Browsing by Author "Mapholi, Ntanganedzeni Olivia"

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    Exploring genetic architecture of tick resistance in South African Nguni cattle
    (Stellenbosch : Stellenbosch University, 2015-12) Mapholi, Ntanganedzeni Olivia; Dzama, Kennedy; Maiwashe, A.; MacNeil, M. D.; Stellenbosch University. Faculty of Agrisciences. Dept. of Animal Sciences.
    ENGLISH ABSTRACT: The broad objective of this study was to identify single nucleotide polymorphisms (SNP) markers associated with tick resistance in South African Nguni cattle and it was addressed by three specific objectives. The first objective was to assess tick load and prevalence in Nguni cattle in different agro-climatic regions of South Africa using tick count data collected monthly from 586 Nguni cattle reared under natural grazing conditions, over two years. Tick counts were assessed under natural challenge at ARC Roodeplaat and Loskop farms (warm climate), and Mukhuthali Nguni Community and University of Fort Hare farms (cool climate). The second objective was to estimate genetic parameters for tick counts in Nguni cattle. The third objective was to identify SNPs associated with tick resistance in Nguni cattle. Counts for each tick species were conducted on each animal in the herd once a month on different body locations, including the head, ears, neck, back, legs, belly, perineum and tail. Distribution of counts was determined using the PROC FREQ (SAS, 2002 - 2010). The tick counts were then analysed with the PROC GLM procedure using the two fixed effect models. Genetic parameters for log-transformed counts were estimated from univariate animal and sire models and bivariate sire models using the ASREML program. Animals were genotyped using Illumina BovineSNP50K assay. After Quality Control (call rate >90%, minor allele frequency > 0.02), 40 436 SNPs were retained for analysis. Association analysis for tick resistance was carried out using two approaches: genome-wide association (GWA) analysis using the GenABEL package and a Regional Heritability Mapping (RHM) analysis. Six tick species were identified: Amblyomma hebraeum (42%), Rhipicephalus evertsi evertsi (22%), Rhipicephalus (Boophilus) spp. (16%), Rhipicephalus appendiculatus (11%), Hyalomma marginatum (5%) and Rhipicephalus simus (4%). Tick infestation was significantly affected by location, season, year, month of counting and age of the animal. Loskop farm, as the warmest location, had the highest tick counts and also showed the largest variation in tick loads. Higher tick counts were also observed in the hot-dry (September to November) and hot-wet (December to February) seasons compared to the other seasons. Amblyomma hebraeum was the dominant tick species across all four locations. Heritability estimates for tick count varied according to season and trait (body part or tick species) and ranged from 0.01±0.01 to 0.26±0.01. Genetic correlations ranged from -0.79±0.33 to 1.00±0.00 among counts for different body parts and 0.00±0.00 to 0.99±0.00 among tick species. Phenotypic correlations ranged from 0.06±0.01 to 0.72±0.01 among body parts and 0.01±0.02 to 0.44±0.01 for tick species. Whole body count was highly correlated to the perineum and the belly. These two traits appear to be the most suitable surrogates for whole body count. Several genomic regions of interest were identified for different traits by both the GWA and RHM approaches. Three genome-wide significant regions on chromosomes 7, 10 and 19 were identified for total tick count on the head, total A. hebraeum ticks and for total number of A. hebraeum in the perineum region. Suggestive significant regions were identified on chromosomes 1, 3, 6, 7, 8, 10, 11, 12, 14, 15, 17, 19 and 26 for several of the tick traits analysed. The GWA approach identified more genomic regions than did the RHM approach. These findings provide information that would be useful in developing strategies for genetic improvement of tick resistance through selection. The chromosome regions identified as harbouring quantitative trait loci (QTL) underlying variation in tick burden form the basis for further analyses to identify specific candidate genes related to cattle tick resistance and provide the potential for marker-assisted selection in Nguni.