Quantitative trait loci (QTL) mapping in livestock allows the identification of genes that determine the genetic variation affecting traits of economic interest. We analyzed the birth weight and weight at 60 days QTL segregating on bovine chromosome BTA14 in a F2 resource population using genotypes produced from seven microsatellite markers. Phenotypes were derived from 346 F2 progeny produced from crossing Bos indicus Gyr x Holstein Bos taurus F1 parents. Interval analysis to detect QTL for birth weight revealed the presence of a QTL (p < 0.05) at 1 centimorgan (cM) from the centromere with an additive effect of 1.210 ± 0.438 kg. Interval analysis for weight at 60 days revealed the presence of a QTL (p < 0.05) at 0 cM from the centromere with an additive effect of 2.122 ± 0.735 kg. The region to which the QTL were assigned is described in the literature as responsible for some growth traits, milk yield, milk composition, fat deposition and has also been related to reproductive traits such as daughter pregnancy rate and ovulation rate. The effects of the QTL described on other traits were not investigated.

The effect of the bovine major histocompatibility complex (BoLA) locus on animal health may be due to a direct action of its alleles on immune functions, whereas its indirect effect on production traits might be explained by the better general health conditions of more productive animals. In the present study, the BoLA-DRB3 gene was investigated in 1058 cows belonging to seven Brazilian Gyr Dairy herds (Bos indicus, Zebu cattle). A total of 37 alleles were identified, 15 of them described for the first time in a Zebu breed. A highly significant association (p < 0.02) was observed between allele *54 and a decrease (-26.1 kg) in milk protein yield and there was a significant association (p < 0.05) between this allele and lower (-26.07 kg) milk fat yield. There was also a significant association (p < 0.05) between allele *6 and decreased (-12.47 kg) milk protein and allele *7 and increased (12.72 kg) milk protein. There were also indications of association (p < 0.10) between somatic cell score (SCS) and alleles *3 (SCS increased by 0.54 units) and *31 (SCS increased by 0.46 units). The highly significant association of allele *54 with lower protein yield suggests the possible use of this allele in marker-assisted selection programs.

Segregation between a genetic marker and a locus influencing a quantitative trait in a well delineated population is the basis for success in mapping quantitative trait loci (QTL). To detect bovine chromosome 5 (BTA5) birth weight QTL we genotyped 294 F2 Gyr (Bos indicus) x Holstein (Bos taurus) crossbreed cattle for five microsatellite markers. A linkage map was constructed for the markers and an interval analysis for the presence of QTL was performed. The linkage map indicated differences in the order of two markers relative to the reference map (<A HREF="http://www.marc.usda.gov/">http://www.marc.usda.gov</A>). Interval analysis detected a QTL controlling birth weight (p < 0.01) at 69 centimorgans (cM) from the most centromeric marker with an effect of 0.32 phenotypic standard-error. These results support other studies with crossbred Bos taurus x Bos indicus populations.

O procedimento de mapeamento por intervalo baseado em modelo aleatório foi aplicado para estudar sua robustez e propriedades no mapeamento de dois QTLs, em populações constituídas de famílias de meios-irmãos. Sob o modelo aleatório, a localização e os componentes de variância foram estimados usando o método da máxima verossimilhança. A estimação dos parâmetros foi baseada na metodologia de pares de irmãos. As proporções de genes idênticos por descendência (IBD) dos QTLs foram estimadas a partir das proporções IBD de dois marcadores flanqueadores. As estimativas dos parâmetros dos QTLs (localizações e componentes de variância) e do poder de detecção, em uma característica com h² = 0,25, foram obtidas usando dados simulados, variando-se o número de famílias, o tamanho das famílias e a proporção da variância genética devida aos QTLs e à posição dos QTLs, localizados no mesmo intervalo, em intervalos adjacentes e não-adjacentes. Os fatores que mais influenciaram as estimativas dos parâmetros foram as proporções da variância devida aos QTLs, ao número e ao tamanho das famílias. As estimativas mais viesadas dos componentes dos QTLs e poligênico foram obtidas com a análise dos registros de somente dez famílias. Com número suficiente de famílias e de indivíduos nas famílias e altas proporções de variância genética devida aos QTLs, o modelo aleatório pode detectar QTL com alto poder, apresentando estimativas das posições com boa acurácia, se não existirem dois QTLs no mesmo intervalo. Para o mapeamento fino e estimativas apropriadas da variância dos QTLs, métodos mais sofisticados devem ser utilizados.

An interval mapping procedure based on the random model approach was applied to investigate its robustness and properties for QTL mapping in populations with prevailing half-sib structures. Under random model, QTL location and variance components were estimated using maximum likelihood procedures. The estimation of parameters was based on sib-pair approach. The proportions of genes identical-by-descent (IBD) at the two QTLs were estimated from the IBD at two flanking marker loci. Estimates for QTLs parameters (locations and variance components) and power of detection, in a trait with h² = 0.25, were obtained using simulated data, varying the number of families, number of half-sibs by families, proportion of QTL variance and QTLs positions. These QTLs were positioned at the same interval, at adjacent intervals and at no adjacent intervals. The most important factors influencing the estimates of QTLs parameters and power were the proportion of variance due to QTLs the number and size of the half-sibs families. The most biased variance components estimations were obtained when sample size was ten. Given a sufficient number of families and high proportions of genetic variance due to QTLs, the random model approach can detect a QTLs with high power and provides accurate estimates of the QTLs position if two QTLs are not present in the same interval. For fine QTL mapping and proper estimation of QTLs variance, more sophisticated methods are required.