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Luigi-Sierra MG, Martínez A, Macri M, Delgado JV, Castelló A, Alvarez JF, Such X, Jordana J, Amills M. Single and longitudinal genome-wide association studies for dairy traits available in goats with three recorded lactations. Anim Genet 2024; 55:257-264. [PMID: 38131417 DOI: 10.1111/age.13391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/01/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023]
Abstract
Milk yield and composition phenotypes are systematically recorded across several lactations in goats, but the majority of genome-wide association studies (GWAS) performed so far have rather ignored the longitudinal nature of such data. Here, we have used two different GWAS approaches to analyse data from three lactations recorded in Murciano-Granadina goats. In Analysis 1, independent GWAS have been carried out for each trait and lactation, while a single longitudinal GWAS, jointly considering all data, has been performed in Analysis 2. In both analyses, genome-wide significant QTL for lactose percentage on chromosome 2 (129.77-131.01 Mb) and for milk protein percentage on the chromosome 6 (74.8-94.6 Mb) casein gene cluster region were detected. In Analysis 1, several QTL were not replicated in all three lactations, possibly due to the existence of lactation-specific genetic determinants. In Analysis 2, we identified several genome-wide significant QTL related to milk yield and protein content that were not uncovered in Analysis 1. The increased number of QTL identified in Analysis 2 suggests that the longitudinal GWAS is particularly well suited for the genetic analysis of dairy traits. Moreover, our data confirm that variability within or close to the casein complex is the main genetic determinant of milk protein percentage in Murciano-Granadina goats.
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Affiliation(s)
- Maria Gracia Luigi-Sierra
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Amparo Martínez
- Departamento de Genética, Universidad de Córdoba, Córdoba, Spain
| | - Martina Macri
- Departamento de Genética, Universidad de Córdoba, Córdoba, Spain
| | | | - Anna Castelló
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Javier Fernández Alvarez
- Asociación Nacional de Criadores de Caprino de Raza Murciano-Granadina (CAPRIGRAN), Granada, Spain
| | - Xavier Such
- Group of Research in Ruminants (G2R), Department of Animal and Food Science, Universitat Autònoma de Barcelona (UAB), Bellaterra, Spain
| | - Jordi Jordana
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Marcel Amills
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Campus Universitat Autònoma de Barcelona, Bellaterra, Spain
- Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, Bellaterra, Spain
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2
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Jiang W, Mooney MH, Shirali M. Unveiling the Genetic Landscape of Feed Efficiency in Holstein Dairy Cows: Insights into Heritability, Genetic Markers, and Pathways via Meta-Analysis. J Anim Sci 2024; 102:skae040. [PMID: 38354297 PMCID: PMC10957122 DOI: 10.1093/jas/skae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 02/09/2024] [Indexed: 02/16/2024] Open
Abstract
Improving the feeding efficiency of dairy cows is a key component to improve the utilization of land resources and meet the demand for high-quality protein. Advances in genomic methods and omics techniques have made it possible to breed more efficient dairy cows through genomic selection. The aim of this review is to obtain a comprehensive understanding of the biological background of feed efficiency (FE) complex traits in purebred Holstein dairy cows including heritability estimate, and genetic markers, genes, and pathways participating in FE regulation mechanism. Through a literature search, we systematically reviewed the heritability estimation, molecular genetic markers, genes, biomarkers, and pathways of traits related to feeding efficiency in Holstein dairy cows. A meta-analysis based on a random-effects model was performed to combine reported heritability estimates of FE complex. The heritability of residual feed intake, dry matter intake, and energy balance was 0.20, 0.34, and 0.22, respectively, which proved that it was reasonable to include the related traits in the selection breeding program. For molecular genetic markers, a total of 13 single-nucleotide polymorphisms and copy number variance loci, associated genes, and functions were reported to be significant across populations. A total of 169 reported candidate genes were summarized on a large scale, using a higher threshold (adjusted P value < 0.05). Then, the subsequent pathway enrichment of these genes was performed. The important genes reported in the articles were included in a gene list and the gene list was enriched by gene ontology (GO):biological process (BP), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis. Three GO:BP terms and four KEGG terms were statistically significant, which mainly focused on adenosine triphosphate (ATP) synthesis, electron transport chain, and OXPHOS pathway. Among these pathways, involved genes such as ATP5MC2, NDUFA, COX7A2, UQCR, and MMP are particularly important as they were previously reported. Twenty-nine reported biological mechanisms along with involved genes were explained mainly by four biological pathways (insulin-like growth factor axis, lipid metabolism, oxidative phosphorylation pathways, tryptophan metabolism). The information from this study will be useful for future studies of genomic selection breeding and genetic structures influencing animal FE. A better understanding of the underlying biological mechanisms would be beneficial, particularly as it might address genetic antagonism.
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Affiliation(s)
- Wentao Jiang
- Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, BT9 5DL, UK
- Agri-Food and Biosciences Institute, Large Park, Hillsborough, BT26 6DR, UK
| | - Mark H Mooney
- Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, BT9 5DL, UK
| | - Masoud Shirali
- Institute for Global Food Security, School of Biological Sciences, Queen’s University Belfast, Belfast, BT9 5DL, UK
- Agri-Food and Biosciences Institute, Large Park, Hillsborough, BT26 6DR, UK
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3
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Pazzola M, Stocco G, Ferragina A, Bittante G, Dettori ML, Vacca GM, Cipolat-Gotet C. Cheese yield and nutrients recovery in the curd predicted by Fourier-transform spectra from individual sheep milk samples. J Dairy Sci 2023; 106:6759-6770. [PMID: 37230879 DOI: 10.3168/jds.2023-23349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/22/2023] [Indexed: 05/27/2023]
Abstract
The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as "water" and "fingerprint" regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.
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Affiliation(s)
- Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | - Giorgia Stocco
- Department of Veterinary Science, University of Parma, 43126 Parma, Italy.
| | - Alessandro Ferragina
- Food Quality and Sensory Science Department, Teagasc Food Research Centre, Dublin D15 KN3K, Ireland
| | - Giovanni Bittante
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE) University of Padova, 35020 Legnaro, PD, Italy
| | - Maria Luisa Dettori
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
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Singh A, Malla WA, Kumar A, Jain A, Thakur MS, Khare V, Tiwari SP. Review: genetic background of milk fatty acid synthesis in bovines. Trop Anim Health Prod 2023; 55:328. [PMID: 37749432 DOI: 10.1007/s11250-023-03754-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 09/12/2023] [Indexed: 09/27/2023]
Abstract
Milk fat composition is an important trait for the dairy industry as it directly influences the nutritional and technological properties of milk and other dairy products. The synthesis of milk fat is a complex process regulated by a network of genes. Thus, understanding the genetic variation and molecular mechanisms regulating milk fat synthesis will help to improve the nutritional quality of dairy products. In this review, we provide an overview of milk fat synthesis in bovines along with the candidate genes involved in the pathway. We also discuss de novo synthesis of fatty acids (ACSS, ACACA, FASN), uptake of FAs (FATP, FAT, LPL), intracellular activation and channelling of FAs (ACSL, FABP), elongation (EVOLV6), desaturation (SCD, FADS), formation of triglycerides (GPAM, AGPAT, LIPIN, DGAT), and milk lipid secretion (BTN1A1, XDH, PLIN2). The genetic variability of individual fatty acids will help to develop selection strategies for obtaining a healthier milk fat profile in bovines. Thus, this review will offer a potential understanding of the molecular mechanisms that regulate milk fat synthesis in bovines.
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Affiliation(s)
- Akansha Singh
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, M.P, 482001, India.
| | - Waseem Akram Malla
- ICMR-National Institute of Malaria Research, Field Unit Guwahati, Assam, 781022, India
| | - Amit Kumar
- ICAR- Indian Veterinary Research Institute, Izatnagar, Bareilly, U.P, 243122, India
| | - Asit Jain
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, M.P, 482001, India
| | - Mohan Singh Thakur
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, M.P, 482001, India
| | - Vaishali Khare
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, M.P, 482001, India
| | - Sita Prasad Tiwari
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, M.P, 482001, India
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Atashi H, Chen Y, Wilmot H, Vanderick S, Hubin X, Soyeurt H, Gengler N. Single-step genome-wide association for selected milk fatty acids in Dual-Purpose Belgian Blue cows. J Dairy Sci 2023; 106:6299-6315. [PMID: 37479585 DOI: 10.3168/jds.2022-22432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 03/17/2023] [Indexed: 07/23/2023]
Abstract
The aim of this study was to estimate genetic parameters and identify genomic regions associated with selected individual and groups of milk fatty acids (FA) predicted by milk mid-infrared spectrometry in Dual-Purpose Belgian Blue cows. The used data were 69,349 test-day records of milk yield, fat percentage, and protein percentage along with selected individual and groups FA of milk (g/dL milk) collected from 2007 to 2020 on 7,392 first-parity (40,903 test-day records), and 5,185 second-parity (28,446 test-day records) cows distributed in 104 herds in the Walloon Region of Belgium. Data of 28,466 SNPs, located on 29 Bos taurus autosomes (BTA), of 1,699 animals (639 males and 1,060 females) were used. Random regression test-day models were used to estimate genetic parameters through the Bayesian Gibbs sampling method. The SNP solutions were estimated using a single-step genomic best linear unbiased prediction approach. The proportion of genetic variance explained by each 25-SNP sliding window (with an average size of ~2 Mb) was calculated, and regions accounting for at least 1.0% of the total additive genetic variance were used to search for candidate genes. Average daily heritability estimated for the included milk FA traits ranged from 0.01 (C4:0) to 0.48 (C12:0) and 0.01 (C4:0) to 0.42 (C12:0) in the first and second parities, respectively. Genetic correlations found between milk yield and the studied individual milk FA, except for C18:0, C18:1 trans, C18:1 cis-9, were positive. The results showed that fat percentage and protein percentage were positively genetically correlated with all studied individual milk FA. Genome-wide association analyses identified 11 genomic regions distributed over 8 chromosomes [BTA1, BTA4, BTA10, BTA14 (4 regions), BTA19, BTA22, BTA24, and BTA26] associated with the studied FA traits, though those found on BTA14 partly overlapped. The genomic regions identified differed between parities and lactation stages. Although these differences in genomic regions detected may be due to the power of quantitative trait locus detection, it also suggests that candidate genes underlie the phenotypic expression of the studied traits may vary between parities and lactation stages. These findings increase our understanding about the genetic background of milk FA and can be used for the future implementation of genomic evaluation to improve milk FA profile in Dual-Purpose Belgian Blue cows.
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Affiliation(s)
- H Atashi
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; Department of Animal Science, Shiraz University, 71441-13131 Shiraz, Iran.
| | - Y Chen
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - H Wilmot
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium; National Fund for Scientific Research (F.R.S.-FNRS), 1000 Brussels, Belgium
| | - S Vanderick
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - X Hubin
- Elevéo asbl Awé Group, 5590 Ciney, Belgium
| | - H Soyeurt
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
| | - N Gengler
- TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
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Jayawardana JMDR, Lopez-Villalobos N, McNaughton LR, Hickson RE. Genomic Regions Associated with Milk Composition and Fertility Traits in Spring-Calved Dairy Cows in New Zealand. Genes (Basel) 2023; 14:genes14040860. [PMID: 37107618 PMCID: PMC10137527 DOI: 10.3390/genes14040860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/05/2023] Open
Abstract
The objective of this study was to identify genomic regions and genes that are associated with the milk composition and fertility traits of spring-calved dairy cows in New Zealand. Phenotypic data from the 2014–2015 and 2021–2022 calving seasons in two Massey University dairy herds were used. We identified 73 SNPs that were significantly associated with 58 potential candidate genes for milk composition and fertility traits. Four SNPs on chromosome 14 were highly significant for both fat and protein percentages, and the associated genes were DGAT1, SLC52A2, CPSF1, and MROH1. For fertility traits, significant associations were detected for intervals from the start of mating to first service, the start of mating to conception, first service to conception, calving to first service, and 6-wk submission, 6-wk in-calf, conception to first service in the first 3 weeks of the breeding season, and not in calf and 6-wk calving rates. Gene Ontology revealed 10 candidate genes (KCNH5, HS6ST3, GLS, ENSBTAG00000051479, STAT1, STAT4, GPD2, SH3PXD2A, EVA1C, and ARMH3) that were significantly associated with fertility traits. The biological functions of these genes are related to reducing the metabolic stress of cows and increasing insulin secretion during the mating period, early embryonic development, foetal growth, and maternal lipid metabolism during the pregnancy period.
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Affiliation(s)
- J. M. D. R. Jayawardana
- School of Agriculture and Environment, Massey University, Palmerston North 4410, New Zealand
- Department of Animal Science, Faculty of Animal Science and Export Agriculture, Uva Wellassa University, Badulla 90000, Sri Lanka
| | | | - Lorna R. McNaughton
- Livestock Improvement Corporation, Private Bag 3016, Hamilton 3240, New Zealand
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Narayana SG, de Jong E, Schenkel FS, Fonseca PA, Chud TC, Powel D, Wachoski-Dark G, Ronksley PE, Miglior F, Orsel K, Barkema HW. Underlying genetic architecture of resistance to mastitis in dairy cattle: A systematic review and gene prioritization analysis of genome-wide association studies. J Dairy Sci 2022; 106:323-351. [DOI: 10.3168/jds.2022-21923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 08/01/2022] [Indexed: 11/05/2022]
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Khan MZ, Ma Y, Ma J, Xiao J, Liu Y, Liu S, Khan A, Khan IM, Cao Z. Association of DGAT1 With Cattle, Buffalo, Goat, and Sheep Milk and Meat Production Traits. Front Vet Sci 2021; 8:712470. [PMID: 34485439 PMCID: PMC8415568 DOI: 10.3389/fvets.2021.712470] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 07/19/2021] [Indexed: 12/26/2022] Open
Abstract
Milk fatty acids are essential for many dairy product productions, while intramuscular fat (IMF) is associated with the quality of meat. The triacylglycerols (TAGs) are the major components of IMF and milk fat. Therefore, understanding the polymorphisms and genes linked to fat synthesis is important for animal production. Identifying quantitative trait loci (QTLs) and genes associated with milk and meat production traits has been the objective of various mapping studies in the last decade. Consistently, the QTLs on chromosomes 14, 15, and 9 have been found to be associated with milk and meat production traits in cattle, goat, and buffalo and sheep, respectively. Diacylglycerol O-acyltransferase 1 (DGAT1) gene has been reported on chromosomes 14, 15, and 9 in cattle, goat, and buffalo and sheep, respectively. Being a key role in fat metabolism and TAG synthesis, the DGAT1 has obtained considerable attention especially in animal milk production. In addition to milk production, DGAT1 has also been a subject of interest in animal meat production. Several polymorphisms have been documented in DGAT1 in various animal species including cattle, buffalo, goat, and sheep for their association with milk production traits. In addition, the DGAT1 has also been studied for their role in meat production traits in cattle, sheep, and goat. However, very limited studies have been conducted in cattle for association of DGAT1 with meat production traits in cattle. Moreover, not a single study reported the association of DGAT1 with meat production traits in buffalo; thus, further studies are warranted to fulfill this huge gap. Keeping in view the important role of DGAT1 in animal production, the current review article was designed to highlight the major development and new insights on DGAT1 effect on milk and meat production traits in cattle, buffalo, sheep, and goat. Moreover, we have also highlighted the possible future contributions of DGAT1 for the studied species.
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Affiliation(s)
- Muhammad Zahoor Khan
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
- Faculty of Veterinary and Animal Sciences, Gomal University, Dera Ismail Khan, Pakistan
| | - Yulin Ma
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jiaying Ma
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Jianxin Xiao
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Yue Liu
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Shuai Liu
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Adnan Khan
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Ibrar Muhammad Khan
- Anhui Provincial Laboratory of Local Livestock and Poultry Genetical Resource Conservation and Breeding, College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Zhijun Cao
- State Key Laboratory of Animal Nutrition, Beijing Engineering Technology Research Center of Raw Milk Quality and Safety Control, College of Animal Science and Technology, China Agricultural University, Beijing, China
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9
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Gutierrez-Reinoso MA, Aponte PM, Garcia-Herreros M. Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review. Animals (Basel) 2021; 11:599. [PMID: 33668747 PMCID: PMC7996307 DOI: 10.3390/ani11030599] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Revised: 11/23/2020] [Accepted: 11/24/2020] [Indexed: 12/16/2022] Open
Abstract
Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.
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Affiliation(s)
- Miguel A. Gutierrez-Reinoso
- Facultad de Ciencias Agropecuarias y Recursos Naturales, Carrera de Medicina Veterinaria, Universidad Técnica de Cotopaxi (UTC), Latacunga 05-0150, Ecuador
- Laboratorio de Biotecnología Animal, Departamento de Ciencia Animal, Facultad de Ciencias Veterinarias, Universidad de Concepción (UdeC), Chillán 3780000, Chile
| | - Pedro M. Aponte
- Colegio de Ciencias Biológicas y Ambientales (COCIBA), Universidad San Francisco de Quito (USFQ), Quito 170157, Ecuador
- Campus Cumbayá, Instituto de Investigaciones en Biomedicina “One-health”, Universidad San Francisco de Quito (USFQ), Quito 170157, Ecuador
| | - Manuel Garcia-Herreros
- Instituto Nacional de Investigação Agrária e Veterinária (INIAV), 2005-048 Santarém, Portugal
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10
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Lu H, Wang Y, Bovenhuis H. Phenotypic and genetic effects of season on milk production traits in dairy cattle in the Netherlands. J Dairy Sci 2021; 104:4486-4497. [PMID: 33612205 DOI: 10.3168/jds.2020-19615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/29/2020] [Indexed: 12/13/2022]
Abstract
Milk production systems in several countries show considerable differences between seasons. For example, in the Netherlands, cows are kept inside and fed silage in winter, whereas they are on pasture in summer. The differences between seasons affect milk yield and composition and might influence the genetic background of milk production traits. The objective of this study was to estimate phenotypic and genetic effects of season on milk production traits. For this purpose, 19,286 test-day milk production records of 1,800 first-parity Dutch Holstein-Frisian cows were available, and these cows were genotyped using a 50K SNP panel. Phenotypic effects of season were significant for all milk production traits. Effects of season were large for milk fat yield, fat content, and protein content. Genetic correlations between milk production traits in different seasons showed that genotype by season interaction effects were relatively small for most milk production traits. The genetic background of protein content and lactose content seems to be sensitive to seasonal effects. Furthermore, the genetic correlations between spring and autumn differed significantly from unity for almost all milk production traits. A genome-wide association study for genotype by season interaction identified chromosomal regions on BTA3, BTA14, BTA20, and BTA25 that showed genotype by season interaction effects, including a region containing DGAT1, which showed interaction effects for fat content and protein content.
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Affiliation(s)
- Haibo Lu
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, the Netherlands
| | - Yachun Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs (MARA), National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, 100193, Beijing, P.R. China
| | - Henk Bovenhuis
- Animal Breeding and Genomics, Wageningen University and Research, 6700AH, Wageningen, the Netherlands.
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11
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Liu L, Zhou J, Chen CJ, Zhang J, Wen W, Tian J, Zhang Z, Gu Y. GWAS-Based Identification of New Loci for Milk Yield, Fat, and Protein in Holstein Cattle. Animals (Basel) 2020; 10:ani10112048. [PMID: 33167458 PMCID: PMC7694478 DOI: 10.3390/ani10112048] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/01/2020] [Accepted: 11/03/2020] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Understanding the genetic architecture underlying milk production traits in cattle is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we performed a genome-wide association study for milk production and quality traits in Holstein cattle. In the total of ten significant single-nucleotide polymorphisms (SNPs) associated with milk fat and protein, six are located in previously reported quantitative traits locus (QTL) regions. The study not only identified the effect of DGAT1 gene on milk fat and protein but also found several novel candidate genes. In addition, some pleiotropic SNPs and QTLs were identified that associated with more than two traits, these results could provide some basis for molecular breeding in dairy cattle. Abstract High-yield and high-quality of milk are the primary goals of dairy production. Understanding the genetic architecture underlying these milk-related traits is beneficial so that genetic variants can be targeted toward the genetic improvement. In this study, we measured five milk production and quality traits in Holstein cattle population from China. These traits included milk yield, fat, and protein. We used the estimated breeding values as dependent variables to conduct the genome-wide association studies (GWAS). Breeding values were estimated through pedigree relationships by using a linear mixed model. Genotyping was carried out on the individuals with phenotypes by using the Illumina BovineSNP150 BeadChip. The association analyses were conducted by using the fixed and random model Circulating Probability Unification (FarmCPU) method. A total of ten single-nucleotide polymorphisms (SNPs) were detected above the genome-wide significant threshold (p < 4.0 × 10−7), including six located in previously reported quantitative traits locus (QTL) regions. We found eight candidate genes within distances of 120 kb upstream or downstream to the associated SNPs. The study not only identified the effect of DGAT1 gene on milk fat and protein, but also discovered novel genetic loci and candidate genes related to milk traits. These novel genetic loci would be an important basis for molecular breeding in dairy cattle.
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Affiliation(s)
- Liyuan Liu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Jinghang Zhou
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Chunpeng James Chen
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
| | - Juan Zhang
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
| | - Wan Wen
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Jia Tian
- Animal Husbandry Workstation, Yinchuan 750001, Ningxia, China; (W.W.); (J.T.)
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, Washington, DC 99164, USA;
- Correspondence: (Z.Z.); (Y.G.)
| | - Yaling Gu
- School of Agriculture, Ningxia University, Yinchuan 750021, Ningxia, China; (L.L.); (J.Z.); (J.Z.)
- Correspondence: (Z.Z.); (Y.G.)
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Lu H, Bovenhuis H. Phenotypic and genetic effects of pregnancy on milk production traits in Holstein-Friesian cattle. J Dairy Sci 2020; 103:11597-11604. [PMID: 32981723 DOI: 10.3168/jds.2020-18561] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Accepted: 07/20/2020] [Indexed: 11/19/2022]
Abstract
Pregnancy is a prerequisite for the initiation of lactation and for maintaining the milk production cycle. Pregnancy affects milk production and therefore should be accounted for in the genetic evaluation. Furthermore, there might be genetic differences in pregnancy effects on milk composition. The objective of this study was to estimate phenotypic and genetic effects of pregnancy on milk production traits. For this purpose, test-day records and conception dates of 1,359 first-parity Holstein-Friesian cows were analyzed. Significant effects of pregnancy on all milk production traits were detected except somatic cell score (e.g., the cumulative effects of pregnancy on milk yield were -247 kg). The pregnancy effects on milk yield, lactose yield, protein yield, fat yield, and fat content were small during early gestation (<150 d) and substantially increased in late gestation. The effects of pregnancy on milk protein yield were relatively stronger than those on fat yield. The effects of pregnancy on milk production traits differed for DGAT1 genotypes. Milk yield, lactose yield, protein yield, and fat yield of DGAT1 AA cows were more affected by pregnancy than that of DGAT1 KK cows (e.g., the cumulative effects of pregnancy on milk yield were negligible for DGAT1 KK cows and were -443 kg for DGAT1 AA cows). These results suggest that DGAT1 KK cows may be more suitable for shortening or omitting the dry period than DGAT1 AA cows.
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Affiliation(s)
- Haibo Lu
- Animal Breeding and Genomics, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands
| | - Henk Bovenhuis
- Animal Breeding and Genomics, Wageningen University and Research, PO Box 338, 6700 AH, Wageningen, the Netherlands.
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