A simple genetic architecture underlies morphological variation in dogs

Abstract

Domestic dogs exhibit tremendous phenotypic diversity, including a greater variation in body size than any other terrestrial mammal. Here, we generate a high density map of canine genetic variation by genotyping 915 dogs from 80 domestic dog breeds, 83 wild canids, and 10 outbred African shelter dogs across 60,968 single-nucleotide polymorphisms (SNPs). Coupling this genomic resource with external measurements from breed standards and individuals as well as skeletal measurements from museum specimens, we identify 51 regions of the dog genome associated with phenotypic variation among breeds in 57 traits. The complex traits include average breed body size and external body dimensions and cranial, dental, and long bone shape and size with and without allometric scaling. In contrast to the results from association mapping of quantitative traits in humans and domesticated plants, we find that across dog breeds, a small number of quantitative trait loci (< or = 3) explain the majority of phenotypic variation for most of the traits we studied. In addition, many genomic regions show signatures of recent selection, with most of the highly differentiated regions being associated with breed-defining traits such as body size, coat characteristics, and ear floppiness. Our results demonstrate the efficacy of mapping multiple traits in the domestic dog using a database of genotyped individuals and highlight the important role human-directed selection has played in altering the genetic architecture of key traits in this important species.

Figure 1. Analysis of 10 individuals from each of 59 breeds, one population of village dogs, and wolves.

(A) LD decay curves based on mean r², including mean LD decay when dogs are selected from 10 different breeds (“between breed” LD). (B) Distribution of long runs of homozygosity in each group. (C) Number of haplotypes across all autosomal 15-SNP windows and number of autozygous individuals per breed at each genomic position computed using 10 individuals per breed. Each window can contain 1–20 different haplotypes and each genomic position can have 0–10 individuals appearing autozygous.

Figure 2. F_ST for each SNP across the 79 CanMap breeds.

Red indicates SNPs with known associations to morphological traits (dark red to fur traits). Mean F_ST was 0.28 (SNPs with F_STs between 0.2 and 0.4 are not plotted here).

Figure 3. Genome-wide association scans across the breeds using allele frequencies of the SNPs and breed-average phenotypes for (A) log(body weight), (B) ear erectness (floppy versus erect ears), and (C) allometric snout length.

The p values of the SNPs were computed using the linear mixed model method for (A and C) and weighted permutation method for (B). SNPs passing Bonferroni correction are marked with orange circles; SNPs included in best-fit predictive models are marked with gray dashes. P-P plots for the scans are shown in the right-hand column. (D) Matrix showing phenotype identity (upper diagonal) is uncorrelated with genome-wide IBS (lower diagonal) between dog breeds for body weight and ear type. Genome-wide IBS is plotted as a scaled value where 0 corresponds to the lowest amount of IBS between any two breeds (0.62) and 1 corresponds to the highest amount of IBS (0.83). Boxers are not shown since their IBS values are low in comparison to other breeds due to the SNP ascertainment bias on the array.

Figure 4. Summary of associations across genomic regions for multiple traits.

Each row corresponds to a trait (either absolute or proportional), and each column corresponds to a genomic region that has been found associated with at least one trait. The shading of each rectangle shows the R² statistic of the single marker model for the trait for all significant associations (p<5.0e-5 for absolute external traits, p<1.0e-4 for skeletal and proportional traits after correcting for population structure). When multiple SNPs in the region are significant, the largest value of the R² statistics is reported.

Figure 5. Correlation between observed and predicted log(body weight) using regression models based on breed-average data.

Plots show correlation with observed breed-average values (1st column), 249 individually phenotyped breed dogs (2nd column), and 50 non-breed village dogs with individual measurements. (A) The predictive model using a single SNP, CFA15.44226659; (B–D) the predictive models using 2, 3, and 6 top SNPs (in order after CFA15.44226659, CFAX.106866624, CFA4.42351982, CFAX.86813164, CFA10.11440860, and CFA7.46842856).