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A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation

Core collection is an ideal resource for genome-wide association studies (GWAS). A subcore collection is a subset of a core collection. A strategy was proposed for finding the optimal sampling percentage on plant subcore collection based on Monte Carlo simulation. A cotton germplasm group of 168 acc...

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Detalles Bibliográficos
Autores principales: Wang, Jiancheng, Guan, Yajing, Wang, Yang, Zhu, Liwei, Wang, Qitian, Hu, Qijuan, Hu, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918405/
https://www.ncbi.nlm.nih.gov/pubmed/24574893
http://dx.doi.org/10.1155/2014/503473
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author Wang, Jiancheng
Guan, Yajing
Wang, Yang
Zhu, Liwei
Wang, Qitian
Hu, Qijuan
Hu, Jin
author_facet Wang, Jiancheng
Guan, Yajing
Wang, Yang
Zhu, Liwei
Wang, Qitian
Hu, Qijuan
Hu, Jin
author_sort Wang, Jiancheng
collection PubMed
description Core collection is an ideal resource for genome-wide association studies (GWAS). A subcore collection is a subset of a core collection. A strategy was proposed for finding the optimal sampling percentage on plant subcore collection based on Monte Carlo simulation. A cotton germplasm group of 168 accessions with 20 quantitative traits was used to construct subcore collections. Mixed linear model approach was used to eliminate environment effect and GE (genotype × environment) effect. Least distance stepwise sampling (LDSS) method combining 6 commonly used genetic distances and unweighted pair-group average (UPGMA) cluster method was adopted to construct subcore collections. Homogeneous population assessing method was adopted to assess the validity of 7 evaluating parameters of subcore collection. Monte Carlo simulation was conducted on the sampling percentage, the number of traits, and the evaluating parameters. A new method for “distilling free-form natural laws from experimental data” was adopted to find the best formula to determine the optimal sampling percentages. The results showed that coincidence rate of range (CR) was the most valid evaluating parameter and was suitable to serve as a threshold to find the optimal sampling percentage. The principal component analysis showed that subcore collections constructed by the optimal sampling percentages calculated by present strategy were well representative.
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spelling pubmed-39184052014-02-26 A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation Wang, Jiancheng Guan, Yajing Wang, Yang Zhu, Liwei Wang, Qitian Hu, Qijuan Hu, Jin ScientificWorldJournal Research Article Core collection is an ideal resource for genome-wide association studies (GWAS). A subcore collection is a subset of a core collection. A strategy was proposed for finding the optimal sampling percentage on plant subcore collection based on Monte Carlo simulation. A cotton germplasm group of 168 accessions with 20 quantitative traits was used to construct subcore collections. Mixed linear model approach was used to eliminate environment effect and GE (genotype × environment) effect. Least distance stepwise sampling (LDSS) method combining 6 commonly used genetic distances and unweighted pair-group average (UPGMA) cluster method was adopted to construct subcore collections. Homogeneous population assessing method was adopted to assess the validity of 7 evaluating parameters of subcore collection. Monte Carlo simulation was conducted on the sampling percentage, the number of traits, and the evaluating parameters. A new method for “distilling free-form natural laws from experimental data” was adopted to find the best formula to determine the optimal sampling percentages. The results showed that coincidence rate of range (CR) was the most valid evaluating parameter and was suitable to serve as a threshold to find the optimal sampling percentage. The principal component analysis showed that subcore collections constructed by the optimal sampling percentages calculated by present strategy were well representative. Hindawi Publishing Corporation 2014-01-20 /pmc/articles/PMC3918405/ /pubmed/24574893 http://dx.doi.org/10.1155/2014/503473 Text en Copyright © 2014 Jiancheng Wang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Jiancheng
Guan, Yajing
Wang, Yang
Zhu, Liwei
Wang, Qitian
Hu, Qijuan
Hu, Jin
A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title_full A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title_fullStr A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title_full_unstemmed A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title_short A Strategy for Finding the Optimal Scale of Plant Core Collection Based on Monte Carlo Simulation
title_sort strategy for finding the optimal scale of plant core collection based on monte carlo simulation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918405/
https://www.ncbi.nlm.nih.gov/pubmed/24574893
http://dx.doi.org/10.1155/2014/503473
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