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（由适用提供）：尽管基因组范围和候选基因关联研究的进展继续确定有助于出生缺陷的常见遗传变异（婴儿死亡率的主要原因[54]），但最近的证据表明，发现的地区仅占一小部分风险。结果，研究人员正在将重点转移到研究稀有变体上，这些变体可能导致这种缺失的先天缺陷的遗传力。对于这种关联研究，三人设计（基因分型为受影响的孩子，父母都是父母）是优选的，因为它对人口子结构是强大的，并且只需要测序三个人而不是较大的家庭。然而，当使用序列数据分析稀有变体时，使用三重奏数据分析使用三重奏数据的常见变体的建立方法会受到降低。这是迫切需要开发使用三重奏设计共同测试常见和稀有变体的方法，以全面地对与先天缺陷相关的遗传因素进行建模，以确定推动与疾病风险关联的遗传变异。当前分析稀有变体的方法主要集中在将稀有变体汇总在一个区域中，并对该区域进行全球测试。为了进一步提高功率，某些方法作为协变量中的模型中包含常见变体。在几种可以从统计上识别驱动关联的稀有变体的最新方法中，所有方法都是基于案例控制设计的，这需要出生缺陷的研究人员，这些研究人员已经收集了三重奏数据才能找到一组外部控件。这不是理想的。强烈需要使用三重奏数据共同分析常见和稀有变体的方法，以统计识别鉴定与先天缺陷相关的遗传变异（稀有和常见）。我们建议通过以下目的来满足这一需求：（1）使用可以识别驱动该关联的变体的三重量数据来开发一种贝叶斯随机搜索变量选择方法，以用于常见和稀有变体分析；（2）使用先天性心脏缺陷的模拟和真实数据将全球组件与现有三重奏数据的现有全局测试进行比较。为了实现这些目标，我们将使用期望最大化算法快速计算拟议的研究具有创新性，因为它将开发出新的且强大的统计方法论，因此我们将开发一个新型的三重奏数据贝叶斯模型。拟议的研究之所以重要，是因为它将产生一种新的强大且广泛适用的方法，用于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，而实际数据的应用可以提高我们对先天性心脏缺陷的病因的理解，从而有可能改善先天性心脏缺陷的遗传性凝聚力和预防策略。