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Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies
BACKGROUND: Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratificatio...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717828/ https://www.ncbi.nlm.nih.gov/pubmed/26781556 http://dx.doi.org/10.1186/s12863-016-0336-y |
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author | Li, Yumei Xiang, Yang |
author_facet | Li, Yumei Xiang, Yang |
author_sort | Li, Yumei |
collection | PubMed |
description | BACKGROUND: Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratification have been developed to test the association between multiple markers and disease-susceptibility genes. Only a few methods focus on testing for maternally mediated genetic effects, which is a critical risk for birth defects. The present study focuses on testing for association and maternally mediated genetic effects with family-based methods. RESULTS: In the present study, we proposed a new method, max_PC integrating principal component analysis, to test association or maternally mediated genetic effects with case-parent data. The proposed method only uses the genotypes of case-parents triads and accommodates missing SNP data. Our results demonstrated that this method is powerful to test association or maternally mediated genetic effects and attractive because it provides a tool for testing the null hypothesis of no association and no maternally mediated genetic effects. Simulations with the permutation procedure as well as an application in the Crohn’s disease study showed that the type I error rates of the proposed statistic were nominal with slightly higher power as compared to those of the max_Z(2) test. CONCLUSIONS: We conclude that the max_PC is a good approach to test association or maternally mediated genetic effects. |
format | Online Article Text |
id | pubmed-4717828 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-47178282016-01-20 Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies Li, Yumei Xiang, Yang BMC Genet Methodology Article BACKGROUND: Major advances in genotyping technology have generated high-density maps of single nucleotide polymorphism (SNP) markers that provide an unprecedented opportunity to identify genes underlying complex traits. Several family-based statistical methods showing robust population stratification have been developed to test the association between multiple markers and disease-susceptibility genes. Only a few methods focus on testing for maternally mediated genetic effects, which is a critical risk for birth defects. The present study focuses on testing for association and maternally mediated genetic effects with family-based methods. RESULTS: In the present study, we proposed a new method, max_PC integrating principal component analysis, to test association or maternally mediated genetic effects with case-parent data. The proposed method only uses the genotypes of case-parents triads and accommodates missing SNP data. Our results demonstrated that this method is powerful to test association or maternally mediated genetic effects and attractive because it provides a tool for testing the null hypothesis of no association and no maternally mediated genetic effects. Simulations with the permutation procedure as well as an application in the Crohn’s disease study showed that the type I error rates of the proposed statistic were nominal with slightly higher power as compared to those of the max_Z(2) test. CONCLUSIONS: We conclude that the max_PC is a good approach to test association or maternally mediated genetic effects. BioMed Central 2016-01-19 /pmc/articles/PMC4717828/ /pubmed/26781556 http://dx.doi.org/10.1186/s12863-016-0336-y Text en © Li and Xiang. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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. |
spellingShingle | Methodology Article Li, Yumei Xiang, Yang Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title | Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title_full | Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title_fullStr | Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title_full_unstemmed | Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title_short | Testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
title_sort | testing association and maternally mediated genetic effects with the principal component analysis in case-parents studies |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4717828/ https://www.ncbi.nlm.nih.gov/pubmed/26781556 http://dx.doi.org/10.1186/s12863-016-0336-y |
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