Body size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes. The 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC. The results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations.

A total of 4466 monthly test day records of 555 Greek Sfakia dairy ewes were used to estimate heritabilities as well as genetic and phenotypic correlations for udder score (US), average daily milk yield (MY), fat percentage (FC) and protein percentage (PC) of milk. Genetic parameters were estimated with a multitrait animal model with correlated residuals. Heritabilities were between 0.17 and 0.24, 0.41 and 0.47, 0.32 and 0.52 and 0.44 and 0.55, for test day US, MY, FC and PC, respectively. Within traits, genetic correlations between test days were between 0.54 and 0.57, 0.71 and 0.96, 0.79 and 0.99 and 0.85 and 0.99 for US, MY, FC and PC, respectively. Genetic correlations between milk traits and US were negative and of very low magnitude (−0.04 to −0.06). Genetic correlations between MY – FC and MY – PC were negative and of varying magnitude, from −0.37 to −0.52 and from −0.44 to −0.50, in the four test days, respectively. Fat and protein percentages were found to be highly, positively, correlated.

Test-day records (n=13677) sampled from 896 ewes in 5-9 (µ=7.5) monthly test-days were used to estimate genetic and phenotypic parameters of test-day yields, lactation milk yield (TMY), length of the milking period (DAYS) and three measures of persistency of milk yield in Boutsico dairy sheep. Τhe measures of persistency were the slope of the regression line (β), the coefficient of variation (CV) of the test-day milk yields and the maximum to average daily milk yield ratio (ΜΑ). The estimates of variance components were obtained under a linear mixed model by restricted maximum likelihood. The heritability of test-day yields ranged from 0.15 to 0.24. DAYS were found to be heritable (h 2 =0.11). Heritability estimates of β, CV and MA were 0.15, 0.13, 0.10, respectively. Selection for maximum lactation yields is expected to result in prolonged milking periods, high rates of decline of yields after peak production, variable test-day yields and higher litter sizes. Selection for flatter lactation curves would reduce lactation yields, increase slightly the length of the milking period and decrease yield variation as well as litter size. The most accurate prediction of TMY was obtained with a linear regression model with the first five test-day records. (Asian-Aust.

Empirical estimations of heritability, systematic effects and predictions of sires’ breeding values (BVs) were obtained under various population structures for simulated populations consisted of n = 400 animals in 5 herds for a trait of medium heritability (h² = 0.30). An infinitesimal additive genetic animal model was assumed while simulating data. Population structure was varied to allow for good and poor connectedness across herds and (non)random association between the genetic and the environmental effects. The impact of the various population structures on the parameter estimation(s) was assessed using Mean Squared Error (MSE) and Pearson’s correlations. Allowing sires to have progenies in more than one herd (good herd connectedness) and random use of sires across herds generally resulted in good parameter estimations. Poor connectedness significantly affected herd effects estimation and BV prediction but not heritability estimation as long as random usage of sires across environments was guaranteed. Selective use of the best sires in the best herds along with poor connectedness resulted in poorest estimations of all parameters examined. In the latter case, heritability was seriously underestimated (h² = 0.06) while highest error, lowest accuracies for the BVs and a remarkable underestimation of the genetic gain were observed. Use of reference sires on a natural mating basis to create genetic links between herds has served a good solution for both heritability and BVs estimation under unfavorable structure. Mating 0.25 of the herd ewes with reference sires resulted in a heritability estimate close to the simulated one. Significantly better estimates of systematic effects and BVs were, however, obtained when 0.5 of the herd ewes were mated by reference sires.