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Goli RC, Mahar K, Manohar PS, Chishi KG, Prabhu IG, Choudhary S, Rathi P, Chinnareddyvari CS, Haritha P, Metta M, Shetkar M, Kumar A, N D CP, Vidyasagar, Sukhija N, Kanaka KK. Insights from homozygous signatures of cervus nippon revealed genetic architecture for components of fitness. Mamm Genome 2024; 35:657-672. [PMID: 39191871 DOI: 10.1007/s00335-024-10064-1] [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: 05/07/2024] [Accepted: 08/13/2024] [Indexed: 08/29/2024]
Abstract
This study investigates the genomic landscape of Sika deer populations, emphasizing the detection and characterization of runs of homozygosity (ROH) and their contribution towards components of fitness. Using 85,001 high-confidence SNPs, the investigation into ROH distribution unveiled nuanced patterns of autozygosity across individuals especially in 2 out of the 8 farms, exhibiting elevated ROH levels and mean genome coverage under ROH segments. The prevalence of shorter ROH segments (0.5-4 Mb) suggests historical relatedness and potential selective pressures within these populations. Intriguingly, despite observed variations in ROH profiles, the overall genomic inbreeding coefficient (FROH) remained relatively low across all farms, indicating a discernible degree of genetic exchange and effective mitigation of inbreeding within the studied Sika deer populations. Consensus ROH (cROH) were found to harbor genes for important functions viz., EGFLAM gene which is involved in the vision function of the eye, SKP2 gene which regulates cell cycle, CAPSL involved in adipogenesis, SPEF2 which is essential for sperm flagellar assembly, DCLK3 involved in the heat stress. This first ever study on ROH in Sika deer, to shed light on the adaptive role of genes in these homozygous regions. The insights garnered from this study have broader implications in the management of genetic diversity in this vulnerable species.
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Affiliation(s)
- Rangasai Chandra Goli
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Karan Mahar
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
| | - Peela Sai Manohar
- ICAR-Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India
| | - Kiyevi G Chishi
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | | | - Sonu Choudhary
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Pallavi Rathi
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Chandana Sree Chinnareddyvari
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
- ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Pala Haritha
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
| | - Muralidhar Metta
- College of Veterinary Science, SVVU, Garividi, Andhra Pradesh, India
| | - Mahantesh Shetkar
- College of Veterinary Sciences and Animal Husbandry, DUVASU, Mathura, Uttar Pradesh, India
| | - Amit Kumar
- ICAR- Indian Institute of Agricultural Biotechnology, Ranchi, Jharkhand, India
| | - Chethan Patil N D
- Department of Agricultural Economics & Extension, Lovely Professional University, Punjab, India
| | - Vidyasagar
- Veterinary College, KVAFSU, Bidar, Karnataka, India
| | - Nidhi Sukhija
- CSB-Central Tasar Research and Training Institute, Ranchi, Jharkhand, India.
| | - K K Kanaka
- ICAR- Indian Institute of Agricultural Biotechnology, Ranchi, Jharkhand, India
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George L, Alex R, Gowane G, Vohra V, Joshi P, Kumar R, Verma A. Weighted single step GWAS reveals genomic regions associated with economic traits in Murrah buffaloes. Anim Biotechnol 2024; 35:2319622. [PMID: 38437001 DOI: 10.1080/10495398.2024.2319622] [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: 03/05/2024]
Abstract
The objective of the present study was to identify genomic regions influencing economic traits in Murrah buffaloes using weighted single step Genome Wide Association Analysis (WssGWAS). Data on 2000 animals, out of which 120 were genotyped using a double digest Restriction site Associated DNA (ddRAD) sequencing approach. The phenotypic data were collected from NDRI, India, on growth traits, viz., body weight at 6M (month), 12M, 18M and 24M, production traits like 305D (day) milk yield, lactation length (LL) and dry period (DP) and reproduction traits like age at first calving (AFC), calving interval (CI) and first service period (FSP). The biallelic genotypic data consisted of 49353 markers post-quality check. The heritability estimates were moderate to high, low to moderate, low for growth, production, reproduction traits, respectively. Important genomic regions explaining more than 0.5% of the total additive genetic variance explained by 30 adjacent SNPs were selected for further analysis of candidate genes. In this study, 105 genomic regions were associated with growth, 35 genomic regions with production and 42 window regions with reproduction traits. Different candidate genes were identified in these genomic regions, of which important are OSBPL8, NAP1L1 for growth, CNTNAP2 for production and ILDR2, TADA1 and POGK for reproduction traits.
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Affiliation(s)
- Linda George
- National Dairy Research Institute, Karnal, India
| | - Rani Alex
- National Dairy Research Institute, Karnal, India
| | - Gopal Gowane
- National Dairy Research Institute, Karnal, India
| | - Vikas Vohra
- National Dairy Research Institute, Karnal, India
| | - Pooja Joshi
- National Dairy Research Institute, Karnal, India
| | - Ravi Kumar
- National Dairy Research Institute, Karnal, India
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3
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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 A, Dige M, Niranjan SK, Ahlawat S, Arora R, Kour A, Vijh RK. Whole genome resequencing revealed genomic variants and functional pathways related to adaptation in Indian yak populations. Anim Biotechnol 2024; 35:2282723. [PMID: 38006247 DOI: 10.1080/10495398.2023.2282723] [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: 11/26/2023]
Abstract
The present study aims to identify genomic variants through a whole genome sequencing (WGS) approach and uncover biological pathways associated with adaptation and fitness in Indian yak populations. A total of 30 samples (10 from each population) were included from Arunachali, Himachali and Ladakhi yak populations. WGS analysis revealed a total of 32171644, 27260825, and 32632460 SNPs and 4865254, 4429941, and 4847513 Indels in the Arunachali, Himachali, and Ladakhi yaks, respectively. Genes such as RYR2, SYNE2, BOLA, HF1, and the novel transcript ENSBGRG00000011079 were found to have the maximum number of high impact variants in all three yak populations, and might play a major role in local adaptation. Functional enrichment analysis of genes harboring high impact SNPs revealed overrepresented pathways related to response to stress, immune system regulation, and high-altitude adaptation. This study provides comprehensive information about genomic variants and their annotation in Indian yak populations, thus would serve as a data resource for researchers working on the yaks. Furthermore, it could be well exploited for better yak conservation strategies by estimating population genetics parameters viz., effective population size, inbreeding, and observed and expected heterozygosity.
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Affiliation(s)
- Amod Kumar
- Animal Genetics Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
| | - Mahesh Dige
- Animal Genetic Resources Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
| | - Saket Kumar Niranjan
- Animal Genetics Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
| | - Sonika Ahlawat
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
| | - Reena Arora
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
| | - Aneet Kour
- ICAR-National Research Centre on Yak, Dirang, India
| | - Ramesh Kumar Vijh
- Animal Genetics Division, ICAR-National Bureau of Animal Genetic Resources (NBAGR), Karnal, India
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Criscione A, Chessari G, Cesarani A, Ablondi M, Asti V, Bigi D, Bordonaro S, Ciampolini R, Cipolat-Gotet C, Congiu M, De Palo P, Landi V, Macciotta NPP, Matassino D, Portolano B, Riggio S, Sabbioni A, Sardina MT, Senczuk G, Tumino S, Vasini M, Ciani E, Mastrangelo S. Analysis of ddRAD-seq data provides new insights into the genomic structure and patterns of diversity in Italian donkey populations. J Anim Sci 2024; 102:skae165. [PMID: 38874306 PMCID: PMC11214105 DOI: 10.1093/jas/skae165] [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: 03/07/2024] [Accepted: 06/13/2024] [Indexed: 06/15/2024] Open
Abstract
With more than 150 recognized breeds, donkeys assume relevant economic importance, especially in developing countries. Even if the estimated number of heads worldwide is 53M, this species received less attention than other livestock species. Italy has traditionally been considered one of the cradles of European donkey breeding, and despite a considerable loss of biodiversity, today still counts nine autochthonous populations. A total of 220 animals belonging to nine different populations were genotyped using the double-digest restriction site associated DNA (ddRAD) sequencing to investigate the pattern of diversity using a multi-technique approach. A total of 418,602,730 reads were generated and successfully demultiplexed to obtain a medium-density SNP genotypes panel with about 27K markers. The diversity indices showed moderate levels of variability. The genetic distances and relationships, largely agree with the breeding history of the donkey populations under investigation. The results highlighted the separation of populations based on their genetic origin or geographical proximity between breeding areas, showed low to moderate levels of admixture, and indicated a clear genetic difference in some cases. For some breeds, the results also validate the success of proper management conservation plans. Identified runs of homozygosity islands, mapped within genomic regions related to immune response and local adaptation, are consistent with the characteristics of the species known for its rusticity and adaptability. This study is the first exhaustive genome-wide analysis of the diversity of Italian donkey populations. The results emphasized the high informativeness of genome-wide markers retrieved through the ddRAD approach. The findings take on great significance in designing and implementing conservation strategies. Standardized genotype arrays for donkey species would make it possible to combine worldwide datasets to provide further insights into the evolution of the genomic structure and origin of this important genetic resource.
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Affiliation(s)
- Andrea Criscione
- Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, Catania, Italy
| | - Giorgio Chessari
- Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, Catania, Italy
- Department of Animal Sciences, Georg-August-University Göttingen, Göttingen, Germany
| | - Alberto Cesarani
- Dipartimento di Agraria, University of Sassari, Sassari, Italy
- Department of Animal and Dairy Science, University of Georgia, Athens, USA
| | - Michela Ablondi
- Dipartimento di Scienze Medico-Veterinarie, University of Parma, Parma, Italy
| | - Vittoria Asti
- Dipartimento di Scienze Medico-Veterinarie, University of Parma, Parma, Italy
| | - Daniele Bigi
- Dipartimento di Scienze e Tecnologie Agro-Alimentari, University of Bologna, Bologna, Italy
| | - Salvatore Bordonaro
- Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, Catania, Italy
| | | | | | - Michele Congiu
- Dipartimento di Agraria, University of Sassari, Sassari, Italy
| | - Pasquale De Palo
- Dipartimento di Medicina Veterinaria, University of Bari Aldo Moro, Valenzano, Italy
| | - Vincenzo Landi
- Dipartimento di Medicina Veterinaria, University of Bari Aldo Moro, Valenzano, Italy
| | | | - Donato Matassino
- Consorzio per la Sperimentazione, Divulgazione e Applicazione di Biotecniche Innovative, Benevento, Italy
| | - Baldassare Portolano
- Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Silvia Riggio
- Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Alberto Sabbioni
- Dipartimento di Scienze Medico-Veterinarie, University of Parma, Parma, Italy
| | - Maria Teresa Sardina
- Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy
| | - Gabriele Senczuk
- Dipartimento di Agricoltura, Ambiente e Alimenti, University of Molise, Campobasso, Italy
| | - Serena Tumino
- Dipartimento di Agricoltura, Alimentazione e Ambiente, University of Catania, Catania, Italy
| | - Matteo Vasini
- Associazione Nazionale Allevatori delle Razze Equine ed Asinine Italiane, ANAREAI, Roma, Italy
| | - Elena Ciani
- Dipartimento di Bioscienze, Biotecnologie e Biofarmaceutica, University of Bari Aldo Moro, Bari, Italy
| | - Salvatore Mastrangelo
- Dipartimento di Scienze Agrarie, Alimentari e Forestali, University of Palermo, Palermo, Italy
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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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Raja TV, Alex R, Singh U, Kumar S, Das AK, Sengar G, Singh AK. Genome wide mining of SNPs and INDELs through ddRAD sequencing in Sahiwal cattle. Anim Biotechnol 2023; 34:4885-4899. [PMID: 37093232 DOI: 10.1080/10495398.2023.2200517] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
The study was conducted in Sahiwal cattle for genome wide identification and annotation of single nucleotide polymorphisms (SNPs) and insertions and deletions (INDELs) in Sahiwal cattle. The double digest restriction-site associated DNA (ddRAD) sequencing, a reduced representation method was used for the identification of variants at nucleotide level. A total of 1,615,211 variants were identified at RD10 and Q30 consisting of 1,480,930 SNPs and 134,281 INDELs with respect to the Bos taurus reference genome. The SNPs were annotated for their location, impact and functional class. The SNPs identified in Sahiwal cattle were found to be associated with a total of 26,229 genes. A total of 1819 SNPs were annotated for 209 candidate genes associated with different production and reproduction traits. The variants identified in the present study may be useful to strengthen the existing bovine SNP chips for reducing the biasness over the taurine cattle breeds. The diversity analysis provides the insight of the genetic architecture of the Sahiwal population Studied. The large genetic variations identified at the nucleotide level provide ample scope for implementing an effective and efficient breed improvement programme for increasing the productivity of Sahiwal cattle.
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Affiliation(s)
- Thiruvothur Venkatesan Raja
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut Cantt, Uttar Pradesh, India
| | - Rani Alex
- ICAR-National Dairy Research Institute, Karnal, Haryana, India
| | - Umesh Singh
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut Cantt, Uttar Pradesh, India
| | - Sushil Kumar
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut Cantt, Uttar Pradesh, India
| | - Achintya Kumar Das
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut Cantt, Uttar Pradesh, India
| | - Gyanendra Sengar
- National Research Centre on Pigs, Rani (Near Airport), Guwahati, Assam, India
| | - Amit Kumar Singh
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut Cantt, Uttar Pradesh, India
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Raja TV, Alex R, Singh U, Kumar S, Das AK, Sengar G, Singh AK, Ghosh A, Saha S, Mitra A. Genome-wide identification and annotation of SNPs for economically important traits in Frieswal™, newly evolved crossbred cattle of India. 3 Biotech 2023; 13:310. [PMID: 37621321 PMCID: PMC10444711 DOI: 10.1007/s13205-023-03701-0] [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: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/26/2023] Open
Abstract
The Frieswal™ is a crossbred cattle evolved by ICAR-Central Institute for Research on Cattle utilizing more than 15,000 cattle maintained at more than 37 military farms spread all over the agro-climatic regions of the country. The ddRAD sequencing method was used to identify and annotate the SNPs and INDELs. The results of variant calling revealed 1,487,851 SNPs and 128,175 INDELs at a read depth of 10. A total of 3,775,079 effects were identified, and majority (66.41%) of the effects were in the intron region of the genome followed by intergenic (21.87%). Majority (99.18%) of the variants had the modifier effect. The results revealed a higher magnitude of transitions as compared to the transversion. The classification of SNPs by functional class revealed a majority of missense (43%) and silent (56%) effects. Out of 26,278 genes identified, 1841 SNPs were annotated in 207 candidate genes responsible for various milk production and reproduction traits. The observed heterozygosity was 0.2804 against the expected heterozygosity value of 0.2978. The overall average inbreeding coefficient (FIS) was 0.0604. The pathway analysis revealed that the prolactin signaling pathway (GO:0038161) was significant biological process complete for both milk production and reproduction traits. The SNP variations can be effectively used as markers for early and accurate identification of the QTLs and for formulating an efficient and effective breed improvement program in Frieswal™ cattle. Supplementary Information The online version contains supplementary material available at 10.1007/s13205-023-03701-0.
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Affiliation(s)
- Thiruvothur Venkatesan Raja
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
| | - Rani Alex
- National Dairy Research Institute, Karnal, Haryana India
| | - Umesh Singh
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
| | - Sushil Kumar
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
| | - Achintya Kumar Das
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
| | - Gyanendra Sengar
- National Research Centre on Pigs, Rani (Near Airport), Guwahati, Assam 781 131 India
| | - Amit Kumar Singh
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
| | - Abhirupa Ghosh
- Division of Bioinformatics, Bose Institute, Unified Campus Salt Lake, College More, EN Block, Sector V, Kolkata, West Bengal 700091 India
| | - Sudipto Saha
- Division of Bioinformatics, Bose Institute, Unified Campus Salt Lake, College More, EN Block, Sector V, Kolkata, West Bengal 700091 India
| | - Abhijit Mitra
- Molecular Genetics Laboratory, Cattle Genetics and Breeding Division, ICAR-Central Institute for Research on Cattle, Meerut, Uttar Pradesh 650 001 India
- Present Address: Animal Husbandry Commissioner, Department of Animal Husbandry and Dairying, Government of India, New Delhi, India
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Masharing N, Sodhi M, Chanda D, Singh I, Vivek P, Tiwari M, Kumari P, Mukesh M. ddRAD sequencing based genotyping of six indigenous dairy cattle breeds of India to infer existing genetic diversity and population structure. Sci Rep 2023; 13:9379. [PMID: 37296129 PMCID: PMC10256769 DOI: 10.1038/s41598-023-32418-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/27/2023] [Indexed: 06/12/2023] Open
Abstract
The present investigation aimed to identify genome wide SNPs and to carry out diversity and population structure study using ddRAD-seq based genotyping of 58 individuals of six indigenous milch cattle breeds (Bos indicus) such as Sahiwal, Gir, Rathi, Tharparkar, Red Sindhi and Kankrej of India. A high percentage of reads (94.53%) were mapped to the Bos taurus (ARS-UCD1.2) reference genome assembly. Following filtration criteria, a total of 84,027 high quality SNPs were identified across the genome of 6 cattle breeds with the highest number of SNPs observed in Gir (34,743), followed by Red Sindhi (13,092), Kankrej (12,812), Sahiwal (8956), Tharparkar (7356) and Rathi (7068). Most of these SNPs were distributed in the intronic regions (53.87%) followed by intergenic regions (34.94%) while only 1.23% were located in the exonic regions. Together with analysis of nucleotide diversity (π = 0.373), Tajima's D (D value ranging from - 0.295 to 0.214), observed heterozygosity (HO ranging from 0.464 to 0.551), inbreeding coefficient (FIS ranging from - 0.253 to 0.0513) suggested for the presence of sufficient within breed diversity in the 6 major milch breeds of India. The phylogenetic based structuring, principal component and admixture analysis revealed genetic distinctness as well as purity of almost all of the 6 cattle breeds. Overall, our strategy has successfully identified thousands of high-quality genome wide SNPs that will further enrich the Bos indicus representation basic information about genetic diversity and structure of 6 major Indian milch cattle breeds which should have implications for better management and conservation of valuable indicine cattle diversity.
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Affiliation(s)
- Nampher Masharing
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
- Animal Biotechnology Center, ICAR-National Dairy Research Institute, Karnal, Haryana, India
| | - Monika Sodhi
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Divya Chanda
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Inderpal Singh
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Prince Vivek
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Manish Tiwari
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
- Animal Biotechnology Center, ICAR-National Dairy Research Institute, Karnal, Haryana, India
| | - Parvesh Kumari
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India
| | - Manishi Mukesh
- Animal Biotechnology Division, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana, India.
- ICAR-NBAGR, Karnal, Haryana, 132001, India.
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Mishra DC, Bhati J, Yadav S, Avashthi H, Sikka P, Jerome A, Balhara AK, Singh I, Rai A, Chaturvedi KK. Comparative expression analysis of water buffalo ( Bubalus bubalis) to identify genes associated with economically important traits. Front Vet Sci 2023; 10:1160486. [PMID: 37252384 PMCID: PMC10213454 DOI: 10.3389/fvets.2023.1160486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/11/2023] [Indexed: 05/31/2023] Open
Abstract
The milk, meat, skins, and draft power of domestic water buffalo (Bubalus bubalis) provide substantial contributions to the global agricultural economy. The world's water buffalo population is primarily found in Asia, and the buffalo supports more people per capita than any other livestock species. For evaluating the workflow, output rate, and completeness of transcriptome assemblies within and between reference-free (RF) de novo transcriptome and reference-based (RB) datasets, abundant bioinformatics studies have been carried out to date. However, comprehensive documentation of the degree of consistency and variability of the data produced by comparing gene expression levels using these two separate techniques is lacking. In the present study, we assessed the variations in the number of differentially expressed genes (DEGs) attained with RF and RB approaches. In light of this, we conducted a study to identify, annotate, and analyze the genes associated with four economically important traits of buffalo, viz., milk volume, age at first calving, post-partum cyclicity, and feed conversion efficiency. A total of 14,201 and 279 DEGs were identified in RF and RB assemblies. Gene ontology (GO) terms associated with the identified genes were allocated to traits under study. Identified genes improve the knowledge of the underlying mechanism of trait expression in water buffalo which may support improved breeding plans for higher productivity. The empirical findings of this study using RNA-seq data-based assembly may improve the understanding of genetic diversity in relation to buffalo productivity and provide important contributions to answer biological issues regarding the transcriptome of non-model organisms.
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Affiliation(s)
- Dwijesh Chandra Mishra
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Jyotika Bhati
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Sunita Yadav
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Himanshu Avashthi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Poonam Sikka
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Andonissamy Jerome
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Ashok Kumar Balhara
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Inderjeet Singh
- ICAR-Central Institute for Research on Buffaloes, Indian Council of Agricultural Research (ICAR), Hisar, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
| | - Krishna Kumar Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Indian Council of Agricultural Research (ICAR), PUSA, New Delhi, India
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Khan A, Singh K, Jaiswal S, Raza M, Jasrotia RS, Kumar A, Gurjar AKS, Kumari J, Nayan V, Iquebal MA, Angadi UB, Rai A, Datta TK, Kumar D. Whole-Genome-Based Web Genomic Resource for Water Buffalo (Bubalus bubalis). Front Genet 2022; 13:809741. [PMID: 35480326 PMCID: PMC9035531 DOI: 10.3389/fgene.2022.809741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 02/14/2022] [Indexed: 11/13/2022] Open
Abstract
Water buffalo (Bubalus bubalis), belonging to the Bovidae family, is an economically important animal as it is the major source of milk, meat, and drought in numerous countries. It is mainly distributed in tropical and subtropical regions with a global population of approximately 202 million. The advent of low cost and rapid sequencing technologies has opened a new vista for global buffalo researchers. In this study, we utilized the genomic data of five commercially important buffalo breeds, distributed globally, namely, Mediterranean, Egyptian, Bangladesh, Jaffrarabadi, and Murrah. Since there is no whole-genome sequence analysis of these five distinct buffalo breeds, which represent a highly diverse ecosystem, we made an attempt for the same. We report the first comprehensive, holistic, and user-friendly web genomic resource of buffalo (BuffGR) accessible at http://backlin.cabgrid.res.in/buffgr/, that catalogues 6028881 SNPs and 613403 InDels extracted from a set of 31 buffalo tissues. We found a total of 7727122 SNPs and 634124 InDels distributed in four breeds of buffalo (Murrah, Bangladesh, Jaffarabadi, and Egyptian) with reference to the Mediterranean breed. It also houses 4504691 SSR markers from all the breeds along with 1458 unique circRNAs, 37712 lncRNAs, and 938 miRNAs. This comprehensive web resource can be widely used by buffalo researchers across the globe for use of markers in marker trait association, genetic diversity among the different breeds of buffalo, use of ncRNAs as regulatory molecules, post-transcriptional regulations, and role in various diseases/stresses. These SNPs and InDelscan also be used as biomarkers to address adulteration and traceability. This resource can also be useful in buffalo improvement programs and disease/breed management.
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Affiliation(s)
- Aamir Khan
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Kalpana Singh
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mustafa Raza
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Rahul Singh Jasrotia
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Animesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anoop Kishor Singh Gurjar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Juli Kumari
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Varij Nayan
- ICAR-Central Institute for Research on Buffaloes, Hisar, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
- *Correspondence: Mir Asif Iquebal,
| | - U. B. Angadi
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anil Rai
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Dinesh Kumar
- Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
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12
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Tyagi SK, Mehrotra A, Singh A, Kumar A, Dutt T, Mishra BP, Pandey AK. Comparative Signatures of Selection Analyses Identify Loci Under Positive Selection in the Murrah Buffalo of India. Front Genet 2021; 12:673697. [PMID: 34737760 PMCID: PMC8560740 DOI: 10.3389/fgene.2021.673697] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 09/17/2021] [Indexed: 12/18/2022] Open
Abstract
India is home to a large and diverse buffalo population. The Murrah breed of North India is known for its milk production, and it has been used in breeding programs in several countries. Selection signature analysis yield valuable information about how the natural and artificial selective pressures have shaped the genomic landscape of modern-day livestock species. Genotype information was generated on six buffalo breeds of India, namely, Murrah, Bhadawari, Mehsana, Pandharpuri, Surti, and Toda using ddRAD sequencing protocol. Initially, the genotypes were used to carry out population diversity and structure analysis among the six breeds, followed by pair-wise comparisons of Murrah with the other five breeds through XP-EHH and F ST methodologies to identify regions under selection in Murrah. Admixture results showed significant levels of Murrah inheritance in all the breeds except Pandharpuri. The selection signature analysis revealed six regions in Murrah, which were identified in more than one pair-wise comparison through both XP-EHH and F ST analyses. The significant regions overlapped with QTLs for milk production, immunity, and body development traits. Genes present in these regions included SLC37A1, PDE9A, PPBP, CXCL6, RASSF6, AFM, AFP, ALB, ANKRD17, CNTNAP2, GPC5, MYLK3, and GPT2. These genes emerged as candidates for future polymorphism studies of adaptability and performance traits in buffaloes. The results also suggested ddRAD sequencing as a useful cost-effective alternative for whole-genome sequencing to carry out diversity analysis and discover selection signatures in Indian buffalo breeds.
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Affiliation(s)
- Shiv K Tyagi
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, India
| | - Arnav Mehrotra
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, India
| | - Akansha Singh
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, India
| | - Amit Kumar
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, India
| | - Triveni Dutt
- Livestock Production and Management, Indian Council of Agricultural Research (ICAR)-Indian Veterinary Research Institute, Bareilly, India
| | - Bishnu P Mishra
- Animal Biotechnology, Indian Council of Agricultural Research (ICAR)-Indian Veterinary Research Institute, Bareilly, India
| | - Ashwni K Pandey
- Animal Genetics Division, ICAR-Indian Veterinary Research Institute, Izatnangar, Bareilly, India
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13
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Niu Q, Zhang T, Xu L, Wang T, Wang Z, Zhu B, Zhang L, Gao H, Song J, Li J, Xu L. Integration of selection signatures and multi-trait GWAS reveals polygenic genetic architecture of carcass traits in beef cattle. Genomics 2021; 113:3325-3336. [PMID: 34314829 DOI: 10.1016/j.ygeno.2021.07.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/05/2021] [Accepted: 07/22/2021] [Indexed: 11/18/2022]
Abstract
Carcass merits are widely considered as economically important traits affecting beef production in the beef cattle industry. However, the genetic basis of carcass traits remains to be well understood. Here, we applied multiple methods, including the Composite of Likelihood Ratio (CLR) and Genome-wide Association Study (GWAS), to explore the selection signatures and candidate variants affecting carcass traits. We identified 11,600 selected regions overlapping with 2214 candidate genes, and most of those were enriched in binding and gene regulation. Notably, we identified 66 and 110 potential variants significantly associated with carcass traits using single-trait and multi-traits analyses, respectively. By integrating selection signatures with single and multi-traits associations, we identified 12 and 27 putative genes, respectively. Several highly conserved missense variants were identified in OR5M13D, NCAPG, and TEX2. Our study supported polygenic genetic architecture of carcass traits and provided novel insights into the genetic basis of complex traits in beef cattle.
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Affiliation(s)
- Qunhao Niu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Tianliu Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Ling Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Tianzhen Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Zezhao Wang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Bo Zhu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Lupei Zhang
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Huijiang Gao
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jiuzhou Song
- Department of Animal and Avian Science, University of Maryland, College Park, USA
| | - Junya Li
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
| | - Lingyang Xu
- Key Laboratory of Animal Genetics Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China.
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14
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Jaiswal S, Jagannadham J, Kumari J, Iquebal MA, Gurjar AKS, Nayan V, Angadi UB, Kumar S, Kumar R, Datta TK, Rai A, Kumar D. Genome Wide Prediction, Mapping and Development of Genomic Resources of Mastitis Associated Genes in Water Buffalo. Front Vet Sci 2021; 8:593871. [PMID: 34222390 PMCID: PMC8253262 DOI: 10.3389/fvets.2021.593871] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 04/30/2021] [Indexed: 12/16/2022] Open
Abstract
Water buffalo (Bubalus bubalis) are an important animal resource that contributes milk, meat, leather, dairy products, and power for plowing and transport. However, mastitis, a bacterial disease affecting milk production and reproduction efficiency, is most prevalent in populations having intensive selection for higher milk yield, especially where the inbreeding level is also high. Climate change and poor hygiene management practices further complicate the issue. The management of this disease faces major challenges, like antibiotic resistance, maximum residue level, horizontal gene transfer, and limited success in resistance breeding. Bovine mastitis genome wide association studies have had limited success due to breed differences, sample sizes, and minor allele frequency, lowering the power to detect the diseases associated with SNPs. In this work, we focused on the application of targeted gene panels (TGPs) in screening for candidate gene association analysis, and how this approach overcomes the limitation of genome wide association studies. This work will facilitate the targeted sequencing of buffalo genomic regions with high depth coverage required to mine the extremely rare variants potentially associated with buffalo mastitis. Although the whole genome assembly of water buffalo is available, neither mastitis genes are predicted nor TGP in the form of web-genomic resources are available for future variant mining and association studies. Out of the 129 mastitis associated genes of cattle, 101 were completely mapped on the buffalo genome to make TGP. This further helped in identifying rare variants in water buffalo. Eighty-five genes were validated in the buffalo gene expression atlas, with the RNA-Seq data of 50 tissues. The functions of 97 genes were predicted, revealing 225 pathways. The mastitis proteins were used for protein-protein interaction network analysis to obtain additional cross-talking proteins. A total of 1,306 SNPs and 152 indels were identified from 101 genes. Water Buffalo-MSTdb was developed with 3-tier architecture to retrieve mastitis associated genes having genomic coordinates with chromosomal details for TGP sequencing for mining of minor alleles for further association studies. Lastly, a web-genomic resource was made available to mine variants of targeted gene panels in buffalo for mastitis resistance breeding in an endeavor to ensure improved productivity and the reproductive efficiency of water buffalo.
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Affiliation(s)
- Sarika Jaiswal
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Jaisri Jagannadham
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Juli Kumari
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mir Asif Iquebal
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Anoop Kishor Singh Gurjar
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Varij Nayan
- Indian Council of Agricultural Research (ICAR)-Central Institute for Research on Buffaloes, Hisar, India
| | - Ulavappa B Angadi
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Sunil Kumar
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Rakesh Kumar
- Animal Biotechnology Centre, Indian Council of Agricultural Research (ICAR)-National Dairy research Institute, Karnal, India
| | - Tirtha Kumar Datta
- Animal Biotechnology Centre, Indian Council of Agricultural Research (ICAR)-National Dairy research Institute, Karnal, India
| | - Anil Rai
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Dinesh Kumar
- Centre for Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Statistics Research Institute, New Delhi, India
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