The genetic structure and diversity of smallholder dairy cattle in Rwanda

Abstract

Previous genomic characterisation of Rwanda dairy cattle predominantly focused on the One Cow per Poor Family (locally called "Girinka") programme. However, smallholder farmers in Rwanda have benefited from other livestock initiatives and development programmes. Capturing and documenting the genetic diversity, is critical in part as a key contribution to genomic resource required to support dairy development in Rwanda. A total of 2,229 crossbred animals located in all dairy-producing regions of Rwanda were sampled. For each animal, a hair sample was collected and genotyped by using the Geneseek Genomic Profiler (GGP, Neogen Geneseek^®) Bovine 50 K (n = 1,917) and GGP Bovine 100 K arrays (n = 312). The combined dataset was subject to quality control, data curation for use in population genetics and genomic analyses. To assess the genetic structure and diversity of the current population, key analyses for population structure were applied: Principal Component Analysis (PCA), population structure and diversity, admixture analysis, measures of heterozygosity, runs of homozygosity (ROH) and minor allelic frequency (MAF). A dataset of global dairy population of European taurine, African indicus and African taurus (n = 250) was used as reference. Results showed that Rwanda cattle population is highly admixed of diverse pure and crossbred animals with average MAF of 33% (standard error; se = 0.001) with proportion of foreign high yielding (taurine) dairy breeds of Jersey Island (18%); 12% non-Island Jersey and 42% Holstein-Friesian ancestries. Two African Bos taurus and five Bos indicus breeds contributed 28% of their genetics. Genetic distances were highest in Gir and N'dama (0.29); and Nelore and N'dama (0.29). There were 1,331 ROH regions and average heterozygosity were high for Rwanda cattle (0.41 se = 0.001). Asides well-established genes in cattle, we found evidence for a variety of novel and less-known genes under selection to be associated with fertility, milk production, innate immunity and environmental adaptation. This observed diversity offers opportunity to decipher the presence and/or lack of genetic variations to initiate short- and long-term breed improvement programmes for adaptation traits, disease resistance, heat tolerance, productivity and profitability of smallholder dairy systems in Rwanda.

Keywords: Admixture; Runs of homozygosity; Rwanda population structure; SNP arrays; Smallholder dairy.

Fig. 1

Relationship between herd size and breed composition of animals (A) and percentage distribution of foreign high yielding (exotic) dairy and indigenous breeds to the Rwanda population (B)

Fig. 2

Principal component analysis of Rwanda cattle vs. global reference population

Fig. 3

UMAP presentation plot of Rwanda cattle vs. global reference population (A) and weighted principal component analysis (WPCA) of Rwanda cattle vs. global reference population (B). A and B provides a more detailed representation of diversity and relationships among the studied populations when compared to the conventional PCA plot in Fig. 2

Fig. 4

Admixture bar plots showing breed proportions and introgression at selected and assumed ancestry assignment clusters K (5, 7, 9 and 11). Each horizontal bar from left to right, represents Ankole (ANK), east African shorthorn zebu (EAZ_SH), Gir (GIR), Holstein (HOL), non-Island Jersey (JER), Island Jersey (Jersey-JI), N’dama (NDG), Nellore (NEL), Rwanda (RWA), Sheko (SHK) and Sahiwal (SHW). The proportion of the bar in each of the k cluster colours corresponds to the average posterior likelihood that the individual is assigned to the cluster indicated by that colour

Fig. 5

Phylogenetic tree showing relationships between reference populations and Rwanda cattle. Breeds are labelled as; Ankole (ANK), east African shorthorn zebu (EAZ_SH), Gir (GIR), Holstein (HOL), non-Island Jersey (JER), Island Jersey (JER_JI), N’dama (NDG), Nellore (NEL), Rwanda (RWA), Sheko (SHK) and Sahiwal (SHW). The red boxes illustrate clusters or subpopulations of cattle breeds represented in the studied population from Rwanda

Fig. 6

Violin plot showing genomic inbreeding coefficient detected for the populations where each coloured violin represents a cattle population

Fig. 7

Sum length of ROH (in mega bases) across the chromosomes in studied population. High peaks and higher sum length (in mega bases) at chromosomes 5 and 20 can be observed in the ROH regions

Fig. 8

Manhattan plot of counts of SNPs occurrences of a SNP by chromosomes in ROHs across individuals in the population

Fig. 9

Evolution of crossbreeding over 15-year (2005–2020) period across the reported provinces in Rwanda (A). Ankole breed was mainly used for crossbreeding with Holstein-Friesian, Jersey breeds than with other breeds in the Eastern (B), Northern (C) and Southern (D) provinces