Detecting genetic signatures of selection is of great interest for many research issues. Common approaches to separate selective from neutral processes focus on the variance of F_ST across loci, as does the original Lewontin and Krakauer (LK) test. Modern developments aim to minimize the false positive rate and to increase the power, by accounting for complex demographic structures. Another stimulating goal is to develop straightforward parametric and computationally tractable tests to deal with massive SNP data sets. Here, we propose an extension of the original LK statistic (T_LK), named T_F–LK, that uses a phylogenetic estimation of the population's kinship ( ) matrix, thus accounting for historical branching and heterogeneity of genetic drift. Using forward simulations of single-nucleotide polymorphisms (SNPs) data under neutrality and selection, we confirm the relative robustness of the LK statistic (T_LK) to complex demographic history but we show that T_F–LK is more powerful in most cases. This new statistic outperforms also a multinomial-Dirichlet-based model [estimation with Markov chain Monte Carlo (MCMC)], when historical branching occurs. Overall, T_F–LK detects 15–35% more selected SNPs than T_LK for low type I errors (P < 0.001). Also, simulations show that T_LK and T_F–LK follow a chi-square distribution provided the ancestral allele frequencies are not too extreme, suggesting the possible use of the chi-square distribution for evaluating significance. The empirical distribution of T_F–LK can be derived using simulations conditioned on the estimated matrix. We apply this new test to pig breeds SNP data and pinpoint outliers using T_F–LK, otherwise undetected using the less powerful T_LK statistic. This new test represents one solution for compromise between advanced SNP genetic data acquisition and outlier analyses.

We herein report new evidence that the QTL effect on chromosome 20 in Finnish Ayrshire can be explained by variation in two distinct genes, growth hormone receptor (GHR) and prolactin receptor (PRLR). In a previous study in Holstein–Friesian dairy cattle an F279Y polymorphism in the transmembrane domain of GHR was found to be associated with an effect on milk yield and composition. The result of our multimarker regression analysis suggests that in Finnish Ayrshire two QTL segregate on the chromosomal region including GHR and PRLR. By sequencing the coding sequences of GHR and PRLR and the sequence of three GHR promoters from the pooled samples of individuals of known QTL genotype, we identified two substitutions that were associated with milk production traits: the previously reported F-to-Y substitution in the transmembrane domain of GHR and an S-to-N substitution in the signal peptide of PRLR. The results provide strong evidence that the effect of PRLR S18N polymorphism is distinct from the GHR F279Y effect. In particular, the GHR F279Y has the highest influence on protein percentage and fat percentage while PRLR S18N markedly influences protein and fat yield. Furthermore, an interaction between the two loci is suggested.

The genetic architecture of behavioral traits in dogs is of great interest to owners, breeders, and professionals involved in animal welfare, as well as to scientists studying the genetics of animal (including human) behavior. The genetic component of dog behavior is supported by between-breed differences and some evidence of within-breed variation. However, it is a challenge to gather sufficiently large datasets to dissect the genetic basis of complex traits such as behavior, which are both time-consuming and logistically difficult to measure, and known to be influenced by nongenetic factors. In this study, we exploited the knowledge that owners have of their dogs to generate a large dataset of personality traits in Labrador Retrievers. While accounting for key environmental factors, we demonstrate that genetic variance can be detected for dog personality traits assessed using questionnaire data. We identified substantial genetic variance for several traits, including fetching tendency and fear of loud noises, while other traits revealed negligibly small heritabilities. Genetic correlations were also estimated between traits; however, due to fairly large SEs, only a handful of trait pairs yielded statistically significant estimates. Genomic analyses indicated that these traits are mainly polygenic, such that individual genomic regions have small effects, and suggested chromosomal associations for six of the traits. The polygenic nature of these traits is consistent with previous behavioral genetics studies in other species, for example in mouse, and confirms that large datasets are required to quantify the genetic variance and to identify the individual genes that influence behavioral traits.

Hip dysplasia remains one of the most serious hereditary diseases occurring in dogs despite long-standing evaluation schemes designed to aid selection for healthy joints. Many researchers have recommended the use of estimated breeding values (EBV) to improve the rate of genetic progress from selection against hip and elbow dysplasia (another common developmental orthopaedic disorder), but few have empirically quantified the benefits of their use. This study aimed to both determine recent genetic trends in hip and elbow dysplasia, and evaluate the potential improvements in response to selection that publication of EBV for such diseases would provide, across a wide range of pure-bred dog breeds.