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A non-threshold region-specific method for detecting rare variants in complex diseases

Author

Listed:
  • Ai-Ru Hsieh
  • Dao-Peng Chen
  • Amrita Sengupta Chattopadhyay
  • Ying-Ju Li
  • Chien-Ching Chang
  • Cathy S J Fann

Abstract

A region-specific method, NTR (non-threshold rare) variant detection method, was developed—it does not use the threshold for defining rare variants and accounts for directions of effects. NTR also considers linkage disequilibrium within the region and accommodates common and rare variants simultaneously. NTR weighs variants according to minor allele frequency and odds ratio to combine the effects of common and rare variants on disease occurrence into a single score and provides a test statistic to assess the significance of the score. In the simulations, under different effect sizes, the power of NTR increased as the effect size increased, and the type I error of our method was controlled well. Moreover, NTR was compared with several other existing methods, including the combined multivariate and collapsing method (CMC), weighted sum statistic method (WSS), sequence kernel association test (SKAT), and its modification, SKAT-O. NTR yields comparable or better power in simulations, especially when the effects of linkage disequilibrium between variants were at least moderate. In an analysis of diabetic nephropathy data, NTR detected more confirmed disease-related genes than the other aforementioned methods. NTR can thus be used as a complementary tool to help in dissecting the etiology of complex diseases.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0188566
    DOI: 10.1371/journal.pone.0188566
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    References listed on IDEAS

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    1. 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.
    2. Teri A. Manolio & Francis S. Collins & Nancy J. Cox & David B. Goldstein & Lucia A. Hindorff & David J. Hunter & Mark I. McCarthy & Erin M. Ramos & Lon R. Cardon & Aravinda Chakravarti & Judy H. Cho &, 2009. "Finding the missing heritability of complex diseases," Nature, Nature, vol. 461(7265), pages 747-753, October.
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