FastSKAT: Sequence kernel association tests for very large sets of markers

The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the geno...

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Published in:Genetic epidemiology Vol. 42; no. 6; pp. 516 - 527
Main Authors: Lumley, Thomas, Brody, Jennifer, Peloso, Gina, Morrison, Alanna, Rice, Kenneth
Format: Journal Article
Language:English
Published: United States Wiley Subscription Services, Inc 01.09.2018
Subjects:
Algorithms
Chromosomes, Human - metabolism
Computer applications
convolution
Eigenvalues
genetic association
Genetic Association Studies
Genetic diversity
Genetic Markers
Genotypes
Histones - metabolism
Humans
Lanczos algorithm
Phenotypes
randomized trace estimator
Sequence Analysis, DNA
Statistics as Topic
stochastic singular value decomposition
Time Factors
genetic association
Lanczos algorithm
randomized trace estimator
convolution
stochastic singular value decomposition
ISSN:0741-0395, 1098-2272, 1098-2272
Online Access:Get full text
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Summary:The sequence kernel association test (SKAT) is widely used to test for associations between a phenotype and a set of genetic variants that are usually rare. Evaluating tail probabilities or quantiles of the null distribution for SKAT requires computing the eigenvalues of a matrix related to the genotype covariance between markers. Extracting the full set of eigenvalues of this matrix (an n×n matrix, for n subjects) has computational complexity proportional to n3. As SKAT is often used when n>104, this step becomes a major bottleneck in its use in practice. We therefore propose fastSKAT, a new computationally inexpensive but accurate approximations to the tail probabilities, in which the k largest eigenvalues of a weighted genotype covariance matrix or the largest singular values of a weighted genotype matrix are extracted, and a single term based on the Satterthwaite approximation is used for the remaining eigenvalues. While the method is not particularly sensitive to the choice of k, we also describe how to choose its value, and show how fastSKAT can automatically alert users to the rare cases where the choice may affect results. As well as providing faster implementation of SKAT, the new method also enables entirely new applications of SKAT that were not possible before; we give examples grouping variants by topologically associating domains, and comparing chromosome‐wide association by class of histone marker.
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ISSN:0741-0395
1098-2272
1098-2272
DOI:10.1002/gepi.22136