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The aim of this paper is to propose a novel estimation method of using genetic-predicted observations to estimate trans-ancestry genetic correlations, which describes how genetic architecture of complex traits varies among populations, in…

Methodology · Statistics 2022-03-24 Bingxin Zhao , Xiaochen Yang , Hongtu Zhu

Assessing the statistical power to detect susceptibility variants plays a critical role in GWA studies both from the prospective and retrospective points of view. Power is empirically estimated by simulating phenotypes under a disease model…

Applications · Statistics 2012-01-25 Vittorio Perduca , Christine Sinoquet , Raphael Mourad , Gregory Nuel

Deep neural networks (DNN) have been used successfully in many scientific problems for their high prediction accuracy, but their application to genetic studies remains challenging due to their poor interpretability. In this paper, we…

Machine Learning · Computer Science 2021-10-01 Peyman H. Kassani , Fred Lu , Yann Le Guen , Zihuai He

Genome-wide association studies (GWAS) are commonly employed to study the genetic basis of complex traits and diseases, and a key question is how much heritability could be explained by all variants in GWAS. One widely used approach that…

Genomics · Quantitative Biology 2023-06-27 Hon-Cheong So , Xiao Xue , Pak-Chung Sham

Large-scale genome-wide association studies (GWAS) have offered an exciting opportunity to discover putative causal genes or risk factors associated with diseases by using SNPs as instrumental variables (IVs). However, conventional…

Methodology · Statistics 2023-10-27 Ben Dai , Chunlin Li , Haoran Xue , Wei Pan , Xiaotong Shen

Imaging genetic studies aim to find associations between genetic variants and imaging quantitative traits. Traditional genome-wide association studies (GWAS) are based on univariate statistical tests, but when multiple traits are analyzed…

Genomics · Quantitative Biology 2022-04-04 Muhammad Ammar Malik , Alexander S. Lundervold , Tom Michoel

Genome-wide association studies (GWAS) have successfully identified over two hundred thousand genotype-trait associations. Yet some challenges remain. First, complex traits are often associated with many single nucleotide polymorphisms…

Methodology · Statistics 2023-02-07 Aastha Khatiwada , Ayse Selen Yilmaz , Bethany J. Wolf , Maciej Pietrzak , Dongjun Chung

Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In these studies, association testing is done on a variant-by-variant basis, possibly missing out on…

The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoff is that if we have a good model of the features X, then we can identify salient features…

Machine Learning · Statistics 2019-05-30 Jaime Roquero Gimenez , James Zou

Genome-wide association studies (GWAS) have achieved great success in the genetic study of Alzheimer's disease (AD). Collaborative imaging genetics studies across different research institutions show the effectiveness of detecting genetic…

Machine Learning · Computer Science 2017-04-28 Qingyang Li , Dajiang Zhu , Jie Zhang , Derrek Paul Hibar , Neda Jahanshad , Yalin Wang , Jieping Ye , Paul M. Thompson , Jie Wang

Genome-wide association studies (GWAS) are widely used to discover genetic variants associated with diseases. To control false positives, all findings from GWAS need to be verified with additional evidences, even for associations discovered…

Genomics · Quantitative Biology 2026-03-12 Wei Jiang , Jing-Hao Xue , Weichuan Yu

Motivated by the inquiries of weak signals in underpowered genome-wide association studies (GWASs), we consider the problem of retaining true signals that are not strong enough to be individually separable from a large amount of noise. We…

Methodology · Statistics 2024-02-05 X. Jessie Jeng , Yifei Hu , Quan Sun , Yun Li

Genome-Wide Association Studies (GWAS) explain only a small fraction of heritability for most complex human phenotypes. Genomic heritability estimates the variance explained by the SNPs on the whole genome using mixed models and accounts…

Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently,…

Methodology · Statistics 2019-03-05 Bingxin Zhao , Hongtu Zhu

Motivated by empirical arguments that are well-known from the genome-wide association studies (GWAS) literature, we study the statistical properties of linear mixed models (LMMs) applied to GWAS. First, we study the sensitivity of LMMs to…

Quantitative Methods · Quantitative Biology 2021-11-09 Haohan Wang , Bryon Aragam , Eric Xing

Identifying variants that carry substantial information on the trait of interest remains a core topic in genetic studies. In analyzing the EADB-UKBB dataset to identify genetic variants associated with Alzheimer's disease (AD), however, we…

Applications · Statistics 2025-12-04 Jiaqi Gu , Zhaomeng Chen , Zihuai He

Recent studies demonstrate that effective healthcare can benefit from using the human genomic information. For instance, analysis of tumor genomes has revealed 140 genes whose mutations contribute to cancer. As a result, many institutions…

Cryptography and Security · Computer Science 2017-03-09 Md Nazmus Sadat , Md Momin Al Aziz , Noman Mohammed , Feng Chen , Shuang Wang , Xiaoqian Jiang

Taking advantages of high-throughput genotyping technology of single nucleotide polymorphism (SNP), large genome-wide association studies (GWASs) have been considered as the promise to unravel the complex relationships between genotypes and…

Genomics · Quantitative Biology 2018-11-20 Xuan Guo

Understanding the genetic basis of complex traits is a longstanding challenge in the field of genomics. Genome-wide association studies (GWAS) have identified thousands of variant-trait associations, but most of these variants are located…

Molecular Networks · Quantitative Biology 2024-11-01 Marc Subirana-Granés , Jill Hoffman , Haoyu Zhang , Christina Akirtava , Sutanu Nandi , Kevin Fotso , Milton Pividori

Identifying which variables do influence a response while controlling false positives pervades statistics and data science. In this paper, we consider a scenario in which we only have access to summary statistics, such as the values of…