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相关论文: Genome-wide Causation Studies of Complex Diseases

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Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~10^4-10^5) are needed to…

基因组学 · 定量生物学 2013-08-20 Inti Pedroso

Genome-wide association studies(GWAS) have proven to be highly useful in revealing the genetic basis of complex diseases. At present, most GWAS are studies of a particular single disease diagnosis against controls. However, in practice, an…

基因组学 · 定量生物学 2021-01-01 Liangying Yin , Carlos Kwan-long Chau , Yu-Ping Lin , Pak-Chung Sham , Hon-Cheong So

Genome-wide association studies (GWAS) have emerged as a rich source of genetic clues into disease biology, and they have revealed strong genetic correlations among many diseases and traits. Some of these genetic correlations may reflect…

统计方法学 · 统计学 2018-11-28 Luke J. O'Connor , Alkes L. Price

For the vast majority of genome wide association studies (GWAS) published so far, statistical analysis was performed by testing markers individually. In this article we present some elementary statistical considerations which clearly show…

应用统计 · 统计学 2010-10-04 Florian Frommlet , Felix Ruhaltinger , Piotr Twarog , Malgorzata Bogdan

Motivation: Genome-wide association studies (GWASs), which assay more than a million single nucleotide polymorphisms (SNPs) in thousands of individuals, have been widely used to identify genetic risk variants for complex diseases. However,…

计算工程、金融与科学 · 计算机科学 2015-01-27 Ben Teng , Can Yang , Jiming Liu , Zhipeng Cai , Xiang Wan

Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations.…

定量方法 · 定量生物学 2020-12-21 Sezin Kircali Ata , Min Wu , Yuan Fang , Le Ou-Yang , Chee Keong Kwoh , Xiao-Li Li

The majority of common diseases are influenced by multiple genetic and environmental factors such as Cancer. Even though uncovering the main causes of disease is deemed difficult due to the complexity of gene-gene and gene-environment…

其他计算机科学 · 计算机科学 2017-05-10 Layan Nahlawi

It is widely recognized nowadays that complex diseases are caused by, amongst the others, multiple genetic factors. The recent advent of genome-wide association study (GWA) has triggered a wave of research aimed at discovering genetic…

应用统计 · 统计学 2011-09-30 Nguyen Xuan Vinh

Genome-wide association studies (GWASs) have been extensively adopted to depict the underlying genetic architecture of complex diseases. Motivated by GWASs' limitations in identifying small effect loci to understand complex traits'…

统计方法学 · 统计学 2023-10-09 Xinran Qi , Michael E. Belloy , Jiaqi Gu , Xiaoxia Liu , Hua Tang , Zihuai He

Genome-Wide Association Studies (GWAS) identify associations between genetic variants and disease; however, moving beyond associations to causal mechanisms is critical for therapeutic target prioritization. The recently proposed Knowledge…

A computationally simple genome-wide association study (GWAS) algorithm for estimating the main and epistatic effects of markers or single nucleotide polymorphisms (SNPs) is proposed. It is based on the intuitive assumption that changes of…

定量方法 · 定量生物学 2017-08-08 Lev V. Utkin , Irina L. Utkina

Motivation: Genome-Wide Association Studies (GWAS) seek to identify causal genomic variants associated with rare human diseases. The classical statistical approach for detecting these variants is based on univariate hypothesis testing, with…

统计方法学 · 统计学 2018-10-22 Florent Guinot , Marie Szafranski , Christophe Ambroise , Franck Samson

Genome-wide association studies (GWAS) provide a means of examining the common genetic variation underlying a range of traits and disorders. In addition, it is hoped that GWAS may provide a means of differentiating affected from unaffected…

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…

统计方法学 · 统计学 2010-10-25 Nan M. Laird , Christoph Lange

Summary statistics of genome-wide association studies (GWAS) teach causal relationship between millions of genetic markers and tens and thousands of phenotypes. However, underlying biological mechanisms are yet to be elucidated. We can…

机器学习 · 统计学 2019-01-25 Yongjin Park , Abhishek Sarkar , Khoi Nguyen , Manolis Kellis

Combining data from several case-control genome-wide association (GWA) studies can yield greater efficiency for detecting associations of disease with single nucleotide polymorphisms (SNPs) than separate analyses of the component studies.…

统计方法学 · 统计学 2010-10-26 Ruth M. Pfeiffer , Mitchell H. Gail , David Pee

Genetic association studies have been a popular approach for assessing the association between common Single Nucleotide Polymorphisms (SNPs) and complex diseases. However, other genomic data involved in the mechanism from SNPs to disease,…

应用统计 · 统计学 2014-04-28 Yen-Tsung Huang , Tyler J. VanderWeele , Xihong Lin

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…

机器学习 · 计算机科学 2018-08-30 Basma Abdulaimma , Paul Fergus , Carl Chalmers

We present an alternative method for genome-wide association studies (GWAS) that is more powerful than the regular GWAS method for locus detection. The regular GWAS method suffers from a substantial multiple-testing burden because of the…

Motivation: Genome-wide association studies (GWAS) have successfully identified thousands of genetic risk loci for complex traits and diseases. Most of these GWAS loci lie in regulatory regions of the genome and the gene through which each…

定量方法 · 定量生物学 2022-10-31 Leonardo Martini , Adriano Fazzone , Michele Gentili , Luca Becchetti , Brian Hobbs
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