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

• 批准号: 9249077
• 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
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9249077
• 关键词: 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; 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; Structure; Sum; Techniques; Testing; United States; United States National Institutes of Health; Validation; Variant; base; case control; congenital heart disorder; cost; database of Genotypes and Phenotypes; design; disability; disorder risk; expectation; genetic analysis; genetic variant; genome-wide; high risk; improved; innovation; novel; population stratification; public health relevance; rare variant; screening; tool; treatment strategy; validation studies

 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继续确定导致出生缺陷的常见遗传变异（婴儿死亡的主要原因[54]），但最近的证据表明，发现的基因座仅占风险的一小部分。因此，研究人员正在将重点转移到研究可能导致出生缺陷遗传性缺失的罕见变异上。三人设计（受影响的孩子和父母双方的基因分型）是首选的关联研究，因为它是强大的人口亚结构，只需要测序三个人，而不是更大的家庭。然而，当使用序列数据分析罕见变异体时，使用三重数据分析常见变异体的已建立方法受到降低的功效的阻碍。因此，迫切需要开发使用三重设计联合测试常见和罕见变异的方法，以全面建模与出生缺陷相关的遗传因素，以识别驱动与疾病风险相关的遗传变异。目前用于分析罕见变异的方法主要集中在汇集区域中的罕见变异并对该区域进行全局测试。为了进一步提高功效，一些方法包括模型中的常见变量作为协变量。在最近几种可以统计识别驱动关联的罕见变异的方法中，所有方法都是基于病例对照设计，这需要出生缺陷研究人员一直在收集三重数据来找到一组外部对照。这不是理想的。强烈需要可以使用三重数据联合分析常见和罕见变异的方法，以这样的方式可以统计地识别驱动与出生缺陷相关的遗传变异（罕见和常见）。我们建议满足这一需求，具有以下目标：（1）开发贝叶斯随机搜索变量选择方法，用于使用三重数据进行常见和罕见变异分析，可以识别驱动关联的变异;以及（2）将全局组件的性能与使用先天性心脏病的模拟和真实的数据的三重数据的现有全局测试进行比较。为了实现这些目标，我们将为三人组数据开发一种新的贝叶斯模型，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体（罕见和常见）的估计值。拟议的研究是创新的，因为它将开发新的和强大的统计方法。这项拟议中的研究意义重大，因为它将产生一种新的强大和广泛适用的方法，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研究，应用于真实的数据可以提高我们对先天性心脏缺陷病因的理解，潜在地改善先天性心脏缺陷的遗传咨询和预防策略。