Abstract
Genome-wide association studies have identified many causal candidate loci associated with common complex phenotypes, such as type-2 diabetes and obesity. However, most of these studies have been drawn from non-randomised case/control experiments, where the units exposed to one group generally differ from those exposed to the other group. The aim of this study was to address the issues arising from non-randomised case/control experiments. In order to achieve this, we have proposed a post-hoc association analysis using subsets of samples selected by the proposed matching technique. This method was applied to two different binary traits, type-2 diabetes and obesity, in Korean subjects. It identified nine and two additional variants for type-2 diabetes and obesity, respectively, which were not identified using the total dataset. Our study demonstrates that the proposed a post-hoc genomewide association analysis can determine additional candidate causal variants responsible for common complex phenotypes.
| Original language | English |
|---|---|
| Pages (from-to) | 197-209 |
| Number of pages | 13 |
| Journal | International Journal of Data Mining and Bioinformatics |
| Volume | 14 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Biochemistry, Genetics and Molecular Biology(all)
- Library and Information Sciences
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A post-hoc genome-wide association study using matched samples. / Gim, Jungsoo; Choi, Sungkyoung; Im, Jongho; Kim, Jae Kwang; Park, Taesung.
In: International Journal of Data Mining and Bioinformatics, Vol. 14, No. 3, 01.01.2016, p. 197-209.Research output: Contribution to journal › Article
TY - JOUR
T1 - A post-hoc genome-wide association study using matched samples
AU - Gim, Jungsoo
AU - Choi, Sungkyoung
AU - Im, Jongho
AU - Kim, Jae Kwang
AU - Park, Taesung
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Genome-wide association studies have identified many causal candidate loci associated with common complex phenotypes, such as type-2 diabetes and obesity. However, most of these studies have been drawn from non-randomised case/control experiments, where the units exposed to one group generally differ from those exposed to the other group. The aim of this study was to address the issues arising from non-randomised case/control experiments. In order to achieve this, we have proposed a post-hoc association analysis using subsets of samples selected by the proposed matching technique. This method was applied to two different binary traits, type-2 diabetes and obesity, in Korean subjects. It identified nine and two additional variants for type-2 diabetes and obesity, respectively, which were not identified using the total dataset. Our study demonstrates that the proposed a post-hoc genomewide association analysis can determine additional candidate causal variants responsible for common complex phenotypes.
AB - Genome-wide association studies have identified many causal candidate loci associated with common complex phenotypes, such as type-2 diabetes and obesity. However, most of these studies have been drawn from non-randomised case/control experiments, where the units exposed to one group generally differ from those exposed to the other group. The aim of this study was to address the issues arising from non-randomised case/control experiments. In order to achieve this, we have proposed a post-hoc association analysis using subsets of samples selected by the proposed matching technique. This method was applied to two different binary traits, type-2 diabetes and obesity, in Korean subjects. It identified nine and two additional variants for type-2 diabetes and obesity, respectively, which were not identified using the total dataset. Our study demonstrates that the proposed a post-hoc genomewide association analysis can determine additional candidate causal variants responsible for common complex phenotypes.
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UR - http://www.scopus.com/inward/citedby.url?scp=84959421187&partnerID=8YFLogxK
U2 - 10.1504/IJDMB.2016.074870
DO - 10.1504/IJDMB.2016.074870
M3 - Article
AN - SCOPUS:84959421187
VL - 14
SP - 197
EP - 209
JO - International Journal of Data Mining and Bioinformatics
JF - International Journal of Data Mining and Bioinformatics
SN - 1748-5673
IS - 3
ER -