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Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model
Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8954869/ https://www.ncbi.nlm.nih.gov/pubmed/35328009 http://dx.doi.org/10.3390/genes13030455 |
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author | Li, Shijing Zhou, Fujie Shen, Jiayu Zhang, Hui Wen, Yongxian |
author_facet | Li, Shijing Zhou, Fujie Shen, Jiayu Zhang, Hui Wen, Yongxian |
author_sort | Li, Shijing |
collection | PubMed |
description | Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are of great significance in revealing the genetic mechanism of biological development. There have been a lot of studies on single-gene region association analysis, but the application of functional linear models in multi-gene region association analysis is still less. In this paper, a functional multi-gene region association analysis test method is proposed based on the functional linear model. From the three directions of common multi-gene region method, multi-gene region weighted method and multi-gene region loci weighted method, that test method is studied combined with computer simulation. The following conclusions are obtained through computer simulation: (a) The functional multi-gene region association analysis test method has higher power than the functional single gene region association analysis test method; (b) The functional multi-gene region weighted method performs better than the common functional multi-gene region method; (c) the functional multi-gene region loci weighted method is the best method for association analysis on three directions of the common multi-gene region method; (d) the performance of the Step method and Multi-gene region loci weighted Step for multi-gene regions is the best in general. Functional multi-gene region association analysis test method can theoretically provide a feasible method for the study of complex traits affected by multiple genes. |
format | Online Article Text |
id | pubmed-8954869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89548692022-03-26 Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model Li, Shijing Zhou, Fujie Shen, Jiayu Zhang, Hui Wen, Yongxian Genes (Basel) Article Genome-wide association analysis is an important approach to identify genetic variants associated with complex traits. Complex traits are not only affected by single gene loci, but also by the interaction of multiple gene loci. Studies of association between gene regions and quantitative traits are of great significance in revealing the genetic mechanism of biological development. There have been a lot of studies on single-gene region association analysis, but the application of functional linear models in multi-gene region association analysis is still less. In this paper, a functional multi-gene region association analysis test method is proposed based on the functional linear model. From the three directions of common multi-gene region method, multi-gene region weighted method and multi-gene region loci weighted method, that test method is studied combined with computer simulation. The following conclusions are obtained through computer simulation: (a) The functional multi-gene region association analysis test method has higher power than the functional single gene region association analysis test method; (b) The functional multi-gene region weighted method performs better than the common functional multi-gene region method; (c) the functional multi-gene region loci weighted method is the best method for association analysis on three directions of the common multi-gene region method; (d) the performance of the Step method and Multi-gene region loci weighted Step for multi-gene regions is the best in general. Functional multi-gene region association analysis test method can theoretically provide a feasible method for the study of complex traits affected by multiple genes. MDPI 2022-03-02 /pmc/articles/PMC8954869/ /pubmed/35328009 http://dx.doi.org/10.3390/genes13030455 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, Shijing Zhou, Fujie Shen, Jiayu Zhang, Hui Wen, Yongxian Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title | Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title_full | Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title_fullStr | Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title_full_unstemmed | Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title_short | Simulation Research on the Methods of Multi-Gene Region Association Analysis Based on a Functional Linear Model |
title_sort | simulation research on the methods of multi-gene region association analysis based on a functional linear model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8954869/ https://www.ncbi.nlm.nih.gov/pubmed/35328009 http://dx.doi.org/10.3390/genes13030455 |
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