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A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic

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  • Bo Eskerod Madsen
  • Sharon R Browning

Abstract

Resequencing is an emerging tool for identification of rare disease-associated mutations. Rare mutations are difficult to tag with SNP genotyping, as genotyping studies are designed to detect common variants. However, studies have shown that genetic heterogeneity is a probable scenario for common diseases, in which multiple rare mutations together explain a large proportion of the genetic basis for the disease. Thus, we propose a weighted-sum method to jointly analyse a group of mutations in order to test for groupwise association with disease status. For example, such a group of mutations may result from resequencing a gene. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated genes, both on simulated and Encode data. Using the weighted-sum method, a resequencing study can identify a disease-associated gene with an overall population attributable risk (PAR) of 2%, even when each individual mutation has much lower PAR, using 1,000 to 7,000 affected and unaffected individuals, depending on the underlying genetic model. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the weighted-sum method, are used.Author Summary: Resequencing is an emerging tool for the identification of rare disease-associated mutations. Recent studies have shown that groups of multiple rare mutations together can explain a large proportion of the genetic basis for some diseases. Therefore, we propose a new statistical method for analysing a group of mutations in order to test for groupwise association with disease status. We compare the proposed weighted-sum method to alternative methods and show that it is powerful for identifying disease-associated groups of mutations, both on computer-simulated and real data. By using computer simulations, we further show that resequencing a few thousand individuals is sufficient to perform a genome-wide study of all human genes, if the proposed method is used. This study thus demonstrates that resequencing studies can identify important genetic associations, provided that specialised analysis methods, such as the proposed weighted-sum method, are used.

Suggested Citation

  • Bo Eskerod Madsen & Sharon R Browning, 2009. "A Groupwise Association Test for Rare Mutations Using a Weighted Sum Statistic," PLOS Genetics, Public Library of Science, vol. 5(2), pages 1-11, February.
  • Handle: RePEc:plo:pgen00:1000384
    DOI: 10.1371/journal.pgen.1000384
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    Citations

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    Cited by:

    1. Wan-Yu Lin & Xiang-Yang Lou & Guimin Gao & Nianjun Liu, 2014. "Rare Variant Association Testing by Adaptive Combination of P-values," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-7, January.
    2. Ai-Ru Hsieh & Dao-Peng Chen & Amrita Sengupta Chattopadhyay & Ying-Ju Li & Chien-Ching Chang & Cathy S J Fann, 2017. "A non-threshold region-specific method for detecting rare variants in complex diseases," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-16, November.
    3. Diana Chang & Alon Keinan, 2012. "Predicting Signatures of “Synthetic Associations” and “Natural Associations” from Empirical Patterns of Human Genetic Variation," PLOS Computational Biology, Public Library of Science, vol. 8(7), pages 1-9, July.
    4. Morgan Mayer-Jochimsen & Shannon Fast & Nathan L Tintle, 2013. "Assessing the Impact of Differential Genotyping Errors on Rare Variant Tests of Association," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-10, March.
    5. Gaurav Bhatia & Vikas Bansal & Olivier Harismendy & Nicholas J Schork & Eric J Topol & Kelly Frazer & Vineet Bafna, 2010. "A Covering Method for Detecting Genetic Associations between Rare Variants and Common Phenotypes," PLOS Computational Biology, Public Library of Science, vol. 6(10), pages 1-12, October.
    6. Yao-Hwei Fang & Yen-Feng Chiu, 2013. "A Novel Support Vector Machine-Based Approach for Rare Variant Detection," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-9, August.
    7. ChangJiang Xu & Martin Ladouceur & Zari Dastani & J Brent Richards & Antonio Ciampi & Celia M T Greenwood, 2012. "Multiple Regression Methods Show Great Potential for Rare Variant Association Tests," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    8. Ruixue Fan & Shaw-Hwa Lo, 2013. "A Robust Model-free Approach for Rare Variants Association Studies Incorporating Gene-Gene and Gene-Environmental Interactions," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-14, December.

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