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One of the main issues in genomic selection was the huge unbalance between number of markers and phenotypes available. In this work, principal component analysis is used to reduce the number of predictors for calculating direct genomic breeding values (DGV) for production and functional traits. 2093 Italian Holstein bulls were genotyped with the 54 K Illumina beadchip, and 39,555 SNP markers were retained after data editing. Principal Components (PC) were extracted from SNP matrix, and 15,207 PC explaining 99% of the original variance were retained and used as predictors. Bulls born before 2001 were included in the reference population, younger animals in the test population. A BLUP model was used to estimate the effect of principal component on deregressed proof (DRPF) for 35 traits and results were compared to those obtained by using SNP genotypes as predictors either with BLUP or with Bayes_A models. Correlations between DGV and DRPF did not substantially differ among the three methods except for milk fat content. The lowest prediction bias was obtained for the method based on the use of principal component. Regression coefficients of DRPF on DGV were lower than one for the approach based on the use of PC and higher than one for the other two methods. The use of PC as predictors resulted in a large reduction of number of predictors (approximately 38%) and of computational time that was approximately 2% of the time needed to estimate SNP effects with the other two methods. Accuracies of genomic predictions were in most of cases only slightly higher than those of the traditional pedigree index, probably due to the limited size of the considered population., (© 2012 Blackwell Verlag GmbH.)

Genomic evaluations for 161,341 Holsteins were computed by using 311,725 of 777,962 markers on the Illumina BovineHD Genotyping BeadChip (HD). Initial edits with 1,741 HD genotypes from 5 breeds revealed that 636,967 markers were usable but that half were redundant. Holstein genotypes were from 1,510 animals with HD markers, 82,358 animals with 45,187 (50K) markers, 1,797 animals with 8,031 (8K) markers, 20,177 animals with 6,836 (6K) markers, 52,270 animals with 2,683 (3K) markers, and 3,229 nongenotyped dams (0K) with >90% of haplotypes imputable because they had 4 or more genotyped progeny. The Holstein HD genotypes were from 1,142 US, Canadian, British, and Italian sires, 196 other sires, 138 cows in a US Department of Agriculture research herd (Beltsville, MD), and 34 other females. Percentages of correctly imputed genotypes were tested by applying the programs findhap and FImpute to a simulated chromosome for an earlier population that had only 1,112 animals with HD genotypes and none with 8K genotypes. For each chip, 1% of the genotypes were missing and 0.02% were incorrect initially. After imputation of missing markers with findhap, percentages of genotypes correct were 99.9% from HD, 99.0% from 50K, 94.6% from 6K, 90.5% from 3K, and 93.5% from 0K. With FImpute, 99.96% were correct from HD, 99.3% from 50K, 94.7% from 6K, 91.1% from 3K, and 95.1% from 0K genotypes. Accuracy for the 3K and 6K genotypes further improved by approximately 2 percentage points if imputed first to 50K and then to HD instead of imputing all genotypes directly to HD. Evaluations were tested by using imputed actual genotypes and August 2008 phenotypes to predict deregressed evaluations of US bulls proven after August 2008. For 28 traits tested, the estimated genomic reliability averaged 61.1% when using 311,725 markers vs. 60.7% when using 45,187 markers vs. 29.6% from the traditional parent average. Squared correlations with future data were slightly greater for 16 traits and slightly less for 12 with HD than with 50K evaluations. The observed 0.4 percentage point average increase in reliability was less favorable than the 0.9 expected from simulation but was similar to actual gains from other HD studies. The largest HD and 50K marker effects were often located at very similar positions. The single-breed evaluation tested here and previous single-breed or multibreed evaluations have not produced large gains. Increasing the number of HD genotypes used for imputation above 1,074 did not improve the reliability of Holstein genomic evaluations., (Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components. Models for capturing different forms of interaction, e.g., chromosome-specific, are presented. Implementations can be carried out using software for likelihood-based or Bayesian inference.

The aim of this paper was to characterize the diversity among haplotypes based on 22 single nucleotide polymorphisms (SNPs) and one deletion within four casein genes in two geographically distant goat populations, the Sicilian Girgentana breed and the Norwegian goat breed. Forty Girgentana goats were genotyped for the aforementioned polymorphisms and the resulting data set was compared with 436 goats from the Norwegian population previously genotyped for these markers. Several casein gene polymorphisms were not in Hardy-Weinberg equilibrium either in Girgentana, or in the Norwegian breed. The SNP haplotype frequencies for the four casein genes were calculated and despite the large geographical distance and phenotypic divergence between these breeds, a proportion of casein loci haplotypes were found to be identical between both Norwegian and Girgentana goats. However, for the CSN2 gene there were no common haplotypes between the two populations. The level of linkage disequilibrium between the casein genes was less in the Girgentana population than in the Norwegian population.

A total of 13,066 first-lactation test-day records of 2,277 Valle del Belice ewes from 17 flocks were used to estimate genetic parameters for somatic cell scores (SCS) and milk production traits, using a repeatability test-day animal model. Heritability estimates were low and ranged from 0.09 to 0.14 for milk, fat, and protein yields, and contents. For SCS, the heritability of 0.14 was relatively high. The repeatabilities were moderate and ranged from 0.29 to 0.47 for milk production traits. The repeatability for SCS was 0.36. Flock-test-day explained a large proportion of the variation for milk production traits, but it did not have a big effect on SCS. The genetic correlations of fat and protein yields with fat and protein percentages were positive and high, indicating a strong association between these traits. The genetic correlations of milk production traits with SCS were positive and ranged from 0.16 to 0.31. The results showed that SCS is a heritable trait in Valle del Belice sheep and that single-trait selection for increased milk production will also increase SCS.

The mitochondrial hypervariable region 1 (HVR1) sequence of 67 goats belonging to the Girgentana, Maltese and Derivata di Siria breeds was partially sequenced in order to present the first phylogenetic characterization of Sicilian goat breeds. These sequences were compared with published sequences of Indian and Pakistani domestic goats and wild goats. Mitochondrial lineage A was observed in most of the Sicilian goats. However, three Girgentana haplotypes were highly divergent from the Capra hircus clade, indicating that a new mtDNA lineage in domestic goats was found.

A Bayesian method was developed to handle QTL analyses of multiple experimental data of outbred populations with heterogeneity of variance between sexes for all random effects. The method employed a scaled reduced animal model with random polygenic and QTL allelic effects. A parsimonious model specification was applied by choosing assumptions regarding the covariance structure to limit the number of parameters to estimate. Markov chain Monte Carlo algorithms were applied to obtain marginal posterior densities. Simulation demonstrated that joint analysis of multiple environments is more powerful than separate single trait analyses of each environment. Measurements on broiler BW obtained from 2 experiments concerning growth efficiency and carcass traits were used to illustrate the method. The population consisted of 10 full-sib families from a cross between 2 broiler lines. Microsatellite genotypes were determined on generations 1 and 2, and phenotypes were collected on groups of generation 3 animals. The model included a polygenic correlation, which had a posterior mean of 0.70 in the analyses. The reanalysis agreed on the presence of a QTL in marker bracket MCW0058-LEI0071 accounting for 34% of the genetic variation in males and 24% in females in the growth efficiency experiment. In the carcass experiment, this QTL accounted for 19% of the genetic variation in males and 6% in females.

A study on heat stress in Mediterranean dairy sheep was undertaken with the objective to examine the relationship between milk production and heat stress, to estimate the additive genetic variances of milk production traits and heat tolerance, and to investigate the possibility of future selection for increased heat tolerance. Production data included 59,661 test-day records belonging to 6624 lactations of 4428 lactating ewes from 17 flocks collected from 1994 through 2003. The traits investigated were daily milk yield, fat and protein percentage, and daily yield of fat-plus-protein. The pedigree file consisted of 5306 animals; in addition to the 4428 animals with records, 188 male and 690 female ancestors were included. Heat stress was modeled by using data from a weather station. Apart from the effects of the weather conditions of the milk recording test-day, the effects of the preceding 1, 2, and 3 d were determined. Because longer periods of heat stress might have a more severe effect than shorter periods, 2-, 3-, and 4-d periods were also considered, by averaging the weather data measurements. Fixed regression analyses were based on models that included effects of flock nested within year of test-day, DIM (days in milk) class x parity class, and several types of weather indicators. The preferred model using the temperature-humidity index (THI) gave a smoother pattern than did the model with temperature x humidity interaction. Both daily milk and fat-plus-protein yield appeared to decrease at THI > or = 23, in all periods considered. Based on the 4-d period, yield decreased for each unit increase of THI above 23 [-62.8 g/unit (-4.2%) for daily milk yield and -8.9 g/unit (-4.9%) for daily fat-plus-protein yield]. Fat and protein percentages appeared to be unaffected by heat stress. A test-day repeatability model was applied for estimation of genetic parameters. The genetic correlations between the general additive effect and the additive effect of heat tolerance were negative (approximately -0.8) for both daily milk and fat-plus-protein yields in all periods considered. Therefore, milk yield is antagonistic with heat tolerance, and selection only for increased milk production will reduce heat tolerance.

A Bayesian method for QTL analysis that is capable of accounting for heterogeneity of variance between sexes, is introduced. The Bayesian method uses a parsimonious model that includes scaling parameters for polygenic and QTL allelic effects per sex. Furthermore, the method employs a reduced animal model to increase computational efficiency. Markov Chain Monte Carlo techniques were applied to obtain estimates of genetic parameters. In comparison with previous regression analyses, the Bayesian method 1) estimates dispersion parameters and polygenic effects, 2) uses individual observations instead of offspring averages, and 3) estimates fixed effect levels and covariates and heterogeneity of variance between sexes simultaneously with other parameters, taking uncertainties fully into account. Broiler data collected in a feed efficiency and a carcass experiment were used to illustrate QTL analysis based on the Bayesian method. The experiments were conducted in a population consisting of 10 full-sib families of a cross between two broiler lines. Microsatellite genotypes were determined on generation 1 and 2 animals and phenotypes were collected on third-generation offspring from mating members from different families. Chromosomal regions that seemed to contain a QTL in previous regression analyses and showed heterogeneity of variance were chosen. Traits analyzed in the feed efficiency experiment were BW at 48 d and growth, feed intake, and feed intake corrected for BW between 23 and 48 d. In the carcass experiment, carcass percentage was analyzed. The Bayesian method was successful in finding QTL in all regions previously detected.

Detection of genes located on the Z-chromosome differs from the detection of genes located on autosomal chromosomes. In the present study, the chicken Z-chromosome is scanned for genes affecting growth traits and feathering. For this purpose, data from a three-generation full-sib-half-sib design was available: parents, full-sib offspring, and half-sib grandoffspring. The parents and full-sib offspring were genotyped for 17 markers on the Z-chromosome. Phenotypic data were only available for grandoffspring. Only the segregation of male chromosomes provides information on the presence of genes, and therefore, a half-sib interval mapping approach was used. The feathering gene was detected significantly and was located between markers ADL0022 and MCW0331. No significant indications were found for the presence of quantitative trait loci affecting growth traits on the Z-chromosome.