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Using parent‐offspring pairs and trios to estimate indirect genetic effects in education Genet. Epidemiol. (IF 2.1) Pub Date : 2024-03-13 Victória Trindade Pons, Annique Claringbould, Priscilla Kamphuis, Albertine J. Oldehinkel, Hanna M. van Loo
We investigated indirect genetic effects (IGEs), also known as genetic nurture, in education with a novel approach that uses phased data to include parent‐offspring pairs in the transmitted/nontransmitted study design. This method increases the power to detect IGEs, enhances the generalizability of the findings, and allows for the study of effects by parent‐of‐origin. We validated and applied this
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Causation and familial confounding as explanations for the associations of polygenic risk scores with breast cancer: Evidence from innovative ICE FALCON and ICE CRISTAL analyses Genet. Epidemiol. (IF 2.1) Pub Date : 2024-03-13 Shuai Li, Gillian S. Dite, Robert J. MacInnis, Minh Bui, Tuong L. Nguyen, Vivienne F. C. Esser, Zhoufeng Ye, James G. Dowty, Enes Makalic, Joohon Sung, Graham G. Giles, Melissa C. Southey, John L. Hopper
A polygenic risk score (PRS) combines the associations of multiple genetic variants that could be due to direct causal effects, indirect genetic effects, or other sources of familial confounding. We have developed new approaches to assess evidence for and against causation by using family data for pairs of relatives (Inference about Causation from Examination of FAmiliaL CONfounding [ICE FALCON]) or
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Are trait‐associated genes clustered together in a gene network? Genet. Epidemiol. (IF 2.1) Pub Date : 2024-03-13 Hyun Jung Koo, Wei Pan
Genome‐wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD) and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network‐based approaches, paired with network diffusion methods, have been proposed to prioritize
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Unveiling challenges in Mendelian randomization for gene–environment interaction Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-29 Malka Gorfine, Conghui Qu, Ulrike Peters, Li Hsu
Gene–environment (GxE) interactions play a crucial role in understanding the complex etiology of various traits, but assessing them using observational data can be challenging due to unmeasured confounders for lifestyle and environmental risk factors. Mendelian randomization (MR) has emerged as a valuable method for assessing causal relationships based on observational data. This approach utilizes
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Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis‐multivariable Mendelian randomization to GLP1R gene region Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-21 Ashish Patel, Dipender Gill, Dmitry Shungin, Christos S. Mantzoros, Lotte Bjerre Knudsen, Jack Bowden, Stephen Burgess
Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop
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Making sense of breast cancer risk estimates Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-09 John O'Quigley
Individual probabilistic assessments on the risk of cancer, primary or secondary, will not be understood by most patients. That is the essence of our arguments in this paper. Greater understanding can be achieved by extensive, intensive, and detailed counseling. But since probability itself is a concept that easily escapes our everyday intuition—consider the famous Monte Hall paradox—then it would
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Gene-based association tests in family samples using GWAS summary statistics Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-05 Peng Wang, Xiao Xu, Ming Li, Xiang-Yang Lou, Siqi Xu, Baolin Wu, Guimin Gao, Ping Yin, Nianjun Liu
Genome-wide association studies (GWAS) have led to rapid growth in detecting genetic variants associated with various phenotypes. Owing to a great number of publicly accessible GWAS summary statistics, and the difficulty in obtaining individual-level genotype data, many existing gene-based association tests have been adapted to require only GWAS summary statistics rather than individual-level data
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Revealing genomic heterogeneity and commonality: A penalized integrative analysis approach accounting for the adjacency structure of measurements Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-05 Xindi Wang, Yu Jiang, Yifan Sun
Advancements in high-throughput genomic technologies have revolutionized the field of disease biomarker identification by providing large-scale genomic data. There is an increasing focus on understanding the relationships among diverse patient groups with distinct disease subtypes and characteristics. Complex diseases exhibit both heterogeneity and shared genomic factors, making it essential to investigate
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Mitigating type 1 error inflation and power loss in GxE PRS: Genotype–environment interaction in polygenic risk score models Genet. Epidemiol. (IF 2.1) Pub Date : 2024-02-01 Dovini Jayasinghe, Md. Moksedul Momin, Kerri Beckmann, Elina Hyppönen, Beben Benyamin, S. Hong Lee
The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype–environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype–environment interaction polygenic risk
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Interval estimate of causal effect in summary data based Mendelian randomization in the presence of winner's curse Genet. Epidemiol. (IF 2.1) Pub Date : 2024-01-28 Kai Wang
This research focuses on the interval estimation of the causal effect of an exposure on an outcome using the summary data-based Mendelian randomization (SMR) method while accounting for the winner's curse caused by the selection of single nucleotide polymorphism instruments. This issue is understudied and is important as the point estimate is biased. Since Fieller's theorem and its variations are not
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simmrd: An open-source tool to perform simulations in Mendelian randomization Genet. Epidemiol. (IF 2.1) Pub Date : 2024-01-23 Noah Lorincz-Comi, Yihe Yang, Xiaofeng Zhu
Mendelian randomization (MR) has become a popular tool for inferring causality of risk factors on disease. There are currently over 45 different methods available to perform MR, reflecting this extremely active research area. It would be desirable to have a standard simulation environment to objectively evaluate the existing and future methods. We present simmrd, an open-source software for performing
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DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree Genet. Epidemiol. (IF 2.1) Pub Date : 2023-11-28 Xuechan Li, John Pura, Andrew Allen, Kouros Owzar, Jianfeng Lu, Matthew Harms, Jichun Xie
Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions
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Bias and mean squared error in Mendelian randomization with invalid instrumental variables Genet. Epidemiol. (IF 2.1) Pub Date : 2023-11-16 Lu Deng, Sheng Fu, Kai Yu
Mendelian randomization (MR) is a statistical method that utilizes genetic variants as instrumental variables (IVs) to investigate causal relationships between risk factors and outcomes. Although MR has gained popularity in recent years due to its ability to analyze summary statistics from genome-wide association studies (GWAS), it requires a substantial number of single nucleotide polymorphisms (SNPs)
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Limitation of permutation-based differential correlation analysis Genet. Epidemiol. (IF 2.1) Pub Date : 2023-11-10 Hoseung Song, Michael C. Wu
The comparison of biological systems, through the analysis of molecular changes under different conditions, has played a crucial role in the progress of modern biological science. Specifically, differential correlation analysis (DCA) has been employed to determine whether relationships between genomic features differ across conditions or outcomes. Because ascertaining the null distribution of test
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Correction to “Abstracts” Genet. Epidemiol. (IF 2.1) Pub Date : 2023-11-09
(2023), Abstracts. Genetic Epidemiology, 47: 520–581. https://doi.org/10.1002/gepi.22539 In the originally published Abstracts, there were authors missing for “Two-sample Mendelian Randomization Study of Circulating Metabolites and Prostate Cancer Risk in Hispanic Populations” (abstract 49). The correct authors and affiliations appear below and have been updated on the online version of the abstracts
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Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region Genet. Epidemiol. (IF 2.1) Pub Date : 2023-10-13 Lars L. J. van der Burg, Liesbeth C. de Wreede, Henning Baldauf, Jürgen Sauter, Johannes Schetelig, Hein Putter, Stefan Böhringer
Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer-cell immunoglobulin-like receptor (KIR) genes. These genes are
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Data-adaptive and pathway-based tests for association studies between somatic mutations and germline variations in human cancers Genet. Epidemiol. (IF 2.1) Pub Date : 2023-10-11 Zhongyuan Chen, Han Liang, Peng Wei
Cancer is a disease driven by a combination of inherited genetic variants and somatic mutations. Recently available large-scale sequencing data of cancer genomes have provided an unprecedented opportunity to study the interactions between them. However, previous studies on this topic have been limited by simple, low statistical power tests such as Fisher's exact test. In this paper, we design data-adaptive
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ioSearch: An approach for identifying interacting multiomics biomarkers using a novel algorithm with application on breast cancer data sets Genet. Epidemiol. (IF 2.1) Pub Date : 2023-10-05 Sarmistha Das, Deo Kumar Srivastava
Identification of biomarkers by integrating multiple omics together is important because complex diseases occur due to an intricate interplay of various genetic materials. Traditional single-omics association tests neither explore this crucial interomics dependence nor identify moderately weak signals due to the multiple-testing burden. Conversely, multiomics data integration imparts complementary
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Statistical methods to detect mother–father genetic interaction effects on risk of infertility: A genome-wide approach Genet. Epidemiol. (IF 2.1) Pub Date : 2023-08-28 Siri N. Skodvin, Håkon K. Gjessing, Astanand Jugessur, Julia Romanowska, Christian M. Page, Elizabeth C. Corfield, Yunsung Lee, Siri E. Håberg, Miriam Gjerdevik
Infertility is a heterogeneous phenotype, and for many couples, the causes of fertility problems remain unknown. One understudied hypothesis is that allelic interactions between the genotypes of the two parents may influence the risk of infertility. Our aim was, therefore, to investigate how allelic interactions can be modeled using parental genotype data linked to 15,789 pregnancies selected from
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Inference of causal metabolite networks in the presence of invalid instrumental variables with GWAS summary data Genet. Epidemiol. (IF 2.1) Pub Date : 2023-08-13 Siyi Chen, Zhaotong Lin, Xiaotong Shen, Ling Li, Wei Pan
We propose structural equation models (SEMs) as a general framework to infer causal networks for metabolites and other complex traits. Traditionally SEMs are used only for individual-level data under the assumption that all instrumental variables (IVs) are valid. To overcome these limitations, we propose both one- and two-sample approaches for causal network inference based on SEMs that can: (1) perform
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Sensitivity analyses gain relevance by fixing parameters observable during the empirical analyses Genet. Epidemiol. (IF 2.1) Pub Date : 2023-07-07 Gibran Hemani, Apostolos Gkatzionis, Kate Tilling, George Davey Smith
In 2017 we presented the MR Steiger method, a sensitivity analysis in Mendelian randomization (MR) for inferring causal directions between variables (Hemani et al., 2017). We discussed many of its potential limitations including that unmeasured confounding under certain extreme circumstances could lead to the wrong inferred causal direction. Lutz et al. (2022) propose an R package (UCRMS) for performing
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Comparison of regmed and BayesNetty for exploring causal models with many variables Genet. Epidemiol. (IF 2.1) Pub Date : 2023-06-27 Richard Howey, Heather J. Cordell
Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with
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A gene-based association test of interactions for maternal–fetal genotypes identifies genes associated with nonsyndromic congenital heart defects Genet. Epidemiol. (IF 2.1) Pub Date : 2023-06-21 Manyan Huang, Chen Lyu, Nianjun Liu, Wendy N. Nembhard, John S. Witte, Charlotte A. Hobbs, Ming Li
The risk of congenital heart defects (CHDs) may be influenced by maternal genes, fetal genes, and their interactions. Existing methods commonly test the effects of maternal and fetal variants one-at-a-time and may have reduced statistical power to detect genetic variants with low minor allele frequencies. In this article, we propose a gene-based association test of interactions for maternal–fetal genotypes
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Phenotypic variance partitioning by transcriptomic gene expression levels and environmental variables for anthropometric traits using GTEx data Genet. Epidemiol. (IF 2.1) Pub Date : 2023-06-15 Pastor Jullian Fabres, S. Hong Lee
Phenotypic variation in human is the results of genetic variation and environmental influences. Understanding the contribution of genetic and environmental components to phenotypic variation is of great interest. The variance explained by genome-wide single nucleotide polymorphisms (SNPs) typically represents a small proportion of the phenotypic variance for complex traits, which may be because the
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Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes Genet. Epidemiol. (IF 2.1) Pub Date : 2023-05-09 Ozvan Bocher, Gaëlle Marenne, Emmanuelle Génin, Hervé Perdry
Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome
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A Brief History behind the journal Genetic Epidemiology and the International Genetic Epidemiology Society Genet. Epidemiol. (IF 2.1) Pub Date : 2023-05-05 Dabeeru C. Rao
This commentary briefly describes the process and steps that underlie the launching of the journal Genetic Epidemiology in 1984 and the International Genetic Epidemiology Society (IGES, to be pronounced as “I guess”) in 1992.
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Gene-level association analysis of bivariate ordinal traits with functional regressions Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-26 Shuqi Wang, Chi-Yang Chiu, Alexander F. Wilson, Joan E. Bailey-Wilson, Elvira Agron, Emily Y. Chew, Jaeil Ahn, Momiao Xiong, Ruzong Fan
In genetic studies, many phenotypes have multiple naturally ordered discrete values. The phenotypes can be correlated with each other. If multiple correlated ordinal traits are analyzed simultaneously, the power of analysis may increase significantly while the false positives can be controlled well. In this study, we propose bivariate functional ordinal linear regression (BFOLR) models using latent
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The sequence kernel association test for multicategorical outcomes Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-19 Zhiwen Jiang, Haoyu Zhang, Thomas U. Ahearn, Montserrat Garcia-Closas, Nilanjan Chatterjee, Hongtu Zhu, Xiang Zhan, Ni Zhao
Disease heterogeneity is ubiquitous in biomedical and clinical studies. In genetic studies, researchers are increasingly interested in understanding the distinct genetic underpinning of subtypes of diseases. However, existing set-based analysis methods for genome-wide association studies are either inadequate or inefficient to handle such multicategorical outcomes. In this paper, we proposed a novel
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Fast and accurate recurrent event analysis for genome-wide association studies Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-15 Jasper P. Hof, Sita H. Vermeulen, Anthony C. C. Coolen, Tessel E. Galesloot
Many diseases recur after recovery, for example, recurrences in cancer and infections. However, research is often focused on analysing only time-to-first recurrence, thereby ignoring any subsequent recurrences that may occur after the first. Statistical models for the analysis of recurrent events are available, of which the extended Cox proportional hazards frailty model is the current state-of-the-art
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RoPE: A robust profile likelihood method for differential gene expression analysis Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-12 Lehang Zhong, Lisa J. Strug
Variation in RNA-Seq data creates modeling challenges for differential gene expression (DE) analysis. Statistical approaches address conventional small sample sizes and implement empirical Bayes or non-parametric tests, but frequently produce different conclusions. Increasing sample sizes enable proposal of alternative DE paradigms. Here we develop RoPE, which uses a data-driven adjustment for variation
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Bias correction for inverse variance weighting Mendelian randomization Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-10 Ninon Mounier, Zoltán Kutalik
Inverse-variance weighted two-sample Mendelian randomization (IVW-MR) is the most widely used approach that utilizes genome-wide association studies (GWAS) summary statistics to infer the existence and the strength of the causal effect between an exposure and an outcome. Estimates from this approach can be subject to different biases due to the use of weak instruments and winner's curse, which can
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Effect of case and control definitions on genome-wide association study (GWAS) findings Genet. Epidemiol. (IF 2.1) Pub Date : 2023-04-06 Monica Isgut, Kijoung Song, Margaret G. Ehm, May Dongmei Wang, Jonathan Davitte
Genome-wide association studies (GWAS) have significantly advanced our understanding of the genetic underpinnings of diseases, but case and control cohort definitions for a given disease can vary between different published studies. For example, two GWAS for the same disease using the UK Biobank data set might use different data sources (i.e., self-reported questionnaires, hospital records, etc.) or
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Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-23 Muhammad Shoaib, Qiang Ye, Heidi IglayReger, Meng H. Tan, Michael Boehnke, Charles F. Burant, Scott A. Soleimanpour, Sarah A. Gagliano Taliun
Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the
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New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-19 Claudia Coscia, Esther Molina-Montes, Raquel Benítez, Evangelina López de Maturana, Alfonso Muriel, Núria Malats, Teresa Pérez
The application of causal mediation analysis (CMA) considering the mediation effect of a third variable is increasing in epidemiological studies; however, this requires fitting strong assumptions on confounding bias. To address this limitation, we propose an extension of CMA combining it with Mendelian randomization (MRinCMA). We applied the new approach to analyse the causal effect of obesity and
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MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-19 Dapeng Shi, Yuquan Wang, Ziyong Zhang, Yunlong Cao, Yue-Qing Hu
Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for
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Gene–environment interaction analysis via deep learning Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-19 Shuni Wu, Yaqing Xu, Qingzhao Zhang, Shuangge Ma
Gene–environment (G–E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted on G–E interaction analysis, and the existing methods are mostly based on regression techniques. In many fields including biomedicine and omics, it has been increasingly recognized that deep learning may outperform regression with its unique flexibility
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Bayesian multivariant fine mapping using the Laplace prior Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-05 Kevin Walters, Hannuun Yaacob
Currently, the only effect size prior that is routinely implemented in a Bayesian fine-mapping multi-single-nucleotide polymorphism (SNP) analysis is the Gaussian prior. Here, we show how the Laplace prior can be deployed in Bayesian multi-SNP fine mapping studies. We compare the ranking performance of the posterior inclusion probability (PIP) using a Laplace prior with the ranking performance of the
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A fast linkage method for population GWAS cohorts with related individuals Genet. Epidemiol. (IF 2.1) Pub Date : 2023-02-05 Gregory J. M. Zajac, Sarah A. Gagliano Taliun, Carlo Sidore, Sarah E. Graham, Bjørn O. Åsvold, Ben Brumpton, Jonas B. Nielsen, Wei Zhou, Maiken Gabrielsen, Anne H. Skogholt, Lars G. Fritsche, David Schlessinger, Francesco Cucca, Kristian Hveem, Cristen J. Willer, Gonçalo R. Abecasis
Linkage analysis, a class of methods for detecting co-segregation of genomic segments and traits in families, was used to map disease-causing genes for decades before genotyping arrays and dense SNP genotyping enabled genome-wide association studies in population samples. Population samples often contain related individuals, but the segregation of alleles within families is rarely used because traditional
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Study of effect modifiers of genetically predicted CETP reduction Genet. Epidemiol. (IF 2.1) Pub Date : 2023-01-26 Marc-André Legault, Amina Barhdadi, Isabel Gamache, Audrey Lemaçon, Louis-Philippe Lemieux Perreault, Jean-Christophe Grenier, Marie-Pierre Sylvestre, Julie G. Hussin, David Rhainds, Jean-Claude Tardif, Marie-Pierre Dubé
Genetic variants in drug targets can be used to predict the long-term, on-target effect of drugs. Here, we extend this principle to assess how sex and body mass index may modify the effect of genetically predicted lower CETP levels on biomarkers and cardiovascular outcomes. We found sex and body mass index (BMI) to be modifiers of the association between genetically predicted lower CETP and lipid biomarkers
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Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank Genet. Epidemiol. (IF 2.1) Pub Date : 2023-01-24 Zihuan Liu, Wei Dai, Shiying Wang, Yisha Yao, Heping Zhang
Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions
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Joint analysis of multiple phenotypes for extremely unbalanced case-control association studies Genet. Epidemiol. (IF 2.1) Pub Date : 2023-01-24 Hongjing Xie, Xuewei Cao, Shuanglin Zhang, Qiuying Sha
In genome-wide association studies (GWAS) for thousands of phenotypes in biobanks, most binary phenotypes have substantially fewer cases than controls. Many widely used approaches for joint analysis of multiple phenotypes produce inflated type I error rates for such extremely unbalanced case-control phenotypes. In this research, we develop a method to jointly analyze multiple unbalanced case-control
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Weak and pleiotropy robust sex-stratified Mendelian randomization in the one sample and two sample settings Genet. Epidemiol. (IF 2.1) Pub Date : 2023-01-22 Vasilios Karageorgiou, Jess Tyrrell, Trevelyan J. Mckinley, Jack Bowden
Mendelian randomization (MR) leverages genetic data as an instrumental variable to provide estimates for the causal effect of an exposure X on a health outcome Y that is robust to confounding. Unfortunately, horizontal pleiotropy—the direct association of a genetic variant with multiple phenotypes—is highly prevalent and can easily render a genetic variant an invalid instrument.
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Improved two-step testing of genome-wide gene–environment interactions Genet. Epidemiol. (IF 2.1) Pub Date : 2022-12-26 Eric S. Kawaguchi, Andre E. Kim, Juan Pablo Lewinger, W. James Gauderman
Two-step tests for gene–environment (G×E$G\times E$) interactions exploit marginal single-nucleotide polymorphism (SNP) effects to improve the power of a genome-wide interaction scan. They combine a screening step based on marginal effects used to “bin” SNPs for weighted hypothesis testing in the second step to deliver greater power over single-step tests while preserving the genome-wide Type I error
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Efficient identification of trait-associated loss-of-function variants in the UK Biobank cohort by exome-sequencing based genotype imputation Genet. Epidemiol. (IF 2.1) Pub Date : 2022-12-09 Wen-Yuan Yu, Shan-Shan Yan, Shu-Han Zhang, Jing-Jing Ni, Bin-Li, Yu-Fang Pei, Lei Zhang
The large-scale open access whole-exome sequencing (WES) data of the UK Biobank ~200,000 participants is accelerating a new wave of genetic association studies aiming to identify rare and functional loss-of-function (LoF) variants associated with complex traits and diseases. We proposed to merge the WES genotypes and the genome-wide genotyping (GWAS) genotypes of 167,000 UKB homogeneous European participants
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Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons Genet. Epidemiol. (IF 2.1) Pub Date : 2022-12-05 Jiacong Du, Xiang Zhou, Dylan Clark-Boucher, Wei Hao, Yongmei Liu, Jennifer A. Smith, Bhramar Mukherjee
Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, H 0 : α β = 0 ${H}_{0}:\alpha \beta =0$ ( α $\alpha $ : effect of the exposure on the mediator after adjusting for confounders; β $\beta $ : effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of
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Adaptive Bayesian variable clustering via structural learning of breast cancer data Genet. Epidemiol. (IF 2.1) Pub Date : 2022-11-15 Riddhi Pratim Ghosh, Arnab K. Maity, Mohsen Pourahmadi, Bani K. Mallick
The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize
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Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries Genet. Epidemiol. (IF 2.1) Pub Date : 2022-11-09 Jerry Z. Zhang, Lacey W. Heinsberg, Mohanraj Krishnan, Nicola L. Hawley, Tanya J. Major, Jenna C. Carlson, Jennie Harré Hindmarsh, Huti Watson, Muhammad Qasim, Lisa K. Stamp, Nicola Dalbeth, Rinki Murphy, Guangyun Sun, Hong Cheng, Take Naseri, Muagututi'a S. Reupena, Erin E. Kershaw, Ranjan Deka, Stephen T. McGarvey, Ryan L. Minster, Tony R. Merriman, Daniel E. Weeks
The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association
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Sparse prediction informed by genetic annotations using the logit normal prior for Bayesian regression tree ensembles Genet. Epidemiol. (IF 2.1) Pub Date : 2022-11-09 Charles Spanbauer, Wei Pan
Using high-dimensional genetic variants such as single nucleotide polymorphisms (SNP) to predict complex diseases and traits has important applications in basic research and other clinical settings. For example, predicting gene expression is a necessary first step to identify (putative) causal genes in transcriptome-wide association studies. Due to weak signals, high-dimensionality, and linkage disequilibrium
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Statistical methods for cis-Mendelian randomization with two-sample summary-level data Genet. Epidemiol. (IF 2.1) Pub Date : 2022-10-23 Apostolos Gkatzionis, Stephen Burgess, Paul J. Newcombe
Mendelian randomization (MR) is the use of genetic variants to assess the existence of a causal relationship between a risk factor and an outcome of interest. Here, we focus on two-sample summary-data MR analyses with many correlated variants from a single gene region, particularly on cis-MR studies which use protein expression as a risk factor. Such studies must rely on a small, curated set of variants
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Mediation analysis of multiple mediators with incomplete omics data Genet. Epidemiol. (IF 2.1) Pub Date : 2022-09-20 John Kidd, Chelsea K. Raulerson, Karen L. Mohlke, Dan-Yu Lin
There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing
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An empirical Bayes approach to improving population-specific genetic association estimation by leveraging cross-population data Genet. Epidemiol. (IF 2.1) Pub Date : 2022-09-18 Li Hsu, Anna Kooperberg, Alexander P. Reiner, Charles Kooperberg
Populations of non-European ancestry are substantially underrepresented in genome-wide association studies (GWAS). As genetic effects can differ between ancestries due to possibly different causal variants or linkage disequilibrium patterns, a meta-analysis that includes GWAS of all populations yields biased estimation in each of the populations and the bias disproportionately impacts non-European
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An exploration of linkage fine-mapping on sequences from case-control studies Genet. Epidemiol. (IF 2.1) Pub Date : 2022-09-01 Payman Nickchi, Charith Karunarathna, Jinko Graham
Linkage analysis maps genetic loci for a heritable trait by identifying genomic regions with excess relatedness among individuals with similar trait values. Analysis may be conducted on related individuals from families, or on samples of unrelated individuals from a population. For allelically heterogeneous traits, population-based linkage analysis can be more powerful than genotypic-association analysis
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Statistical learning for sparser fine-mapped polygenic models: The prediction of LDL-cholesterol Genet. Epidemiol. (IF 2.1) Pub Date : 2022-08-08 Carlo Maj, Christian Staerk, Oleg Borisov, Hannah Klinkhammer, Ming Wai Yeung, Peter Krawitz, Andreas Mayr
Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping
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Investigating the prediction of CpG methylation levels from SNP genotype data to help elucidate relationships between methylation, gene expression and complex traits Genet. Epidemiol. (IF 2.1) Pub Date : 2022-08-05 James J. Fryett, Andrew P. Morris, Heather J. Cordell
As popularised by PrediXcan (and related methods), transcriptome-wide association studies (TWAS), in which gene expression is imputed from single-nucleotide polymorphism (SNP) genotypes and tested for association with a phenotype, are a popular approach for investigating the role of gene expression in complex traits. Like gene expression, DNA methylation is an important biological process and, being
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Genetic heterogeneity: Challenges, impacts, and methods through an associative lens Genet. Epidemiol. (IF 2.1) Pub Date : 2022-08-04 Alexa A. Woodward, Ryan J. Urbanowicz, Adam C. Naj, Jason H. Moore
Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead
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Including diverse and admixed populations in genetic epidemiology research Genet. Epidemiol. (IF 2.1) Pub Date : 2022-07-16 Amke Caliebe, Fasil Tekola-Ayele, Burcu F. Darst, Xuexia Wang, Yeunjoo E. Song, Jiang Gui, Ronnie A. Sebro, David J. Balding, Mohamad Saad, Marie-Pierre Dubé
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology
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Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference Genet. Epidemiol. (IF 2.1) Pub Date : 2022-07-05 Yutong Luo, Ruzong Fan
To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular
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Control for population stratification in genetic association studies based on GWAS summary statistics Genet. Epidemiol. (IF 2.1) Pub Date : 2022-06-29 Shijia Yan, Qiuying Sha, Shuanglin Zhang
Over the past years, genome-wide association studies (GWAS) have generated a wealth of new information. Summary data from many GWAS are now publicly available, promoting the development of many statistical methods for association studies based on GWAS summary statistics, which avoids the increasing challenges associated with individual-level genotype and phenotype data sharing. However, for population-based
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Sparse group variable selection for gene–environment interactions in the longitudinal study Genet. Epidemiol. (IF 2.1) Pub Date : 2022-06-29 Fei Zhou, Xi Lu, Jie Ren, Kun Fan, Shuangge Ma, Cen Wu
Penalized variable selection for high-dimensional longitudinal data has received much attention as it can account for the correlation among repeated measurements while providing additional and essential information for improved identification and prediction performance. Despite the success, in longitudinal studies, the potential of penalization methods is far from fully understood for accommodating