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Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator

Functional linear regression models have been widely used in the gene association analysis of complex traits. These models retain all the genetic information in the data and take full advantage of spatial information in genetic variation data, which leads to brilliant detection power. However, the s...

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Autores principales: Wang, Jingyu, Zhou, Fujie, Li, Cheng, Yin, Ning, Liu, Huiming, Zhuang, Binxian, Huang, Qingyu, Wen, Yongxian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137544/
https://www.ncbi.nlm.nih.gov/pubmed/37107592
http://dx.doi.org/10.3390/genes14040834
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author Wang, Jingyu
Zhou, Fujie
Li, Cheng
Yin, Ning
Liu, Huiming
Zhuang, Binxian
Huang, Qingyu
Wen, Yongxian
author_facet Wang, Jingyu
Zhou, Fujie
Li, Cheng
Yin, Ning
Liu, Huiming
Zhuang, Binxian
Huang, Qingyu
Wen, Yongxian
author_sort Wang, Jingyu
collection PubMed
description Functional linear regression models have been widely used in the gene association analysis of complex traits. These models retain all the genetic information in the data and take full advantage of spatial information in genetic variation data, which leads to brilliant detection power. However, the significant association signals identified by the high-power methods are not all the real causal SNPs, because it is easy to regard noise information as significant association signals, leading to a false association. In this paper, a method based on the sparse functional data association test (SFDAT) of gene region association analysis is developed based on a functional linear regression model with local sparse estimation. The evaluation indicators CSR and DL are defined to evaluate the feasibility and performance of the proposed method with other indicators. Simulation studies show that: (1) SFDAT performs well under both linkage equilibrium and linkage disequilibrium simulation; (2) SFDAT performs successfully for gene regions (including common variants, low-frequency variants, rare variants and mix variants); (3) With power and type I error rates comparable to OLS and Smooth, SFDAT has a better ability to handle the zero regions. The Oryza sativa data set is analyzed by SFDAT. It is shown that SFDAT can better perform gene association analysis and eliminate the false positive of gene localization. This study showed that SFDAT can lower the interference caused by noise while maintaining high power. SFDAT provides a new method for the association analysis between gene regions and phenotypic quantitative traits.
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spelling pubmed-101375442023-04-28 Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator Wang, Jingyu Zhou, Fujie Li, Cheng Yin, Ning Liu, Huiming Zhuang, Binxian Huang, Qingyu Wen, Yongxian Genes (Basel) Article Functional linear regression models have been widely used in the gene association analysis of complex traits. These models retain all the genetic information in the data and take full advantage of spatial information in genetic variation data, which leads to brilliant detection power. However, the significant association signals identified by the high-power methods are not all the real causal SNPs, because it is easy to regard noise information as significant association signals, leading to a false association. In this paper, a method based on the sparse functional data association test (SFDAT) of gene region association analysis is developed based on a functional linear regression model with local sparse estimation. The evaluation indicators CSR and DL are defined to evaluate the feasibility and performance of the proposed method with other indicators. Simulation studies show that: (1) SFDAT performs well under both linkage equilibrium and linkage disequilibrium simulation; (2) SFDAT performs successfully for gene regions (including common variants, low-frequency variants, rare variants and mix variants); (3) With power and type I error rates comparable to OLS and Smooth, SFDAT has a better ability to handle the zero regions. The Oryza sativa data set is analyzed by SFDAT. It is shown that SFDAT can better perform gene association analysis and eliminate the false positive of gene localization. This study showed that SFDAT can lower the interference caused by noise while maintaining high power. SFDAT provides a new method for the association analysis between gene regions and phenotypic quantitative traits. MDPI 2023-03-30 /pmc/articles/PMC10137544/ /pubmed/37107592 http://dx.doi.org/10.3390/genes14040834 Text en © 2023 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
Wang, Jingyu
Zhou, Fujie
Li, Cheng
Yin, Ning
Liu, Huiming
Zhuang, Binxian
Huang, Qingyu
Wen, Yongxian
Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title_full Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title_fullStr Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title_full_unstemmed Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title_short Gene Association Analysis of Quantitative Trait Based on Functional Linear Regression Model with Local Sparse Estimator
title_sort gene association analysis of quantitative trait based on functional linear regression model with local sparse estimator
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137544/
https://www.ncbi.nlm.nih.gov/pubmed/37107592
http://dx.doi.org/10.3390/genes14040834
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