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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...

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Autores principales: Li, Shijing, Li, Shiqin, Su, Shaoqiang, Zhang, Hui, Shen, Jiayu, Wen, Yongxian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
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.
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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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