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Genetic Epidemiology

Fernando Pires Hartwig, Neil Martin Davies, George Davey Smith
Mendelian randomization (MR) has been increasingly used to strengthen causal inference in observational epidemiology. Methodological developments in the field allow detecting and/or adjusting for different potential sources of bias, mainly bias due to horizontal pleiotropy (or "off-target" genetic effects). Another potential source of bias is nonrandom matching between spouses (i.e., assortative mating). In this study, we performed simulations to investigate the bias caused in MR by assortative mating...
July 3, 2018: Genetic Epidemiology
Amit D Joshi, Xin Li, Peter Kraft, Jiali Han
The human MC1R gene is highly polymorphic among lightly pigmented populations, and several variants in the MC1R gene have been associated with increased risk of both melanoma and nonmelanoma skin cancers. The functional consequences of MC1R gene variants have been studied in vitro and in vivo in postulated causal pathways, such as G-protein-coupled signaling transduction, pigmentation, immune response, inflammatory response, cell proliferation, and extracellular matrix adhesion. In a case-control study nested within the Nurses' Health Study, we utilized hierarchical modeling approaches, incorporating quantitative information from these functional studies, to examine the association between particular MC1R alleles and the risk of skin cancers...
July 3, 2018: Genetic Epidemiology
Thomas Madsen, Danielle Braun, Gang Peng, Giovanni Parmigiani, Lorenzo Trippa
The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci...
June 25, 2018: Genetic Epidemiology
Thomas Lumley, Jennifer Brody, Gina Peloso, Alanna Morrison, Kenneth Rice
The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n×n matrix, for n subjects) has computational complexity proportional to n3 . As SKAT is often used when n>104, this step becomes a major bottleneck in its use in practice...
June 22, 2018: Genetic Epidemiology
Bin Zhu, Lisa Mirabello, Nilanjan Chatterjee
In rare variant association studies, aggregating rare and/or low frequency variants, may increase statistical power for detection of the underlying susceptibility gene or region. However, it is unclear which variants, or class of them, in a gene contribute most to the association. We proposed a subregion-based burden test (REBET) to simultaneously select susceptibility genes and identify important underlying subregions. The subregions are predefined by shared common biologic characteristics, such as the protein domain or functional impact...
June 22, 2018: Genetic Epidemiology
Umut Özbek, Hui-Min Lin, Yan Lin, Daniel E Weeks, Wei Chen, John R Shaffer, Shaun M Purcell, Eleanor Feingold
In a genome-wide association study (GWAS), association between genotype and phenotype at autosomal loci is generally tested by regression models. However, X-chromosome data are often excluded from published analyses of autosomes because of the difference between males and females in number of X chromosomes. Failure to analyze X-chromosome data at all is obviously less than ideal, and can lead to missed discoveries. Even when X-chromosome data are included, they are often analyzed with suboptimal statistics...
June 13, 2018: Genetic Epidemiology
Iryna Lobach, Joshua Sampson, Siarhei Lobach, Li Zhang
Genome-wide association studies (GWAS) often measure gene-environment interactions (G × E). We consider the problem of accurately estimating a G × E in a case-control GWAS when a subset of the controls have silent, or undiagnosed, disease and the frequency of the silent disease varies by the environmental variable. We show that using case-control status without accounting for misdiagnosis can lead to biased estimates of the G × E. We further propose a pseudolikelihood approach to remove the bias and accurately estimate how the relationship between the genetic variant and the true disease status varies by the environmental variable...
June 13, 2018: Genetic Epidemiology
Rafael A Nafikov, Alejandro Q Nato, Harkirat Sohi, Bowen Wang, Lisa Brown, Andrea R Horimoto, Badri N Vardarajan, Sandra M Barral, Giuseppe Tosto, Richard P Mayeux, Timothy A Thornton, Elizabeth Blue, Ellen M Wijsman
Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available...
June 3, 2018: Genetic Epidemiology
Jacob M Keaton, Chuan Gao, Meijian Guan, Jacklyn N Hellwege, Nicholette D Palmer, James S Pankow, Myriam Fornage, James G Wilson, Adolfo Correa, Laura J Rasmussen-Torvik, Jerome I Rotter, Yii-Der I Chen, Kent D Taylor, Stephen S Rich, Lynne E Wagenknecht, Barry I Freedman, Maggie C Y Ng, Donald W Bowden
Although type 2 diabetes (T2D) results from metabolic defects in insulin secretion and insulin sensitivity, most of the genetic risk loci identified to date relates to insulin secretion. We reported that T2D loci influencing insulin sensitivity may be identified through interactions with insulin secretion loci, thereby leading to T2D. Here, we hypothesize that joint testing of variant main effects and interaction effects with an insulin secretion locus increases power to identify genetic interactions leading to T2D...
April 24, 2018: Genetic Epidemiology
Richard Barfield, Helian Feng, Alexander Gusev, Lang Wu, Wei Zheng, Bogdan Pasaniuc, Peter Kraft
Integrating genome-wide association (GWAS) and expression quantitative trait locus (eQTL) data into transcriptome-wide association studies (TWAS) based on predicted expression can boost power to detect novel disease loci or pinpoint the susceptibility gene at a known disease locus. However, it is often the case that multiple eQTL genes colocalize at disease loci, making the identification of the true susceptibility gene challenging, due to confounding through linkage disequilibrium (LD). To distinguish between true susceptibility genes (where the genetic effect on phenotype is mediated through expression) and colocalization due to LD, we examine an extension of the Mendelian randomization (MR) egger regression method that allows for LD while only requiring summary association data for both GWAS and eQTL...
July 2018: Genetic Epidemiology
Lauren Mak, Minghao Li, Chen Cao, Paul Gordon, Maja Tarailo-Graovac, Chad Bousman, Pei Wang, Quan Long
Power estimations are important for optimizing genotype-phenotype association study designs. However, existing frameworks are designed for common disorders, and thus ill-suited for the inherent challenges of studies for low-prevalence conditions such as rare diseases and infrequent adverse drug reactions. These challenges include small sample sizes and the need to leverage genetic annotation resources in association analyses for the purpose of ranking potential causal genes. We present SimPEL, a simulation-based program providing power estimations for the design of low-prevalence condition studies...
July 2018: Genetic Epidemiology
Xiaobo Guo, Junxian Zhu, Qiao Fan, Mingguang He, Xueqin Wang, Heping Zhang
Multiple correlated phenotypes are frequently collected in genome-wide association studies (GWASs), and a systematic, simultaneous analysis of multiple phenotypes can integrate the signals from single phenotypes, therefore increasing the power of detecting genetic signals. However, fundamental questions remain open, including the conditions and reasons under which the multivariate analysis is beneficial, how a highly significant signal arises in the multivariate analysis. To understand these issues, we propose to decompose the multivariate model into a series of simple univariate models...
July 2018: Genetic Epidemiology
Silviu-Alin Bacanu, Kenneth S Kendler
To argue for increased sample collection for disorders without significant findings, researchers resorted to plotting, for multiple traits, the number of significant findings as a function of the sample size. However, for polygenic traits, the prevalence of the disorder confounds the relationship between the number of significant findings and the sample size. To adjust the number of significant findings for prevalence, we develop a method that uses the expected noncentrality of the contrast between liabilities of cases and controls...
July 2018: Genetic Epidemiology
Ni Zhao, Xiang Zhan, Katherine A Guthrie, Caroline M Mitchell, Joseph Larson
The human microbiome is a dynamic system that changes due to diseases, medication, change in diet, etc. The paired design is a common approach to evaluate the microbial changes while controlling for the inherent differences between people. For example, microbiome data may be collected from the same individuals before and after a treatment. Two challenges exist in analyzing this type of data. First, microbiome data are compositional such that the reads for all taxa in each sample are constrained to sum to a constant...
July 2018: Genetic Epidemiology
Jingjing Yang, Sai Chen, Gonçalo Abecasis
Meta-analysis is now an essential tool for genetic association studies, allowing them to combine large studies and greatly accelerating the pace of genetic discovery. Although the standard meta-analysis methods perform equivalently as the more cumbersome joint analysis under ideal settings, they result in substantial power loss under unbalanced settings with various case-control ratios. Here, we investigate the power loss problem by the standard meta-analysis methods for unbalanced studies, and further propose novel meta-analysis methods performing equivalently to the joint analysis under both balanced and unbalanced settings...
June 2018: Genetic Epidemiology
Eszter Szekely, Tae-Hwi Linus Schwantes-An, Cristina M Justice, Jeremy A Sabourin, Philip R Jansen, Ryan L Muetzel, Wendy Sharp, Henning Tiemeier, Heejong Sung, Tonya J White, Alexander F Wilson, Philip Shaw
Genome-wide association studies (GWASs) are unraveling the genetics of adult brain neuroanatomy as measured by cross-sectional anatomic magnetic resonance imaging (aMRI). However, the genetic mechanisms that shape childhood brain development are, as yet, largely unexplored. In this study we identify common genetic variants associated with childhood brain development as defined by longitudinal aMRI. Genome-wide single nucleotide polymorphism (SNP) data were determined in two cohorts: one enriched for attention-deficit/hyperactivity disorder (ADHD) (LONG cohort: 458 participants; 119 with ADHD) and the other from a population-based cohort (Generation R: 257 participants)...
June 2018: Genetic Epidemiology
Xiaoyu Liang, Qiuying Sha, Yeonwoo Rho, Shuanglin Zhang
Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases...
June 2018: Genetic Epidemiology
Pratyaydipta 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 P Epstein, Debashis Ghosh
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...
June 2018: Genetic Epidemiology
Stephannie Shih, Yen-Tsung Huang, Hwai-I Yang
Previous work suggested a genetic component affecting the risk of hepatocellular carcinoma (HCC) and mediation analyses have elucidated potential indirect pathways of these genetic effects. Specifically, the effects of alcohol dehydrogenase (ADH1B) and aldehyde dehydrogenase (ALDH2) genes on HCC risk vary based on alcohol consumption habits. However, alcohol consumption may not be the only mediator in the identified pathway: factors related to alcohol consumption may contribute to the same indirect pathway...
June 2018: Genetic Epidemiology
Emily Baker, Karl Michael Schmidt, Rebecca Sims, Michael C O'Donovan, Julie Williams, Peter Holmans, Valentina Escott-Price, With The Gerad Consortium
Polygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set-based analyses by leveraging public genome-wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set-based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets...
June 2018: Genetic Epidemiology
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