Selection of Cattle for Disease Resistance

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Sukanta Basak M.V.Sc. 1 st Year (AGB) NDRI, Karnal Presented by : Selection of Cattle for Disease Resistance Credit Seminar On

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( Tomar , 2004) Selection Artificial Selection Natural Selection Ecological Selection Sexual Selection Selection is a process of giving preference to certain individuals in a population to reproduce than other individuals which are denied the opportunity to produce next generation. Selection

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Disease Resistance

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Resistance : The animal’s ability to resist infection or moderate the parasite lifecycle Tolerance: Animal’s ability to withstand the pathogenic effects of infection Genetic Resistance : Genetic component to resist pathogens (Bishop & Mackenzie, 2003)

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Nutrition Age Genetics Management System Biological Status Pathogen Season Immune System Immunological Background Epidemiology Preventive Measure Stress

Resistance comes…:

Resistance comes… Adaptability Protective Mechanisms Immune System Healing Capacity The combined function of all these mechanisms provides cattle with a resistance to disease

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Resistance/sensitivity depending on pathogenic stage during an infectious process Pathogenic stage Resistant animal Susceptible animal Exposure to the pathogen No infection Contamination First stage of infection Local infection Multiplication of the pathogen Systemic infection Elimination of the pathogen Chronic infection Clinical consequence No sign Expression of the disease Issue Survival Death ( Lantier and Khang , 1988)

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↑ Cost of production ↑ Chance in Resistance to drugs Poor Resilience Selective breeding

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Fixation of alleles diminish resistance Microbial resistance to antibiotics Emergence of new diseases (BSE) Increase in disease transmission Wildlife to livestock transmission ( Gummow , 2010) Therapeutic treatment costs higher Consumer expectations : Milk & milk product free of drug residue Animals to be fed without additives ,antibiotics Selection leads… ( Sudhan & Sharma, 2010)

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Extensive use of taurine germplasm under genetic improvement program disseminate undesirable allele in Indian native cattle breeds A2 A1 13 Allelic variation of β -casein genes Health Disorders p=0.355 Safe Consumption q=0.645 A1A2: More common in Holstein Friesian and Jersey A2A2: Higher frequency in the crossbreds ( Sodhi et al., 2012)

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A ll animals exhibit same clinical symptoms? Evidence for genetic variation in aspect of disease resistance has been documented for more than 50 diseases in all major domestic livestock species (Bishop et al., 2005)

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Diseases No of cattle breed Tryapanosomioasis 17 Tick infestation/burden 17 Tick borne disease (unspecified) 4 Anaplasmosis 2 Piroplasmosis / Babesiosis 4 Heartwater / Cowdriosis 1 Internal parasites/worms 2 Bovine leukosis 9 Foot rot ( Bacteroides nodusos ) 1 Total 59 (FAO, 2007) Cattle - Resistance or tolerance to specific diseases or parasites

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Diseases Area No of breed Breed Name Trypanosomiasis North &West Africa 15 N’Dama , Baoule , Namchl , Samba East Africa 2 Sheko , Jiddu Tick Burden South Africa 8 Ngunl , Angonl South Asia 4 Javanese Zebu, Thai Europe & the Caucasus 1 Zebu of Azerbaijan South America 1 Romosinuano South West Pacific 3 Australian Milking Zebu, Sahiwal Internal Parasitosis South Africa 1 Madagascar Zebu South East Asia 1 Javanese Zebu

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Diseases Area No of breed Breed Name Tick Borne Diseases (Unspecific) North & West Africa 2 Baoule , Ghana Shorthorn Southern Africa 1 Angoni Anaplasmosis Europe & caucasus 2 Cinisara , Modicana Babesiosis North & Western Africa 2 N’Dama , Noire Pie de Meknes Europe & caucasus 1 Modicana Heartwater / Cowdriosis Europe & caucasus 1 Damara Leukosis Central Asia 1 Bestuzhevskaya Europe & Caucasus 7 Istobenskaya , Yurinskaya Foot Rot Europe & Caucasus 1 Sayaguesa (FAO, 2007)

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Disease/Parasite Resistance (Breed) Compare (Breeds) Experimental condition Result Reference Haemonchus contortus N’Dama Zebu Village herd ( Gambala ) Fewer abomasal worm, lower FEC Claxton & leaperre , 1991 Ticks ( Amblyomma varlegatum , Hyalomma spp. ) N’Dama N’Dama X Zebu Field condition ( Gambala ) Fewer Ticks Mattioli et al., 1993 Ticks ( Anaplasma marglnale ) N’Dama Gobra zebu Field condition ( Gambala ) Lower serological prevalence & fewer ticks Mattioli et al., 1995 Theileria annulata Sahiwal Holstein -Friesian Artificial infection Less severe clinical symptom Glass et al ., (2005) (FAO, 2007)

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Trait types No of QTL Milk yield 345 Somatic cell score 123 Milk Fat % 108 M paratuberculosis susceptability 77 Fertility 550 Mastitis 257 Disease susceptibility 106 Feed conversion 70 Conformation 61 Life time production 50 General health parameter 46 Organ disorder 18 Parasitic resistance 17 Semen quality 11 (http://www.animalgenome.org/QTLdb/cattle.html) Traits type & QTL no in Cattle

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QTL map for milk production traits in exotic cattle

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• P < 0 . 001 • 0 . 001 < P < 0 . 01 • 0 . 01 < P < 0 . 05 (Mackinnon & Georges, 1998)

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Genetic correlations between various health traits & 305-day milk yield in dairy cattle SCS -Somatic Cell Score, RFM - Retention of Fetal Membrane ( Donagh et al., 2011) SCS Mastitis Lameness Milk Fever Cystic Ovaries Metritis Ketosis RFM Reference NA NA 0.27 -0.67 -0.06 NA 0.77 NA Uribe et al. , 1995 NA 0.21 (0.06) 0.29 (0.11) 0.19 NA NA NA NA Pryce et al. , 1997 NA 0.15 0.24 NA 0.23 0.02 NA -0.28 Van Dorp et al. , 1998 0.15 (0.06) 0.45 (0.09) NA NA NA NA NA NA Rupp et al. , 1999 NA 0.35 (0.10) NA 0.27 (0.12) 0.70 (0.15) NA NA NA Kadarmideen et al. , 2000 NA 0.09 0.02 NA 0.10 -0.05 0.12 NA Wald et al. , 2004

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Selection for D isease Resistance ↑ Cost for Disease Measurement f or disease Resistance Prospect for genetic improvement for disease resistance Genetic variability of disease resistance

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Conventionally selection for disease Resistance based On Treatment Records Host Immune Responses Host Biological Responses Pathogen Responses

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We are interested in the genetic component(s) influencing the host and/or pathogen responses Major genes Polygenic effects Host – Pathogen interaction Marker Assisted Selection for microbial diseases Few major genes discoveries Focus on general immunity response

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(Bishop et al ., 2011) Selection based on phenotypes will be feasible in case of endemic diseases which poses challenges to animals e.g. - Mastitis and nematode infection in ruminants Principles Selection for disease resistance Traits that indicate the response of animals or phenotypes Choice of proper DNA markers against diseases

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Molecular Evolution (Bishop et al. , 2011) Genomic Selection

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( Boichard et al., 2010 ) Breeding Objectives

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Can we select cattle to be disease resistant ?

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Maybe! Not in my lifetime Sometimes Huh? Sure Absolutely Not Absolutely

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Pedigree analysis Test matings Biochemical Screening DNA markers Gene therapy Clinical screening

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Mastitis Nematode parasitic infection (under grazing condition) External parasitic infestation Eye diseases Respiratory disorders Tuberculosis B rucellosis Paratuberculosis Foot and mouth diseases Bovine spongiform encephalopathy (BSE) Feed-borne toxins Ketosis Common diseases affect cattle (Morris et al. , 2007)

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Approximately 125 known genetic disorders

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Mastitis is one of the most prevalent diseases of dairy cattle, which causes huge economic losses to the dairy industry world wide. The incidence rate of clinical mastitis was 25%−60%. ( Ruegg et al., 2003) Indian dairy industry looses Rs. 61 billion corer due to mastitis Rs. 44 billion corer for sub-clinical mastitis Rs. 17 billion corer for clinical mastitis ( Burman et al., 2002) Infectious Diseases… Mastitis is a global problem as it adversely affects animal health, quality of milk and economics of milk production and every country including developed ones suffer huge financial losses. (Sharma et al., 2007)

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Direct selection for clinical mastitis ( Gianola and Foulley , 1983) Mastitis Data All-or- None trait Linear models or Threshold model Variance component and Breeding value estimation Cattle Selection

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Traits Reasons References SCC Medium to high genetic correlation Mrode & Swanson, 1996 Udder conformation Improving accuracy of selection Mrode et al., 1998 Udder depth and fore udder attachment Most frequently associated Rupp and Boichard , 2003 Test-day SCC Better model Haas et al., 2003 Milking speed, Electrical conductivity in milk & Markers of immune response Associated either with CM & SCC Norberg et al. , 2004 Indirect selection for mastitis

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( Interbull , 2008) The international genetic evaluations include mastitis resistance, calving traits, fertility, workability traits and longevity as complements to production and conformation traits In countries where direct selection for clinical mastitis is not an option it is better to use SCC than ignoring mastitis in the breeding programme ( Heringstad et al. , 2000) Selection for reduced somatic cell count in dairy cattle decreased incidence of mastitis (Shook and Schutz , 1992)

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Disease/Pathogen Candidate gene Cattle type Referrences Mycobacterium bovis SLC11A1 Exotic Qureshi et al ., 1996 Salmonella dublin SLC11A1 Exotic Qureshi et al ., 1996 Jhone’s disease TLR4, SLC11A1 Exotic Pinedo et al., 2009 Mastitis MHC II , LTF, IL 8, CCL2, FEZF2 & TLR 4 Exotic Ogorevc et al., 2009 TLR4 Exotic Sharma et al., 2006 CARD15 Sahiwal Soumya et al., 2011 IL8 Sahiwal Hazra et al., 2011 Brucella abortus SLC11A1 Zebu & Cross breed Kumar et al ., 2011 Candidate Gene Associate with Disease Resistance

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Bulk milk somatic cell count, % of cows with SCC >250,000 cells/ml per test-day % of cows with new infections Culling rate because of mastitis. The currently used primary parameters to analyse the herd situation in the mastitis control program ( Sudhan & Sharma, 2010)

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Bovine Success Story Modelling studies played major role on genetic resistance to disease Genetic-epidemiological models of nematode infections shows substantial reductions in parasite challenge by increasing host resistance (Bishop & Stear , 1997) Mixed hidden Markov model (HMM) developed for predicting breeding values of a biomarker (somatic cell score) that helps individual probabilities of health and disease (mastitis) ( Detilleux et al., 2007)

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( Budeli et al. , 2009) REML model (Repeatability animal model) as follows: y = Xb + Za + Wc + e y = vector of normalized tick count observations b =vector of fixed effects of contemporary group and age a =vector of random direct additive genetic effects of the animals c =vector of random permanent environmental effects E = vector of random residual effects unique to each observation X, Z & W= incidence matrices relating the fixed and random effects respectively in observations in y Genetic variation for tick count exists in the Bonsmara cattle & Genetic selection for tick resistance is feasible even though genetic progress may be slow

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Consideration of breeding programme for enhanced resistance Need to genetically improve resistance has to be established . The benefit of archiving improved resistance including epidemiological benefits need to be assed. (Bishop et al. , 2011) Davies et al (2009) describe an approach in which diseases are ranked in terms of their importance and also in terms of their amenability to genetic selection

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Quantitative genetic approaches to breeding for genetic resistance to disease in cattle Additive genetic variation between breeds Additive genetic variation within breeds Non-additive genetic variation between breeds (Heterosis) Non-additive genetic variation within breed (Inbreeding)

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Steps to get disease resistance strains

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Transgenic animal posses various resistance to BVD, Rabies, FMD, Brucellosis, Mycoplasmal pneumonia, Pseudorabies , IBR on its Progeny ( Adison et al., 1989) Annie (2000) GEM (2005)

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Constraints in selection for disease resistance Microbes can change their genetic makeup much faster than we can change the host’s genetic ability to resist them.

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Genetic correlation between production and disease resistance are often antagonstic . Milk yield in cattle has antagnostic correlation with metabolic, physiologic and metabolic disease traits. (Van Drop et al., 1998) Animals bred for resistance to one pathogen are also more resistant to another pathogen. Data regarding the immune response, disease resistance and economic production traits are not always available.

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It is not easy to select for disease resistance to more than few diseases as it could potentially remove considerable selection pressure from existing selection goals. (Bishop et al.,2011) Improve reference populations for prediction of genomically enhanced genetic merit, especially for low heritability health and functional traits, such as somatic cell count, mastitis, calving difficulty, longevity and female fertility. ( João Dürr et al., 2011)

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Project in India… Project Title Nodal Officer Principle Investigator Identification of mastitis resistant cattle and buffaloes NDRI Dr. I. D. Gupta Exploration of DNA markers for resistance/ susceptibility to mastitis in crossbred cattle IVRI Dr. B. Bhushan Study of genetic variability in the Bovine Cytokines ( Bos indicus ) NBAGR Dr. J. Behl Delineating polymorphism and evolution of Toll like receptors in Indian native ( Bos indicus ) cattle breeds. NBAGR Dr. M. Sodhi

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Characterization available genetic resources Genetic enhancement and prebreeding Functional genomics, proteomics, phenomics etc. Gene mining Molecular breeding through tools like marker-aided selection and gene stacking Customized genetic engineering (development of trait-specific transgenics) (ICAR Vison 2030) Future Prospective…

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Conclusion Long period of selection on production most functional traits leads to deterioration up to a critical point and it need to be restored Genomic selection with phenotypic recording offers new opportunities in genetic progress New techniques and sources could be used to generate new data for best selection of cattle for disease resistance without compromising production efficiency and product quality.

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THANK YOU