Combining landscape genomics and ecological modelling to investigate local adaptation of indigenous Ugandan cattle to East Coast fever

Abstract

63East Coast fever (ECF) is a fatal sickness affecting cattle populations of eastern, central, 64and southern Africa. The disease is transmitted by the tick Rhipicephalus appendiculatus, and 65 caused by the protozoan Theileria parva parva, which invades host lymphocytes and promotes 66 their clonal expansion. Importantly, indigenous cattle show tolerance to infection in ECF-67 endemically stable areas. Here, the putative genetic bases underlying ECF-tolerance were 68 investigated using molecular data and epidemiological information from 823 indigenous cattle 69 from Uganda. Vector distribution and host infection risk were estimated over the study area and 70 subsequently tested as triggers of local adaptation by means of landscape genomics analysis. We 71 identified 41 and seven candidate adaptive loci for tick resistance and infection tolerance, 72 respectively. Among the genes associated with the candidate adaptive loci are PRKG1 and SLA2. 73 PRKG1 was already described as associated with tick resistance in indigenous South African 74 cattle, due to its role into inflammatory response. SLA2 is part of the regulatory pathways 75 involved into lymphocytes' proliferation. Additionally, local ancestry analysis suggested the 76 zebuine origin of the genomic region candidate for tick resistance. 77 Author summary 78The tick-borne parasite Theileria parva parva infects cattle populations of eastern, central 79 and southern Africa, by causing a highly fatal pathology called "East Coast fever". The disease is 80 especially severe for the exotic breeds imported to Africa, as well as outside the endemic areas of 81 East Africa. In these regions, indigenous cattle populations can survive to infection, and this 82 5 tolerance might result from unique adaptations evolved to fight the disease. We investigated this 83 hypothesis by using a method named "landscape genomics", with which we compared the genetic 84 characteristics of indigenous Ugandan cattle coming from areas at different infection risk, and 85 located genomic sites potentially attributable to tolerance. In particular, the method pinpointed 86 two genes, one (PRKG1) involved into inflammatory response and potentially affecting East 87Coast fever vector attachment, the other (SLA2) involved into lymphocytes proliferation, a 88 process activated by T. parva parva infection. Our findings can orientate future research on the 89 genetic basis of East Coast fever-tolerance, and derive from a general method that can be applied 90 to investigate adaptation in analogous host-vector-parasite systems. Characterization of the 91 genetic factors underlying East Coast-fever-tolerance represents an essential step towards 92 enhancing sustainability and productivity of local agroecosystems. 93

Rapid identification and interpretation of gene–environment associations using the new R.SamBada landscape genomics pipeline

“…sam β ada is written in C++ with a particular emphasis on high‐performance computing (HPC). Since its publication, sam β ada , as applied alone or in combination with other methods, proved useful to target putative genomic regions underlying local adaptation in a wide variety of species, including domestic animals such as swine and cattle (Cesconeto et al, ; Vajana et al, ), wild animals such as the freshwater sculpin and European pond turtle (Lucek, Keller, Nolte, & Seehausen, ; Pereira, Teixeira, & Velo‐Antón, ), and many different plant species including the European beech and the cow‐tail fir (Cuervo‐Alarcon et al, ; Shih, Chang, Chung, Chiang, & Hwang, ).…”

Combining Landscape Genomics and Ecological Modelling to Investigate Local Adaptation of Indigenous Ugandan Cattle to East Coast Fever

“…The authors are grateful to the members of the NextGen Consortium, and to Graeme S. Cumming, who kindly provided the R. appendiculatus occurrence dataset used in the present study. A previous version of this manuscript appeared as a chapter of the Ph.D. thesis by Vajana ( 2017 ), and was released as pre-print to bioRχiv (Vajana et al, 2018 ).…”

Agroecologies defined by species distribution models improve model fit of genotype by environment interactions to identify the best performing chicken breeds for smallholder systems

“…The realized niche is smaller and refers to those parts of the fundamental niche where the species can survive despite the presence of competitors or negative interactions. In recent years, the conceptual framework for SDMs has been extended by livestock scientists and used to identify environmental predictors associated with habitat suitability, productive performance (e.g., growth rate), population differentiation, and local adaptation in livestock populations (Vajana et al, 2018;Vallejo-Trujillo et al, 2018;Lozano-Jaramillo, 2019;Lozano-Jaramillo et al, 2019b;Gheyas et al, 2021;Kebede et al, 2021;Gebru et al, 2022;Trujillo et al, 2022) performed SDMs to calculate the percentage area suitable for improved chicken breeds across administrative (political) boundaries of Ethiopia. Spatial modeling improves genetic evaluation of livestock for smallholders (Selle et al, 2020) and is applicable for definition of agroecologies in multienvironment performance testing of candidate breeds.…”