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Identifying rare haplotype-environment interactions using Logistic Bayesian Lasso

使用逻辑贝叶斯套索识别罕见的单倍型-环境相互作用

基本信息

批准号：
8508230
负责人：
Swati Biswas
金额：
$ 8.23万
依托单位：
UNIVERSITY OF TEXAS DALLAS
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-10 至 2015-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8508230
关键词：
Age related macular degeneration Cancer Etiology Case-Control Studies Code Communities Complement Factor H Complex Computer software Data Data Analyses Data Set Databases Detection Disease Drug Formulations Environment Environmental Risk Factor Frequencies Funding Genes Genotype Goals Haplotypes Health Heritability Lasso Logistics Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of pancreas Malignant neoplasm of prostate Malignant neoplasm of urinary bladder Methods Mining Modeling Names Noise Phenotype Population Prevention Rare Diseases Renal Cell Carcinoma Renal carcinoma Role Sampling Scheme Second Look Surgery Signal Transduction Statistical Methods Technology Time Variant Writing base cancer epidemiology case control cost database of Genotypes and Phenotypes design flexibility gene environment interaction genetic variant genome wide association study improved meetings melanoma method development next generation sequencing non-genetic programs prospective prototype simulation user friendly software

项目摘要

DESCRIPTION (provided by applicant): Rare variants have been heralded as key to uncovering \missing heritability" in complex diseases such as cancers. These variants can now be genotyped using next-generation sequencing technologies; nonetheless, rare haplotypes may also result from combination of common SNPs available from Genome-Wide Association Studies (GWAS). In this regard, there may be a great deal of treasure that are yet to be mined from the GWAS data to explore the common disease rare variant hypothesis. Recently, we have proposed an approach named Logistic Bayesian LASSO (LBL) to identify association with rare haplotypes in a case-control setting. LBL is an adaptation of the Bayesian counterpart of penalized regression approach LASSO. Our approach is able to weed out unassociated (especially common) haplotypes to achieve enough noise reduction so that the signals contained in the associated rare haplotypes can be more easily detected. Using LBL, we were able to implicate a specific rare haplotype for Age-related Macular Degeneration (AMD) in the Complement Factor H (CFH) gene for the first time. In addition to rare variants, gene-environment interaction (GXE) is believed to be another important contributor to missing heritability. LBL has a flexible framework that can incorporate non-genetic (environmental) covariates and gene- environment interactions. In this project we propose methods for exploring interactions between rare haplotypes and environmental factors in cancer epidemiology, rst in the setting of simple random sampling and then for stratified random sampling. We will develop methods both with and without the assumption of gene-environment independence. The methods will be extensively studied through simulations under a variety of settings. They will be applied to several cancer datasets available from NIH's database of Genotypes and Phenotypes (dbGaP) and the AMD data. Further, the method for stratified sampling will be used to analyze the NCI-sponsored Kidney Cancer Case-Control Study, wherein the controls were selected by stratified sampling using frequency matching with cases. We will implement the proposed methods in a well-documented user-friendly software and make it available to the larger scientific community.

描述（由申请人提供）：罕见变异已被宣布为发现复杂疾病（如癌症）中“缺失遗传性”的关键。这些变体现在可以使用下一代测序技术进行基因分型;尽管如此，罕见的单倍型也可能是由全基因组关联研究（GWAS）提供的常见SNP组合引起的。在这方面，可能有大量的宝藏尚未从GWAS数据中挖掘出来，以探索常见疾病罕见变异假说。最近，我们提出了一种名为Logistic贝叶斯LASSO（LBL）的方法，以确定与罕见的单倍型在病例对照设置。LBL是惩罚回归方法LASSO的贝叶斯对应物的适应。我们的方法能够剔除不相关的（特别是常见的）单倍型，以实现足够的降噪，从而可以更容易地检测到相关的罕见单倍型中包含的信号。使用LBL，我们能够首次在补体因子H（CFH）基因中发现一种与AMD相关的罕见单倍型。除了罕见的变异，基因-环境相互作用（GXE）被认为是缺失遗传性的另一个重要因素。LBL有一个灵活的框架，可以纳入非遗传（环境）协变量和基因-环境相互作用。在这个项目中，我们提出了在癌症流行病学中探索罕见单倍型和环境因素之间相互作用的方法，首先是在简单随机抽样的背景下，然后是分层随机抽样。我们将发展的方法，有和没有基因环境独立的假设。这些方法将通过各种设置下的模拟进行广泛研究。它们将被应用于NIH基因型和表型数据库（dbGaP）和AMD数据中的几个癌症数据集。此外，分层抽样的方法将用于分析NCI申办的肾癌病例对照研究，其中对照是通过使用与病例频率匹配的分层抽样选择的。我们将在一个记录良好的用户友好的软件中实施所提出的方法，并将其提供给更大的科学界。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Comparison of haplotype-based tests for detecting gene-environment interactions with rare variants.

用于检测基因与环境与罕见变异的相互作用的基于单倍型的测试的比较。

DOI：
10.1093/bib/bbz031
发表时间：
2020
期刊：
Briefings in bioinformatics
影响因子：
9.5
作者：
Papachristou,Charalampos;Biswas,Swati
通讯作者：
Biswas,Swati

Population-based association and gene by environment interactions in Genetic Analysis Workshop 18.

遗传分析研讨会 18 中基于群体的关联和环境相互作用的基因。

DOI：
10.1002/gepi.21825
发表时间：
2014
期刊：
Genetic epidemiology
影响因子：
2.1
作者：
Satten,GlenA;Biswas,Swati;Papachristou,Charalampos;Turkmen,Asuman;König,InkeR
通讯作者：
König,InkeR

Association of rare haplotypes on ULK4 and MAP4 genes with hypertension.