TY - JOUR
T1 - FastSKAT: Sequence kernel association tests for very large sets of markers.
JF - Genet Epidemiol
Y1 - 2018
A1 - Lumley, Thomas
A1 - Brody, Jennifer
A1 - Peloso, Gina
A1 - Morrison, Alanna
A1 - Rice, Kenneth
KW - Algorithms
KW - Chromosomes, Human
KW - Genetic Association Studies
KW - Genetic Markers
KW - Histones
KW - Humans
KW - Sequence Analysis, DNA
KW - Statistics as Topic
KW - Time Factors
AB -
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 n . As SKAT is often used when n104 , 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.
VL - 42
IS - 6
ER -