Genome-wide association analysis with matched samples discloses additional novel risk loci

Jungsoo Gim, Sungkyoung Choi, Jongho Im, Jae Kwang Kim, Taesung Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Genome-wide association studies have identified many candidate causal variants associated with common complex diseases and traits, but most of them have been drawn from nonrandomized case/control designs. In nonrandomized experiments, the results drawn from two different groups can be misleading because the units exposed to one group generally differ systematically from the units exposed to the other group. Propensity score is widely used to group case and control units for a more direct and significant comparison even with nonrandomized experiments. This propensity score matching can help with prioritizing additional uncovered variants on disease risk via sub-group analysis in genome-wide association studies. The aim of this work is to propose a post-hoc association test based on the subsets of samples. For that purpose, this paper presents a new paradigm for a post-hoc genome-wide association test when the sample size of controls are larger than that of cases: selecting control samples by equating the distribution of covariates in the case and control groups and re-performing association analysis upon these matched samples. We demonstrated the feasibility of this approach by applying it to 2752 type II diabetes patients in 8842 Korean population. Genome-wide association approach with matched samples is able to disclose 9 additional novel variants and 7 out of 9 have not identified from the association test of whole control samples. The process described here can successfully be combined with other types of case/control studies with large covariate information. This indicates that there a possibility of obtaining additional candidate causal variants responsible for common diseases through genome-wide association analysis with matched samples.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
EditorsHuiru Zheng, Xiaohua Tony Hu, Yadong Wang, Jin-Kao Hao, David Gilbert, Daniel Berrar, Kwang-Hyun Cho, Werner Dubitzky
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5-10
Number of pages6
ISBN (Electronic)9781479956692
DOIs
Publication statusPublished - 2014 Dec 29
Event2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 - Belfast, United Kingdom
Duration: 2014 Nov 22014 Nov 5

Publication series

NameProceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014

Other

Other2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014
CountryUnited Kingdom
CityBelfast
Period14/11/214/11/5

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics

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    Gim, J., Choi, S., Im, J., Kim, J. K., & Park, T. (2014). Genome-wide association analysis with matched samples discloses additional novel risk loci. In H. Zheng, X. T. Hu, Y. Wang, J-K. Hao, D. Gilbert, D. Berrar, K-H. Cho, & W. Dubitzky (Eds.), Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 (pp. 5-10). [6999379] (Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2014.6999379