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Chen Y, Atashi H, Qu J, Delhez P, Runcie D, Soyeurt H, Gengler N. Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding. J Dairy Sci 2024:S0022-0302(24)00975-5. [PMID: 38969006 DOI: 10.3168/jds.2023-24319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 06/10/2024] [Indexed: 07/07/2024]
Abstract
With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).
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Affiliation(s)
- Yansen Chen
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium.
| | - Hadi Atashi
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium; Department of Animal Science, Shiraz University, 71441-13131 Shiraz, Iran
| | - Jiayi Qu
- Department of Animal Science, University of California Davis, CA 95616 Davis, USA
| | - Pauline Delhez
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Daniel Runcie
- Department of Plant Sciences, University of California Davis, CA 95616 Davis, USA
| | - Hélène Soyeurt
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
| | - Nicolas Gengler
- TERRA Teaching and Research Center, University of Liège, Gembloux Agro-Bio Tech (ULiège-GxABT), 5030 Gembloux, Belgium
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Singh A, Kumar A, Thakur MS, Khare V, Jain A, Tiwari SP. Genetic analysis of milk minerals in dairy cattle: a review. J Appl Genet 2024; 65:375-381. [PMID: 38286942 DOI: 10.1007/s13353-024-00832-9] [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: 12/30/2022] [Revised: 12/22/2023] [Accepted: 01/19/2024] [Indexed: 01/31/2024]
Abstract
Mineral composition in milk can affect its nutritional as well as physio-chemical properties of milk and is considered an important trait in the evaluation of milk quality. The composition and concentration of milk minerals could be altered with natural source of variation including nutrition and genetics. The effect of diet on milk minerals is well studied. However, genetic effects on the milk minerals have recently gained the attention. This review provides an overview of the genetic variation of milk minerals, and the genomic regions associated with mineral concentration in the milk are also discussed. The difference of milk minerals between breeds and the genetic parameters including heritability estimates and correlation among minerals indicates that milk minerals are under strong genetic control. Recently, the genome-wide association study (GWAS) has explored several regions associated with milk minerals and thus provides a new genetic source for improving the milk quality through genomics-assisted breeding. Hence, a combination of the qualitative and molecular approaches can be exploited to improving the nutritional quality of cattle milk in terms of its mineral composition.
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Affiliation(s)
- Akansha Singh
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, 482001, M.P, India.
| | - Amit Kumar
- ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, U.P., India
| | - Mohan Singh Thakur
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, 482001, M.P, India
| | - Vaishali Khare
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, 482001, M.P, India
| | - Asit Jain
- College of Veterinary Science and Animal Husbandry, NDVSU, Jabalpur, 482001, M.P, India
| | - Sita Prasad Tiwari
- Nanaji Deshmukh Veterinary Science University, Jabalpur, 482004, M.P., India
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Lopdell TJ, Trevarton AJ, Moody J, Prowse-Wilkins C, Knowles S, Tiplady K, Chamberlain AJ, Goddard ME, Spelman RJ, Lehnert K, Snell RG, Davis SR, Littlejohn MD. A common regulatory haplotype doubles lactoferrin concentration in milk. Genet Sel Evol 2024; 56:22. [PMID: 38549172 PMCID: PMC11234695 DOI: 10.1186/s12711-024-00890-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
BACKGROUND Bovine lactoferrin (Lf) is an iron absorbing whey protein with antibacterial, antiviral, and antifungal activity. Lactoferrin is economically valuable and has an extremely variable concentration in milk, partly driven by environmental influences such as milking frequency, involution, or mastitis. A significant genetic influence has also been previously observed to regulate lactoferrin content in milk. Here, we conducted genetic mapping of lactoferrin protein concentration in conjunction with RNA-seq, ChIP-seq, and ATAC-seq data to pinpoint candidate causative variants that regulate lactoferrin concentrations in milk. RESULTS We identified a highly-significant lactoferrin protein quantitative trait locus (pQTL), as well as a cis lactotransferrin (LTF) expression QTL (cis-eQTL) mapping to the LTF locus. Using ChIP-seq and ATAC-seq datasets representing lactating mammary tissue samples, we also report a number of regions where the openness of chromatin is under genetic influence. Several of these also show highly significant QTL with genetic signatures similar to those highlighted through pQTL and eQTL analysis. By performing correlation analysis between these QTL, we revealed an ATAC-seq peak in the putative promotor region of LTF, that highlights a set of 115 high-frequency variants that are potentially responsible for these effects. One of the 115 variants (rs110000337), which maps within the ATAC-seq peak, was predicted to alter binding sites of transcription factors known to be involved in lactation-related pathways. CONCLUSIONS Here, we report a regulatory haplotype of 115 variants with conspicuously large impacts on milk lactoferrin concentration. These findings could enable the selection of animals for high-producing specialist herds.
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Affiliation(s)
- Thomas J Lopdell
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand.
| | - Alexander J Trevarton
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Janelle Moody
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Claire Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Sarah Knowles
- Auckland War Memorial Museum, Victoria Street West, Auckland, New Zealand
| | - Kathryn Tiplady
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
| | - Michael E Goddard
- Agriculture Victoria, AgriBio, Centre for AgriBiosciences, Bundoora, VIC, Australia
- Faculty of Veterinarian and Agricultural Science, The University of Melbourne, Parkville, VIC, Australia
| | - Richard J Spelman
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Klaus Lehnert
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Russell G Snell
- School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Stephen R Davis
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
| | - Mathew D Littlejohn
- Research & Development, Livestock Improvement Corporation, Ruakura Road, Hamilton, New Zealand
- AL Rae Centre for Genetics and Breeding, Massey University, Palmerston North, New Zealand
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Selionova M, Trukhachev V, Aibazov M, Sermyagin A, Belous A, Gladkikh M, Zinovieva N. Genome-Wide Association Study of Milk Composition in Karachai Goats. Animals (Basel) 2024; 14:327. [PMID: 38275787 PMCID: PMC10812594 DOI: 10.3390/ani14020327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/10/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024] Open
Abstract
This study is first to perform a genome-wide association study (GWAS) to investigate the milk quality traits in Karachai goats. The objective of the study was to identify candidate genes associated with milk composition traits based on the identification and subsequent analysis of all possible SNPs, both genome-wide (high-confidence) and suggestive (subthreshold significance). To estimate the milk components, 22 traits were determined, including several types of fatty acids. DNA was extracted from ear tissue or blood samples. A total of 167 Karachai goats were genotyped using an Illumina GoatSNP53K BeadChip panel (Illumina Inc., San Diego, CA, USA). Overall, we identified 167 highly significant and subthreshold SNPs associated with the milk components of Karachai goats. A total of 10 SNPs were located within protein-coding genes and 33 SNPs in close proximity to them (±0.2 Mb). The largest number of genome-wide significant SNPs was found on chromosomes 2 and 8 and some of them were associated with several traits. The greatest number of genome-wide significant SNPs was identified for crude protein and lactose (6), and the smallest number-only 1 SNP-for freezing point depression. No SNPs were identified for monounsaturated and polyunsaturated fatty acids. Functional annotation of all 43 SNPs allowed us to identify 66 significant candidate genes on chromosomes 1, 2, 3, 4, 5, 8, 10, 13, 16, 18, 21, 23, 25, 26, and 27. We considered these genes potential DNA markers of the fatty acid composition of Karachai goat milk. Also, we found 12 genes that had a polygenic effect: most of them were simultaneously associated with the dry matter content and fatty acids (METTL, SLC1A 8, PHACTR1, FMO2, ECI1, PGP, ABCA3, AMDHD2). Our results suggest that the genes identified in our study affecting the milk components in Karachai goats differed from those identified in other breeds of dairy goats.
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Affiliation(s)
- Marina Selionova
- Subdepartment of Animal Breeding, Genetics and Biotechnology, Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Timiryazevskaya Street, 41, 127434 Moscow, Russia (M.G.)
| | - Vladimir Trukhachev
- Subdepartment of Animal Breeding, Genetics and Biotechnology, Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Timiryazevskaya Street, 41, 127434 Moscow, Russia (M.G.)
| | - Magomet Aibazov
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy 60, 142132 Podolsk, Moscow Region, Russia; (M.A.); (A.S.); (A.B.); (N.Z.)
| | - Alexander Sermyagin
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy 60, 142132 Podolsk, Moscow Region, Russia; (M.A.); (A.S.); (A.B.); (N.Z.)
| | - Anna Belous
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy 60, 142132 Podolsk, Moscow Region, Russia; (M.A.); (A.S.); (A.B.); (N.Z.)
| | - Marianna Gladkikh
- Subdepartment of Animal Breeding, Genetics and Biotechnology, Russian State Agrarian University—Moscow Timiryazev Agricultural Academy, Timiryazevskaya Street, 41, 127434 Moscow, Russia (M.G.)
| | - Natalia Zinovieva
- L. K. Ernst Federal Research Center for Animal Husbandry, Dubrovitsy 60, 142132 Podolsk, Moscow Region, Russia; (M.A.); (A.S.); (A.B.); (N.Z.)
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Gualdrón Duarte JL, Yuan C, Gori AS, Moreira GCM, Takeda H, Coppieters W, Charlier C, Georges M, Druet T. Sequenced-based GWAS for linear classification traits in Belgian Blue beef cattle reveals new coding variants in genes regulating body size in mammals. Genet Sel Evol 2023; 55:83. [PMID: 38017417 PMCID: PMC10683324 DOI: 10.1186/s12711-023-00857-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/17/2023] [Indexed: 11/30/2023] Open
Abstract
BACKGROUND Cohorts of individuals that have been genotyped and phenotyped for genomic selection programs offer the opportunity to better understand genetic variation associated with complex traits. Here, we performed an association study for traits related to body size and muscular development in intensively selected beef cattle. We leveraged multiple trait information to refine and interpret the significant associations. RESULTS After a multiple-step genotype imputation to the sequence-level for 14,762 Belgian Blue beef (BBB) cows, we performed a genome-wide association study (GWAS) for 11 traits related to muscular development and body size. The 37 identified genome-wide significant quantitative trait loci (QTL) could be condensed in 11 unique QTL regions based on their position. Evidence for pleiotropic effects was found in most of these regions (e.g., correlated association signals, overlap between credible sets (CS) of candidate variants). Thus, we applied a multiple-trait approach to combine information from different traits to refine the CS. In several QTL regions, we identified strong candidate genes known to be related to growth and height in other species such as LCORL-NCAPG or CCND2. For some of these genes, relevant candidate variants were identified in the CS, including three new missense variants in EZH2, PAPPA2 and ADAM12, possibly two additional coding variants in LCORL, and candidate regulatory variants linked to CCND2 and ARMC12. Strikingly, four other QTL regions associated with dimension or muscular development traits were related to five (recessive) deleterious coding variants previously identified. CONCLUSIONS Our study further supports that a set of common genes controls body size across mammalian species. In particular, we added new genes to the list of those associated with height in both humans and cattle. We also identified new strong candidate causal variants in some of these genes, strengthening the evidence of their causality. Several breed-specific recessive deleterious variants were identified in our QTL regions, probably as a result of the extreme selection for muscular development in BBB cattle.
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Affiliation(s)
- José Luis Gualdrón Duarte
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium.
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium.
| | - Can Yuan
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Ann-Stephan Gori
- Walloon Breeders Association, Rue des Champs Elysées, 4, 5590, Ciney, Belgium
| | - Gabriel C M Moreira
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Haruko Takeda
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Wouter Coppieters
- GIGA Genomic Platform, GIGA-R, University of Liège, Avenue de l'Hôpital, 1, 4000, Liège, Belgium
| | - Carole Charlier
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Michel Georges
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
| | - Tom Druet
- Unit of Animal Genomics, GIGA-R & Faculty of Veterinary Medicine, University of Liège, Avenue de l'Hôpital, 1, Liège, 4000, Belgium
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Trebes H, Wang Y, Reynolds E, Tiplady K, Harland C, Lopdell T, Johnson T, Davis S, Harris B, Spelman R, Couldrey C. Identification of candidate novel production variants on the Bos taurus chromosome X. J Dairy Sci 2023; 106:7799-7815. [PMID: 37562645 DOI: 10.3168/jds.2022-23095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/26/2023] [Indexed: 08/12/2023]
Abstract
Chromosome X is often excluded from bovine genetic studies due to complications caused by the sex specific nature of the chromosome. As chromosome X is the second largest cattle chromosome and makes up approximately 6% of the female genome, finding ways to include chromosome X in dairy genetic studies is important. Using female animals and treating chromosome X as an autosome, we performed X chromosome inclusive genome-wide association studies in the selective breeding environment of the New Zealand dairy industry, aiming to identify chromosome X variants associated with milk production traits. We report on the findings of these genome-wide association studies and their potential effect within the dairy industry. We identify missense mutations in the MOSPD1 and CCDC160 genes that are associated with decreased milk volume and protein production and increased fat production. Both of these mutations are exonic SNP that are more prevalent in the Jersey breed than in Holstein-Friesians. Of the 2 candidates proposed it is likely that only one is causal, though we have not been able to identify which is more likely.
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Affiliation(s)
- H Trebes
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand.
| | - Y Wang
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - E Reynolds
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - K Tiplady
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - C Harland
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - T Lopdell
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - T Johnson
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - S Davis
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - B Harris
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - R Spelman
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
| | - C Couldrey
- Research and Development, Livestock Improvement Corporation, Hamilton 3240, New Zealand
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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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Lopdell TJ. Using QTL to Identify Genes and Pathways Underlying the Regulation and Production of Milk Components in Cattle. Animals (Basel) 2023; 13:ani13050911. [PMID: 36899768 PMCID: PMC10000085 DOI: 10.3390/ani13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 02/23/2023] [Accepted: 02/28/2023] [Indexed: 03/06/2023] Open
Abstract
Milk is a complex liquid, and the concentrations of many of its components are under genetic control. Many genes and pathways are known to regulate milk composition, and the purpose of this review is to highlight how the discoveries of quantitative trait loci (QTL) for milk phenotypes can elucidate these pathways. The main body of this review focuses primarily on QTL discovered in cattle (Bos taurus) as a model species for the biology of lactation, and there are occasional references to sheep genetics. The following section describes a range of techniques that can be used to help identify the causative genes underlying QTL when the underlying mechanism involves the regulation of gene expression. As genotype and phenotype databases continue to grow and diversify, new QTL will continue to be discovered, and although proving the causality of underlying genes and variants remains difficult, these new data sets will further enhance our understanding of the biology of lactation.
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Stocco G, Dadousis C, Pazzola M, Vacca GM, Dettori ML, Mariani E, Cipolat-Gotet C. Prediction accuracies of cheese-making traits using Fourier-transform infrared spectra in goat milk. Food Chem 2023; 403:134403. [DOI: 10.1016/j.foodchem.2022.134403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/04/2022] [Accepted: 09/22/2022] [Indexed: 10/14/2022]
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Villar-Hernández BDJ, Amalfitano N, Cecchinato A, Pazzola M, Vacca GM, Bittante G. Phenotypic Analysis of Fourier-Transform Infrared Milk Spectra in Dairy Goats. Foods 2023; 12:foods12040807. [PMID: 36832882 PMCID: PMC9955890 DOI: 10.3390/foods12040807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/07/2023] [Accepted: 02/09/2023] [Indexed: 02/16/2023] Open
Abstract
The infrared spectrum of bovine milk is used to predict many interesting traits, whereas there have been few studies on goat milk in this regard. The objective of this study was to characterize the major sources of variation in the absorbance of the infrared spectrum in caprine milk samples. A total of 657 goats belonging to 6 breeds and reared on 20 farms under traditional and modern dairy systems were milk-sampled once. Fourier-transform infrared (FTIR) spectra were taken (2 replicates per sample, 1314 spectra), and each spectrum contained absorbance values at 1060 different wavenumbers (5000 to 930 × cm-1), which were treated as a response variable and analyzed one at a time (i.e., 1060 runs). A mixed model, including the random effects of sample/goat, breed, flock, parity, stage of lactation, and the residual, was used. The pattern and variability of the FTIR spectrum of caprine milk was similar to those of bovine milk. The major sources of variation in the entire spectrum were as follows: sample/goat (33% of the total variance); flock (21%); breed (15%); lactation stage (11%); parity (9%); and the residual unexplained variation (10%). The entire spectrum was segmented into five relatively homogeneous regions. Two of them exhibited very large variations, especially the residual variation. These regions are known to be affected by the absorbance of water, although they also exhibited wide variations in the other sources of variation. The average repeatability of these two regions were 45% and 75%, whereas for the other three regions it was about 99%. The FTIR spectrum of caprine milk could probably be used to predict several traits and to authenticate the origin of goat milk.
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Affiliation(s)
| | - Nicolò Amalfitano
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Alessio Cecchinato
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
| | - Michele Pazzola
- Department of Veterinary Medicine, University of Sassari, 07100 Sassari, Italy
| | | | - Giovanni Bittante
- Department of Agronomy, Food and Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell’Università 16, 35020 Legnaro, Italy
- Correspondence:
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Prowse-Wilkins CP, Lopdell TJ, Xiang R, Vander Jagt CJ, Littlejohn MD, Chamberlain AJ, Goddard ME. Genetic variation in histone modifications and gene expression identifies regulatory variants in the mammary gland of cattle. BMC Genomics 2022; 23:815. [PMID: 36482302 PMCID: PMC9733386 DOI: 10.1186/s12864-022-09002-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 11/10/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Causal variants for complex traits, such as eQTL are often found in non-coding regions of the genome, where they are hypothesised to influence phenotypes by regulating gene expression. Many regulatory regions are marked by histone modifications, which can be assayed by chromatin immunoprecipitation followed by sequencing (ChIP-seq). Sequence reads from ChIP-seq form peaks at putative regulatory regions, which may reflect the amount of regulatory activity at this region. Therefore, eQTL which are also associated with differences in histone modifications are excellent candidate causal variants. RESULTS We assayed the histone modifications H3K4Me3, H3K4Me1 and H3K27ac and mRNA in the mammary gland of up to 400 animals. We identified QTL for peak height (histone QTL), exon expression (eeQTL), allele specific expression (aseQTL) and allele specific binding (asbQTL). By intersecting these results, we identify variants which may influence gene expression by altering regulatory regions of the genome, and may be causal variants for other traits. Lastly, we find that these variants are found in putative transcription factor binding sites, identifying a mechanism for the effect of many eQTL. CONCLUSIONS We find that allele specific and traditional QTL analysis often identify the same genetic variants and provide evidence that many eQTL are regulatory variants which alter activity at regulatory regions of the bovine genome. Our work provides methodological and biological updates on how regulatory mechanisms interplay at multi-omics levels.
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Affiliation(s)
- Claire P Prowse-Wilkins
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia.
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia.
| | - Thomas J Lopdell
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240, New Zealand
| | - Ruidong Xiang
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Christy J Vander Jagt
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
| | - Mathew D Littlejohn
- Research and Development, Livestock Improvement Corporation, Private Bag 3016, Hamilton, 3240, New Zealand
| | - Amanda J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
| | - Michael E Goddard
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Victoria, 3082, Australia
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, 3010, Australia
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Tiplady KM, Lopdell TJ, Sherlock RG, Johnson TJ, Spelman RJ, Harris BL, Davis SR, Littlejohn MD, Garrick DJ. Comparison of the genetic characteristics of directly measured and Fourier-transform mid-infrared-predicted bovine milk fatty acids and proteins. J Dairy Sci 2022; 105:9763-9791. [DOI: 10.3168/jds.2022-22089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 07/21/2022] [Indexed: 11/17/2022]
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van den Berg I, Ho PN, Nguyen TV, Haile-Mariam M, Luke TDW, Pryce JE. Using mid-infrared spectroscopy to increase GWAS power to detect QTL associated with blood urea nitrogen. Genet Sel Evol 2022; 54:27. [PMID: 35436852 PMCID: PMC9014603 DOI: 10.1186/s12711-022-00719-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/05/2022] [Indexed: 11/20/2022] Open
Abstract
Blood urea nitrogen (BUN) is an indicator trait for urinary nitrogen excretion. Measuring BUN level requires a blood sample, which limits the number of records that can be obtained. Alternatively, BUN can be predicted using mid-infrared (MIR) spectroscopy of a milk sample and thus records become available on many more cows through routine milk recording processes. The genetic correlation between MIR predicted BUN (MBUN) and BUN is 0.90. Hence, genetically, BUN and MBUN can be considered as the same trait. The objective of our study was to perform genome-wide association studies (GWAS) for BUN and MBUN, compare these two GWAS and detect quantitative trait loci (QTL) for both traits, and compare the detected QTL with previously reported QTL for milk urea nitrogen (MUN). The dataset used for our analyses included 2098 and 18,120 phenotypes for BUN and MBUN, respectively, and imputed whole-genome sequence data. The GWAS for MBUN was carried out using either the full dataset, the 2098 cows with records for BUN, or 2000 randomly selected cows, so that the dataset size is comparable to that for BUN. The GWAS results for BUN and MBUN were very different, in spite of the strong genetic correlation between the two traits. We detected 12 QTL for MBUN, on bovine chromosomes 2, 3, 9, 11, 12, 14 and X, and one QTL for BUN on chromosome 13. The QTL detected on chromosomes 11, 14 and X overlapped with QTL detected for MUN. The GWAS results were highly sensitive to the subset of records used. Hence, caution is warranted when interpreting GWAS based on small datasets, such as for BUN. MBUN may provide an attractive alternative to perform a more powerful GWAS to detect QTL for BUN.
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