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Pedigree reconstruction and population structure using SNP markers in Gir cattle.

Authors :
Garcia AO
Otto PI
Glatzl Junior LA
Rocha RFB
Dos Santos MG
de Oliveira DA
da Silva MVGB
Panetto JCDC
Machado MA
Verneque RDS
GuimarĂ£es SEF
Source :
Journal of applied genetics [J Appl Genet] 2023 May; Vol. 64 (2), pp. 329-340. Date of Electronic Publication: 2023 Jan 16.
Publication Year :
2023

Abstract

Our objective was to establish a SNPs panel for pedigree reconstruction using microarrays of different densities and evaluate the genomic relationship coefficient of the inferred pedigree, in addition to analyzing the population structure based on genomic analyses in Gir cattle. For parentage analysis and genomic relationship, 16,205 genotyped Gir animals (14,458 females and 1747 males) and 1810 common markers to the four SNP microarrays were used. For population structure analyses, including linkage disequilibrium, effective population size, and runs of homozygosity (ROH), genotypes from 21,656 animals were imputed. Likelihood ratio (LR) approach was used to reconstruct the pedigree, deepening the pedigree and showing it is well established in terms of recent information. Coefficients for each relationship category of the inferred pedigree were adequate. Linkage disequilibrium showed rapid decay. We detected a decrease in the effective population size over the last 50 generations, with the average generation interval around 9.08 years. Higher ROH-based inbreeding coefficient in a class of short ROH segments, with moderate to high values, was also detected, suggesting bottlenecks in the Gir genome. Breeding strategies to minimize inbreeding and avoid massive use of few proven sires with high genetic value are suggested to maintain genetic variability in future generations. In addition, we recommend reducing the generation interval to maximize genetic progress and increase effective population size.<br /> (© 2023. The Author(s), under exclusive licence to Institute of Plant Genetics Polish Academy of Sciences.)

Details

Language :
English
ISSN :
2190-3883
Volume :
64
Issue :
2
Database :
MEDLINE
Journal :
Journal of applied genetics
Publication Type :
Academic Journal
Accession number :
36645582
Full Text :
https://doi.org/10.1007/s13353-023-00747-x