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 (F ) 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 F 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.

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The main objective of this study was to estimate genetic trends for 3.7% FCM, fat yield, days open, and predicted body weight after calving in six experimental dairy herds owned by the State Farm Division of the North Carolina Department of Agriculture. Body weights were predicted from heart girths measured at or before the first test day after calving. Data analyzed were 23,052 records from 8575 cows, daughters of 681 bulls. Heritabilities and breeding values were estimated using the multiple-trait, derivative-free REML programs and a single-trait repeatability model. Breeding values of cows were averaged by and regressed on birth year to estimate genetic trends. Genetic correlations between traits were estimated by correlating breeding values. Estimates of heritability were 0.25 for 3.7% FCM, 0.28 for fat yield, 0.03 for days open, and 0.17 for predicted body weight. Unfavorable genetic relationships were found between yields and days open and between yields and body weight. Genetically, cows that were heavier after calving produced less milk and fat but conceived earlier than smaller cows. Genetic changes in yields and days open were greater for cows born after 1970, but the greatest genetic changes were after 1980 (FCM, 94.7 kg/yr; fat yield, 3.46 kg/yr; days open, 1.1 d/yr). Breeding values for body weight increased for cows born from 1950 to 1970, decreased until 1980, and increased for later parities. The results of our study suggest that AI organizations may need to include fertility traits in progeny testing and relax the emphasis on increased body weight.