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Many common diseases are highly polygenic, modulated by a large number genetic factors with small effects on susceptibility to disease. These small effects are difficult to map reliably in genetic association studies. To address this…

Quantitative Methods · Quantitative Biology 2012-08-23 Peter Carbonetto , Matthew Stephens

2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactorial disorder with a complex aetiology. The genetic…

Machine Learning · Computer Science 2018-08-30 Basma Abdulaimma , Paul Fergus , Carl Chalmers

With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive genetic predictors in disease prediction. There exists, therefore, a critical need for developing…

Methodology · Statistics 2025-04-02 Changshuai Wei , Ming Li , Yalu Wen , Chengyin Ye , Qing Lu

In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships between exposures and outcomes based on observational data. However, numerous genetic studies have shown…

Methodology · Statistics 2026-04-10 Julien St-Pierre , Archer Y. Yang , Mireille E. Schnitzer , Marc-André Legault

Substantial progress has been made in identifying single genetic variants predisposing to common complex diseases. Nonetheless, the genetic etiology of human diseases remains largely unknown. Human complex diseases are likely influenced by…

Methodology · Statistics 2014-05-27 Zihuai He , Min Zhang , Xiaowei Zhan , Qing Lu

With the recent advent of high-throughput genotyping techniques, genetic data for genome-wide association studies (GWAS) have become increasingly available, which entails the development of efficient and effective statistical approaches.…

Applications · Statistics 2015-02-04 Jiahan Li , Wei Zhong , Runze Li , Rongling Wu

Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables (IVs) to infer the causal effect of a modifiable exposure on an outcome. Although recent years have seen many extensions of basic MR…

Methodology · Statistics 2022-03-15 Sai Li , Ting Ye

Alzheimer's disease is the most common cause of dementia. It is the fifth-leading cause of death among elderly people. With high genetic heritability (79%), finding disease causal genes is a crucial step in find treatment for AD. Following…

Applications · Statistics 2018-11-14 Sicheng Hao , Rui Wang , Yu Zhang , Hui Zhan

Genome-wide association studies (GWA studies or GWAS) investigate the relationships between genetic variants such as single-nucleotide polymorphisms (SNPs) and individual traits. Recently, incorporating biological priors together with…

Machine Learning · Statistics 2017-09-13 Tao Yang , Paul Thompson , Sihai Zhao , Jieping Ye

Identifying phenotypes plays an important role in furthering our understanding of disease biology through practical applications within healthcare and the life sciences. The challenge of dealing with the complexities and noise within…

Applications · Statistics 2023-04-28 Andre Vauvelle , Hamish Tomlinson , Aaron Sim , Spiros Denaxas

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene…

Methodology · Statistics 2024-09-23 Mariyam Khan , Adriaan-Alexander Ludl , Sean Bankier , Johan Bjorkegren , Tom Michoel

Genome-Wide Association Studies (GWAS) offer an exciting and promising new research avenue for finding genes for complex diseases. Traditional case-control and cohort studies offer many advantages for such designs. Family-based association…

Methodology · Statistics 2010-10-25 Nan M. Laird , Christoph Lange

The widely used genetic pleiotropic analysis of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high…

Machine Learning · Statistics 2015-12-04 Panpan Wang , Mohammad Rahman , Li Jin , Momiao Xiong

The approval success rate of drug candidates is very low with the majority of failure due to safety and efficacy. Increasingly available high dimensional information on targets, drug molecules and indications provides an opportunity for ML…

Machine Learning · Computer Science 2022-07-27 Onuralp Soylemez

Obesity is widely recognized as a serious and pervasive health concern. We study obesity through body mass index (BMI), which is known to be highly heritable, and identify important genetic risk factors for BMI from hundreds of thousands of…

Applications · Statistics 2026-03-31 Jiantong Wang , Heng Lian , Yan Yu , Tianhai Zu , Heping Zhang

Replication helps ensure that a genotype-phenotype association observed in a genome-wide association (GWA) study represents a credible association and is not a chance finding or an artifact due to uncontrolled biases. We discuss…

Methodology · Statistics 2010-10-26 Peter Kraft , Eleftheria Zeggini , John P. A. Ioannidis

In Genome-Wide Association Studies (GWAS), heritability is defined as the fraction of variance of an outcome explained by a large number of genetic predictors in a high-dimensional polygenic linear model. This work studies the asymptotic…

Statistics Theory · Mathematics 2025-02-27 David Azriel , Samuel Davenport , Armin Schwartzman

Genomic data arising from a genome-wide association study (GWAS) are often not only of large-scale, but also incomplete. A specific form of their incompleteness is missing values with non-ignorable missingness mechanism. The intrinsic…

Methodology · Statistics 2021-11-11 Siru Wang , Guoqi Qian

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either…

Populations and Evolution · Quantitative Biology 2016-10-26 Anand Bhaskar , Adel Javanmard , Thomas A. Courtade , David Tse

Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. In this paper, we leverage recent advances in…

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