pleioR: A powerful and fast test and software for the study of pleiotropy in systems involving many traits with biobank-sized data

pleioR：一个强大而快速的测试和软件，用于研究涉及生物库大小数据的许多性状的系统中的多效性

• 批准号: 10187158
• 负责人: Gustavo de los Campos
• 金额: $ 7.83万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10187158
• 关键词: Address; Adopted; Adoption; Affect; All of Us Research Program; Big Data; Biological; Chromosome Mapping; Code; Communities; Complex; Computer software; Data; Data Analyses; Data Set; Data Store; Development; Disease; Documentation; Environment; Etiology; Genes; Genetic; Genetic Predisposition to Disease; Genetic study; Genomic Segment; Genotype; Genotype-Tissue Expression Project; Grant; Health; Human; Human Genome; Individual; Knowledge; Link; Maps; Memory; Metabolic syndrome; Methodology; Methods; Modernization; Performance; Phenotype; Privatization; Property; Quantitative Trait Loci; Research; Risk; Sample Size; Sampling; Statistical Study; Syndrome; System; Testing; Variant; advanced disease; biobank; genetic analysis; genome wide association study; genomic locus; improved; novel; open source; pleiotropism; preservation; risk prediction; risk variant; simulation; software development; tool; trait; user-friendly

Pleiotropy (i.e., variants that confer risk to multiple characters) leads to the genetic correlation between traits and underlies the development of many syndromes. The identification of variants with pleiotropic effects on health- related traits can improve the biological understanding of gene action and disease etiology, and can help to advance disease-risk prediction. However, mapping pleiotropic risk loci is statistically and computationally challenging. Schaid et al. (Genetics, 2016) proposed an intersection-union sequential test that addresses the statistical challenges emerging in multi-trait genome-wide association analyses. Schaid’s sequential Likelihood Ratio Test (sLRT) is powerful, provides adequate error control, and leads to easy-to-interpret results. However, the adoption of the methodology remains limited because the proposed test and the existing software do not scale to big data (hundreds of thousands of individuals, millions of SNPs, many traits). Therefore, we propose to develop an alternative to the sLRT that achieves the same power but involves computations that scale to big data. Our approach adopts the intersection-union sequential testing framework but uses a Wald test and an approximation that substantially reduces the computational burden. Preliminary results presented in this grant show that the proposed test, and the beta C++ implementation we developed, has the power and error-control performance of the sLRT, it is considerably faster (by a factor of about 300), and scales to big data. In this project, we will (Aim 1) conduct extensive simulations to assess the statistical properties of the proposed test. (Aim 2) We will integrate memory mapping with optimized in-memory computations to develop open-source software that will implement the proposed test within the R environment, in a software package that will scale to big-data analysis. (Aim 3) Finally, we will use the methods and software developed in Aim 3, together with data from the UK-Biobank, to study the genetic underpinnings of Metabolic Syndrome. The advent of biobank data has opened unprecedented opportunities for mapping genetic loci affecting complex biological networks. However, more efficient data analysis tools are needed to unleash the potential of modern biobanks. This proposal will: (i) Develop novel methods for mapping risk loci affecting systems of traits. (ii) Develop and share with the research community software that can be used to analyze multidimensional phenotypes with big data. (iii) Advance knowledge of the genetic basis of Metabolic Syndrome.

多效性（即，将会议风险与多个字符风险）导致特征与 基础许多综合症的发展。鉴定具有多效影响对健康的变体 - 相关特征可以改善对基因作用和疾病病因的生物学理解，并可以帮助促进 疾病风险预测。 但是，在统计学上，映射多效风险基因座在统计上是受到挑战的。 Schaid等。 （遗传学，2016年） 提出了一个相交联合顺序测试，该测试解决了多特征中出现的统计挑战 全基因组关联分析。 Schaid的顺序可能比率测试（SLRT）功能强大，提供了足够的 错误控制，并导致易于解释结果。但是，该方法的采用仍然有限 因为拟议的测试和现有软件没有扩展到大数据（成千上万的个人， 数百万个SNP，许多特征）。因此，我们建议开发出实现同样的SLRT的替代方案 功率，但涉及将扩展到大数据的计算。 我们的方法采用了交叉点 - 工会顺序测试框架，但使用了WALD测试和近似 这大大减少了计算伯嫩。这笔赠款中提出的初步结果表明 拟议的测试以及我们开发的βC++实现，具有功率和错误控制性能 SLRT，它的周到更快（约为300倍），并扩展到大数据。 在该项目中，我们将（AIM 1）进行广泛的模拟来评估拟议测试的统计特性。 （AIM 2）我们将将内存映射与优化的内存计算集成以开发开源 将在R环境中实现拟议测试的软件，以扩展为 大数据分析。 （AIM 3）最后，我们将使用AIM 3中开发的方法和软件以及来自 英国 - 生物银行研究代谢综合征的遗传基础。 生物库数据的冒险已经为绘制遗传局部影响复合物的始终开辟了机会 生物网络。但是，需要更有效的数据分析工具来释放现代的潜力 生物库。该建议将：（i）开发新的方法来绘制风险本地影响特征系统。 （ii）发展 并与研究社区软件分享，可用于分析具有大的多维表型 数据。 （iii）提前了解代谢综合征的遗传基础。