Model for fitting longitudinal traits subject to threshold response applied to genetic evaluation for heat tolerance

A semi-parametric non-linear longitudinal hierarchical model is presented. The model assumes that individual variation exists both in the degree of the linear change of performance (slope) beyond a particular threshold of the independent variable scale and in the magnitude of the threshold itself; these individual variations are attributed to genetic and environmental components. During implementation via a Bayesian MCMC approach, threshold levels were sampled using a Metropolis step because their fully conditional posterior distributions do not have a closed form. The model was tested by simulation following designs similar to previous studies on genetics of heat stress. Posterior means of parameters of interest, under all simulation scenarios, were close to their true values with the latter always being included in the uncertain regions, indicating an absence of bias. The proposed models provide flexible tools for studying genotype by environmental interaction as well as for fitting other longitudinal traits subject to abrupt changes in the performance at particular points on the independent variable scale

Genotype by environment interaction for 450-day weight of Nelore cattle analyzed by reaction norm models

Genotype by environment interactions (GEI) have attracted increasing attention in tropical breeding programs because of the variety of production systems involved. In this work, we assessed GEI in 450-day adjusted weight (W450) Nelore cattle from 366 Brazilian herds by comparing traditional univariate single-environment model analysis (UM) and random regression first order reaction norm models for six environmental variables: standard deviations of herd-year (RRMw) and herd-year-season-management (RRMw-m) groups for mean W450, standard deviations of herd-year (RRMg) and herd-year-season-management (RRMg-m) groups adjusted for 365-450 days weight gain (G450) averages, and two iterative algorithms using herd-year-season-management group solution estimates from a first RRMw-m and RRMg-m analysis (RRMITw-m and RRMITg-m, respectively). The RRM results showed similar tendencies in the variance components and heritability estimates along environmental gradient. Some of the variation among RRM estimates may have been related to the precision of the predictor and to correlations between environmental variables and the likely components of the weight trait. GEI, which was assessed by estimating the genetic correlation surfaces, had values < 0.5 between extreme environments in all models. Regression analyses showed that the correlation between the expected progeny differences for UM and the corresponding differences estimated by RRM was higher in intermediate and favorable environments than in unfavorable environments (p < 0.0001)

Use of multi-trait and random regression models to identify genetic variation in tolerance to porcine reproductive and respiratory syndrome virus

Background: A host can adopt two response strategies to infection: resistance (reduce pathogen load) and tolerance (minimize impact of infection on performance). Both strategies may be under genetic control and could thus be targeted for genetic improvement. Although there is evidence that supports a genetic basis for resistance to porcine reproductive and respiratory syndrome (PRRS), it is not known whether pigs also differ genetically in tolerance. We determined to what extent pigs that have been shown to vary genetically in resistance to PRRS also exhibit genetic variation in tolerance. Multi-trait linear mixed models and random regression sire models were fitted to PRRS Host Genetics Consortium data from 1320 weaned pigs (offspring of 54 sires) that were experimentally infected with a virulent strain of PRRS virus to obtain genetic parameter estimates for resistance and tolerance. Resistance was defined as the inverse of within-host viral load (VL) from 0 to 21 (VL21) or 0 to 42 (VL42) days post-infection and tolerance as the slope of the reaction-norm of average daily gain (ADG21, ADG42) on VL21 or VL42. Results: Multi-trait analysis of ADG associated with either low or high VL was not indicative of genetic variation in tolerance. Similarly, random regression models for ADG21 and ADG42 with a tolerance slope fitted for each sire did not result in a better fit to the data than a model without genetic variation in tolerance. However, the distribution of data around average VL suggested possible confounding between level and slope estimates of the regression lines. Augmenting the data with simulated growth rates of non-infected half-sibs (ADG0) helped resolve this statistical confounding and indicated that genetic variation in tolerance to PRRS may exist if genetic correlations between ADG0 and ADG21 or ADG42 are low to moderate. Conclusions: Evidence for genetic variation in tolerance of pigs to PRRS was weak when based on data from infected piglets only. However, simulations indicated that genetic variance in tolerance may exist and could be detected if comparable data on uninfected relatives were available. In conclusion, of the two defense strategies, genetics of tolerance is more difficult to elucidate than genetics of resistance.</p

Antineoplastic Drugs as a Potential Risk Factor in Occupational Settings: Mechanisms of Action at the Cell Level, Genotoxic Effects, and Their Detection Using Different Biomarkers

U članku je prikazana osnovna podjela antineoplastičnih lijekova prema mehanizmima djelovanja na razini stanice. Objašnjeni su mehanizmi genotoksičnosti najvažnijih vrsta lijekova koji se primjenjuju u okviru uobičajenih protokola za liječenje zloćudnih novotvorina. Navedena je važeća klasifi kacija antineoplastika prema kancerogenom potencijalu, podaci o mutagenom potencijalu te je prikazana njihova podjela u skladu s anatomsko-terapijsko-kemijskim sustavom klasifi kacije. Sustavno su prikazani najvažniji rezultati svjetskih i hrvatskih istraživanja na populacijama radnika izloženih antineoplasticima, provedenih u razdoblju 1980.-2009. s pomoću četiri najčešće primjenjivane metode: analize izmjena sestrinskih kromatida, analize kromosomskih aberacija, mikronukleus-testa i komet-testa. Objašnjena su osnovna načela navedenih metoda te raspravljene njihove prednosti i nedostaci. Biološki pokazatelji daju važne podatke o individualnoj osjetljivosti profesionalno izloženih ispitanika koji mogu poslužiti unaprjeđenju postojećih uvjeta rada i upravljanju rizicima pri izloženosti genotoksičnim agensima. Na osnovi prednosti i nedostataka citogenetičkih metoda zaključeno je da je mikronukleus-test, koji podjednako uspješno dokazuje klastogene i aneugene učinke, jedna od najboljih metoda dostupnih za otkrivanje štetnih djelovanja antineoplastičnih lijekova koji su u aktivnoj primjeni.This article brings an overview of the mechanisms of action of antineoplastic drugs used in the clinical setting. It also describes the genotoxic potentials of the most important classes of antineoplastic drugs involved in standard chemotherapy protocols. Classifi cation of antineoplastic drugs according to the IARC monographs on the evaluation of carcinogenic risks to humans is accompanied by data on their mutagenicity and the most recent updates in the Anatomical Therapeutic Chemical (ATC) Classifi cation System. We report the main fi ndings of biomonitoring studies that were conducted in exposed healthcare workers all over the world between 1980 and 2009 using four biomarkers: sister chromatid exchanges, chromosome aberrations, micronuclei. and the comet assay. The methods are briefl y explained and their advantages and disadvantages discussed. Biomarkers provide important information on individual genome sensitivity, which eventually might help to improve current working practices and to manage the risks related with exposure to genotoxic agents. Taking into consideration all known advantages and drawbacks of the existing cytogenetic methods, the micronucleus assay, which is able to detect both clastogenic and aneugenic action, is the most suitable biomarker for assessing harmful effects of antineoplastic drugs currently used in health care