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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome

Han Chen1, Jennifer E Huffman2, Jennifer A Brody3

  • 1Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Center for Precision Health, School of Public Health and School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.

American Journal of Human Genetics
|January 15, 2019
PubMed
Summary

We developed new statistical tests called variant-set mixed model association tests (SMMAT) for analyzing whole-genome sequencing data. These tests efficiently handle complex genetic data, improving rare variant association studies.

Keywords:
TOPMedgeneralized linear mixed modelpopulation structurerare variantsrelatednessvariant set association testwhole-genome sequencing

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Whole-genome sequencing (WGS) generates vast amounts of data, necessitating advanced statistical methods for genetic association studies.
  • Existing variant-set tests (e.g., burden test, SKAT) have limitations in handling complex samples with population structure and relatedness, common in large-scale WGS projects.
  • There is a need for computationally efficient and powerful methods to analyze rare variants in large, complex datasets.

Purpose of the Study:

  • To propose novel variant-set mixed model association tests (SMMAT) for analyzing rare variants in large-scale WGS studies.
  • To develop methods applicable to both continuous and binary traits within a generalized linear mixed model framework.
  • To address the challenges posed by population structure and relatedness in complex study samples.

Main Methods:

  • Developed SMMAT using the generalized linear mixed model framework.
  • Implemented a shared null model that requires fitting only once per genome-wide analysis, enhancing computational efficiency.
  • Applied the tests to simulated data and a real dataset from the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program.

Main Results:

  • SMMAT correctly controls type I error rates for both continuous and binary traits in the presence of population structure and relatedness.
  • The proposed methods are computationally efficient and suitable for large-scale WGS studies.
  • Demonstrated the utility of SMMAT in analyzing plasma fibrinogen levels in a large TOPMed cohort.

Conclusions:

  • SMMAT provides a powerful and efficient approach for rare variant association testing in large-scale WGS studies with complex sample structures.
  • The methods are robust to population structure and relatedness, making them suitable for diverse genomic datasets.
  • SMMAT facilitates genetic discovery in precision medicine initiatives like TOPMed.