Identifying individual risk rare variants using protein structure guided local tests (POINT)

Rachel Marceau West, Wenbin Lu, Daniel M. Rotroff, Melaine A. Kuenemann, Sheng Mao Chang, Michael C. Wu, Michael J. Wagner, John B. Buse, Alison A. Motsinger-Reif, Denis Fourches, Jung Ying Tzeng

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

Rare variants are of increasing interest to genetic association studies because of their etiological contributions to human complex diseases. Due to the rarity of the mutant events, rare variants are routinely analyzed on an aggregate level. While aggregation analyses improve the detection of global-level signal, they are not able to pinpoint causal variants within a variant set. To perform inference on a localized level, additional information, e.g., biological annotation, is often needed to boost the information content of a rare variant. Following the observation that important variants are likely to cluster together on functional domains, we propose a protein structure guided local test (POINT) to provide variant-specific association information using structure-guided aggregation of signal. Constructed under a kernel machine framework, POINT performs local association testing by borrowing information from neighboring variants in the 3-dimensional protein space in a data-adaptive fashion. Besides merely providing a list of promising variants, POINT assigns each variant a p-value to permit variant ranking and prioritization. We assess the selection performance of POINT using simulations and illustrate how it can be used to prioritize individual rare variants in PCSK9, ANGPTL4 and CETP in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) clinical trial data.

原文English
文章編號e1006722
期刊PLoS computational biology
15
發行號2
DOIs
出版狀態Published - 2019 二月

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

指紋 深入研究「Identifying individual risk rare variants using protein structure guided local tests (POINT)」主題。共同形成了獨特的指紋。

引用此