A Novel Trio-based Bayesian Method to Identify Rare Variants for Birth Defects

一种新的基于三重奏的贝叶斯方法来识别出生缺陷的罕见变异

基本信息

批准号：
9035008
负责人：
MICHAEL D SWARTZ
金额：
$ 7.7万
依托单位：
UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-06-01 至 2018-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9035008
关键词：
Accounting Affect Algorithms Automobile Driving Bayesian Method Bayesian Modeling Benchmarking Biological Candidate Disease Gene Child Child health care Childhood Clinical Congenital Abnormality Congenital Heart Defects Couples Data Data Set Disease Etiology Evolution Family Foundations Gene Frequency Genes Genetic Genetic Counseling Genetic Models Genetic Variation Genotype Healthcare Hereditary Disease Heritability Human Individual Infant Mortality Intervention Lead Logistic Regressions Measures Methodology Methods Minor Mission Modeling Morbidity - disease rate National Heart, Lung, and Blood Institute Newborn Infant Parents Pediatric Cardiac Genomics Consortium Performance Population Prevalence Prevention strategy Property Public Health Research Research Personnel Risk Sampling Sequence Analysis Speed Stratification Structure Sum Techniques Testing United States Validation Variant Weight base case control congenital heart disorder cost database of Genotypes and Phenotypes design disability disorder risk expectation genetic variant genome-wide high risk improved innovation novel public health relevance rare variant screening tool treatment strategy validation studies

项目摘要

DESCRIPTION (provided by applicant): Although advances in genome wide and candidate gene association studies continue to identify common genetic variants contributing to birth defects (a leading cause of infant mortality [54]), recent evidence indicates that discovered loci only account for a small fraction of risk. As a result, researchers are shifting focus to investigae rare variants that could be responsible for this missing heritability of birth defects. The trio design (genotyping the affected child and both parents) is preferred for such association studies because it is robust to population substructure and only requires sequencing three people, rather than larger families. However established methods for analyzing common variants using trio data are hindered by reduced power when analyzing rare variants using sequence data. Thus, there is a critical need to develop methods that jointly test for common and rare variants using the trio design to comprehensively model the genetic factors associated with birth defects in order to identify the genetic variants driving the association with disease risk. Current methods for analyzing rare variants primarily focus on pooling the rare variants in a region and performing a global test for that region. To further increase power, some methods include common variants in the model as covariates. Of the few most recent methods that can statistically identify rare variants that drive the association, all are based on the case control design, which requires birth defect researchers who have been collecting trio data to find an external set of controls. This is less than ideal. There is a strong need for methods that can jointly analyze common and rare variants using trio data in such a way that can statistically identify the genetic variants (rare and common) that drive association with birth defects. We propose to fulfill this need with the following aims: (1) Develop a Bayesian stochastic search variable selection method for common and rare variant analysis using trio data that can identify the variants driving the association; and (2) Compare the performance of the global component to existing global tests for trio data using both simulated and real data for congenital heart defects. To achieve these aims, we will develop a novel Bayesian model for trio data, using the Expectation-Maximization algorithm to quickly compute the modes of the Bayesian posterior distribution as estimates for genetic regions as well as specific variants (rare and common). The proposed research is innovative because it will develop new and powerful statistical methodology. The proposed research is significant because it will produce a new powerful and widely applicable method for uncovering the genetic basis for birth defects and other childhood diseases. Ultimately, the new methods will improve birth defects research, and the application to real data can improve our understanding of the etiology of congenital heart defects, potentially improving genetic counseling and prevention strategies for congenital heart defects.

描述（由申请人提供）：尽管全基因组和候选基因关联研究的进展不断发现导致出生缺陷（婴儿死亡的主要原因[54]）的常见遗传变异，但最近的证据表明，发现的基因座仅占一小部分。因此，研究人员将注意力转向研究罕见变异，这些变异可能是导致出生缺陷遗传性缺失的原因。三重设计（对受影响的孩子和父母双方进行基因分型）适用于此类关联研究，因为它是稳健的。人口然而，使用三人组数据分析常见变异的现有方法在使用序列数据分析罕见变异时会受到限制，因此迫切需要开发联合测试的方法。常见和罕见变异使用三重奏设计对与出生缺陷相关的遗传因素进行全面建模，以确定与疾病风险相关的遗传变异，目前分析罕见变异的方法主要集中于汇集一个区域的罕见变异并进行分析。为了进一步增强该地区的实力，一些人进行了全球测试。在可以从技术上识别驱动关联的罕见变异的几种最新方法中，所有方法都基于病例对照设计，这需要一直在收集三重数据的出生缺陷研究人员来找到一个模型。这并不理想，迫切需要能够使用三组数据联合分析常见和罕见变异的方法，以便从技术上识别与出生相关的遗传变异（罕见和常见）。我们建议通过以下目标来满足这一需求： (1) 使用三重数据开发一种用于常见和罕见变异分析的贝叶斯随机搜索变量选择方法，该方法可以识别驱动关联的变异；(2) 使用模拟数据将全局组件的性能与现有的三重数据全局测试进行比较；为了实现这些目标，我们将为三重数据开发一种新颖的贝叶斯模型，使用期望最大化算法快速计算贝叶斯后验分布的模式作为遗传区域以及特定变异的估计。（罕见且常见）。这项研究具有创新性，因为它将开发新的、强大的统计方法。这项研究意义重大，因为它将产生一种新的强大且广泛适用的方法来揭示出生缺陷和其他儿童疾病的遗传基础。最终，新方法将改善出生缺陷的研究，并且对真实数据的应用可以提高我们对先天性心脏病病因学的理解，有可能改善先天性心脏病的遗传咨询和预防策略。