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

多效性（即，赋予多个性状风险的变异）导致性状之间的遗传相关性， 是许多综合症的基础对健康具有多效性的变异体的鉴定- 相关性状可以提高对基因作用和疾病病因学的生物学理解， 疾病风险预测 然而，绘制多效性风险基因座在统计学和计算上具有挑战性。Schaid等人（Genetics，2016） 提出了一个交叉联合序贯测试，解决了多性状中出现的统计挑战， 全基因组关联分析。Schaid的序贯似然比检验（sLRT）是强大的，提供了足够的 错误控制，并导致易于解释的结果。然而，采用这一方法的情况仍然有限 因为所提出的测试和现有的软件不能扩展到大数据（数十万个个体， 数以百万计的SNP，许多性状）。因此，我们建议发展另一种轻便铁路系统， 但涉及到大数据的计算。 我们的方法采用了交叉-联合顺序测试框架，但使用了Wald测试和近似 这大大减少了计算负担。初步研究结果显示， 建议的测试，以及我们开发的beta C++实现，具有 sLRT，它的速度要快得多（大约是300倍），并且可以扩展到大数据。 在这个项目中，我们将（目标1）进行广泛的模拟，以评估拟议的测试的统计特性。 (Aim 2）我们将整合内存映射与优化的内存计算，开发开源 将在R环境中实施拟议测试的软件，其软件包将扩展到 大数据分析(Aim 3）最后，我们将使用目标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。 生物库数据的出现为绘制影响复杂性的遗传位点提供了前所未有的机会。 生物网络然而，需要更有效的数据分析工具来释放现代数据分析的潜力。 生物银行这项建议将：（一）发展新的方法，绘制影响性状系统的风险基因座。(ii)发展 并与研究社区分享可用于分析大规模多维度表型的软件， 数据(iii)了解代谢综合征的遗传基础。

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.