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Genomic studies of milk-related traits in water buffalo (Bubalus bubalis) based on single-step genomic best linear unbiased prediction and random regression models
- Source :
- Scopus, Repositório Institucional da UNESP, Universidade Estadual Paulista (UNESP), instacron:UNESP
- Publication Year :
- 2021
- Publisher :
- American Dairy Science Association, 2021.
-
Abstract
- Made available in DSpace on 2021-06-25T11:12:57Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-05-01 Genomic selection has been widely implemented in many livestock breeding programs, but it remains incipient in buffalo. Therefore, this study aimed to (1) estimate variance components incorporating genomic information in Murrah buffalo; (2) evaluate the performance of genomic prediction for milk-related traits using single- and multitrait random regression models (RRM) and the single-step genomic best linear unbiased prediction approach; and (3) estimate longitudinal SNP effects and candidate genes potentially associated with time-dependent variation in milk, fat, and protein yields, as well as somatic cell score (SCS) in multiple parities. The data used to estimate the genetic parameters consisted of a total of 323,140 test-day records. The average daily heritability estimates were moderate (0.35 ± 0.02 for milk yield, 0.22 ± 0.03 for fat yield, 0.42 ± 0.03 for protein yield, and 0.16 ± 0.03 for SCS). The highest heritability estimates, considering all traits studied, were observed between 20 and 280 d in milk (DIM). The genetic correlation estimates at different DIM among the evaluated traits ranged from −0.10 (156 to 185 DIM for SCS) to 0.61 (36 to 65 DIM for fat yield). In general, direct selection for any of the traits evaluated is expected to result in indirect genetic gains for milk yield, fat yield, and protein yield but also increase SCS at certain lactation stages, which is undesirable. The predicted RRM coefficients were used to derive the genomic estimated breeding values (GEBV) for each time point (from 5 to 305 DIM). In general, the tuning parameters evaluated when constructing the hybrid genomic relationship matrices had a small effect on the GEBV accuracy and a greater effect on the bias estimates. The SNP solutions were back-solved from the GEBV predicted from the Legendre random regression coefficients, which were then used to estimate the longitudinal SNP effects (from 5 to 305 DIM). The daily SNP effect for 3 different lactation stages were performed considering 3 different lactation stages for each trait and parity: from 5 to 70, from 71 to 150, and from 151 to 305 DIM. Important genomic regions related to the analyzed traits and parities that explain more than 0.50% of the total additive genetic variance were selected for further analyses of candidate genes. In general, similar potential candidate genes were found between traits, but our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the traits across parities. These results contribute to a better understanding of the genetic architecture of milk production traits in dairy buffalo and reinforce the relevance of incorporating genomic information to genetically evaluate longitudinal traits in dairy buffalo. Furthermore, the candidate genes identified can be used as target genes in future functional genomics studies. Department of Animal Sciences Purdue University Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP) Centre for Genetic Improvement of Livestock Department of Animal Biosciences University of Guelph Department of Animal and Avian Science University of Maryland Department of Animal Science College of Agricultural and Veterinary Sciences São Paulo State University (UNESP)
- Subjects :
- Candidate gene
Buffaloes
Genome-wide association study
Best linear unbiased prediction
mastitis
Genetic correlation
Murrah buffalo
longitudinal trait
03 medical and health sciences
Pregnancy
Statistics
Genetic variation
Genetics
Animals
Lactation
Murrah
030304 developmental biology
0303 health sciences
genome-wide association study
biology
0402 animal and dairy science
Genomics
04 agricultural and veterinary sciences
Heritability
biology.organism_classification
040201 dairy & animal science
Genetic architecture
Milk
Phenotype
Female
Animal Science and Zoology
lactation curve
Food Science
Subjects
Details
- ISSN :
- 00220302
- Volume :
- 104
- Database :
- OpenAIRE
- Journal :
- Journal of Dairy Science
- Accession number :
- edsair.doi.dedup.....8dc05926338cb4799be0b266be8d77f9