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Genetic Epidemiology

Ting-Ting Hou, Feng Lin, Shasha Bai, Mario A Cleves, Hai-Ming Xu, Xiang-Yang Lou
The manifestation of complex traits is influenced by gene-gene and gene-environment interactions, and the identification of multifactor interactions is an important but challenging undertaking for genetic studies. Many complex phenotypes such as disease severity are measured on an ordinal scale with more than two categories. A proportional odds model can improve statistical power for these outcomes, when compared to a logit model either collapsing the categories into two mutually exclusive groups or limiting the analysis to pairs of categories...
November 2, 2018: Genetic Epidemiology
Matthew O Goodman, Lori Chibnik, Tianxi Cai
Commonly in biomedical research, studies collect data in which an outcome measure contains informative excess zeros; for example, when observing the burden of neuritic plaques (NPs) in brain pathology studies, those who show none contribute to our understanding of neurodegenerative disease. The outcome may be characterized by a mixture distribution with one component being the "structural zero" and the other component being a Poisson distribution. We propose a novel variance components score test of genetic association between a set of genetic markers and a zero-inflated count outcome from a mixture distribution...
October 24, 2018: Genetic Epidemiology
Jeremy A Sabourin, Cheryl D Cropp, Heejong Sung, Lawrence C Brody, Joan E Bailey-Wilson, Alexander F Wilson
Results from association studies are traditionally corroborated by replicating the findings in an independent data set. Although replication studies may be comparable for the main trait or phenotype of interest, it is unlikely that secondary phenotypes will be comparable across studies, making replication problematic. Alternatively, there may simply not be a replication sample available because of the nature or frequency of the phenotype. In these situations, an approach based on complementary pairs stability selection for genome-wide association study (ComPaSS-GWAS), is proposed as an ad-hoc alternative to replication...
October 18, 2018: Genetic Epidemiology
Kelsey E Grinde, Qibin Qi, Timothy A Thornton, Simin Liu, Aladdin H Shadyab, Kei Hang K Chan, Alexander P Reiner, Tamar Sofer
Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns...
October 15, 2018: Genetic Epidemiology
Svetlana Cherlin, Darren Plant, John C Taylor, Marco Colombo, Athina Spiliopoulou, Evan Tzanis, Ann W Morgan, Michael R Barnes, Paul McKeigue, Jennifer H Barrett, Costantino Pitzalis, Anne Barton, Matura Consortium, Heather J Cordell
Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium...
October 12, 2018: Genetic Epidemiology
Sebastian J Teran Hidalgo, Tingyu Zhu, Mengyun Wu, Shuangge Ma
Clustering has been widely conducted in the analysis of gene expression data. For complex diseases, it has played an important role in identifying unknown functions of genes, serving as the basis of other analysis, and others. A common limitation of most existing clustering approaches is to assume that genes are separated into disjoint clusters. As genes often have multiple functions and thus can belong to more than one functional cluster, the disjoint clustering results can be unsatisfactory. In addition, due to the small sample sizes of genetic profiling studies and other factors, there may not be sufficient evidence to confirm the specific functions of some genes and cluster them definitively into disjoint clusters...
October 9, 2018: Genetic Epidemiology
Iryna Lobach
Genetic studies are continuing to generate volumes and variety of data that can be used to examine the genetic effects. Often the effect of a genetic variant varies by nongenetic measures, what is traditionally defined as gene-environment interaction (G×E). If the G×E term is neglected, estimates of the main effects can be substantially biased. We derive a general and convenient approximation to the magnitude of bias in the estimates due to omitting the G×E term. We show that the approximation is reasonably accurate in finite samples...
October 9, 2018: Genetic Epidemiology
Robert C Elston
This is the 100th year anniversary of Fisher's 1918 paper "The correlation between relatives on the supposition of Mendelian inheritance" (Transactions of the Royal Society of Edinburgh 1918, 52 pp 899-438). Fisher's work has had a strong influence on today's genetic epidemiology and this brief autobiographical note highlights a few of the ways his influence on me has affected the field. Although I once took a course of lectures from Fisher, it was mainly his writings that influenced my statistical thinking...
October 8, 2018: Genetic Epidemiology
Diptavo Dutta, Laura Scott, Michael Boehnke, Seunggeun Lee
In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT)...
October 8, 2018: Genetic Epidemiology
Ian B Stanaway, Taryn O Hall, Elisabeth A Rosenthal, Melody Palmer, Vivek Naranbhai, Rachel Knevel, Bahram Namjou-Khales, Robert J Carroll, Krzysztof Kiryluk, Adam S Gordon, Jodell Linder, Kayla Marie Howell, Brandy M Mapes, Frederick T J Lin, Yoonjung Yoonie Joo, M Geoffrey Hayes, Ali G Gharavi, Sarah A Pendergrass, Marylyn D Ritchie, Mariza de Andrade, Damien C Croteau-Chonka, Soumya Raychaudhuri, Scott T Weiss, Matt Lebo, Sami S Amr, David Carrell, Eric B Larson, Christopher G Chute, Laura Jarmila Rasmussen-Torvik, Megan J Roy-Puckelwartz, Patrick Sleiman, Hakon Hakonarson, Rongling Li, Elizabeth W Karlson, Josh F Peterson, Iftikhar J Kullo, Rex Chisholm, Joshua Charles Denny, Gail P Jarvik, David R Crosslin
The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome-wide variant genotype data...
October 8, 2018: Genetic Epidemiology
James J Lee, Matt McGue, William G Iacono, Carson C Chow
To infer that a single-nucleotide polymorphism (SNP) either affects a phenotype or is linkage disequilibrium with a causal site, we must have some assurance that any SNP-phenotype correlation is not the result of confounding with environmental variables that also affect the trait. In this study, we study the properties of linkage disequilibrium (LD) Score regression, a recently developed method for using summary statistics from genome-wide association studies to ensure that confounding does not inflate the number of false positives...
September 24, 2018: Genetic Epidemiology
Alexandre Bureau, Ferdouse Begum, Margaret A Taub, Jacqueline B Hetmanski, Margaret M Parker, Hasan Albacha-Hejazi, Alan F Scott, Jeffrey C Murray, Mary L Marazita, Joan E Bailey-Wilson, Terri H Beaty, Ingo Ruczinski
We previously demonstrated how sharing of rare variants (RVs) in distant affected relatives can be used to identify variants causing a complex and heterogeneous disease. This approach tested whether single RVs were shared by all sequenced affected family members. However, as with other study designs, joint analysis of several RVs (e.g., within genes) is sometimes required to obtain sufficient statistical power. Further, phenocopies can lead to false negatives for some causal RVs if complete sharing among affected is required...
September 24, 2018: Genetic Epidemiology
Wei Xu, Meiling Hao
No abstract text is available yet for this article.
September 24, 2018: Genetic Epidemiology
Tao Wang, Xiaonan Xue, Xianhong Xie, Kenny Ye, Xiaofeng Zhu, Robert C Elston
Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power...
September 20, 2018: Genetic Epidemiology
Zeynep Baskurt, Lisa J Strug
The likelihood function represents statistical evidence given data and a model. The evidential paradigm (EP), an alternative to Bayesian and Frequentist paradigms, provides considerable theory demonstrating evidence strength for different parameter values via the ratio of likelihoods at different parameter values; thus, enabling inference directly from the likelihood function. The likelihood function, however, can be difficult to compute; for example, in genetic association studies with a binary outcome in large pedigrees...
September 17, 2018: Genetic Epidemiology
Xiang Zhan, Lingzhou Xue, Haotian Zheng, Anna Plantinga, Michael C Wu, Daniel J Schaid, Ni Zhao, Jun Chen
Recent research has highlighted the importance of the human microbiome in many human disease and health conditions. Most current microbiome association analyses focus on unrelated samples; such methods are not appropriate for analysis of data collected from more advanced study designs such as longitudinal and pedigree studies, where outcomes can be correlated. Ignoring such correlations can sometimes lead to suboptimal results or even possibly biased conclusions. Thus, new methods to handle correlated outcome data in microbiome association studies are needed...
September 15, 2018: Genetic Epidemiology
(no author information available yet)
No abstract text is available yet for this article.
October 2018: Genetic Epidemiology
Jenna C Carlson, Nichole L Nidey, Azeez Butali, Carmen J Buxo, Kaare Christensen, Frederic W-D Deleyiannis, Jacqueline T Hecht, L Leigh Field, Lina M Moreno-Uribe, Ieda M Orioli, Fernando A Poletta, Carmencita Padilla, Alexandre R Vieira, Seth M Weinberg, George L Wehby, Eleanor Feingold, Jeffrey C Murray, Mary L Marazita, Elizabeth J Leslie
Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is the most common craniofacial birth defect in humans and is notable for its apparent sexual dimorphism where approximately twice as many males are affected as females. The sources of this disparity are largely unknown, but interactions between genetic and sex effects are likely contributors. We examined gene-by-sex (G × S) interactions in a worldwide sample of 2,142 NSCL/P cases and 1,700 controls recruited from 13 countries. First, we performed genome-wide joint tests of the genetic (G) and G × S effects genome-wide using logistic regression assuming an additive genetic model and adjusting for 18 principal components of ancestry...
October 2018: Genetic Epidemiology
Brandon J Coombes, Saonli Basu, Matt McGue
Interaction between genes and environments (G×E) can be well investigated in families due to the shared genes and environment among family members. However, the majority of the current tests of G×E interaction between a set of variants and an environment are only suitable for studies with unrelated subjects. In this paper, we extend several G×E interaction tests to a linear mixed model framework to study interaction between a set of correlated environments and a candidate gene in families. The correlated environments can either be modeled separately or jointly in one model...
October 2018: Genetic Epidemiology
Li-Chu Chien, Yen-Feng Chiu
Here, we describe a retrospective mega-analysis framework for gene- or region-based multimarker rare variant association tests. Our proposed mega-analysis association tests allow investigators to combine longitudinal and cross-sectional family- and/or population-based studies. This framework can be applied to a continuous, categorical, or survival trait. In addition to autosomal variants, the tests can be applied to conduct mega-analyses on X-chromosome variants. Tests were built on study-specific region- or gene-level quasiscore statistics and, therefore, do not require estimates of effects of individual rare variants...
October 2018: Genetic Epidemiology
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