Views on GWAS statistical analysis

Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate sta...

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Detalles Bibliográficos
Autores principales: Cao, Xiaowen, Xing, Li, He, Hua, Zhang, Xuekui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2020
Materias:
Views
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7434950/
https://www.ncbi.nlm.nih.gov/pubmed/32831520
http://dx.doi.org/10.6026/97320630016393
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author Cao, Xiaowen
Xing, Li
He, Hua
Zhang, Xuekui
author_facet Cao, Xiaowen
Xing, Li
He, Hua
Zhang, Xuekui
author_sort Cao, Xiaowen
collection PubMed
description Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis.
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spelling pubmed-74349502020-08-21 Views on GWAS statistical analysis Cao, Xiaowen Xing, Li He, Hua Zhang, Xuekui Bioinformation Views Genome-wide association study (GWAS) is a popular approach to investigate relationships between genetic information and diseases. A number of associations are tested in a study and the results are often corrected using multiple adjustment methods. It is observed that GWAS studies suffer adequate statistical power for reliability. Hence, we document known models for reliability assessment using improved statistical power in GWAS analysis. Biomedical Informatics 2020-05-31 /pmc/articles/PMC7434950/ /pubmed/32831520 http://dx.doi.org/10.6026/97320630016393 Text en © 2020 Biomedical Informatics http://creativecommons.org/licenses/by/3.0/ This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.
spellingShingle Views
Cao, Xiaowen
Xing, Li
He, Hua
Zhang, Xuekui
Views on GWAS statistical analysis
title Views on GWAS statistical analysis
title_full Views on GWAS statistical analysis
title_fullStr Views on GWAS statistical analysis
title_full_unstemmed Views on GWAS statistical analysis
title_short Views on GWAS statistical analysis
title_sort views on gwas statistical analysis
topic Views
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7434950/
https://www.ncbi.nlm.nih.gov/pubmed/32831520
http://dx.doi.org/10.6026/97320630016393
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AT xingli viewsongwasstatisticalanalysis
AT hehua viewsongwasstatisticalanalysis
AT zhangxuekui viewsongwasstatisticalanalysis

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