• Publications
  • Influence
Rare-variant association testing for sequencing data with the sequence kernel association test.
The sequence kernel association test (SKAT) is proposed, a supervised, flexible, computationally efficient regression method to test for association between genetic variants (common and rare) in a region and a continuous or dichotomous trait while easily adjusting for covariates. Expand
Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies.
A unified approach for testing the association between rare variants and phenotypes in sequencing association studies is proposed and it is shown that the unified test corresponds to the optimal test in an extended family of SKAT tests, which is referred to as SKAT-O. Expand
Optimal tests for rare variant effects in sequencing association studies.
This paper proposes a class of tests that include burden tests and SKAT as special cases, and derives an optimal test within this class that maximizes power, and shows that this optimal test outperforms burden testsand SKAT in a wide range of scenarios. Expand
Sequence kernel association tests for the combined effect of rare and common variants.
Several sequence kernel association tests are introduced to evaluate the cumulative effect of rare and common variants and can achieve substantial increases in power compared with the most commonly used tests, including the burden and variance-component tests. Expand
Rare-variant association analysis: study designs and statistical tests.
An overview of statistical issues in rare-variant association studies with a focus on study designs and statistical tests is provided and various gene- or region-based association tests are compared in terms of their assumptions and performance. Expand
General framework for meta-analysis of rare variants in sequencing association studies.
The proposed meta-analysis methods for commonly used gene- or region-based rare variants tests, such as burden tests and variance component tests, are applicable to meta- analysis of multiple ancestry groups and are essentially as powerful as joint analysis by directly pooling individual level genotype data. Expand
Rare Variant Association Testing for Sequencing Data Using the Sequence Kernel Association Test ( SKAT )
*These authors contributed equally to this work. 1 Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA 2 Department of Biostatistics, HarvardExpand
Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies
SAIGE is a scalable and accurate generalized mixed model association test that can efficiently analyze large data sets while controlling for unbalanced case-control ratios and sample relatedness, as shown by applying SAIGE to the UK Biobank data for > 1,400 binary phenotypes. Expand
The Genome Architecture of the Collaborative Cross Mouse Genetic Reference Population
The Collaborative Cross Consortium reports here on the development of a unique genetic resource population, a multiparental recombinant inbred panel derived from eight laboratory mouse inbred strains, which shows that founder haplotypes are inherited at the expected frequency. Expand
Test for interactions between a genetic marker set and environment in generalized linear models.
If the main effects of multiple SNPs in a set are associated with a disease/trait, the classical single SNP-GE interaction analysis can be biased and have an inflated Type 1 error rate, a computationally efficient and powerful gene-environment set association test (GESAT) in generalized linear models is proposed. Expand