1
|
Sharko FS, Khatib A, Prokhortchouk EB. Genomic Estimated Breeding Value of Milk Performance and Fertility Traits in the Russian Black-and-White Cattle Population. Acta Naturae 2022; 14:109-122. [PMID: 35441049 PMCID: PMC9013432 DOI: 10.32607/actanaturae.11648] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 01/14/2022] [Indexed: 11/20/2022] Open
Abstract
A breakthrough in cattle breeding was achieved with the incorporation of animal
genomic data into breeding programs. The introduction of genomic selection has
a major impact on traditional genetic assessment systems and animal genetic
improvement programs. Since 2010, genomic selection has been officially
introduced in the evaluation of the breeding and genetic potential of cattle in
Europe, the U.S., Canada, and many other developed countries. The purpose of
this study is to develop a system for a genomic evaluation of the breeding
value of the domestic livestock of Black-and-White and Russian Holstein cattle
based on 3 milk performance traits: daily milk yield (kg), daily milk fat (%),
and daily milk protein content (%) and 6 fertility traits: age at first calving
(AFC), calving interval (CI), calving to first insemination interval (CFI),
interval between first and last insemination (IFL), days open (DO), and number
of services (NS). We built a unified database of breeding animals from 523
breeding farms in the Russian Federation. The database included pedigree
information on 2,551,529 cows and 69,131 bulls of the Russian Holstein and
Black-and-White cattle breeds, as well as information on the milk performance
of 1,597,426 cows with 4,771,366 completed lactations. The date of birth of the
animals included in the database was between 1975 and 2017. Genotyping was
performed in 672 animals using a BovineSNP50 v3 DNA Analysis BeadChip
microarray (Illumina, USA). The genomic estimated breeding value (GEBV) was
evaluated only for 644 animals (427 bulls and 217 cows) using the single-step
genomic best linear unbiased prediction – animal model (ssGBLUP-AM). The
mean genetic potential was +0.88 and +1.03 kg for the daily milk yield, -0.002%
for the milk fat content, and –0.003 and 0.001% for the milk protein
content in the cows and bulls, respectively. There was negative genetic
progress in the fertility traits in the studied population between 1975 and
2017. The reliability of the estimated breeding value (EBV) for genotyped bulls
ranged from 89 to 93% for the milk performance traits and 85 to 90% for the
fertility traits, whereas the reliability of the genomic estimated breeding
value (GEBV) varied 54 to 64% for the milk traits and 23 to 60% for the
fertility traits. This result shows that it is possible to use the genomic
estimated breeding value with rather high reliability to evaluate the domestic
livestock of Russian Holstein and Black-and-White cattle breeds for fertility
and milk performance traits. This system of genomic evaluation may help bring
domestic breeding in line with modern competitive practices and estimate the
breeding value of cattle at birth based on information on the animal’s
genome.
Collapse
Affiliation(s)
- F. S. Sharko
- Laboratory of vertebrate genomics and epigenomics, Federal Research Centre “Fundamentals of Biotechnology” of the Russian Academy of Sciences, Moscow, 119071 Russia
| | - A. Khatib
- Laboratory I-Gene, ZAO “Genoanalytica”, Moscow, 119234 Russia
- Department of biotechnology, faculty of Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
- Atomic Energy Commission of Syria (AECS), Department of Agriculture, Damascus, 6091 Syria
| | - E. B. Prokhortchouk
- Laboratory of vertebrate genomics and epigenomics, Federal Research Centre “Fundamentals of Biotechnology” of the Russian Academy of Sciences, Moscow, 119071 Russia
- Laboratory I-Gene, ZAO “Genoanalytica”, Moscow, 119234 Russia
| |
Collapse
|
2
|
Oliveira HR, Lourenco DAL, Masuda Y, Misztal I, Tsuruta S, Jamrozik J, Brito LF, Silva FF, Cant JP, Schenkel FS. Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle. J Dairy Sci 2019; 102:9995-10011. [PMID: 31477296 DOI: 10.3168/jds.2019-16821] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/08/2019] [Indexed: 11/19/2022]
Abstract
Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 lactations and the other considering SCS in the first 3 lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among lactations. Among the lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third lactations were more similar to each other than for the first lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.
Collapse
Affiliation(s)
- H R Oliveira
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil.
| | - D A L Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - Y Masuda
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - I Misztal
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - S Tsuruta
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - J Jamrozik
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada
| | - L F Brito
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - F F Silva
- Department of Animal Sciences, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil
| | - J P Cant
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - F S Schenkel
- Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G 2W1, Canada
| |
Collapse
|
3
|
Oliveira HR, Brito LF, Lourenco DAL, Silva FF, Jamrozik J, Schaeffer LR, Schenkel FS. Invited review: Advances and applications of random regression models: From quantitative genetics to genomics. J Dairy Sci 2019; 102:7664-7683. [PMID: 31255270 DOI: 10.3168/jds.2019-16265] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2019] [Accepted: 05/02/2019] [Indexed: 12/23/2022]
Abstract
An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.
Collapse
Affiliation(s)
- H R Oliveira
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil
| | - L F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - D A L Lourenco
- Department of Animal and Dairy Science, University of Georgia, Athens 30602
| | - F F Silva
- Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-000, Brazil
| | - J Jamrozik
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada
| | - L R Schaeffer
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada
| | - F S Schenkel
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, ON, N1G2W1, Canada.
| |
Collapse
|