Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects
ABSTRACT
With the aim of improving detection of novel single‐nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of...
Source: Genetic Epidemiology - April 1, 2017 Category: Epidemiology Authors: Miguel Pereira, John R. Thompson, Christian X. Weichenberger, Duncan C. Thomas, Cosetta Minelli Tags: RESEARCH ARTICLE Source Type: research
A genome ‐wide linkage and association analysis of imputed insertions and deletions with cardiometabolic phenotypes in Mexican Americans: The Insulin Resistance Atherosclerosis Family Study
In conclusion, these results confirm the feasibility of imputing INDELs from array‐based single nucleotide polymorphism (SNP) genotypes. Analysis of these variants using association and linkage replicated previously identified SNP signals and identified multiple novel INDEL signals. These results support the inclusion of INDELs into genetic studies to more fully interrogate the spectrum of genetic variation. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - April 1, 2017 Category: Epidemiology Authors: Chuan Gao, Fang ‐Chi Hsu, Latchezar M. Dimitrov, Hayrettin Okut, Yii‐Der I. Chen, Kent D. Taylor, Jerome I. Rotter, Carl D. Langefeld, Donald W. Bowden, Nicholette D. Palmer Tags: RESEARCH ARTICLE Source Type: research
A combination test for detection of gene ‐environment interaction in cohort studies
ABSTRACT
Identifying gene‐environment (G‐E) interactions can contribute to a better understanding of disease etiology, which may help researchers develop disease prevention strategies and interventions. One big criticism of studying G‐E interaction is the lack of power due to sample size. Studies often restrict the interaction search to the top few hundred hits from a genome‐wide association study or focus on potential candidate genes. In this paper, we test interactions between a candidate gene and an environmental factor to improve power by analyzing multiple variants within a gene. We extend recently developed s...
Source: Genetic Epidemiology - March 31, 2017 Category: Epidemiology Authors: Brandon Coombes, Saonli Basu, Matt McGue Tags: Research Article Source Type: research
On the association analysis of genome ‐sequencing data: A spatial clustering approach for partitioning the entire genome into nonoverlapping windows
ABSTRACT
For the association analysis of whole‐genome sequencing (WGS) studies, we propose an efficient and fast spatial‐clustering algorithm. Compared to existing analysis approaches for WGS data, that define the tested regions either by sliding or consecutive windows of fixed sizes along variants, a meaningful grouping of nearby variants into consecutive regions has the advantage that, compared to sliding window approaches, the number of tested regions is likely to be smaller. In comparison to consecutive, fixed‐window approaches, our approach is likely to group nearby variants together. Given existing biological e...
Source: Genetic Epidemiology - March 20, 2017 Category: Epidemiology Authors: Heide Loehlein Fier, Dmitry Prokopenko, Julian Hecker, Michael H. Cho, Edwin K. Silverman, Scott T. Weiss, Rudolph E. Tanzi, Christoph Lange Tags: Research Article Source Type: research
Semiparametric methods for estimation of a nonlinear exposure ‐outcome relationship using instrumental variables with application to Mendelian randomization
We present two novel IV methods for investigating the shape of the exposure‐outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to esti...
Source: Genetic Epidemiology - March 19, 2017 Category: Epidemiology Authors: James R. Staley, Stephen Burgess Tags: RESEARCH ARTICLE Source Type: research
Are rare variants really independent?
In this report, we show that two commonly used LD measures are not capable of detecting LD when rare variants are involved. We present this argument from two perspectives, both the LD measures themselves and the computational issues associated with them. To address these issues, we propose an alternative LD measure, the polychoric correlation, that was originally designed for detecting associations among categorical variables. Using simulated as well as the 1000 Genomes data, we explore the performances of LD measures in detail and discuss their implications in association studies. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 16, 2017 Category: Epidemiology Authors: Asuman Turkmen, Shili Lin Tags: Brief Report Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - March 5, 2017 Category: Epidemiology Tags: Issue Information Source Type: research
Gene ‐based segregation method for identifying rare variants in family‐based sequencing studies
ABSTRACT
Whole‐exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene‐based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission. This test takes into account different degrees of relatedness in families, the number of fun...
Source: Genetic Epidemiology - February 12, 2017 Category: Epidemiology Authors: Dandi Qiao, Christoph Lange, Nan M. Laird, Sungho Won, Craig P. Hersh, Jarrett Morrow, Brian D. Hobbs, Sharon M. Lutz, Ingo Ruczinski, Terri H. Beaty, Edwin K. Silverman, Michael H. Cho Tags: Research Article Source Type: research
Genetic risk models: Influence of model size on risk estimates and precision
ABSTRACT
Disease risk estimation plays an important role in disease prevention. Many studies have found that the ability to predict risk improves as the number of risk single‐nucleotide polymorphisms (SNPs) in the risk model increases. However, the width of the confidence interval of the risk estimate is often not considered in the evaluation of the risk model. Here, we explore how the risk and the confidence interval width change as more SNPs are added to the model in the order of decreasing effect size, using both simulated data and real data from studies of abdominal aortic aneurysms and age‐related macular degenera...
Source: Genetic Epidemiology - January 31, 2017 Category: Epidemiology Authors: Ying Shan, Gerard Tromp, Helena Kuivaniemi, Diane T. Smelser, Shefali S. Verma, Marylyn D. Ritchie, James R. Elmore, David J. Carey, Yvette P. Conley, Michael B. Gorin, Daniel E. Weeks Tags: Research Article Source Type: research
Adaptive testing for multiple traits in a proportional odds model with applications to detect SNP ‐brain network associations
ABSTRACT
There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but ...
Source: Genetic Epidemiology - January 31, 2017 Category: Epidemiology Authors: Junghi Kim, Wei Pan, Tags: Research Article Source Type: research
Detecting association of rare and common variants based on cross ‐validation prediction error
ABSTRACT
Despite the extensive discovery of disease‐associated common variants, much of the genetic contribution to complex traits remains unexplained. Rare variants may explain additional disease risk or trait variability. Although sequencing technology provides a supreme opportunity to investigate the roles of rare variants in complex diseases, detection of these variants in sequencing‐based association studies presents substantial challenges. In this article, we propose novel statistical tests to test the association between rare and common variants in a genomic region and a complex trait of interest based on cross...
Source: Genetic Epidemiology - January 31, 2017 Category: Epidemiology Authors: Xinlan Yang, Shuaichen Wang, Shuanglin Zhang, Qiuying Sha Tags: Research Article Source Type: research
Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies
ABSTRACT
Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero‐inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya‐Gamma random variables. Hierarchical centering and...
Source: Genetic Epidemiology - January 23, 2017 Category: Epidemiology Authors: Lizhen Xu, Andrew D. Paterson, Wei Xu Tags: Research Article Source Type: research
A powerful statistical framework for generalization testing in GWAS, with application to the HCHS/SOL
ABSTRACT
In genome‐wide association studies (GWAS), “generalization” is the replication of genotype‐phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family‐wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow‐up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses. It also fails to leverage the two‐stage design to increa...
Source: Genetic Epidemiology - January 16, 2017 Category: Epidemiology Authors: Tamar Sofer, Ruth Heller, Marina Bogomolov, Christy L. Avery, Mariaelisa Graff, Kari E. North, Alex P. Reiner, Timothy A. Thornton, Kenneth Rice, Yoav Benjamini, Cathy C. Laurie, Kathleen F. Kerr Tags: Research Article Source Type: research
Issue Information
(Source: Genetic Epidemiology)
Source: Genetic Epidemiology - January 9, 2017 Category: Epidemiology Tags: Issue Information Source Type: research
Rare variant association test with multiple phenotypes
This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability. (Source: Genetic Epidemiology)
Source: Genetic Epidemiology - December 31, 2016 Category: Epidemiology Authors: Selyeong Lee, Sungho Won, Young Jin Kim, Yongkang Kim, , Bong ‐Jo Kim, Taesung Park Tags: Research Article Source Type: research
