Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle

Detalhes bibliográficos
Autor(a) principal: Braz, Camila U. [UNESP]
Data de Publicação: 2019
Outros Autores: Taylor, Jeremy F., Bresolin, Tiago [UNESP], Espigolan, Rafael [UNESP], Feitosa, Fabieli L. B. [UNESP], Carvalheiro, Roberto [UNESP], Baldi, Fernando [UNESP], De Albuquerque, Lucia G. [UNESP], De Oliveira, Henrique N. [UNESP]
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1186/s12863-019-0713-4
http://hdl.handle.net/11449/190044
Resumo: Background: Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness. Results: The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. Conclusions: GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.
id UNSP_234e41621837d3a5346e53b36a191852
oai_identifier_str oai:repositorio.unesp.br:11449/190044
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattleAdditive genetic varianceBeef cattleGWAAHaplotypeMeat tendernessBackground: Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness. Results: The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. Conclusions: GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.Animal Science Department São Paulo State University (Unesp)Division of Animal Sciences University of MissouriAnimal Science Department São Paulo State University (Unesp)Universidade Estadual Paulista (Unesp)University of MissouriBraz, Camila U. [UNESP]Taylor, Jeremy F.Bresolin, Tiago [UNESP]Espigolan, Rafael [UNESP]Feitosa, Fabieli L. B. [UNESP]Carvalheiro, Roberto [UNESP]Baldi, Fernando [UNESP]De Albuquerque, Lucia G. [UNESP]De Oliveira, Henrique N. [UNESP]2019-10-06T17:00:29Z2019-10-06T17:00:29Z2019-01-14info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s12863-019-0713-4BMC Genetics, v. 20, n. 1, 2019.1471-2156http://hdl.handle.net/11449/19004410.1186/s12863-019-0713-42-s2.0-85059949040Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengBMC Geneticsinfo:eu-repo/semantics/openAccess2024-06-07T18:44:43Zoai:repositorio.unesp.br:11449/190044Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:04:15.621422Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
title Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
spellingShingle Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
Braz, Camila U. [UNESP]
Additive genetic variance
Beef cattle
GWAA
Haplotype
Meat tenderness
title_short Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
title_full Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
title_fullStr Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
title_full_unstemmed Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
title_sort Sliding window haplotype approaches overcome single SNP analysis limitations in identifying genes for meat tenderness in Nelore cattle
author Braz, Camila U. [UNESP]
author_facet Braz, Camila U. [UNESP]
Taylor, Jeremy F.
Bresolin, Tiago [UNESP]
Espigolan, Rafael [UNESP]
Feitosa, Fabieli L. B. [UNESP]
Carvalheiro, Roberto [UNESP]
Baldi, Fernando [UNESP]
De Albuquerque, Lucia G. [UNESP]
De Oliveira, Henrique N. [UNESP]
author_role author
author2 Taylor, Jeremy F.
Bresolin, Tiago [UNESP]
Espigolan, Rafael [UNESP]
Feitosa, Fabieli L. B. [UNESP]
Carvalheiro, Roberto [UNESP]
Baldi, Fernando [UNESP]
De Albuquerque, Lucia G. [UNESP]
De Oliveira, Henrique N. [UNESP]
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
University of Missouri
dc.contributor.author.fl_str_mv Braz, Camila U. [UNESP]
Taylor, Jeremy F.
Bresolin, Tiago [UNESP]
Espigolan, Rafael [UNESP]
Feitosa, Fabieli L. B. [UNESP]
Carvalheiro, Roberto [UNESP]
Baldi, Fernando [UNESP]
De Albuquerque, Lucia G. [UNESP]
De Oliveira, Henrique N. [UNESP]
dc.subject.por.fl_str_mv Additive genetic variance
Beef cattle
GWAA
Haplotype
Meat tenderness
topic Additive genetic variance
Beef cattle
GWAA
Haplotype
Meat tenderness
description Background: Traditional single nucleotide polymorphism (SNP) genome-wide association analysis (GWAA) can be inefficient because single SNPs provide limited genetic information about genomic regions. On the other hand, using haplotypes in the statistical analysis may increase the extent of linkage disequilibrium (LD) between haplotypes and causal variants and may also potentially capture epistastic interactions between variants within a haplotyped locus, providing an increase in the power and robustness of the association studies. We performed GWAA (413,355 SNP markers) using haplotypes based on variable-sized sliding windows and compared the results to a single-SNP GWAA using Warner-Bratzler shear force measured in the longissimus thorasis muscle of 3161 Nelore bulls to ascertain the optimal window size for identifying the genomic regions that influence meat tenderness. Results: The GWAA using single SNPs identified eight variants influencing meat tenderness on BTA 3, 4, 9, 10 and 11. However, thirty-three putative meat tenderness QTL were detected on BTA 1, 3, 4, 5, 8, 9, 10, 11, 15, 17, 18, 24, 25, 26 and 29 using variable-sized sliding haplotype windows. Analyses using sliding window haplotypes of 3, 5, 7, 9 and 11 SNPs identified 57, 61, 42, 39, and 21% of all thirty-three putative QTL regions, respectively; however, the analyses using the 3 and 5 SNP haplotypes, cumulatively detected 88% of the putative QTL. The genes associated with variation in meat tenderness participate in myogenesis, neurogenesis, lipid and fatty acid metabolism and skeletal muscle structure or composition processes. Conclusions: GWAA using haplotypes based on variable-sized sliding windows allowed the detection of more QTL than traditional single-SNP GWAA. Analyses using smaller haplotypes (3 and 5 SNPs) detected a higher proportion of the putative QTL.
publishDate 2019
dc.date.none.fl_str_mv 2019-10-06T17:00:29Z
2019-10-06T17:00:29Z
2019-01-14
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1186/s12863-019-0713-4
BMC Genetics, v. 20, n. 1, 2019.
1471-2156
http://hdl.handle.net/11449/190044
10.1186/s12863-019-0713-4
2-s2.0-85059949040
url http://dx.doi.org/10.1186/s12863-019-0713-4
http://hdl.handle.net/11449/190044
identifier_str_mv BMC Genetics, v. 20, n. 1, 2019.
1471-2156
10.1186/s12863-019-0713-4
2-s2.0-85059949040
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv BMC Genetics
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129487889498112