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Genetic diversity and population structure of a Peruvian cattle herd using SNP data

New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucl...

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Autores principales: Corredor, Flor-Anita, Figueroa, Deyanira, Estrada, Richard, Salazar, Wilian, Quilcate, Carlos, Vásquez, Héctor V., Gonzales, Jhony, Maicelo, Jorge L., Medina, Percy, Arbizu, Carlos I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036791/
https://www.ncbi.nlm.nih.gov/pubmed/36968592
http://dx.doi.org/10.3389/fgene.2023.1073843
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author Corredor, Flor-Anita
Figueroa, Deyanira
Estrada, Richard
Salazar, Wilian
Quilcate, Carlos
Vásquez, Héctor V.
Gonzales, Jhony
Maicelo, Jorge L.
Medina, Percy
Arbizu, Carlos I.
author_facet Corredor, Flor-Anita
Figueroa, Deyanira
Estrada, Richard
Salazar, Wilian
Quilcate, Carlos
Vásquez, Héctor V.
Gonzales, Jhony
Maicelo, Jorge L.
Medina, Percy
Arbizu, Carlos I.
author_sort Corredor, Flor-Anita
collection PubMed
description New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucleus cattle herd of four commercial breeds from a Peruvian institution were determined. This nucleus comprises Brahman (N = 9), Braunvieh (N = 9), Gyr (N = 5), and Simmental (N = 15) breeds. Additionally, samples from a locally adapted creole cattle, the Arequipa Fighting Bull (AFB, N = 9), were incorporated. Female individuals were genotyped with the GGPBovine100K and males with the BovineHD. Quality control, and the proportion of polymorphic SNPs, minor allele frequency, expected heterozygosity, observed heterozygosity, and inbreeding coefficient were estimated for the five breeds. Admixture, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were performed. Also, a dendrogram was constructed using the Neighbor-Joining clustering algorithm. The genetic diversity indices in all breeds showed a high proportion of polymorphic SNPs, varying from 51.42% in Gyr to 97.58% in AFB. Also, AFB showed the highest expected heterozygosity estimate (0.41 ± 0.01), while Brahman the lowest (0.33 ± 0.01). Besides, Braunvieh possessed the highest observed heterozygosity (0.43 ± 0.01), while Brahman the lowest (0.37 ± 0.02), indicating that Brahman was less diverse. According to the molecular variance analysis, 75.71% of the variance occurs within individuals, whereas 24.29% occurs among populations. The pairwise genetic differentiation estimates (F(ST)) between breeds showed values that ranged from 0.08 (Braunvieh vs. AFB) to 0.37 (Brahman vs. Braunvieh). Similarly, pairwise Reynold’s distance ranged from 0.09 (Braunvieh vs. AFB) to 0.46 (Brahman vs. Braunvieh). The dendrogram, similar to the PCA, identified two groups, showing a clear separation between Bos indicus (Brahman and Gyr) and B. taurus breeds (Braunvieh, Simmental, and AFB). Simmental and Braunvieh grouped closely with the AFB cattle. Similar results were obtained for the population structure analysis with K = 2. The results from this study would contribute to the appropriate management, avoiding loss of genetic variability in these breeds and for future improvements in this nucleus. Additional work is needed to speed up the breeding process in the Peruvian cattle system.
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spelling pubmed-100367912023-03-25 Genetic diversity and population structure of a Peruvian cattle herd using SNP data Corredor, Flor-Anita Figueroa, Deyanira Estrada, Richard Salazar, Wilian Quilcate, Carlos Vásquez, Héctor V. Gonzales, Jhony Maicelo, Jorge L. Medina, Percy Arbizu, Carlos I. Front Genet Genetics New-generation sequencing technologies, among them SNP chips for massive genotyping, are useful for the effective management of genetic resources. To date, molecular studies in Peruvian cattle are still scarce. For the first time, the genetic diversity and population structure of a reproductive nucleus cattle herd of four commercial breeds from a Peruvian institution were determined. This nucleus comprises Brahman (N = 9), Braunvieh (N = 9), Gyr (N = 5), and Simmental (N = 15) breeds. Additionally, samples from a locally adapted creole cattle, the Arequipa Fighting Bull (AFB, N = 9), were incorporated. Female individuals were genotyped with the GGPBovine100K and males with the BovineHD. Quality control, and the proportion of polymorphic SNPs, minor allele frequency, expected heterozygosity, observed heterozygosity, and inbreeding coefficient were estimated for the five breeds. Admixture, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were performed. Also, a dendrogram was constructed using the Neighbor-Joining clustering algorithm. The genetic diversity indices in all breeds showed a high proportion of polymorphic SNPs, varying from 51.42% in Gyr to 97.58% in AFB. Also, AFB showed the highest expected heterozygosity estimate (0.41 ± 0.01), while Brahman the lowest (0.33 ± 0.01). Besides, Braunvieh possessed the highest observed heterozygosity (0.43 ± 0.01), while Brahman the lowest (0.37 ± 0.02), indicating that Brahman was less diverse. According to the molecular variance analysis, 75.71% of the variance occurs within individuals, whereas 24.29% occurs among populations. The pairwise genetic differentiation estimates (F(ST)) between breeds showed values that ranged from 0.08 (Braunvieh vs. AFB) to 0.37 (Brahman vs. Braunvieh). Similarly, pairwise Reynold’s distance ranged from 0.09 (Braunvieh vs. AFB) to 0.46 (Brahman vs. Braunvieh). The dendrogram, similar to the PCA, identified two groups, showing a clear separation between Bos indicus (Brahman and Gyr) and B. taurus breeds (Braunvieh, Simmental, and AFB). Simmental and Braunvieh grouped closely with the AFB cattle. Similar results were obtained for the population structure analysis with K = 2. The results from this study would contribute to the appropriate management, avoiding loss of genetic variability in these breeds and for future improvements in this nucleus. Additional work is needed to speed up the breeding process in the Peruvian cattle system. Frontiers Media S.A. 2023-03-10 /pmc/articles/PMC10036791/ /pubmed/36968592 http://dx.doi.org/10.3389/fgene.2023.1073843 Text en Copyright © 2023 Corredor, Figueroa, Estrada, Salazar, Quilcate, Vásquez, Gonzales, Maicelo, Medina and Arbizu. 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
Corredor, Flor-Anita
Figueroa, Deyanira
Estrada, Richard
Salazar, Wilian
Quilcate, Carlos
Vásquez, Héctor V.
Gonzales, Jhony
Maicelo, Jorge L.
Medina, Percy
Arbizu, Carlos I.
Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title_full Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title_fullStr Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title_full_unstemmed Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title_short Genetic diversity and population structure of a Peruvian cattle herd using SNP data
title_sort genetic diversity and population structure of a peruvian cattle herd using snp data
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036791/
https://www.ncbi.nlm.nih.gov/pubmed/36968592
http://dx.doi.org/10.3389/fgene.2023.1073843
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