1
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Pallotti S, Picciolini M, Antonini M, Renieri C, Napolioni V. Genome-wide scan for runs of homozygosity in South American Camelids. BMC Genomics 2023; 24:470. [PMID: 37605116 PMCID: PMC10440933 DOI: 10.1186/s12864-023-09547-3] [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: 04/27/2023] [Accepted: 07/31/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Alpaca (Vicugna pacos), llama (Lama glama), vicugna (Vicugna vicugna) and guanaco (Lama guanicoe), are the camelid species distributed over the Andean high-altitude grasslands, the Altiplano, and the Patagonian arid steppes. Despite the wide interest on these animals, most of the loci under selection are still unknown. Using whole-genome sequencing (WGS) data we investigated the occurrence and the distribution of Runs Of Homozygosity (ROHs) across the South American Camelids (SACs) genome to identify the genetic relationship between the four species and the potential signatures of selection. RESULTS A total of 37 WGS samples covering the four species was included in the final analysis. The multi-dimensional scaling approach showed a clear separation between the four species; however, admixture analysis suggested a strong genetic introgression from vicugna and llama to alpaca. Conversely, very low genetic admixture of the guanaco with the other SACs was found. The four species did not show significant differences in the number, length of ROHs (100-500 kb) and genomic inbreeding values. Longer ROHs (> 500 kb) were found almost exclusively in alpaca. Seven overlapping ROHs were shared by alpacas, encompassing nine loci (FGF5, LOC107034918, PRDM8, ANTXR2, LOC102534792, BSN, LOC116284892, DAG1 and RIC8B) while nine overlapping ROHs were found in llama with twenty-five loci annotated (ERC2, FZD9, BAZ1B, BCL7B, LOC116284208, TBL2, MLXIPL, PHF20, TRNAD-AUC, LOC116284365, RBM39, ARFGEF2, DCAF5, EXD2, HSPB11, LRRC42, LDLRAD1, TMEM59, LOC107033213, TCEANC2, LOC102545169, LOC116278408, SMIM15, NDUFAF2 and RCOR1). Four overlapping ROHs, with three annotated loci (DLG1, KAT6B and PDE4D) and three overlapping ROHs, with seven annotated genes (ATP6V1E1, BCL2L13, LOC116276952, BID, KAT6B, LOC116282667 and LOC107034552), were detected for vicugna and guanaco, respectively. CONCLUSIONS The signatures of selection revealed genomic areas potentially selected for production traits as well as for natural adaptation to harsh environment. Alpaca and llama hint a selection driven by environment as well as by farming purpose while vicugna and guanaco showed selection signals for adaptation to harsh environment. Interesting, signatures of selection on KAT6B gene were identified for both vicugna and guanaco, suggesting a positive effect on wild populations fitness. Such information may be of interest to further ecological and animal production studies.
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Affiliation(s)
- Stefano Pallotti
- Genomic And Molecular Epidemiology (GAME) Lab, School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy.
| | | | - Marco Antonini
- Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Roma, Italy
| | - Carlo Renieri
- School of Pharmacy and Health Products, University of Camerino, Camerino, Italy
| | - Valerio Napolioni
- Genomic And Molecular Epidemiology (GAME) Lab, School of Biosciences and Veterinary Medicine, University of Camerino, Camerino, Italy
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2
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Ruvinskiy D, Igoshin A, Yurchenko A, Ilina AV, Larkin DM. Resequencing the Yaroslavl cattle genomes reveals signatures of selection and a rare haplotype on BTA28 likely to be related to breed phenotypes. Anim Genet 2022; 53:680-684. [PMID: 35711120 PMCID: PMC9541747 DOI: 10.1111/age.13230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 03/12/2022] [Accepted: 06/04/2022] [Indexed: 11/29/2022]
Abstract
The genomes of local livestock could shed light on their genetic history, mechanisms of adaptations to environments and unique genetics. Herein we look into the genetics and adaptations of the Russian native dairy Yaroslavl cattle breed using 22 resequenced individuals and comparing them with two related breeds (Russian Kholmogory and Holstein), and to the taurine set of the 1000 Bull Genomes Project (Run 9). HapFLK analysis with Kholmogory and Holstein breeds (using Yakut cattle as outgroup) resulted in 22 regions under selection (q‐value < 0.01) on 11 chromosomes assigned to Yaroslavl cattle, including a strong signature of selection in the region of the KIT gene on BTA6. The FST (fixation index) with the 1000 Bull Genomes Dataset showed 48 non‐overlapping top (0.1%) FST regions of which three overlapped HapFLK regions. We identified 1982 highly differentiated (FST > 0.40) missense mutations in the Yaroslavl genomes. These genes were enriched in the epidermal growth factor and calcium‐binding functional categories. The top FST intervals contained eight genes with allele frequencies quite different between the Yaroslavl and Kholmogory breeds and the rest of the 1000 Bull Genomes Dataset, including KAT6B, which had a nearly Yaroslavl breed‐specific deleterious missense mutation with the highest FST in our dataset (0.99). This gene is a part of a long haplotype containing other genes from FST and hapFLK analyses and with a negative association with weight and carcass traits according to the genotyping of 30 phenotyped Yaroslavl cattle individuals. Our work provides the industry with candidate genetic variants to be focused on in breed improvement efforts.
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Affiliation(s)
- Daniil Ruvinskiy
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia.,Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
| | - Alexander Igoshin
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia
| | - Andrey Yurchenko
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia
| | - Anna V Ilina
- Federal Williams Research Center of Forage Production & Agroecology, Scientific Research Institute of Livestock Breeding and Forage Production, Yaroslavl Region, Russia
| | - Denis M Larkin
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), Novosibirsk, Russia.,Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.,Royal Veterinary College, University of London, London, UK
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3
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Cortellari M, Barbato M, Talenti A, Bionda A, Carta A, Ciampolini R, Ciani E, Crisà A, Frattini S, Lasagna E, Marletta D, Mastrangelo S, Negro A, Randi E, Sarti FM, Sartore S, Soglia D, Liotta L, Stella A, Ajmone-Marsan P, Pilla F, Colli L, Crepaldi P. The climatic and genetic heritage of Italian goat breeds with genomic SNP data. Sci Rep 2021; 11:10986. [PMID: 34040003 PMCID: PMC8154919 DOI: 10.1038/s41598-021-89900-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 04/29/2021] [Indexed: 02/04/2023] Open
Abstract
Local adaptation of animals to the environment can abruptly become a burden when faced with rapid climatic changes such as those foreseen for the Italian peninsula over the next 70 years. Our study investigates the genetic structure of the Italian goat populations and links it with the environment and how genetics might evolve over the next 50 years. We used one of the largest national datasets including > 1000 goats from 33 populations across the Italian peninsula collected by the Italian Goat Consortium and genotyped with over 50 k markers. Our results showed that Italian goats can be discriminated in three groups reflective of the Italian geography and its geo-political situation preceding the country unification around two centuries ago. We leveraged the remarkable genetic and geographical diversity of the Italian goat populations and performed landscape genomics analysis to disentangle the relationship between genotype and environment, finding 64 SNPs intercepting genomic regions linked to growth, circadian rhythm, fertility, and inflammatory response. Lastly, we calculated the hypothetical future genotypic frequencies of the most relevant SNPs identified through landscape genomics to evaluate their long-term effect on the genetic structure of the Italian goat populations. Our results provide an insight into the past and the future of the Italian local goat populations, helping the institutions in defining new conservation strategy plans that could preserve their diversity and their link to local realities challenged by climate change.
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Affiliation(s)
- Matteo Cortellari
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy
| | - Mario Barbato
- grid.8142.f0000 0001 0941 3192Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Andrea Talenti
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy ,grid.4305.20000 0004 1936 7988The Roslin Institute, University of Edinburgh, Easter Bush Campus, Midlothian, EH25 9RG UK
| | - Arianna Bionda
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy
| | - Antonello Carta
- Unità di Ricerca di Genetica e Biotecnologie, Agris Sardegna, 07100 Sassari, Italy
| | - Roberta Ciampolini
- grid.5395.a0000 0004 1757 3729Dipartimento di Scienze Veterinarie, Università di Pisa, Viale delle Piagge 2, 56124 Pisa, Italy
| | - Elena Ciani
- grid.7644.10000 0001 0120 3326Dipartimento di Bioscienze Biotecnologie e Biofarmaceutica, Università degli Studi di Bari, Via Orabona 4, 70126 Bari, Italy
| | - Alessandra Crisà
- Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) - Research Centre for Animal Production and Acquaculture, 00015 Monterotondo, Rome, Italy
| | - Stefano Frattini
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy
| | - Emiliano Lasagna
- grid.9027.c0000 0004 1757 3630Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121 Perugia, Italy
| | - Donata Marletta
- grid.8158.40000 0004 1757 1969Department of Agriculture, Food and Environment, University of Catania, Via Valdisavoia 5, 95123 Catania, Italy
| | - Salvatore Mastrangelo
- grid.10776.370000 0004 1762 5517Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, 90128 Palermo, Italy
| | - Alessio Negro
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy
| | - Ettore Randi
- grid.5117.20000 0001 0742 471XDepartment of Chemistry and Bioscience, Faculty of Engineering and Science, University of Aalborg, Aalborg, Denmark
| | - Francesca M. Sarti
- grid.9027.c0000 0004 1757 3630Department of Agricultural, Food and Environmental Sciences, University of Perugia, 06121 Perugia, Italy
| | - Stefano Sartore
- grid.7605.40000 0001 2336 6580Dipartimento di Scienze Veterinarie, Università degli Studi di Torino, largo Braccini 2, 10095 Grugliasco, Italy
| | - Dominga Soglia
- grid.7605.40000 0001 2336 6580Dipartimento di Scienze Veterinarie, Università degli Studi di Torino, largo Braccini 2, 10095 Grugliasco, Italy
| | - Luigi Liotta
- grid.10438.3e0000 0001 2178 8421Dipartimento di Scienze Veterinarie, University of Messina, Messina, Italy
| | - Alessandra Stella
- grid.5326.20000 0001 1940 4177Institute of Biology and Biotechnology in Agriculture, National Research Council (CNR), Milan, Italy
| | - Paolo Ajmone-Marsan
- grid.8142.f0000 0001 0941 3192Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Fabio Pilla
- grid.10373.360000000122055422Dipartimento Agricoltura, Ambiente e Alimenti Universitá degli Studi del Molise, 86100 Campobasso, Italy
| | - Licia Colli
- grid.8142.f0000 0001 0941 3192Dipartimento di Scienze Animali, della Nutrizione e degli Alimenti and BioDNA Centro di ricerca sulla Biodiversità e sul DNA Antico, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy
| | - Paola Crepaldi
- grid.4708.b0000 0004 1757 2822Dipartimento di Scienze Agrarie e Ambientali – Produzione, Territorio, Agroenergia, Università degli Studi di Milano, Via Celoria 2, 20133 Milan, Italy
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4
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Thakor PB, Hinsu AT, Bhatia DR, Shah TM, Nayee N, Sudhakar A, Rank DN, Joshi CG. High-throughput genotype-based population structure analysis of selected buffalo breeds. Transl Anim Sci 2021; 5:txab033. [PMID: 33981962 PMCID: PMC8103726 DOI: 10.1093/tas/txab033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 05/01/2021] [Indexed: 11/29/2022] Open
Abstract
India is considered as the home tract of some of the best buffalo breeds. However, the genetic structure of the Indian river buffalo is poorly understood. Hence, there is a need to characterize the populations and understand the genetic structure of various buffalo breeds for selection and to design breeding strategies. In this study, we have analyzed genetic variability and population structure of seven buffalo breeds from their respective geographical regions using Axiom Buffalo Genotyping Array. Diversity, as measured by expected heterozygosity, ranged from 0.364 in Surti to 0.384 in Murrah breed, and pair-wise FST values revealed the lowest genetic distance between Murrah and Nili-Ravi (0.0022), while the highest between Surti and Pandharpuri (0.030). Principal component analysis and structure analysis unveiled the differentiation of Surti, Pandharpuri, and Jaffarabadi in first two principal components and at K = 4, respectively, while remaining breeds were grouped together as a separate single cluster and admixed. Murrah and Mehsana showed early linkage disequilibrium (LD) decay, while Surti breed showed late decay. In LD blocks to quantitative trait locis (QTLs) concordance analysis, 4.65% of concordance was observed with 873 LD blocks overlapped with 2,330 QTLs. Overall, total 4,090 markers were identified from all LD blocks for six types of traits. Results of this study indicated that these single-nucleotide polymorphism (SNP) markers could differentiate phenotypically distinct breeds like Surti, Pandharpuri, and Jaffarabadi but not others. So, there is a need to develop SNP chip based on SNP markers identified by sequence information of local breeds.
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Affiliation(s)
- Prakash B Thakor
- Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India
| | - Ankit T Hinsu
- Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India
| | - Dhruv R Bhatia
- Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India
| | - Tejas M Shah
- Department of Animal Biotechnology, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India
| | - Nilesh Nayee
- National Dairy Development Board, Anand 388001, India
| | - A Sudhakar
- National Dairy Development Board, Anand 388001, India
| | - Dharamshibhai N Rank
- Department of Animal Genetics and Breeding, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India
| | - Chaitanya G Joshi
- Department of Animal Biotechnology, College of Veterinary Science and Animal Husbandry, Anand Agriculture University, Anand 388001, India.,Gujarat Biotechnology Research Centre, Gandhinagar 382017, India
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5
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Moscarelli A, Sardina MT, Cassandro M, Ciani E, Pilla F, Senczuk G, Portolano B, Mastrangelo S. Genome-wide assessment of diversity and differentiation between original and modern Brown cattle populations. Anim Genet 2020; 52:21-31. [PMID: 33174276 DOI: 10.1111/age.13019] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/08/2020] [Indexed: 02/06/2023]
Abstract
Identifying genomic regions involved in the differences between breeds can provide information on genes that are under the influence of both artificial and natural selection. The aim of this study was to assess the genetic diversity and differentiation among four different Brown cattle populations (two original vs. two modern populations) and to characterize the distribution of runs of homozygosity (ROH) islands using the Illumina Bovine SNP50 BeadChip genotyping data. After quality control, 34 735 SNPs and 106 animals were retained for the analyses. Larger heterogeneity was highlighted for the original populations. Patterns of genetic differentiation, multidimensional scaling, and the neighboring joining tree distinguished the modern from the original populations. The FST -outlier identified several genes putatively involved in the genetic differentiation between the two groups, such as stature and growth, behavior, and adaptability to local environments. The ROH islands within both the original and the modern populations overlapped with QTL associated with relevant traits. In modern Brown (Brown Swiss and Italian Brown), ROH islands harbored candidate genes associated with milk production traits, in evident agreement with the artificial selection conducted to improve this trait in these populations. In original Brown (Original Braunvieh and Braunvieh), we identified candidate genes related with fat deposition, confirming that breeding strategies for the original Brown populations aimed to produce dual-purpose animals. Our study highlighted the presence of several genomic regions that vary between Brown populations, in line with their different breeding histories.
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Affiliation(s)
- A Moscarelli
- Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, 90128, Italy
| | - M T Sardina
- Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, 90128, Italy
| | - M Cassandro
- Dipartimento di Agronomia Animali Alimenti Risorse naturali e Ambiente, University of Padova, Legnaro, 35020, Italy
| | - E Ciani
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, University of Bari, Bari, 70124, Italy
| | - F Pilla
- Dipartimento Agricoltura, Ambiente e Alimenti, University of Molise, Campobasso, 86100, Italy
| | - G Senczuk
- Dipartimento Agricoltura, Ambiente e Alimenti, University of Molise, Campobasso, 86100, Italy
| | - B Portolano
- Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, 90128, Italy
| | - S Mastrangelo
- Dipartimento Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, 90128, Italy
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6
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van den Berg I, Hayes BJ, Chamberlain AJ, Goddard ME. Overlap between eQTL and QTL associated with production traits and fertility in dairy cattle. BMC Genomics 2019; 20:291. [PMID: 30987590 PMCID: PMC6466667 DOI: 10.1186/s12864-019-5656-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 03/29/2019] [Indexed: 01/26/2023] Open
Abstract
Background Identifying causative mutations or genes through which quantitative trait loci (QTL) act has proven very difficult. Using information such as gene expression may help to identify genes and mutations underlying QTL. Our objective was to identify regions associated both with production traits or fertility and with gene expression, in dairy cattle. We used three different approaches to discover QTL that are also expression QTL (eQTL): 1) estimate the correlation between local genomic estimated breeding values (GEBV) and gene expression, 2) investigate whether the 300 intervals explaining most genetic variance for a trait contain more eQTL than 300 randomly selected intervals, and 3) a colocalisation analysis. Phenotypes and genotypes up to sequence level of 35,775 dairy bulls and cows were used for QTL mapping, and gene expression and genotypes of 131 cows were used to identify eQTL. Results With all three approaches, we identified some overlap between eQTL and QTL, though the majority of QTL in our dataset did not seem to be eQTL. The most significant associations between QTL and eQTL were found for intervals on chromosome 18, where local GEBV for all traits showed a strong association with the expression of the FUK and DDX19B. Intervals whose local GEBV for a trait correlated highly significantly with the expression of a nearby gene explained only a very small part of the genetic variance for that trait. It is likely that part of these correlations were due to linkage disequilibrium (LD) in the interval. While the 300 intervals explaining most genetic variance explained most of the GEBV variance, they contained only slightly more eQTL than 300 randomly selected intervals that explained a minimal portion of the GEBV variance. Furthermore, some variants showed a high colocalisation probability, but this was only the case for few variants. Conclusions Several reasons may have contributed to the low level of overlap between QTL and eQTL detected in our study, including a lack of power in the eQTL study and long-range LD making it difficult to separate QTL and eQTL. Furthermore, it may be that eQTL explain only a small fraction of QTL. Electronic supplementary material The online version of this article (10.1186/s12864-019-5656-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- I van den Berg
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, Australia. .,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.
| | - B J Hayes
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia.,Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, Queensland, 4067, Australia
| | - A J Chamberlain
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia
| | - M E Goddard
- Faculty of Veterinary & Agricultural Science, University of Melbourne, Parkville, Victoria, Australia.,Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia
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7
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Identification of genomic regions harboring diversity between Holstein and two local endangered breeds, Modenese and Maremmana. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.07.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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8
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Liu JJ, Liang AX, Campanile G, Plastow G, Zhang C, Wang Z, Salzano A, Gasparrini B, Cassandro M, Yang LG. Genome-wide association studies to identify quantitative trait loci affecting milk production traits in water buffalo. J Dairy Sci 2017; 101:433-444. [PMID: 29128211 DOI: 10.3168/jds.2017-13246] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2017] [Accepted: 09/13/2017] [Indexed: 01/03/2023]
Abstract
Water buffalo is the second largest resource of milk supply around the world, and it is well known for its distinctive milk quality in terms of fat, protein, lactose, vitamin, and mineral contents. Understanding the genetic architecture of milk production traits is important for future improvement by the buffalo breeding industry. The advance of genome-wide association studies (GWAS) provides an opportunity to identify potential genetic variants affecting important economical traits. In the present study, GWAS was performed for 489 buffaloes with 1,424 lactation records using the 90K Affymetrix Buffalo SNP Array (Affymetrix/Thermo Fisher Scientific, Santa Clara, CA). Collectively, 4 candidate single nucleotide polymorphisms (SNP) in 2 genomic regions were found to associate with buffalo milk production traits. One region affecting milk fat and protein percentage was located on the equivalent of Bos taurus autosome (BTA)3, spanning 43.3 to 43.8 Mb, which harbored the most likely candidate genes MFSD14A, SLC35A3, and PALMD. The other region on the equivalent of BTA14 at 66.5 to 67.0 Mb contained candidate genes RGS22 and VPS13B and influenced buffalo total milk yield, fat yield, and protein yield. Interestingly, both of the regions were reported to have quantitative trait loci affecting milk performance in dairy cattle. Furthermore, we suggest that buffaloes with the C allele at AX-85148558 and AX-85073877 loci and the G allele at AX-85106096 locus can be selected to improve milk fat yield in this buffalo-breeding program. Meanwhile, the G allele at AX-85063131 locus can be used as the favorable allele for improving milk protein percentage. Genomic prediction showed that the reliability of genomic estimated breeding values (GEBV) of 6 milk production traits ranged from 0.06 to 0.22, and the correlation between estimated breeding values and GEBV ranged from 0.23 to 0.35. These findings provide useful information to understand the genetic basis of buffalo milk properties and may play a role in accelerating buffalo breeding programs using genomic approaches.
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Affiliation(s)
- J J Liu
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agriculture University, Wuhan, Hubei, China 430070; Hubei Province's Engineering Research Center in Buffalo Breeding and Products, Wuhan, Hubei, China 430070
| | - A X Liang
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agriculture University, Wuhan, Hubei, China 430070; Hubei Province's Engineering Research Center in Buffalo Breeding and Products, Wuhan, Hubei, China 430070
| | - G Campanile
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy 80137
| | - G Plastow
- Department of Agricultural, Food, and Nutritional Sciences, University of Alberta, Edmonton, AB, Canada T6G 2C8
| | - C Zhang
- Department of Agricultural, Food, and Nutritional Sciences, University of Alberta, Edmonton, AB, Canada T6G 2C8
| | - Z Wang
- Department of Agricultural, Food, and Nutritional Sciences, University of Alberta, Edmonton, AB, Canada T6G 2C8
| | - A Salzano
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy 80137
| | - B Gasparrini
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy 80137
| | - M Cassandro
- Department of Agronomy, Food, Natural Resources, Animal, and Environment, University of Padova, Agripolis, Legnaro, Italy 35020
| | - L G Yang
- Key Lab of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agriculture University, Wuhan, Hubei, China 430070; Hubei Province's Engineering Research Center in Buffalo Breeding and Products, Wuhan, Hubei, China 430070.
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9
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Macciotta NPP, Biffani S, Bernabucci U, Lacetera N, Vitali A, Ajmone-Marsan P, Nardone A. Derivation and genome-wide association study of a principal component-based measure of heat tolerance in dairy cattle. J Dairy Sci 2017; 100:4683-4697. [PMID: 28365122 DOI: 10.3168/jds.2016-12249] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 02/05/2017] [Indexed: 12/19/2022]
Abstract
Heat stress represents a key factor that negatively affects the productive and reproductive performance of farm animals. In the present work, a new measure of tolerance to heat stress for dairy cattle was developed using principal component analysis. Data were from 590,174 test-day records for milk yield, fat and protein percentages, and somatic cell score of 39,261 Italian Holstein cows. Test-day records adjusted for main systematic factors were grouped into 11 temperature-humidity index (THI) classes. Daughter trait deviations (DTD) were calculated for 1,540 bulls as means of the adjusted test-day records for each THI class. Principal component analysis was performed on the DTD for each bull. The first 2 principal components (PC) explained 42 to 51% of the total variance of the system across the 4 traits. The first PC, a measure of the level at which the curve is located, was interpreted as a measure of the level at which the DTD curve was located. The second PC, which shows the slope of increasing or decreases DTD curves, synthesized the behavior of the DTD pattern. Heritability of the 2 component scores was moderate to high for level across all traits (range = 0.23-0.82) and low to moderate for slope (range = 0.16-0.28). For each trait, phenotypic and genetic correlations between level and slope were equal to zero. A genome-wide association analysis was carried out on a subsample of 423 bulls genotyped with the Illumina 50K bovine bead chip (Illumina, San Diego, CA). Two single nucleotide polymorphisms were significantly associated with slope for milk yield, 4 with level for fat percentage, and 2 with level and slope of protein percentage, respectively. The gene discovery was carried out considering windows of 0.5 Mb surrounding the significant markers and highlighted some interesting candidate genes. Some of them have been already associated with the mechanism of heat tolerance as the heat shock transcription factor (HSF1) and the malonyl-CoA-acyl carrier protein transacylase (MCAT). The 2 PC were able to describe the overall level and the slope of response of milk production traits across increasing levels of THI index. Moreover, they exhibited genetic variability and were genetically uncorrelated. These features suggest their use as measures of thermotolerance in dairy cattle breeding schemes.
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Affiliation(s)
- N P P Macciotta
- Dipartimento di Agraria, Università di Sassari, 07100 Sassari, Italy.
| | - S Biffani
- Associazione Italiana Allevatori, 00161 Roma, Italy
| | - U Bernabucci
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - N Lacetera
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - A Vitali
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy
| | - P Ajmone-Marsan
- Istituto di Zootecnica, Università Cattolica del Sacro Cuore, 29122 Piacenza, Italy
| | - A Nardone
- Dipartimento di Scienze Agrarie e Forestali, Università degli Studi della Tuscia-Viterbo, 01100 Viterbo, Italy.
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Sanchez M, Govignon-Gion A, Ferrand M, Gelé M, Pourchet D, Amigues Y, Fritz S, Boussaha M, Capitan A, Rocha D, Miranda G, Martin P, Brochard M, Boichard D. Whole-genome scan to detect quantitative trait loci associated with milk protein composition in 3 French dairy cattle breeds. J Dairy Sci 2016; 99:8203-8215. [DOI: 10.3168/jds.2016-11437] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2016] [Accepted: 06/16/2016] [Indexed: 11/19/2022]
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