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ABSTRACT. The edited data set for the estimation of heritability, genetic and phenotypic correlations of fertility traits contained up to 23,402 records from 10,894 cows calved between 2001 and 2015. Heritability estimates for success in first service (FS), gestation length (GL), number of inseminations (NI), insemination outcome (IO), calving interval (CI), calving birth weight (CBW) and days open (DO) were low and ranged between 0.016 (DO) and 0.123 (GL). Repeatability of fertility traits was estimated to vary from 0.021 (FS) to 0.411 (IO). The genetic correlations between DO × CI, DO × NI and CI × NI were positive and nearly perfect (0.98, 0.88 and 0.88, respectively), while those between DO × IO and CI × IO were negative (-0.98 and -1, respectively). Further, the phenotypic correlations between DO × CI, DO × NI, CI × NI, CBW × IO and SF × IO were 0.99, 0.83, 0.83, 0.99 and 1, respectively, while those between DO × IO, CI × IO, GL × IO and NI × IO were -0.99, -0.99, -0.99 and -1, respectively. Overall genetic parameters imply a good practical management in heat stress conditions will be essential for improving fertility efficiency.Keywords: heritability, repeatability, genetic correlation, phenotypic correlation, heat stress. Análise genética de traços de fertilidade em vacas leiteiras Holstein em climas quentes e temperadosRESUMO. Os dados editados para definir a estimativa de herdabilidade, correlações genéticas e fenotípicas de características de fertilidade continham até 23,402 registros a partir de 10,894 vacas paridas entre 2001 e 2015. As estimativas de herdabilidade para o sucesso no primeiro serviço (SF), duração da gestação (GL), número de inseminações (NI), resultado de inseminação (IO), intervalo entre partos (CI), peso ao nascer (CBW) e dias abertos (DO) foram baixas e variaram entre 0,016 (DO) e 0,123 (GL). A repetitividade das características de fertilidade foi estimada e variou entre 0,021 (SF) e 0,411 (IO). A correlação genética entre DO × CI, DO × NI e CI × NI foi positiva e quase perfeita (0,98, 0,88 e 0,88, respectivamente), enquanto que aquela entre DO × IO e CI × IO foi negativa (-0,98 e -1, respectivamente). A correlação fenotípica entre DO × CI, DO × NI, CI × NI, CBW × IO e SF × IO foi 0,99, 0,83, 0,83, 0,99 e 1, respectivamente, enquanto aquela entre DO × IO, CI × IO, GL × IO e NI × IO foi -0,99, -0,99, -0,99 e -1, respectivamente. Os parâmetros genéticos constatados implicam que será essencial uma gestão bem prática na condição de estresse por calor para melhoria da eficiência da fertilidade.Palavras-chave: herdabilidade, repetitividade, correlação genética, correlação fenotípica, estresse por calor.

Selective breeding has led to gradual changes at the genome level of horses. Deciphering selective pressure patterns is progressive to understand how breeding strategies have shaped the sport horse genome; although, little is known about the genomic regions under selective pressures in sport horse breeds. The major goal of this study was to shed light on genomic regions and biological pathways under selective pressures in sport horses. In this study, whole-genome sequences of 16 modern sport and 35 non-sport horses were used to investigate the genomic selective signals of sport performance, by employing fixation index, nucleotide diversity, and Tajima’s D approaches. A total number of 49 shared genes were identified using these approaches. The functional enrichment analysis for candidate genes revealed novel significant biological processes related to musculoskeletal system development, such as limb development and morphogenesis, having been targeted by selection in sport breeds.

BackgroundGenotype imputation is an important process of predicting unknown genotypes, which uses reference population with dense genotypes to predict missing genotypes for both human and animal genetic variations at a low cost. Machine learning methods specially boosting methods have been used in genetic studies to explore the underlying genetic profile of disease and build models capable of predicting missing values of a marker.MethodsIn this study strategies and factors affecting the imputation accuracy of parent-offspring trios compared from lower-density SNP panels (5 K) to high density (10 K) SNP panel using three different Boosting methods namely TotalBoost (TB), LogitBoost (LB) and AdaBoost (AB). The methods employed using simulated data to impute the un-typed SNPs in parent-offspring trios. Four different datasets of G1 (100 trios with 5 k SNPs), G2 (100 trios with 10 k SNPs), G3 (500 trios with 5 k SNPs), and G4 (500 trio with 10 k SNPs) were simulated. In four datasets all parents were genotyped completely, and offspring genotyped with a lower density panel.ResultsComparison of the three methods for imputation showed that the LB outperformed AB and TB for imputation accuracy. The time of computation were different between methods. The AB was the fastest algorithm. The higher SNP densities resulted the increase of the accuracy of imputation. Larger trios (i.e. 500) was better for performance of LB and TB.ConclusionsThe conclusion is that the three methods do well in terms of imputation accuracy also the dense chip is recommended for imputation of parent-offspring trios.

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