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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
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...
September 11, 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...
September 6, 2018: Genetic Epidemiology
Guillaume Paré, Shihong Mao, Wei Q Deng
Complex traits can share a substantial proportion of their polygenic heritability. However, genome-wide polygenic correlations between pairs of traits can mask heterogeneity in their shared polygenic effects across loci. We propose a novel method (weighted maximum likelihood-regional polygenic correlation [RPC]) to evaluate polygenic correlation between two complex traits in small genomic regions using summary association statistics. Our method tests for evidence that the polygenic effect at a given region affects two traits concurrently...
August 29, 2018: Genetic Epidemiology
Lisa J Strug
Concerns over reproducibility in research has reinvigorated the discourse on P-values as measures of statistical evidence. In a position statement by the American Statistical Association board of directors, they warn of P-value misuse and refer to the availability of alternatives. Despite the common practice of comparing P-values across different hypothesis tests in genetics, it is well-appreciated that P-values must be interpreted alongside the sample size and experimental design used for their computation...
August 18, 2018: 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
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
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...
September 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 <mml:math xmlns:mml=""> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>×</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> matrix, for n subjects) has computational complexity proportional to n3 ...
September 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...
September 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
S Taylor Fischer, Yunxuan Jiang, K Alaine Broadaway, Karen N Conneely, Michael P Epstein
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...
July 2018: Genetic Epidemiology
Jiebiao Wang, Qianying Liu, Brandon L Pierce, Dezheng Huo, Olufunmilayo I Olopade, Habibul Ahsan, Lin S Chen
There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses...
July 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
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