Dataset Information

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

ABSTRACT: With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

SUBMITTER: Chen H

PROVIDER: S-EPMC6372261 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

Chen Han H Huffman Jennifer E JE Brody Jennifer A JA Wang Chaolong C Lee Seunggeun S Li Zilin Z Gogarten Stephanie M SM Sofer Tamar T Bielak Lawrence F LF Bis Joshua C JC Blangero John J Bowler Russell P RP Cade Brian E BE Cho Michael H MH Correa Adolfo A Curran Joanne E JE de Vries Paul S PS Glahn David C DC Guo Xiuqing X Johnson Andrew D AD Kardia Sharon S Kooperberg Charles C Lewis Joshua P JP Liu Xiaoming X Mathias Rasika A RA Mitchell Braxton D BD O'Connell Jeffrey R JR Peyser Patricia A PA Post Wendy S WS Reiner Alex P AP Rich Stephen S SS Rotter Jerome I JI Silverman Edwin K EK Smith Jennifer A JA Vasan Ramachandran S RS Wilson James G JG Yanek Lisa R LR Redline Susan S Smith Nicholas L NL Boerwinkle Eric E Borecki Ingrid B IB Cupples L Adrienne LA Laurie Cathy C CC Morrison Alanna C AC Rice Kenneth M KM Lin Xihong X

American journal of human genetics 20190110 2

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data wit ...[more]

PMID: 30639324

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Project description:MotivationDiagnosis and treatment decisions on genomic data have become widespread as the cost of genome sequencing decreases gradually. In this context, disease-gene association studies are of great importance. However, genomic data are very sensitive when compared to other data types and contains information about individuals and their relatives. Many studies have shown that this information can be obtained from the query-response pairs on genomic databases. In this work, we propose a method that uses secure multi-party computation to query genomic databases in a privacy-protected manner. The proposed solution privately outsources genomic data from arbitrarily many sources to the two non-colluding proxies and allows genomic databases to be safely stored in semi-honest cloud environments. It provides data privacy, query privacy and output privacy by using XOR-based sharing and unlike previous solutions, it allows queries to run efficiently on hundreds of thousands of genomic data.ResultsWe measure the performance of our solution with parameters similar to real-world applications. It is possible to query a genomic database with 3 000 000 variants with five genomic query predicates under 400 ms. Querying 1 048 576 genomes, each containing 1 000 000 variants, for the presence of five different query variants can be achieved approximately in 6 min with a small amount of dedicated hardware and connectivity. These execution times are in the right range to enable real-world applications in medical research and healthcare. Unlike previous studies, it is possible to query multiple databases with response times fast enough for practical application. To the best of our knowledge, this is the first solution that provides this performance for querying large-scale genomic data.Availability and implementationhttps://gitlab.com/DIFUTURE/privacy-preserving-variant-queries.Supplementary informationSupplementary data are available at Bioinformatics online.

Dataset Information

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

Publications

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

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