Mapping of quantitative trait loci controlling trypanotolerance in a cross of tolerant West African N'Dama and susceptible East African Boran cattle

Abstract

Trypanosomosis, or sleeping sickness, is a major disease constraint on livestock productivity in sub-Saharan Africa. To identify quantitative trait loci (QTL) controlling resistance to trypanosomosis in cattle, an experimental cross was made between trypanotolerant African N'Dama (Bos taurus) and trypanosusceptible improved Kenya Boran (Bos indicus) cattle. Sixteen phenotypic traits were defined describing anemia, body weight, and parasitemia. One hundred seventy-seven F2 animals and their parents and grandparents were genotyped at 477 molecular marker loci covering all 29 cattle autosomes. Total genome coverage was 82%. Putative QTL were mapped to 18 autosomes at a genomewise false discovery rate of <0.20. The results are consistent with a single QTL on 17 chromosomes and two QTL on BTA16. Individual QTL effects ranged from Ϸ6% to 20% of the phenotypic variance of the trait. Excluding chromosomes with ambiguous or nontrypanotolerance effects, the allele for resistance to trypanosomosis originated from the N'Dama parent at nine QTL and from the Kenya Boran at five QTL, and at four QTL there is evidence of an overdominant mode of inheritance. These results suggest that selection for trypanotolerance within an F2 cross between N'Dama and Boran cattle could produce a synthetic breed with higher trypanotolerance levels than currently exist in the parental breeds.

Genetic and expression analysis of cattle identifies candidate genes in pathways responding to Trypanosoma congolense infection

“…Hanotte et al (6) localized QTL for parasitemia, body weight, and packed cell volume after T. congolense infection in a Boran × N'Dama F2 cross. We used four strategies to identify candidate quantitative trait genes (QTG) within the large QTL regions.…”

“…Ten major quantitative trait loci (QTL) that control trypanotolerance in cattle have been genetically mapped by our group using an F2 cross between Boran (zebu) and N'Dama cattle (6). However, the positions of the loci have been resolved only to within ±20 cm and lie in gene-rich regions that lack obvious functional candidates, or contain numerous genes with similar potential.…”

Serial analysis of gene expression (SAGE) in bovine trypanotolerance: preliminary results

“…II). Furthermore, the T cell receptor beta cluster is located in the bovine chromosome 4 (Bta4) in the 4q3.1 and 4q3.6 region (IDVGA51-TGLA159/MGTG4B) where Hanotte et al [14] described a QTL strongly associated with the fewer parasites trait (PARMLn) in N'Dama. We also found genes encoding cytokines such as interleukins (IL1 and IL10R) confirming their role in the induction of a cell polyclonal activation, particularly for IgM antibodies [26].…”

“…Neither zootechnical studies [8,9,16,15,23], nor quantitative genetics approaches [39], nor the electrophoretic analysis of targeted proteins [32,31], nor MHC typing [21] have brought significant progress on the trypanotolerance understanding. QTL studies developed more recently, firstly on mice [17][18][19] then on bovine [14] give some more interesting results but they are restricted to small parts of the cattle genome. Considering the limited number of experimental animals used, the confidence interval of the bovine QTLs is too wide to be useful in a marker assisted selection (MAS) programme, or in a positional candidate approach.…”

African Trypanosomiasis

“…52 A unique F 2 resource population derived from trypanotolerant N'Dama bulls crossed with susceptible Boran cows revealed nine QTL influencing the outcome of infection with T. congolense, with a confidence such that more than 90 per cent will have real effects. 53 This cattle-based study was extremely expensive, however, and identified QTL with a relatively low resolution. Nevertheless, trypanotolerance research 44 has, from the start, gained value by comparison with the mouse system, and this synergy continues to grow as the mouse becomes an increasingly powerful model for functional genomics.…”