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A New Testing Strategy to Identify Rare Variants with Either Risk or Protective Effect on Disease

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  • Iuliana Ionita-Laza
  • Joseph D Buxbaum
  • Nan M Laird
  • Christoph Lange

Abstract

Rapid advances in sequencing technologies set the stage for the large-scale medical sequencing efforts to be performed in the near future, with the goal of assessing the importance of rare variants in complex diseases. The discovery of new disease susceptibility genes requires powerful statistical methods for rare variant analysis. The low frequency and the expected large number of such variants pose great difficulties for the analysis of these data. We propose here a robust and powerful testing strategy to study the role rare variants may play in affecting susceptibility to complex traits. The strategy is based on assessing whether rare variants in a genetic region collectively occur at significantly higher frequencies in cases compared with controls (or vice versa). A main feature of the proposed methodology is that, although it is an overall test assessing a possibly large number of rare variants simultaneously, the disease variants can be both protective and risk variants, with moderate decreases in statistical power when both types of variants are present. Using simulations, we show that this approach can be powerful under complex and general disease models, as well as in larger genetic regions where the proportion of disease susceptibility variants may be small. Comparisons with previously published tests on simulated data show that the proposed approach can have better power than the existing methods. An application to a recently published study on Type-1 Diabetes finds rare variants in gene IFIH1 to be protective against Type-1 Diabetes.Author Summary: Risk to common diseases, such as diabetes, heart disease, etc., is influenced by a complex interaction among genetic and environmental factors. Most of the disease-association studies conducted so far have focused on common variants, widely available on genotyping platforms. However, recent advances in sequencing technologies pave the way for large-scale medical sequencing studies with the goal of elucidating the role rare variants may play in affecting susceptibility to complex traits. The large number of rare variants and their low frequencies pose great challenges for the analysis of these data. We present here a novel testing strategy, based on a weighted-sum statistic, that is less sensitive than existing methods to the presence of both risk and protective variants in the genetic region under investigation. We show applications to simulated data and to a real dataset on Type-1 Diabetes.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pgen00:1001289
    DOI: 10.1371/journal.pgen.1001289
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    References listed on IDEAS

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    1. Brendan Maher, 2008. "Personal genomes: The case of the missing heritability," Nature, Nature, vol. 456(7218), pages 18-21, November.
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    1. Chung-Feng Kao & Jia-Rou Liu & Hung Hung & Po-Hsiu Kuo, 2015. "A Robust GWSS Method to Simultaneously Detect Rare and Common Variants for Complex Disease," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-14, April.
    2. Ren-Hua Chung & Wei-Yun Tsai & Eden R Martin, 2014. "Family-Based Association Test Using Both Common and Rare Variants and Accounting for Directions of Effects for Sequencing Data," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-7, September.
    3. Zheng Xu & Song Yan & Cong Wu & Qing Duan & Sixia Chen & Yun Li, 2023. "Next-Generation Sequencing Data-Based Association Testing of a Group of Genetic Markers for Complex Responses Using a Generalized Linear Model Framework," Mathematics, MDPI, vol. 11(11), pages 1-28, June.
    4. Martin Ladouceur & Zari Dastani & Yurii S Aulchenko & Celia M T Greenwood & J Brent Richards, 2012. "The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals," PLOS Genetics, Public Library of Science, vol. 8(2), pages 1-11, February.
    5. 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.
    6. Zheng Xu, 2023. "Association Testing of a Group of Genetic Markers Based on Next-Generation Sequencing Data and Continuous Response Using a Linear Model Framework," Mathematics, MDPI, vol. 11(6), pages 1-32, March.
    7. 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.

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