Integrating genetic and genomic analyses of combined health data across ecotypes to improve disease resistance in indigenous African chickens

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cg.authorship.typesCGIAR and advanced research instituteen
cg.contributor.affiliationUniversity of Edinburghen
cg.contributor.affiliationScotland's Rural Collegeen
cg.contributor.affiliationCentre for Tropical Livestock Genetics and Healthen
cg.contributor.affiliationUniversity of Londonen
cg.contributor.affiliationUniversity of Nottinghamen
cg.contributor.affiliationUniversity of Liverpoolen
cg.contributor.affiliationInternational Livestock Research Instituteen
cg.contributor.affiliationUniversity of Greenwichen
cg.contributor.crpLivestock
cg.contributor.donorBiotechnology and Biological Sciences Research Council, United Kingdomen
cg.contributor.donorDepartment for International Development, United Kingdomen
cg.contributor.donorScottish Governmenten
cg.contributor.donorBill & Melinda Gates Foundationen
cg.coverage.regionAfrica
cg.creator.identifierTadelle Dessie: 0000-0002-1630-0417en
cg.creator.identifierOlivier Hanotte: 0000-0002-2877-4767en
cg.creator.identifierJudy Bettridge: 0000-0002-3917-4660en
cg.howPublishedFormally Publisheden
cg.identifier.doihttps://doi.org/10.3389/fgene.2020.543890en
cg.isijournalISI Journalen
cg.issn1664-8021en
cg.journalFrontiers in Geneticsen
cg.reviewStatusPeer Reviewen
cg.subject.ilriANIMAL BREEDINGen
cg.subject.ilriANIMAL DISEASESen
cg.subject.ilriCHICKENSen
cg.subject.ilriGENETICSen
cg.subject.ilriINDIGENOUS BREEDSen
cg.subject.ilriPOULTRYen
cg.volume11en
dc.contributor.authorBanos, Giorgiosen
dc.contributor.authorLindsay, V.en
dc.contributor.authorDesta, T.T.en
dc.contributor.authorBettridge, Judy M.en
dc.contributor.authorSánchez Molano, E.en
dc.contributor.authorVallejo Trujillo, Adrianaen
dc.contributor.authorMatika, O.en
dc.contributor.authorDessie, Tadelleen
dc.contributor.authorWigley, P.en
dc.contributor.authorChristley, Robert M.en
dc.contributor.authorKaiser, P.en
dc.contributor.authorHanotte, Olivier H.en
dc.contributor.authorPsifidi, A.en
dc.date.accessioned2020-11-26T08:42:41Zen
dc.date.available2020-11-26T08:42:41Zen
dc.identifier.urihttps://hdl.handle.net/10568/110306
dc.titleIntegrating genetic and genomic analyses of combined health data across ecotypes to improve disease resistance in indigenous African chickensen
dcterms.abstractPoultry play an important role in the agriculture of many African countries. The majority of chickens in sub-Saharan Africa are indigenous, raised in villages under semi-scavenging conditions. Vaccinations and biosecurity measures rarely apply, and infectious diseases remain a major cause of mortality and reduced productivity. Genomic selection for disease resistance offers a potentially sustainable solution but this requires sufficient numbers of individual birds with genomic and phenotypic data, which is often a challenge to collect in the small populations of indigenous chicken ecotypes. The use of information across-ecotypes presents an attractive possibility to increase the relevant numbers and the accuracy of genomic selection. In this study, we performed a joint analysis of two distinct Ethiopian indigenous chicken ecotypes to investigate the genomic architecture of important health and productivity traits and explore the feasibility of conducting genomic selection across-ecotype. Phenotypic traits considered were antibody response to Infectious Bursal Disease (IBDV), Marek's Disease (MDV), Fowl Cholera (PM) and Fowl Typhoid (SG), resistance to Eimeria and cestode parasitism, and productivity (body weight and body condition score (BCS)). Combined data from the two chicken ecotypes, Horro (n=384) and Jarso (n=376), were jointly analysed for genetic parameter estimation, genome-wide association studies (GWAS), genomic breeding value (GEBVs) calculation, genomic predictions, whole-genome sequencing (WGS), and pathways analyses. Estimates of across-ecotype heritability were significant and moderate in magnitude (0.22-0.47) for all traits except for SG and BCS. GWAS identified several significant genomic associations with health and productivity traits. The WGS analysis revealed putative candidate genes and mutations for IBDV (TOLLIP, ANGPTL5, BCL9, THEMIS2), MDV (GRM7), SG (MAP3K21), Eimeria (TOM1L1) and cestodes (TNFAIP1, ATG9A, NOS2) parasitism, which warrant further investigation. Reliability of GEBVs increased compared to within-ecotype calculations but accuracy of genomic prediction did not, probably because the genetic distance between the two ecotypes offset the benefit from increased sample size. However, for some traits genomic prediction was only feasible in across-ecotype analysis. Our results generally underpin the potential of genomic selection to enhance health and productivity across-ecotypes. Future studies should establish the required minimum sample size and genetic similarity between ecotypes to ensure accurate joint genomic selection.en
dcterms.accessRightsOpen Access
dcterms.audienceScientistsen
dcterms.available2020-10-09en
dcterms.bibliographicCitationBanos, G., Lindsay, V., Desta, T.T., Bettridge, J., Sanchez-Molano, E., Vallejo-Trujillo, A., Matika, O., Dessie, T., Wigley, P., Christley, R.M., Kaiser, P., Hanotte, O. and Psifidi, A. 2020. Integrating genetic and genomic analyses of combined health data across ecotypes to improve disease resistance in indigenous African chickens. Frontiers in Genetics 11:543890.en
dcterms.issued2020-10-09en
dcterms.languageen
dcterms.licenseCC-BY-4.0
dcterms.publisherFrontiers Mediaen
dcterms.subjectchickensen
dcterms.subjectgeneticsen
dcterms.subjectindigenous breedsen
dcterms.subjectpoultryen
dcterms.subjectanimal diseasesen
dcterms.subjectanimal breedingen
dcterms.typeJournal Article

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