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Comprehensive Approach to Analyzing Rare Genetic Variants

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  • Thomas J Hoffmann
  • Nicholas J Marini
  • John S Witte

Abstract

Recent findings suggest that rare variants play an important role in both monogenic and common diseases. Due to their rarity, however, it remains unclear how to appropriately analyze the association between such variants and disease. A common approach entails combining rare variants together based on a priori information and analyzing them as a single group. Here one must make some assumptions about what to aggregate. Instead, we propose two approaches to empirically determine the most efficient grouping of rare variants. The first considers multiple possible groupings using existing information. The second is an agnostic “step-up” approach that determines an optimal grouping of rare variants analytically and does not rely on prior information. To evaluate these approaches, we undertook a simulation study using sequence data from genes in the one-carbon folate metabolic pathway. Our results show that using prior information to group rare variants is advantageous only when information is quite accurate, but the step-up approach works well across a broad range of plausible scenarios. This agnostic approach allows one to efficiently analyze the association between rare variants and disease while avoiding assumptions required by other approaches for grouping such variants.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0013584
    DOI: 10.1371/journal.pone.0013584
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    Cited by:

    1. Timothy D O’Connor & Adam Kiezun & Michael Bamshad & Stephen S Rich & Joshua D Smith & Emily Turner & NHLBIGO Exome Sequencing Project & ESP Population Genetics, Statistical Analysis Working Group & S, 2013. "Fine-Scale Patterns of Population Stratification Confound Rare Variant Association Tests," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
    2. Gourab De & Wai-Ki Yip & Iuliana Ionita-Laza & Nan Laird, 2013. "Rare Variant Analysis for Family-Based Design," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-9, January.
    3. Rajesh Talluri & Sanjay Shete, 2013. "A Linkage Disequilibrium–Based Approach to Selecting Disease-Associated Rare Variants," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-6, July.
    4. 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.
    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. Yi Nengjun & Xu Shizhong & Lou Xiang-Yang & Mallick Himel, 2014. "Multiple comparisons in genetic association studies: a hierarchical modeling approach," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 13(1), pages 35-48, February.
    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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