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Reconsidering Association Testing Methods Using Single-Variant Test Statistics as Alternatives to Pooling Tests for Sequence Data with Rare Variants

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  • Daniel D Kinnamon
  • Ray E Hershberger
  • Eden R Martin

Abstract

Association tests that pool minor alleles into a measure of burden at a locus have been proposed for case-control studies using sequence data containing rare variants. However, such pooling tests are not robust to the inclusion of neutral and protective variants, which can mask the association signal from risk variants. Early studies proposing pooling tests dismissed methods for locus-wide inference using nonnegative single-variant test statistics based on unrealistic comparisons. However, such methods are robust to the inclusion of neutral and protective variants and therefore may be more useful than previously appreciated. In fact, some recently proposed methods derived within different frameworks are equivalent to performing inference on weighted sums of squared single-variant score statistics. In this study, we compared two existing methods for locus-wide inference using nonnegative single-variant test statistics to two widely cited pooling tests under more realistic conditions. We established analytic results for a simple model with one rare risk and one rare neutral variant, which demonstrated that pooling tests were less powerful than even Bonferroni-corrected single-variant tests in most realistic situations. We also performed simulations using variants with realistic minor allele frequency and linkage disequilibrium spectra, disease models with multiple rare risk variants and extensive neutral variation, and varying rates of missing genotypes. In all scenarios considered, existing methods using nonnegative single-variant test statistics had power comparable to or greater than two widely cited pooling tests. Moreover, in disease models with only rare risk variants, an existing method based on the maximum single-variant Cochran-Armitage trend chi-square statistic in the locus had power comparable to or greater than another existing method closely related to some recently proposed methods. We conclude that efficient locus-wide inference using single-variant test statistics should be reconsidered as a useful framework for devising powerful association tests in sequence data with rare variants.

Suggested Citation

  • Daniel D Kinnamon & Ray E Hershberger & Eden R Martin, 2012. "Reconsidering Association Testing Methods Using Single-Variant Test Statistics as Alternatives to Pooling Tests for Sequence Data with Rare Variants," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
  • Handle: RePEc:plo:pone00:0030238
    DOI: 10.1371/journal.pone.0030238
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    References listed on IDEAS

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    1. Benjamin M Neale & Manuel A Rivas & Benjamin F Voight & David Altshuler & Bernie Devlin & Marju Orho-Melander & Sekar Kathiresan & Shaun M Purcell & Kathryn Roeder & Mark J Daly, 2011. "Testing for an Unusual Distribution of Rare Variants," PLOS Genetics, Public Library of Science, vol. 7(3), pages 1-8, March.
    2. Iuliana Ionita-Laza & Joseph D Buxbaum & Nan M Laird & Christoph Lange, 2011. "A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease," PLOS Genetics, Public Library of Science, vol. 7(2), pages 1-6, February.
    3. Thomas J Hoffmann & Nicholas J Marini & John S Witte, 2010. "Comprehensive Approach to Analyzing Rare Genetic Variants," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-9, November.
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    1. Nanye Long & Samuel P Dickson & Jessica M Maia & Hee Shin Kim & Qianqian Zhu & Andrew S Allen, 2013. "Leveraging Prior Information to Detect Causal Variants via Multi-Variant Regression," PLOS Computational Biology, Public Library of Science, vol. 9(6), pages 1-11, June.
    2. Xinge Jessie Jeng & Zhongyin John Daye & Wenbin Lu & Jung-Ying Tzeng, 2016. "Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-23, June.
    3. Rachel Marceau West & Wenbin Lu & Daniel M Rotroff & Melaine A Kuenemann & Sheng-Mao Chang & Michael C Wu & Michael J Wagner & John B Buse & Alison A Motsinger-Reif & Denis Fourches & Jung-Ying Tzeng, 2019. "Identifying individual risk rare variants using protein structure guided local tests (POINT)," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-24, February.

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