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AP-SKAT: highly-efficient genome-wide rare variant association test

Overview of attention for article published in BMC Genomics, September 2016
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4 tweeters

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8 Dimensions

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25 Mendeley
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Title
AP-SKAT: highly-efficient genome-wide rare variant association test
Published in
BMC Genomics, September 2016
DOI 10.1186/s12864-016-3094-3
Pubmed ID
Authors

Takanori Hasegawa, Kaname Kojima, Yosuke Kawai, Kazuharu Misawa, Takahiro Mimori, Masao Nagasaki

Abstract

Genome-wide association studies have revealed associations between single-nucleotide polymorphisms (SNPs) and phenotypes such as disease symptoms and drug tolerance. To address the small sample size for rare variants, association studies tend to group gene or pathway level variants and evaluate the effect on the set of variants. One of such strategies, known as the sequential kernel association test (SKAT), is a widely used collapsing method. However, the reported p-values from SKAT tend to be biased because the asymptotic property of the statistic is used to calculate the p-value. Although this bias can be corrected by applying permutation procedures for the test statistics, the computational cost of obtaining p-values with high resolution is prohibitive. To address this problem, we devise an adaptive SKAT procedure termed AP-SKAT that efficiently classifies significant SNP sets and ranks them according to the permuted p-values. Our procedure adaptively stops the permutation test when the significance level is outside some confidence interval of the estimated p-value for a binomial distribution. To evaluate the performance, we first compare the power and sample size calculation and the type I error rates estimate of SKAT, SKAT-O, and the proposed procedure using genotype data in the SKAT R package and from 1000 Genome Project. Through computational experiments using whole genome sequencing and SNP array data, we show that our proposed procedure is highly efficient and has comparable accuracy to the standard procedure. For several types of genetic data, the developed procedure could achieve competitive power and sample size under small and large sample size conditions with controlling considerable type I error rates, and estimate p-values of significant SNP sets that are consistent with those estimated by the standard permutation test within a realistic time. This demonstrates that the procedure is sufficiently powerful for recent whole genome sequencing and SNP array data with increasing numbers of phenotypes. Additionally, this procedure can be used in other association tests by employing alternative methods to calculate the statistics.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 20%
Researcher 5 20%
Other 4 16%
Student > Doctoral Student 3 12%
Professor > Associate Professor 2 8%
Other 1 4%
Unknown 5 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 24%
Biochemistry, Genetics and Molecular Biology 6 24%
Medicine and Dentistry 3 12%
Computer Science 2 8%
Environmental Science 1 4%
Other 2 8%
Unknown 5 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 October 2016.
All research outputs
#6,737,685
of 11,293,566 outputs
Outputs from BMC Genomics
#4,005
of 6,784 outputs
Outputs of similar age
#133,751
of 260,223 outputs
Outputs of similar age from BMC Genomics
#146
of 303 outputs
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