Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows.
The objective of this study was to evaluate the proportion of genetic variance explained by single nucleotide polymorphism markers, individually or clustered in 1, 2, and 5 Mb windows, for milk yield, fat yield, protein yield, fat content, protein content, and somatic cell score in Mexican Holstein...
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| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0314888 |
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| author | José G Cortes-Hernández Adriana García-Ruiz Francisco Peñagaricano Hugo H Montaldo Felipe J Ruiz-López |
| author_facet | José G Cortes-Hernández Adriana García-Ruiz Francisco Peñagaricano Hugo H Montaldo Felipe J Ruiz-López |
| author_sort | José G Cortes-Hernández |
| collection | DOAJ |
| description | The objective of this study was to evaluate the proportion of genetic variance explained by single nucleotide polymorphism markers, individually or clustered in 1, 2, and 5 Mb windows, for milk yield, fat yield, protein yield, fat content, protein content, and somatic cell score in Mexican Holstein cattle. The analysis included data from 640,746 lactation records of 358,857 cows born between 1979 and 2019, distributed in 353 herds in 18 states of Mexico. The analysis included genotypic data on 7,713 cows and 577 sires, with information on 88,911 markers previously imputed and filtered by quality control. Genomic scans via the single-step genomic best linear unbiased prediction method were performed using BLUPF90 software. A total of 162 markers were significantly associated (p<0.01) with the phenotypic traits evaluated, and the SNP markers were distributed across chromosomes 1, 3, 5, 6, 10, 12, 14, 16, 18, 20, 22, and 29. When the size of the genomic windows was increased from 1 to 5 Mb, a greater proportion of genetic variance was explained by the SNPs within the window, and a greater number of windows explained more than 1% of the genetic variance. The most significant regions were associated with two or more phenotypic traits, such as one region on chromosome 14 that harbors the DGAT1, EXOSC4, PPP1R16A, and FOXH1 genes, which affect all the traits under study. In general, the utilization of genomic windows resulted in a greater proportion of genetic variance explained by milk production traits. |
| format | Article |
| id | doaj-art-4dc536e0ce4d49ff80a67ce523f852c8 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-4dc536e0ce4d49ff80a67ce523f852c82025-02-09T05:30:40ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01202e031488810.1371/journal.pone.0314888Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows.José G Cortes-HernándezAdriana García-RuizFrancisco PeñagaricanoHugo H MontaldoFelipe J Ruiz-LópezThe objective of this study was to evaluate the proportion of genetic variance explained by single nucleotide polymorphism markers, individually or clustered in 1, 2, and 5 Mb windows, for milk yield, fat yield, protein yield, fat content, protein content, and somatic cell score in Mexican Holstein cattle. The analysis included data from 640,746 lactation records of 358,857 cows born between 1979 and 2019, distributed in 353 herds in 18 states of Mexico. The analysis included genotypic data on 7,713 cows and 577 sires, with information on 88,911 markers previously imputed and filtered by quality control. Genomic scans via the single-step genomic best linear unbiased prediction method were performed using BLUPF90 software. A total of 162 markers were significantly associated (p<0.01) with the phenotypic traits evaluated, and the SNP markers were distributed across chromosomes 1, 3, 5, 6, 10, 12, 14, 16, 18, 20, 22, and 29. When the size of the genomic windows was increased from 1 to 5 Mb, a greater proportion of genetic variance was explained by the SNPs within the window, and a greater number of windows explained more than 1% of the genetic variance. The most significant regions were associated with two or more phenotypic traits, such as one region on chromosome 14 that harbors the DGAT1, EXOSC4, PPP1R16A, and FOXH1 genes, which affect all the traits under study. In general, the utilization of genomic windows resulted in a greater proportion of genetic variance explained by milk production traits.https://doi.org/10.1371/journal.pone.0314888 |
| spellingShingle | José G Cortes-Hernández Adriana García-Ruiz Francisco Peñagaricano Hugo H Montaldo Felipe J Ruiz-López Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. PLoS ONE |
| title | Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. |
| title_full | Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. |
| title_fullStr | Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. |
| title_full_unstemmed | Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. |
| title_short | Uncovering the genetic basis of milk production traits in Mexican Holstein cattle based on individual markers and genomic windows. |
| title_sort | uncovering the genetic basis of milk production traits in mexican holstein cattle based on individual markers and genomic windows |
| url | https://doi.org/10.1371/journal.pone.0314888 |
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