Extent of Linkage Disequilibrium and Effective Population Size in Four South African Sanga Cattle Breeds

Abstract

Knowledge on the extent of linkage disequilibrium (LD) in livestock populations is essential to determine the minimum distance between markers required for effective coverage when conducting genome-wide association studies (GWAS). This study evaluated the extent of LD, persistence of allelic phase and effective population size (Ne) for four Sanga cattle breeds in South Africa including the Afrikaner (n = 44), Nguni (n = 54), Drakensberger (n = 47), and Bonsmara breeds (n = 46), using Angus (n = 31) and Holstein (n = 29) as reference populations. We found that moderate LD extends up to inter-marker distances of 40–60 kb in Angus (0.21) and Holstein (0.21) and up to 100 kb in Afrikaner (0.20). This suggests that genomic selection and association studies performed within these breeds using an average inter-marker r2≥ 0.20 would require about 30,000–50,000 SNPs. However, r2≥ 0.20 extended only up to 10–20 kb in the Nguni and Drakensberger and 20–40 kb in the Bonsmara indicating that 75,000 to 150,000 SNPs would be necessary for GWAS in these breeds. Correlation between alleles at contiguous loci indicated that phase was not strongly preserved between breeds. This suggests the need for breed-specific reference populations in which a much greater density of markers should be scored to identify breed specific haplotypes which may then be imputed into multi-breed commercial populations. Analysis of effective population size based on the extent of LD, revealed Ne = 95 (Nguni), Ne = 87 (Drakensberger), Ne = 77 (Bonsmara), and Ne = 41 (Afrikaner). Results of this study form the basis for implementation of genomic selection programs in the Sanga breeds of South Africa.

Insight into the genetic composition of South African Sanga cattle using SNP data from cattle breeds worldwide

“…Genotypes for four South African Sanga cattle breeds [Afrikaner—AFR (n = 36), Nguni—NGU (n = 50), Drakensberger—DRA (n = 47) and Bonsmara—BON (n = 44)] originated from previous studies [2, 3, 6]. They were generated using the Illumina BovineSNP50 BeadChip v2, which features 54,609 SNPs distributed across the bovine genome with an average spacing of 49.9 kb [16].…”

“…Therefore, they may hold potential for production in harsh and fluctuating South African environments based on their adaptation to the nutritional, parasitic, and pathogenic challenges they are faced with. These breeds are not endangered and have reasonable effective population sizes [4–6]. Given their adaptive characteristics, they are potentially valuable to breeding programs in other regions that face biological stresses such as famine, drought or disease epidemics [7].…”

On the Extent of Linkage Disequilibrium in the Genome of Farm Animals

“…As shown in Figure 4, the distribution of allele frequency drawn from sequence data is a decreasing function that involves a sizable fraction of infrequent alleles. In contrast, frequency distribution in genotyping arrays is rather an increasing function, as SNPs were mainly ascertained aiming at frequent alleles and coverage of the genome during the establishment of the array (also see Fu et al, 2015 andMakina et al, 2015). Given that LD, as measured by r 2 depends on allele frequencies, the difference between the studies is partially due to the biased SNPs selection on the genotyping arrays.…”

Next‐generation metrics for monitoring genetic erosion within populations of conservation concern

“…The linkage disequilibrium (LD) approach, which uses the correlation between alleles at different loci to estimate N e , reflects the inbreeding effective population size in the previous generation when considering unlinked loci (Hare et al, 2011), or even over a longer time-period, when considering linked loci. This property makes it very useful in recently declining or isolated populations, and has been increasingly used in various species (Kijas et al, 2014;Makina et al, 2015;Pazmiño, Maes, Simpfendorfer, Salinas-de-León, & van Herwerden, 2017;Plomion et al, 2014). Hollenbeck, Portnoy, and Gold (2016) used an extension of linkage disequilibrium to estimate N e over a range of time points using SNP genotype data from a single sample per population.…”