Identification of patterns related to linkage groups or disequilibrium by factor analysis

Detalhes bibliográficos
Autor(a) principal: Oliveira,Cristiano Ferreira de
Data de Publicação: 2021
Outros Autores: Teixeira,Gabriely, Temoteo,Alex da Silva, Nascimento,Moysés, Cruz,Cosme Damião
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Ciência Rural
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500401
Resumo: ABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.
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spelling Identification of patterns related to linkage groups or disequilibrium by factor analysislinkage disequilibriumfactor analysisSNPhaplotype blockslinkage groupsQTLABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.Universidade Federal de Santa Maria2021-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500401Ciência Rural v.51 n.5 2021reponame:Ciência Ruralinstname:Universidade Federal de Santa Maria (UFSM)instacron:UFSM10.1590/0103-8478cr20190984info:eu-repo/semantics/openAccessOliveira,Cristiano Ferreira deTeixeira,GabrielyTemoteo,Alex da SilvaNascimento,MoysésCruz,Cosme Damiãoeng2021-03-03T00:00:00ZRevista
dc.title.none.fl_str_mv Identification of patterns related to linkage groups or disequilibrium by factor analysis
title Identification of patterns related to linkage groups or disequilibrium by factor analysis
spellingShingle Identification of patterns related to linkage groups or disequilibrium by factor analysis
Oliveira,Cristiano Ferreira de
linkage disequilibrium
factor analysis
SNP
haplotype blocks
linkage groups
QTL
title_short Identification of patterns related to linkage groups or disequilibrium by factor analysis
title_full Identification of patterns related to linkage groups or disequilibrium by factor analysis
title_fullStr Identification of patterns related to linkage groups or disequilibrium by factor analysis
title_full_unstemmed Identification of patterns related to linkage groups or disequilibrium by factor analysis
title_sort Identification of patterns related to linkage groups or disequilibrium by factor analysis
author Oliveira,Cristiano Ferreira de
author_facet Oliveira,Cristiano Ferreira de
Teixeira,Gabriely
Temoteo,Alex da Silva
Nascimento,Moysés
Cruz,Cosme Damião
author_role author
author2 Teixeira,Gabriely
Temoteo,Alex da Silva
Nascimento,Moysés
Cruz,Cosme Damião
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Oliveira,Cristiano Ferreira de
Teixeira,Gabriely
Temoteo,Alex da Silva
Nascimento,Moysés
Cruz,Cosme Damião
dc.subject.por.fl_str_mv linkage disequilibrium
factor analysis
SNP
haplotype blocks
linkage groups
QTL
topic linkage disequilibrium
factor analysis
SNP
haplotype blocks
linkage groups
QTL
description ABSTRACT: Empirical patterns of linkage disequilibrium (LD) can be used to increase the statistical power of genetic mapping. This study was carried out with the objective of verifying the efficacy of factor analysis (AF) applied to data sets of molecular markers of the SNP type, in order to identify linkage groups and haplotypes blocks. The SNPs data set used was derived from a simulation process of an F2 population, containing 2000 marks with information of 500 individuals. The estimation of the factorial loadings of FA was made in two ways, considering the matrix of distances between the markers (A) and considering the correlation matrix (R). The number of factors (k) to be used was established based on the graph scree-plot and based on the proportion of the total variance explained. Results indicated that matrices A and R lead to similar results. Based on the scree-plot we considered k equal to 10 and the factors interpreted as being representative of the bonding groups. The second criterion led to a number of factors equal to 50, and the factors interpreted as being representative of the haplotypes blocks. This showed the potential of the technique, making it possible to obtain results applicable to any type of population, helping or corroborating the interpretation of genomic studies. The study demonstrated that AF was able to identify patterns of association between markers, identifying subgroups of markers that reflect factor binding groups and also linkage disequilibrium groups.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500401
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-84782021000500401
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0103-8478cr20190984
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de Santa Maria
publisher.none.fl_str_mv Universidade Federal de Santa Maria
dc.source.none.fl_str_mv Ciência Rural v.51 n.5 2021
reponame:Ciência Rural
instname:Universidade Federal de Santa Maria (UFSM)
instacron:UFSM
instname_str Universidade Federal de Santa Maria (UFSM)
instacron_str UFSM
institution UFSM
reponame_str Ciência Rural
collection Ciência Rural
repository.name.fl_str_mv
repository.mail.fl_str_mv
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