Multiple-trait random regression models for the estimation of genetic parameters for milk, fat, and protein yield in buffaloes

In this study, genetic parameters for test-day milk, fat, and protein yield were estimated for the first lactation. The data analyzed consisted of 1,433 first lactations of Murrah buffaloes, daughters of 113 sires from 12 herds in the state of São Paulo, Brazil, with calvings from 1985 to 2007. Ten-month classes of lactation days were considered for the test-day yields. The (co)variance components for the 3 traits were estimated using the regression analyses by Bayesian inference applying an animal model by Gibbs sampling. The contemporary groups were defined as herd-year-month of the test day. In the model, the random effects were additive genetic, permanent environment, and residual. The fixed effects were contemporary group and number of milkings (1 or 2), the linear and quadratic effects of the covariable age of the buffalo at calving, as well as the mean lactation curve of the population, which was modeled by orthogonal Legendre polynomials of fourth order. The random effects for the traits studied were modeled by Legendre polynomials of third and fourth order for additive genetic and permanent environment, respectively, the residual variances were modeled considering 4 residual classes. The heritability estimates for the traits were moderate (from 0.21-0.38), with higher estimates in the intermediate lactation phase. The genetic correlation estimates within and among the traits varied from 0.05 to 0.99. The results indicate that the selection for any trait test day will result in an indirect genetic gain for milk, fat, and protein yield in all periods of the lactation curve. The accuracy associated with estimated breeding values obtained using multi-trait random regression was slightly higher (around 8%) compared with single-trait random regression. This difference may be because to the greater amount of information available per animal. © 2013 American Dairy Science Association.

Influence of genotype-environment interaction on the classification of Nellore bulls in Southern Brazil

The aim of this study was to assess the occurrence of genotype-environment interaction, as well as its effects on the magnitude of genetic parameters and the classification of Nellore breeding bulls for the trait adjusted weight at 205 days (W205) on Southern Brazil. The components of (co)variance were estimated by Bayesian inference, using a linear-linear animal model in a bi-trait analysis. The proposed model for the analyses considers as random the direct additive genetic and maternal effects and residual effects, and as fixed effects the contemporary groups, sex, season of birth and weighing, and calving age as covariable (linear and quadratic effects). The a posteriori mean estimates of the direct heritabilities for W205 in the three States varied from 0.24 in Paraná (PR) to 0.34 in Santa Catarina (SC). The estimates of maternal heritability varied from 0.23 in SC and Rio Grande do Sul (RS) to 0.28 in PR. The a posteriori mean distributions of the genetic correlation varied from 0.52 between SC and RS, to 0.84 between PR and RS, suggesting that the best breeding bulls in SC are not the same as in RS.

Reaction norms for the study of genotype-environment interaction for growth and indicator traits of sexual precocity in Nellore cattle

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic parameters and investigation of genotype x environment interactions in Nellore x Hereford crossbred for resistance to cattle ticks in different regions of Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

The relationship between obesity, low back pain, and lumbar disc degeneration when genetics and the environment are considered: a systematic review of twin studies

BACKGROUND CONTEXT: The relationships between obesity and low back pain (LBP) and lumbar disc degeneration (LDD) remain unclear. It is possible that familial factors, including genetics and early environment, affect these relationships.PURPOSE: To investigate the relationship between obesity-related measures (eg, weight, body mass index [BMI]) and LBP and LDD using twin studies, where the effect of genetics and early environment can be controlled.STUDY DESIGN: A systematic review with meta-analysis.METHODS: MEDLINE, CINAHL, Scopus, Web of Science, and EMBASE databases were searched from the earliest records to August 2014. All cross-sectional and longitudinal observational twin studies identified by the search strategy were considered for inclusion. Two investigators independently assessed the eligibility, conducted the quality assessment, and extracted the data. Metaanalyses (fixed or random effects, as appropriate) were used to pool studies'estimates of association.RESULTS: In total, 11 articles met the inclusion criteria. Five studies were included in the LBP analysis and seven in the LDD analysis. For the LBP analysis, pooling of the five studies showed that the risk of having LBP for individuals with the highest levels of BMI or weight was almost twice that of people with a lower BMI (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.6-2.0; I-2 = 0%). A dose-response relationship was also identified. When genetics and the effects of a shared early environment were adjusted for using a within-pair twin case-control analysis, pooling of three studies showed a reduced but statistically positive association between obesity and prevalence of LBP (OR 1.5; 95% CI 1.1-2.1; I-2 = 0%). However, the association was further diminished and not significant (OR 1.4; 95% CI 0.8-2.3; I-2 = 0%) when pooling included two studies on monozygotic twin pairs only. Seven studies met the inclusion criteria for LDD. When familial factors were not controlled for, body weight was positively associated with LDD in all five cross-sectional studies. Only two cross-sectional studies investigated the relationship between obesity-related measures and LDD accounting for familial factors, and the results were conflicting. One longitudinal study in LBP and three longitudinal studies in LDD found no increase in risk in obese individuals, whether or not familial factors were controlled for.CONCLUSIONS: Findings from this review suggest that genetics and early environment are possible mechanisms underlying the relationship between obesity and LBP; however, a direct causal link between these conditions appears to be weak. Further longitudinal studies using the twin design are needed to better understand the complex mechanisms underlying the associations between obesity, LBP, and LDD.