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Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model
In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequenc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899465/ https://www.ncbi.nlm.nih.gov/pubmed/35265102 http://dx.doi.org/10.3389/fgene.2022.781740 |
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author | Li, Shijing Li, Shiqin Su, Shaoqiang Zhang, Hui Shen, Jiayu Wen, Yongxian |
author_facet | Li, Shijing Li, Shiqin Su, Shaoqiang Zhang, Hui Shen, Jiayu Wen, Yongxian |
author_sort | Li, Shijing |
collection | PubMed |
description | In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits. |
format | Online Article Text |
id | pubmed-8899465 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88994652022-03-08 Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model Li, Shijing Li, Shiqin Su, Shaoqiang Zhang, Hui Shen, Jiayu Wen, Yongxian Front Genet Genetics In the process of growth and development in life, gene expressions that control quantitative traits will turn on or off with time. Studies of longitudinal traits are of great significance in revealing the genetic mechanism of biological development. With the development of ultra-high-density sequencing technology, the associated analysis has tremendous challenges to statistical methods. In this paper, a longitudinal functional data association test (LFDAT) method is proposed based on the function-on-function regression model. LFDAT can simultaneously treat phenotypic traits and marker information as continuum variables and analyze the association of longitudinal quantitative traits and gene regions. Simulation studies showed that: 1) LFDAT performs well for both linkage equilibrium simulation and linkage disequilibrium simulation, 2) LFDAT has better performance for gene regions (include common variants, low-frequency variants, rare variants and mixture), and 3) LFDAT can accurately identify gene switching in the growth and development stage. The longitudinal data of the Oryza sativa projected shoot area is analyzed by LFDAT. It showed that there is the advantage of quick calculations. Further, an association analysis was conducted between longitudinal traits and gene regions by integrating the micro effects of multiple related variants and using the information of the entire gene region. LFDAT provides a feasible method for studying the formation and expression of longitudinal traits. Frontiers Media S.A. 2022-02-21 /pmc/articles/PMC8899465/ /pubmed/35265102 http://dx.doi.org/10.3389/fgene.2022.781740 Text en Copyright © 2022 Li, Li, Su, Zhang, Shen and Wen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Li, Shijing Li, Shiqin Su, Shaoqiang Zhang, Hui Shen, Jiayu Wen, Yongxian Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title | Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title_full | Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title_fullStr | Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title_full_unstemmed | Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title_short | Gene Region Association Analysis of Longitudinal Quantitative Traits Based on a Function-On-Function Regression Model |
title_sort | gene region association analysis of longitudinal quantitative traits based on a function-on-function regression model |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8899465/ https://www.ncbi.nlm.nih.gov/pubmed/35265102 http://dx.doi.org/10.3389/fgene.2022.781740 |
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