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Sukhija N, Malik AA, Devadasan JM, Dash A, Bidyalaxmi K, Ravi Kumar D, Kousalaya Devi M, Choudhary A, Kanaka KK, Sharma R, Tripathi SB, Niranjan SK, Sivalingam J, Verma A. Genome-wide selection signatures address trait specific candidate genes in cattle indigenous to arid regions of India. Anim Biotechnol 2024; 35:2290521. [PMID: 38088885 DOI: 10.1080/10495398.2023.2290521] [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] [Indexed: 02/22/2024]
Abstract
The peculiarity of Indian cattle lies in milk quality, resistance to diseases and stressors as well as adaptability. The investigation addressed selection signatures in Gir and Tharparkar cattle, belonging to arid ecotypes of India. Double digest restriction-site associated DNA sequencing (ddRAD-seq) yielded nearly 26 million high-quality reads from unrelated seven Gir and seven Tharparkar cows. In all, 19,127 high-quality SNPs were processed for selection signature analysis. An approach involving within-population composite likelihood ratio (CLR) statistics and between-population FST statistics was used to capture selection signatures within and between the breeds, respectively. A total of 191 selection signatures were addressed using CLR and FST approaches. Selection signatures overlapping 86 and 73 genes were detected as Gir- and Tharparkar-specific, respectively. Notably, genes related to production (CACNA1D, GHRHR), reproduction (ESR1, RBMS3), immunity (NOSTRIN, IL12B) and adaptation (ADAM22, ASL) were annotated to selection signatures. Gene pathway analysis revealed genes in insulin/IGF pathway for milk production, gonadotropin releasing hormone pathway for reproduction, Wnt signalling pathway and chemokine and cytokine signalling pathway for adaptation. This is the first study where selection signatures are identified using ddRAD-seq in indicine cattle breeds. The study shall help in conservation and leveraging genetic improvements in Gir and Tharparkar cattle.
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Affiliation(s)
- Nidhi Sukhija
- ICAR-National Dairy Research Institute, Karnal, India
| | - Anoop Anand Malik
- TERI School of Advanced Studies, Delhi, India
- The Energy and Resources Institute, North Eastern Regional Centre, Guwahati, India
| | | | | | - Kangabam Bidyalaxmi
- ICAR-National Dairy Research Institute, Karnal, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, India
| | - D Ravi Kumar
- ICAR-National Dairy Research Institute, Karnal, India
| | | | | | - K K Kanaka
- ICAR-National Dairy Research Institute, Karnal, India
- ICAR- Indian Institute of Agricultural Biotechnology, Ranchi, India
| | - Rekha Sharma
- ICAR-National Bureau of Animal Genetic Resources, Karnal, India
| | | | | | | | - Archana Verma
- ICAR-National Dairy Research Institute, Karnal, India
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Kumar H, Panigrahi M, G Strillacci M, Sonejita Nayak S, Rajawat D, Ghildiyal K, Bhushan B, Dutt T. Detection of genome-wide copy number variation in Murrah buffaloes. Anim Biotechnol 2023; 34:3783-3795. [PMID: 37381739 DOI: 10.1080/10495398.2023.2227670] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Riverine Buffaloes, especially the Murrah breed because of their adaptability to harsh climatic conditions, is farmed in many countries to convert low-quality feed into valuable dairy products and meat. Here, we investigated the copy number variations (CNVs) in 296 Murrah buffalo using the Axiom® Buffalo Genotyping Array 90K (Affymetrix, Santa Clara, CA, USA). The CNVs were detected on the autosomes, using the Copy Number Analysis Module (CNAM) using the univariate analysis. 7937 CNVs were detected in 279 Buffaloes, the average length of the CNVs was 119,048.87 bp that ranged between 7800 and 4,561,030 bp. These CNVs were accounting for 10.33% of the buffalo genome, which was comparable to cattle, sheep, and goat CNV analyses. Further, CNVs were merged and 1541 CNVRs were detected using the Bedtools-mergeBed command. 485 genes were annotated within 196 CNVRs that were identified in at least 10 animals of Murrah population. Out of these, 40 CNVRs contained 59 different genes that were associated with 69 different traits. Overall, the study identified a significant number of CNVs and CNVRs in the Murrah breed of buffalo, with a wide range of lengths and frequencies across the autosomes. The identified CNVRs contained genes associated with important traits related to production and reproduction, making them potentially important targets for future breeding and genetic improvement efforts.
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Affiliation(s)
- Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Maria G Strillacci
- Department of Veterinary Medicine and Animal Sciences, University of Milan, Lodi, Italy
| | | | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, India
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Nayak SS, Panigrahi M, Rajawat D, Ghildiyal K, Sharma A, Parida S, Bhushan B, Mishra BP, Dutt T. Comprehensive selection signature analyses in dairy cattle exploiting purebred and crossbred genomic data. Mamm Genome 2023; 34:615-631. [PMID: 37843569 DOI: 10.1007/s00335-023-10021-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 09/24/2023] [Indexed: 10/17/2023]
Abstract
The main objective of the current research was to locate, annotate, and highlight specific areas of the bovine genome that are undergoing intense positive selection. Here, we are analyzing selection signatures in crossbred (Bos taurus X Bos indicus), taurine (Bos taurus), and indicine (Bos indicus) cattle breeds. Indicine cattle breeds found throughout India are known for their higher heat tolerance and disease resilience. More breeds and more methods can provide a better understanding of the selection signature. So, we have worked on nine distinct cattle breeds utilizing seven different summary statistics, which is a fairly extensive approach. In this study, we carried out a thorough genome-wide investigation of selection signatures using bovine 50K SNP data. We have included the genotyped data of two taurine, two crossbreds, and five indicine cattle breeds, for a total of 320 animals. During the 1950s, these indicine (cebuine) cattle breeds were exported with the aim of enhancing the resilience of taurine breeds in Western countries. For this study, we employed seven summary statistics, including intra-population, i.e., Tajima's D, CLR, iHS, and ROH and inter-population statistics, i.e., FST, XP-EHH, and Rsb. The NCBI database, PANTHER 17.0, and CattleQTL database were used for annotation after finding the important areas under selection. Some genes, including EPHA6, CTNNA2, NPFFR2, HS6ST3, NPR3, KCNIP4, LIPK, SDCBP, CYP7A1, NSMAF, UBXN2B, UGDH, UBE2K, and DAB1, were shown to be shared by three or more different approaches. Therefore, it gives evidence of the most intense selection in these areas. These genes are mostly linked to milk production and adaptability traits. This study also reveals selection regions that contain genes which are crucial to numerous biological functions, including those associated with milk production, coat color, glucose metabolism, oxidative stress response, immunity and circadian rhythms.
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Affiliation(s)
- Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India.
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - Anurodh Sharma
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
| | - B P Mishra
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, 132001, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, UP, 243122, India
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Demir E, Moravčíková N, Kaya S, Kasarda R, Doğru H, Bilginer Ü, Balcioğlu MS, Karsli T. Genome-wide genetic variation and population structure of native and cosmopolitan cattle breeds reared in Türkiye. Anim Biotechnol 2023; 34:3877-3886. [PMID: 37471206 DOI: 10.1080/10495398.2023.2235600] [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] [Indexed: 07/22/2023]
Abstract
This is the first comprehensive study to reveal genetic variation and population structure at genome level in six Anatolian (Anatolian Black, East Anatolian Red, South Anatolian Red, South Anatolian Yellow, Turkish Grey Steppe, and Zavot) and two cosmopolitan (Brown Swiss and Holstein Friesian) cattle breeds reared in Türkiye. Being 20 samples from each population, a total of 160 blood samples retrieved from representative herds were utilized to generate genomic libraries by ddRADseq method. Genomic libraries sequenced by Illumina HiSeq X Ten instrument revealed a total of 211,119 bi-allelic SNPs with high call rate. Compared to cosmopolitan cattle breeds, a higher genetic variation was observed in native Turkish cattle with an average of 0.380 observed heterozygosity. Genetic distances were comparatively low between native cattle breeds, whereas the highest genetic distance (0.064) was detected between South Anatolian Yellow and Brown Swiss. Population structure analyses showed that the native Turkish and cosmopolitan cattle breeds were clearly different from each other according to their phylogenetic origin. Besides, a high level of genetic admixture was detected among Anatolian Black, Turkish Grey Steppe, South Anatolian Red, and South Anatolian Yellow, whereas East Anatolian Red and Zavot were distinct from the other native and cosmopolitan cattle breeds. TreeMix algorithm under the assumption of one and two migration events revealed a migration route from Anatolian clade to Anatolian Black, while a second migration edge was drawn from Brown Swiss to East Anatolian Red. This study demonstrates the importance of national conservation studies in the native breeds whose population size has dramatically decreased. In addition, SNP arrays and next-generation sequencing platforms are recommended for future studies to reveal the genetic variation of other local Turkish livestock species to arrange effective conservation programs.
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Affiliation(s)
- Eymen Demir
- Department of Animal Science, Faculty of Agriculture, Akdeniz University, Antalya, Republic of Türkiye
| | - Nina Moravčíková
- Institute of Nutrition and Genomics, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture in Nitra, Slovak Republic
| | - Sarp Kaya
- Department of Medical Services and Techniques, Vocational School of Burdur Health Services, Burdur Mehmet Akif Ersoy University, Burdur, Republic of Türkiye
| | - Radovan Kasarda
- Institute of Nutrition and Genomics, Faculty of Agrobiology and Food Resources, Slovak University of Agriculture in Nitra, Slovak Republic
| | - Huriye Doğru
- Department of Medical Services and Techniques, Vocational School of Burdur Health Services, Burdur Mehmet Akif Ersoy University, Burdur, Republic of Türkiye
| | - Ümit Bilginer
- Department of Animal Science, Faculty of Agriculture, Akdeniz University, Antalya, Republic of Türkiye
| | - Murat Soner Balcioğlu
- Department of Animal Science, Faculty of Agriculture, Akdeniz University, Antalya, Republic of Türkiye
| | - Taki Karsli
- Department of Animal Science, Faculty of Agriculture, Eskisehir Osmangazi University, Eskisehir, Republic of Türkiye
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Saravanan KA, Rajawat D, Kumar H, Nayak SS, Bhushan B, Dutt T, Panigrahi M. Signatures of selection in riverine buffalo populations revealed by genome-wide SNP data. Anim Biotechnol 2023; 34:3343-3354. [PMID: 36384399 DOI: 10.1080/10495398.2022.2145292] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The detection of selection signatures assists in understanding domestication, evolution, and the identification of genomic regions related to adaptation and production traits in buffaloes. The emergence of high-throughput technologies like Next Generation Sequencing and SNP genotyping had expanded our ability to detect these signatures of selection. In this study, we sought to identify signatures of selection in five buffalo populations (Brazilian Murrah, Bulgarian Murrah, Indian Murrah, Nili-Ravi, and Kundi) using Axiom Buffalo 90 K Genotyping Array data. Using seven different methodologies (Tajima's D, CLR, ROH, iHS, FST, FLK and hapFLK), we identified selection signatures in 374 genomic regions, spanning a total of 381 genes and 350 quantitative trait loci (QTLs). Among these, several candidate genes were associated with QTLs for milk production, reproduction, growth and carcass traits. The genes and QTLs reported in this study provide insight into selection signals shaping the genome of buffalo breeds. Our findings can aid in further genomic association studies, genomic prediction, and the implementation of breeding programmes in Indian buffaloes.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
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Rajawat D, Panigrahi M, Nayak SS, Ghildiyal K, Sharma A, Kumar H, Parida S, Bhushan B, Gaur GK, Mishra BP, Dutt T. Uncovering genes underlying coat color variation in indigenous cattle breeds through genome-wide positive selection. Anim Biotechnol 2023; 34:3920-3933. [PMID: 37493405 DOI: 10.1080/10495398.2023.2240387] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
The identification of candidate genes related to pigmentation and under selective sweep provides insights into the genetic basis of pigmentation and the evolutionary forces that have shaped this variation. The selective sweep events in the genes responsible for normal coat color in Indian cattle groups are still unknown. To find coat color genes displaying signs of selective sweeps in the indigenous cattle, we compiled a list of candidate genes previously investigated for their association with coat color and pigmentation. After that, we performed a genome-wide scan of positive selection signatures using the BovineSNP50K Bead Chip in 187 individuals of seven indigenous breeds. We applied a wide range of methods to find evidence of selection, such as Tajima's D, CLR, iHS, varLD, ROH, and FST. We found a total of sixteen genes under selective sweep, that were involved in coat color and pigmentation physiology. These genes are CRIM1 in Gir, MC1R in Sahiwal, MYO5A, PMEL and POMC in Tharparkar, TYRP1, ERBB2, and ASIP in Red Sindhi, MITF, LOC789175, PAX3 and TYR in Ongole, and IRF2, SDR165 and, KIT in Nelore, ADAMTS19 in Hariana. These genes are related to melanin synthesis, the biology of melanocytes and melanosomes, and the migration and survival of melanocytes during development.
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Affiliation(s)
- Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Anurodh Sharma
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Subhashree Parida
- Pharmacology and Toxicology Division, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - B P Mishra
- Animal Biotechnology Division, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly, India
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Saravanan KA, Panigrahi M, Kumar H, Nayak SS, Rajawat D, Bhushan B, Dutt T. Progress and future perspectives of livestock genomics in India: a mini review. Anim Biotechnol 2023; 34:1979-1987. [PMID: 35369840 DOI: 10.1080/10495398.2022.2056046] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
The field of genetics has evolved a lot after the emergence of molecular and advanced genomic technologies. The advent of Next Generation Sequencing, SNP genotyping platforms and simultaneous reduction in the cost of sequencing had opened the door to genomic research in farm animals. There are various applications of genomics in livestock, such as the use of genomic data: (i) to investigate genetic diversity and breed composition/population structure (ii) to identify genetic variants and QTLs related to economically important and ecological traits, genome-wide association studies (GWAS) and genomic signatures of selection; (iii) to enhance breeding programs by genomic selection. Compared to traditional methods, genomic selection is expected to improve selection response by increasing selection accuracy and reducing the generation interval due to early selection. Genomic selection (GS) in developed countries has led to rapid genetic gains, especially in dairy cattle, due to a well-established genetic evaluation system. Indian livestock system is still lagging behind developed nations in adopting these technologies. This review discusses the current status, challenges, and future perspectives of livestock genomics in India.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Bareilly, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Bareilly, UP, India
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Bhowmik N, Seaborn T, Ringwall KA, Dahlen CR, Swanson KC, Hulsman Hanna LL. Genetic Distinctness and Diversity of American Aberdeen Cattle Compared to Common Beef Breeds in the United States. Genes (Basel) 2023; 14:1842. [PMID: 37895190 PMCID: PMC10606367 DOI: 10.3390/genes14101842] [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: 08/11/2023] [Revised: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/29/2023] Open
Abstract
American Aberdeen (AD) cattle in the USA descend from an Aberdeen Angus herd originally brought to the Trangie Agricultural Research Centre, New South Wales, AUS. Although put under specific selection pressure for yearling growth rate, AD remain genomically uncharacterized. The objective was to characterize the genetic diversity and structure of purebred and crossbred AD cattle relative to seven common USA beef breeds using available whole-genome SNP data. A total of 1140 animals consisting of 404 purebred (n = 8 types) and 736 admixed individuals (n = 10 types) was used. Genetic diversity metrics, an analysis of molecular variance, and a discriminant analysis of principal components were employed. When linkage disequilibrium was not accounted for, markers influenced basic diversity parameter estimates, especially for AD cattle. Even so, intrapopulation and interpopulation estimates separate AD cattle from other purebred types (e.g., Latter's pairwise FST ranged from 0.1129 to 0.2209), where AD cattle were less heterozygous and had lower allelic richness than other purebred types. The admixed AD-influenced cattle were intermediate to other admixed types for similar parameters. The diversity metrics separation and differences support strong artificial selection pressures during and after AD breed development, shaping the evolution of the breed and making them genomically distinct from similar breeds.
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Affiliation(s)
- Nayan Bhowmik
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Travis Seaborn
- School of Natural Resource Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Kris A. Ringwall
- Dickinson Research Extension Center, North Dakota State University, Dickinson, ND 58601, USA
| | - Carl R. Dahlen
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
| | - Kendall C. Swanson
- Department of Animal Sciences, North Dakota State University, Fargo, ND 58108, USA
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Anas M, Farooq M, Asif M, Ali WR, Mansoor S. A Novel Insight into the Identification of Potential SNP Markers for the Genomic Characterization of Buffalo Breeds in Pakistan. Animals (Basel) 2023; 13:2543. [PMID: 37570351 PMCID: PMC10416883 DOI: 10.3390/ani13152543] [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: 06/07/2023] [Revised: 07/11/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
Domestic buffaloes (Bubalus bubalis), known as water buffaloes, play a key role as versatile multipurpose agricultural animals in the Asiatic region. Pakistan, with the second-largest buffalo population in the world, holds a rich domestication history of buffaloes. The overall trends in buffalo production demand the genomic characterization of Pakistani buffalo breeds. To this end, the resequencing data of Pakistani breeds, along with buffalo breeds from 13 other countries, were retrieved from our previous study. This dataset, which contained 34,671,886 single-nucleotide polymorphisms (SNPs), was analyzed through a pipeline that was developed to compare possible allele differences among breeds at each SNP position. In contrast, other available tools only check for positional SNP differences for breed-specific markers. In total, 1918, 1549, 404, and 341 breed-specific markers were identified to characterize the Nili, Nili-Ravi, Azakheli, and Kundi breeds of Pakistani buffalo, respectively. Sufficient evidence in the form of phenotypic data, principal component analysis, admixture analysis, and linkage analysis showed that the Nili breed has maintained its distinct breed status despite sharing a close evolutionary relationship with the Nili-Ravi breed of buffalo. In this era of genome science, the conservation of these breeds and the further validation of the given selection markers in larger populations is a pressing need.
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Affiliation(s)
- Muhammad Anas
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Punjab, Pakistan
- Department of Animal Sciences and Center for Nutrition and Pregnancy, North Dakota State University, Fargo, ND 58105, USA
| | - Muhammad Farooq
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Punjab, Pakistan
| | - Muhammad Asif
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Punjab, Pakistan
| | - Waqas Rafique Ali
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Punjab, Pakistan
| | - Shahid Mansoor
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad 38000, Punjab, Pakistan
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Zhao C, Wang D, Teng J, Yang C, Zhang X, Wei X, Zhang Q. Breed identification using breed-informative SNPs and machine learning based on whole genome sequence data and SNP chip data. J Anim Sci Biotechnol 2023; 14:85. [PMID: 37259083 DOI: 10.1186/s40104-023-00880-x] [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: 12/19/2022] [Accepted: 04/05/2023] [Indexed: 06/02/2023] Open
Abstract
BACKGROUND Breed identification is useful in a variety of biological contexts. Breed identification usually involves two stages, i.e., detection of breed-informative SNPs and breed assignment. For both stages, there are several methods proposed. However, what is the optimal combination of these methods remain unclear. In this study, using the whole genome sequence data available for 13 cattle breeds from Run 8 of the 1,000 Bull Genomes Project, we compared the combinations of three methods (Delta, FST, and In) for breed-informative SNP detection and five machine learning methods (KNN, SVM, RF, NB, and ANN) for breed assignment with respect to different reference population sizes and difference numbers of most breed-informative SNPs. In addition, we evaluated the accuracy of breed identification using SNP chip data of different densities. RESULTS We found that all combinations performed quite well with identification accuracies over 95% in all scenarios. However, there was no combination which performed the best and robust across all scenarios. We proposed to integrate the three breed-informative detection methods, named DFI, and integrate the three machine learning methods, KNN, SVM, and RF, named KSR. We found that the combination of these two integrated methods outperformed the other combinations with accuracies over 99% in most cases and was very robust in all scenarios. The accuracies from using SNP chip data were only slightly lower than that from using sequence data in most cases. CONCLUSIONS The current study showed that the combination of DFI and KSR was the optimal strategy. Using sequence data resulted in higher accuracies than using chip data in most cases. However, the differences were generally small. In view of the cost of genotyping, using chip data is also a good option for breed identification.
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Affiliation(s)
- Changheng Zhao
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Dan Wang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Jun Teng
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Cheng Yang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Xinyi Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Xianming Wei
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China
| | - Qin Zhang
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, 271018, China.
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Ryan CA, Berry DP, O’Brien A, Pabiou T, Purfield DC. Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information. Front Genet 2023; 14:1120312. [PMID: 37274789 PMCID: PMC10237237 DOI: 10.3389/fgene.2023.1120312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 05/03/2023] [Indexed: 06/07/2023] Open
Abstract
Introduction: The ability to accurately predict breed composition using genomic information has many potential uses including increasing the accuracy of genetic evaluations, optimising mating plans and as a parameter for genotype quality control. The objective of the present study was to use a database of genotyped purebred and crossbred cattle to compare breed composition predictions using a freely available software, Admixture, with those from a single nucleotide polymorphism Best Linear Unbiased Prediction (SNP-BLUP) approach; a supplementary objective was to determine the accuracy and general robustness of low-density genotype panels for predicting breed composition. Methods: All animals had genotype information on 49,213 autosomal single nucleotide polymorphism (SNPs). Thirteen breeds were included in the analysis and 500 purebred animals per breed were used to establish the breed training populations. Accuracy of breed composition prediction was determined using a separate validation population of 3,146 verified purebred and 4,330 two and three-way crossbred cattle. Results: When all 49,213 autosomal SNPs were used for breed prediction, a minimal absolute mean difference of 0.04 between Admixture vs. SNP-BLUP breed predictions was evident. For crossbreds, the average absolute difference in breed prediction estimates generated using SNP-BLUP and Admixture was 0.068 with a root mean square error of 0.08. Breed predictions from low-density SNP panels were generated using both SNP-BLUP and Admixture and compared to breed prediction estimates using all 49,213 SNPs (representing the gold standard). Breed composition estimates of crossbreds required more SNPs than predicting the breed composition of purebreds. SNP-BLUP required ≥3,000 SNPs to predict crossbred breed composition, but only 2,000 SNPs were required to predict purebred breed status. The absolute mean (standard deviation) difference across all panels <2,000 SNPs was 0.091 (0.054) and 0.315 (0.316) when predicting the breed composition of all animals using Admixture and SNP-BLUP, respectively compared to the gold standard prediction. Discussion: Nevertheless, a negligible absolute mean (standard deviation) difference of 0.009 (0.123) in breed prediction existed between SNP-BLUP and Admixture once ≥3,000 SNPs were considered, indicating that the prediction of breed composition could be readily integrated into SNP-BLUP pipelines used for genomic evaluations thereby avoiding the necessity for a stand-alone software.
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Affiliation(s)
- C. A. Ryan
- Teagasc, Co. Cork, Ireland
- Munster Technological University, Cork, Ireland
| | | | | | - T. Pabiou
- Irish Cattle Breeding Federation, Cork, Ireland
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12
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Lee YS, Son S, Lee HK, Lee RH, Shin D. Elucidating breed-specific variants of native pigs in Korea: insights into pig breeds' genomic characteristics. Anim Cells Syst (Seoul) 2022; 26:338-347. [PMID: 36605594 PMCID: PMC9809348 DOI: 10.1080/19768354.2022.2141316] [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] [Indexed: 11/10/2022] Open
Abstract
Although conserving native pig breeds is important in Korea, research on the genomic aspects to identify breed-specific variations in native pig breeds is uncommon. Single nucleotide polymorphisms (SNPs) can be a powerful source for identifying breed-specific variants. We used whole genome sequencing data, including Jeju Native Pig (JNP), Korean Native Pig (KNP), Korean Wild Boar (KWB), and other western commercial pig breeds to determine native pig breed-specific SNPs. Furthermore, the goal was not only to determine the genomic specificity of native pig breeds but also to identify SNPs that carry breed-specific information (breed-informative SNPs) that can be related to breed characteristics. The representative characteristics of native pigs are their unique meat quality and disease resistance. We surveyed the gene ontology (GO) of native pigs with breed-specific SNPs. Examining the genes associated with GO may contribute to revealing the reasons for the unique characteristics of native pig breeds. The enriched GOs terms were neuron projection development, cell surface receptor signaling pathway, ion homeostasis in JNP, cell adhesion and wound healing in KNP, and DNA repair and reproduction in KWB. We expect that this study of breed-specific SNPs will enable us to gain a deeper understanding of native pigs in Korea.
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Affiliation(s)
- Young-Sup Lee
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Seungwoo Son
- Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Hak-Kyo Lee
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea,Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea
| | - Ra Ham Lee
- Department of Animal Biotechnology, Jeonbuk National University, Jeonju, Republic of Korea, Ra Ham Lee Department of Animal Biotechnology, Jeonbuk National University, Jeonju54896, Republic of Korea; Donghyun Shin Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju54896, Republic of Korea
| | - Donghyun Shin
- Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju, Republic of Korea, Ra Ham Lee Department of Animal Biotechnology, Jeonbuk National University, Jeonju54896, Republic of Korea; Donghyun Shin Department of Agricultural Convergence Technology, Jeonbuk National University, Jeonju54896, Republic of Korea
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Panigrahi M, Kumar H, Saravanan KA, Rajawat D, Sonejita Nayak S, Ghildiyal K, Kaisa K, Parida S, Bhushan B, Dutt T. Trajectory of livestock genomics in South Asia: A comprehensive review. Gene 2022; 843:146808. [PMID: 35973570 DOI: 10.1016/j.gene.2022.146808] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 02/07/2023]
Abstract
Livestock plays a central role in sustaining human livelihood in South Asia. There are numerous and distinct livestock species in South Asian countries. Several of them have experienced genetic development in recent years due to the application of genomic technologies and effective breeding programs. This review discusses genomic studies on cattle, buffalo, sheep, goat, pig, horse, camel, yak, mithun, and poultry. The frontiers covered in this review are genetic diversity, admixture studies, selection signature research, QTL discovery, genome-wide association studies (GWAS), and genomic selection. The review concludes with recommendations for South Asian livestock systems to increasingly leverage genomic technologies, based on the lessons learned from the numerous case studies. This paper aims to present a comprehensive analysis of the dichotomy in the South Asian livestock sector and argues that a realistic approach to genomics in livestock can ensure long-term genetic advancements.
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Affiliation(s)
- Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Kanika Ghildiyal
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Kaiho Kaisa
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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14
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Rajawat D, Panigrahi M, Kumar H, Nayak SS, Parida S, Bhushan B, Gaur GK, Dutt T, Mishra BP. Identification of important genomic footprints using eight different selection signature statistics in domestic cattle breeds. Gene 2022; 816:146165. [PMID: 35026292 DOI: 10.1016/j.gene.2021.146165] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Revised: 12/13/2021] [Accepted: 12/20/2021] [Indexed: 12/25/2022]
Abstract
In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, FST, FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes.
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Affiliation(s)
- Divya Rajawat
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - B P Mishra
- Division of Animal Biotechnology, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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15
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Pal D, Panigrahi M, Chhotaray S, Kumar H, Nayak SS, Rajawat D, Parida S, Gaur GK, Dutt T, Bhushan B. Unraveling genetic admixture in the Indian crossbred cattle by different approaches using Bovine 50K BeadChip. Trop Anim Health Prod 2022; 54:135. [PMID: 35292868 DOI: 10.1007/s11250-022-03133-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 02/24/2022] [Indexed: 11/25/2022]
Abstract
With the upsurge of crossbreeding in India, the admixture levels are highly unpredictable in the composite breeds. Hence, in the present study, 72 Vrindavani animals were assessed for the level of admixture from their known ancestors that are Holstein-Friesian, Jersey, Brown Swiss, and Hariana, through three different software, namely, STRUCTURE, ADMIXTURE, and frappe. The genotype data for ancestral breeds were obtained from a public repository, i.e., DRYAD. The Frieswal crossbred cattle along with ancestral breeds like Holstein-Friesian and Sahiwal were also investigated for the level of admixture with the help of the above-mentioned software. The Frieswal population was found to comprise an average of 62.49, 61.12, and 61.21% of Holstein-Friesian and 37.50, 38.88, and 38.80% of Sahiwal estimated through STRUCTURE, ADMIXTURE, and frappe, respectively. The Vrindavani population was found to consist of on average 39.5, 42.4, and 42.3% of Holstein-Friesian; 22.9, 22.3, and 21.7% of Jersey; 10.7, 10.6, and 11.9% of Brown Swiss; and 26.9, 24.7, and 24.1% of Hariana blood estimated through STRUCTURE, ADMIXTURE, and frappe, respectively. A greater degree of variation was noted in the results from STRUCTURE vs. frappe, STRUCTURE vs. ADMIXTURE than in ADMIXTURE vs. frappe. From this study, we conclude that the admixture analysis based on a single software should be validated through the use of many different approaches for better prediction of admixture levels.
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Affiliation(s)
- Dhan Pal
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India.
| | - Supriya Chhotaray
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
| | - Harshit Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
| | - Sonali Sonejita Nayak
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
| | - Subhashree Parida
- Division of Veterinary Pharmacology & Toxicology, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India
| | - G K Gaur
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, Indian Veterinary Research Institute, Izatnagar, Bareilly, 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Uttar Pradesh, Izatnagar, Bareilly, 243122, India
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Kumar H, Panigrahi M, Saravanan KA, Parida S, Bhushan B, Gaur GK, Dutt T, Mishra BP, Singh RK. SNPs with intermediate minor allele frequencies facilitate accurate breed assignment of Indian Tharparkar cattle. Gene 2021; 777:145473. [PMID: 33549713 DOI: 10.1016/j.gene.2021.145473] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 01/23/2021] [Accepted: 01/28/2021] [Indexed: 10/22/2022]
Abstract
Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed.
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Affiliation(s)
- Harshit Kumar
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Manjit Panigrahi
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India.
| | - K A Saravanan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Subhashree Parida
- Division of Pharmacology & Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Bharat Bhushan
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - G K Gaur
- Division of Animal Genetics, Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - B P Mishra
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
| | - R K Singh
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly 243122, UP, India
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Seo D, Cho S, Manjula P, Choi N, Kim YK, Koh YJ, Lee SH, Kim HY, Lee JH. Identification of Target Chicken Populations by Machine Learning Models Using the Minimum Number of SNPs. Animals (Basel) 2021; 11:ani11010241. [PMID: 33477975 PMCID: PMC7835996 DOI: 10.3390/ani11010241] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 11/16/2022] Open
Abstract
A marker combination capable of classifying a specific chicken population could improve commercial value by increasing consumer confidence with respect to the origin of the population. This would facilitate the protection of native genetic resources in the market of each country. In this study, a total of 283 samples from 20 lines, which consisted of Korean native chickens, commercial native chickens, and commercial broilers with a layer population, were analyzed to determine the optimal marker combination comprising the minimum number of markers, using a 600 k high-density single nucleotide polymorphism (SNP) array. Machine learning algorithms, a genome-wide association study (GWAS), linkage disequilibrium (LD) analysis, and principal component analysis (PCA) were used to distinguish a target (case) group for comparison with control chicken groups. In the processing of marker selection, a total of 47,303 SNPs were used for classifying chicken populations; 96 LD-pruned SNPs (50 SNPs per LD block) served as the best marker combination for target chicken classification. Moreover, 36, 44, and 8 SNPs were selected as the minimum numbers of markers by the AdaBoost (AB), Random Forest (RF), and Decision Tree (DT) machine learning classification models, which had accuracy rates of 99.6%, 98.0%, and 97.9%, respectively. The selected marker combinations increased the genetic distance and fixation index (Fst) values between the case and control groups, and they reduced the number of genetic components required, confirming that efficient classification of the groups was possible by using a small number of marker sets. In a verification study including additional chicken breeds and samples (12 lines and 182 samples), the accuracy did not significantly change, and the target chicken group could be clearly distinguished from the other populations. The GWAS, PCA, and machine learning algorithms used in this study can be applied efficiently, to determine the optimal marker combination with the minimum number of markers that can distinguish the target population among a large number of SNP markers.
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Affiliation(s)
- Dongwon Seo
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea; (D.S.); (S.C.); (P.M.); (S.H.L.)
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
| | - Sunghyun Cho
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea; (D.S.); (S.C.); (P.M.); (S.H.L.)
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
| | - Prabuddha Manjula
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea; (D.S.); (S.C.); (P.M.); (S.H.L.)
| | - Nuri Choi
- SELS Center, Division of Biotechnology, Advanced Institute of Environment and Bioscience, Chonbuk National University, Iksan 54596, Korea;
| | - Young-Kuk Kim
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
- Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea
| | - Yeong Jun Koh
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
- Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Korea
| | - Seung Hwan Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea; (D.S.); (S.C.); (P.M.); (S.H.L.)
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
| | - Hyung-Yong Kim
- Insilicogen Inc., Yongin 16954, Korea
- Correspondence: (H.-Y.K.); (J.H.L.); Tel.: +82-42-821-5779 (J.H.L.)
| | - Jun Heon Lee
- Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea; (D.S.); (S.C.); (P.M.); (S.H.L.)
- Bio-AI Convergence Research Center, Chungnam National University, Daejeon 34134, Korea; (Y.-K.K.); (Y.J.K.)
- Correspondence: (H.-Y.K.); (J.H.L.); Tel.: +82-42-821-5779 (J.H.L.)
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18
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Saravanan KA, Panigrahi M, Kumar H, Parida S, Bhushan B, Gaur GK, Kumar P, Dutt T, Mishra BP, Singh RK. Genome-wide assessment of genetic diversity, linkage disequilibrium and haplotype block structure in Tharparkar cattle breed of India. Anim Biotechnol 2020; 33:297-311. [PMID: 32730141 DOI: 10.1080/10495398.2020.1796696] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Knowledge about genetic diversity is very essential for the management and sustainable utilization of livestock genetic resources. In this study, we presented a comprehensive genome-wide analysis of genetic diversity, ROH, inbreeding, linkage disequilibrium, effective population size and haplotype block structure in Tharparkar cattle of India. A total of 24 Tharparkar animals used in this study were genotyped with Illumina BovineSNP50 array. After quality control, 22,825 biallelic SNPs were retained, which were in HWE, MAF > 0.05 and genotyping rate >90%. The overall mean observed (HO) and expected heterozygosity (HE) were 0.339 ± 0.156 and 0.325 ± 0.129, respectively. The average minor allele frequency was 0.234 with a standard deviation of ± 0.131. We identified a total of 1832 ROH segments and the highest autosomal coverage of 13.87% was observed on chromosome 23. The genomic inbreeding coefficients estimates by FROH, FHOM, FGRM and FUNI were 0.0589, 0.0215, 0.0532 and 0.0160 respectively. The overall mean linkage disequilibrium (LD) for a total of 133,532 pairwise SNPs measured by D' and r2 was 0.6452 and 0.1339, respectively. In addition, we observed a gradual decline in effective population size over the past generations.
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Affiliation(s)
- K A Saravanan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Manjit Panigrahi
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Harshit Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Subhashree Parida
- Division of Pharmacology and Toxicology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Bharat Bhushan
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - G K Gaur
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Pushpendra Kumar
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - B P Mishra
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
| | - R K Singh
- Division of Animal Biotechnology, ICAR-Indian Veterinary Research Institute, Izatnagar, Bareilly, India
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