ArticleAmerican journal of human genetics2019
ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.
Article in American journal of human genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 298 papers, 7 of them syntheses that pooled it.
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Who cites it
298 citing papers in PubMed, 7 syntheses or guidelines pooled it.
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- Computationally efficient meta-analysis of gene-based tests using summary statistics in large-scale genetic studies.Nature genetics · 2025Pooled it
- Scalable and accurate rare variant meta-analysis with Meta-SAIGE.Nature genetics · 2025Pooled it
- Cross-ancestry genome-wide association study identifies new susceptibility genes for preeclampsia.BMC pregnancy and childbirth · 2025Pooled it
- Expression- and splicing-based multi-tissue transcriptome-wide association studies identified multiple genes for breast cancer by estrogen-receptor status.Breast cancer research : BCR · 2024Pooled it
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238 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.
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