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BMC Bioinformatics

Open Access

A new set-valued system identification approach to identifying rare genetic variants for ordered categorical phenotype

  • Wenjian Bi1,
  • Guolian Kang2Email author,
  • Yuehua Cui3,
  • Yun Li4,
  • Christine M Hartford5,
  • Wing Leung5, 6 and
  • Ji-Feng Zhang1
Contributed equally
BMC Bioinformatics201415(Suppl 10):P29

https://doi.org/10.1186/1471-2105-15-S10-P29

Published: 29 September 2014

Background

For phenotype-genotype association studies that involve a phenotype with ordered multiple response categories, we usually either regroup multiple categories of the phenotype into two categories of “cases” and “controls” and then apply the standard logistic regression (LG) model [1, 2], or apply a non-parametric method of Spearman rank correlation [3] or parametric method of ordered logistic (orderLG) regression model [4] which accounts for the ordinal nature of the phenotype. However, these approaches may lose statistical power if the phenotype is obtained by categorizing an observed or complicated unmeasured or immeasurable continuous phenotype or if the underlying genetic variants are rare.

Materials and methods

Therefore, we propose a set-valued (SV) system method, which assumes that the underlying continuous phenotype follows a normal distribution, to identify genetic variants associated with an ordinal categorical phenotype. We couple this model with a set-valued system identification method to identify all underlying key system parameters.

Results

Simulation studies show that SV well controlled the Type I error rate. In the comparison among LG, SV and orderLG methods, LG had significantly lower power than both SV and orderLG due to the disregard of the ordinal nature of the phenotype, and SV had similar or higher power than orderLG. Additionally, the SV association parameter estimate was 2.7-28.7 fold less variable than the orderLG association parameter estimate. Less variability in the association parameter estimate translates to greater power and robustness across the spectrum of minor allele frequencies. These advantages are most pronounced for rare variants or even common variants when sample size is small. For instance, in a simulation with data generated from an additive orderedLG model with an odds ratio of 7.4 for a phenotype with three categories, a single nucleotide polymorphism with minor allele frequency of 0.75% and sample size of 999 (333 per category), the power of SV, orderLG and LG models were 70%, 40% and <1%, respectively, at a significance level of 10-6. When applied to a real data set, the set of variants identified by LG and orderLG was a subset of those identified by SV. Thus, SV can be a competitive alternative to LG or orderLG in genetic association studies such as candidate gene, genome-wide association studies or next generation sequencing studies, for ordered categorical phenotype.

Notes

Authors’ Affiliations

(1)
Key Laboratory of Systems and Control, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
(2)
Department of Biostatistics, St. Jude Children’s Research Hospital
(3)
Department of Statistics and Probability, Michigan State University
(4)
Department of Biostatistics, University of North Carolina
(5)
Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children’s Research Hospital
(6)
Department of Pediatrics, University of Tennessee Health Science Center

References

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Copyright

© Bi et al; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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