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

Abstract

Given the importance of linkage disequilibrium (LD) in gene mapping and evolutionary inferences, I characterize in this review the pattern of LD and discuss the influence of human intervention during domestication, breed establishment, and subsequent genetic improvement on shaping the genome of livestock species. To this end, I summarize data on the profile of LD based on array genotypes vs. sequencing data in cattle and chicken, two major livestock species, and compare to the human case. This comparison provides insights into the real dimension of the pairwise allelic correlation and haplo-block structuring. The dependency of LD on allelic frequency is pictured and a recently introduced metric for moderating it is outlined. In the context of the contact farm animals had with human, the impact of genetic forces including admixture, mutation, recombination rate, selection, and effective population size on LD is discussed. The review further highlights the interplay of LD with runs of homozygosity and concludes with the operational implications of the widely used association and selection mapping studies in relation to LD.

Keywords: allele frequency spectrum (AFS); association mapping; haplotype block; runs of homozygosity; selection mapping.

Figure 1

Surface plot of the dependency of LD on allelic frequency of SNP pairs. The means of r ² are plotted for 45 bins of 0.01 allele frequency each (from Qanbari et al., 2010a).

Figure 2

The behavior of LD as a function of inter-marker distance (Mb) and MAF interval (dMAF). The estimates of r ² (left panel) and ρ ² (right panel) are depicted as surface plots for SNP loci on chromosome 3 of the Italian Tuscan population in HapMap III (from Gianola et al., 2013).

Figure 3

A schematic representation of decay of LD in domestic chicken. r² values are plotted as a function of pair-wise inter-marker distances based on sequence (Seq) versus SNP50K (Array) data in a population of Lohmann brown layer line. The gray dots represent sequence-based r² plotted for each chromosome separately, whereas LD based on array data was simply averaged genome-wide due to the lack of enough LD estimates in shorter distance bins. The black dashed line is fitted as mean LD in each distance bin across chromosomes. The r² values representing sequence data are estimated for sub-samples of all pairwise estimates in macrochromosomes, but include all SNP by SNP relationships in microchromosomes.

Figure 4

Distribution of allelic frequency in domestic chicken. Histogram compares profile of minor allele frequency between 50K array and sequence data in a population of Lohmann brown layer.

Figure 5

The LD-block structuring as a function of SNP density. (Panel A) displays a LD block of length 29 Kb based on estimates of pair-wise D’ among 13 SNPs located on BTA25 in Fleckvieh cattle. (Panel B) displays LD structure in the same region in sequencing resolution consisting of 115 markers. The LD blocks are obtained using “confidence intervals” algorithm (Gabriel et al., 2002) in Haploview (Barrett et al., 2005). LD analysis has been conducted with a constant number of individuals.

Figure 6

A schematic illustration of historical Ne in chicken. The ancestral demography is inferred in sequence resolution for RJF and white (WL) and brown (BL) layers employing the Pairwise Sequentially Markovian Coalescent [PSMC, Li and Durbin (2011)] framework. The scale on the x-axis is years in the past and the scale on the y-axis represents the historical effective population numbers. Orange (RJF), brown (BL), and cyan (WL) lines represent inferred demography for different populations with bootstraps in lighter colors. Note that inferences of bootstraps are depicted only for one sample of each population.