemiology
Stefan Konigorski, Yuan Wang, Candemir Cigsar, Yildiz E Yilmaz
In genetic association studies, it is important to distinguish direct and indirect genetic effects in order to build truly functional models. For this purpose, we consider a directed acyclic graph setting with genetic variants, primary and intermediate phenotypes, and confounding factors. In order to make valid statistical inference on direct genetic effects on the primary phenotype, it is necessary to consider all potential effects in the graph, and we propose to use the estimating equations method with robust Huber-White sandwich standard errors...
December 18, 2017: Genetic Epidemiology
Osvaldo Espin-Garcia, Radu V Craiu, Shelley B Bull
We evaluate two-phase designs to follow-up findings from genome-wide association study (GWAS) when the cost of regional sequencing in the entire cohort is prohibitive. We develop novel expectation-maximization-based inference under a semiparametric maximum likelihood formulation tailored for post-GWAS inference. A GWAS-SNP (where SNP is single nucleotide polymorphism) serves as a surrogate covariate in inferring association between a sequence variant and a normally distributed quantitative trait (QT). We assess test validity and quantify efficiency and power of joint QT-SNP-dependent sampling and analysis under alternative sample allocations by simulations...
December 14, 2017: Genetic Epidemiology
Debashree Ray, Michael Boehnke
Genome-wide association studies (GWAS) for complex diseases have focused primarily on single-trait analyses for disease status and disease-related quantitative traits. For example, GWAS on risk factors for coronary artery disease analyze genetic associations of plasma lipids such as total cholesterol, LDL-cholesterol, HDL-cholesterol, and triglycerides (TGs) separately. However, traits are often correlated and a joint analysis may yield increased statistical power for association over multiple univariate analyses...
December 10, 2017: Genetic Epidemiology
Qing Duan, Zheng Xu, Laura M Raffield, Suhua Chang, Di Wu, Ethan M Lange, Alex P Reiner, Yun Li
Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping...
December 10, 2017: Genetic Epidemiology
Haley A Moss, Goli Samimi, Laura J Havrilesky, Mark E Sherman, Evan R Myers
U.S. guidelines recommend BRCA1/2 mutation testing for women diagnosed with high-grade ovarian cancer (HGOC) to increase recognition of carriers, but most remain unidentified and at risk. Accordingly, an approach termed "Traceback" has been proposed in which probands are retrospectively identified by testing archived pathology specimens, and family members are traced to provide genetic counseling and testing. We used population-based data to estimate the number of family members who might be contacted through such a program...
November 30, 2017: Genetic Epidemiology
Wei Xu, Meiling Hao
The expression of X-chromosome undergoes three possible biological processes: X-chromosome inactivation (XCI), escape of the X-chromosome inactivation (XCI-E), and skewed X-chromosome inactivation (XCI-S). Although these expressions are included in various predesigned genetic variation chip platforms, the X-chromosome has generally been excluded from the majority of genome-wide association studies analyses; this is most likely due to the lack of a standardized method in handling X-chromosomal genotype data...
November 26, 2017: Genetic Epidemiology
Frank Dudbridge, Nora Pashayan, Jian Yang
The substantial heritability of most complex diseases suggests that genetic data could provide useful risk prediction. To date the performance of genetic risk scores has fallen short of the potential implied by heritability, but this can be explained by insufficient sample sizes for estimating highly polygenic models. When risk predictors already exist based on environment or lifestyle, two key questions are to what extent can they be improved by adding genetic information, and what is the ultimate potential of combined genetic and environmental risk scores? Here, we extend previous work on the predictive accuracy of polygenic scores to allow for an environmental score that may be correlated with the polygenic score, for example when the environmental factors mediate the genetic risk...
November 26, 2017: Genetic Epidemiology
Minsun Song, William Wheeler, Neil E Caporaso, Maria Teresa Landi, Nilanjan Chatterjee
Genome-wide association studies (GWAS) are now routinely imputed for untyped single nucleotide polymorphisms (SNPs) based on various powerful statistical algorithms for imputation trained on reference datasets. The use of predicted allele counts for imputed SNPs as the dosage variable is known to produce valid score test for genetic association. In this paper, we investigate how to best handle imputed SNPs in various modern complex tests for genetic associations incorporating gene-environment interactions. We focus on case-control association studies where inference for an underlying logistic regression model can be performed using alternative methods that rely on varying degree on an assumption of gene-environment independence in the underlying population...
November 26, 2017: Genetic Epidemiology
Zhongxue Chen, Yan Lu, Tong Lin, Qingzhong Liu, Kai Wang
It is well known that using proper weights for genetic variants is crucial in enhancing the power of gene- or pathway-based association tests. To increase the power, we propose a general approach that adaptively selects weights among a class of weight families and apply it to the popular sequencing kernel association test. Through comprehensive simulation studies, we demonstrate that the proposed method can substantially increase power under some conditions. Applications to real data are also presented. This general approach can be extended to all current set-based rare variant association tests whose performances depend on variant's weight assignment...
November 26, 2017: Genetic Epidemiology
Desmond D Campbell, Yiming Li, Pak C Sham
Construction of multifactorial disease models from epidemiological findings and their application to disease pedigrees for risk prediction is nontrivial for all but the simplest of cases. Multifactorial Disease Risk Calculator is a web tool facilitating this. It provides a user-friendly interface, extending a reported methodology based on a liability-threshold model. Multifactorial disease models incorporating all the following features in combination are handled: quantitative risk factors (including polygenic scores), categorical risk factors (including major genetic risk loci), stratified age of onset curves, and the partition of the population variance in disease liability into genetic, shared, and unique environment effects...
November 26, 2017: Genetic Epidemiology
Julian Hecker, Xin Xu, F William Townes, Heide Loehlein Fier, Chris Corcoran, Nan Laird, Christoph Lange
For family-based association studies, Horvath et al. proposed an algorithm for the association analysis between haplotypes and arbitrary phenotypes when the phase of the haplotypes is unknown, that is, genotype data is given. Their approach to haplotype analysis maintains the original features of the TDT/FBAT-approach, that is, complete robustness against genetic confounding and misspecification of the phenotype. The algorithm has been implemented in the FBAT and PBAT software package and has been used in numerous substantive manuscripts...
November 21, 2017: Genetic Epidemiology
Jenna C Carlson, Jennifer Standley, Aline Petrin, John R Shaffer, Azeez Butali, Carmen J Buxó, 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 J Leslie
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...
November 10, 2017: Genetic Epidemiology
Nicholas B Larson, Zachary C Fogarty, Melissa C Larson, Kimberly R Kalli, Kate Lawrenson, Simon Gayther, Brooke L Fridley, Ellen L Goode, Stacey J Winham
X-chromosome inactivation (XCI) epigenetically silences transcription of an X chromosome in females; patterns of XCI are thought to be aberrant in women's cancers, but are understudied due to statistical challenges. We develop a two-stage statistical framework to assess skewed XCI and evaluate gene-level patterns of XCI for an individual sample by integration of RNA sequence, copy number alteration, and genotype data. Our method relies on allele-specific expression (ASE) to directly measure XCI and does not rely on male samples or paired normal tissue for comparison...
November 8, 2017: Genetic Epidemiology
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