02617nas a2200301   4500008004100000022001400041245008000055210006900135260001200204300001200216490000700228520176700235653001502002653002302017653003202040653002002072653001302092653001102105653002702116653002402143653001702167100001902184700002002203700001702223700002102240700001802261856003602279       2018                        eng d  a1098-227200aFastSKAT: Sequence kernel association tests for very large sets of markers.0 aFastSKAT Sequence kernel association tests for very large sets o  c2018 09  a516-5270 v423 a<p>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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo>×</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math> matrix, for n subjects) has computational complexity proportional to n . As SKAT is often used when <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>n</mml:mi><mml:mo></mml:mo><mml:msup><mml:mn>10</mml:mn><mml:mn>4</mml:mn></mml:msup></mml:mrow></mml:math> , 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.</p>10aAlgorithms10aChromosomes, Human10aGenetic Association Studies10aGenetic Markers10aHistones10aHumans10aSequence Analysis, DNA10aStatistics as Topic10aTime Factors1 aLumley, Thomas1 aBrody, Jennifer1 aPeloso, Gina1 aMorrison, Alanna1 aRice, Kenneth  uhttps://chs-nhlbi.org/node/7921