Analysis of Climate Change in Croatia Based on Calculation of Temperature Thresholds

The equivalent of the phenological data for the beginning of active vegetation and the provision of plant growth stage development with heat is the date when the average daily air temperature exceeds a certain threshold or biological minimum, and is determined from the average annual temperature pattern. The aim of the paper was to calculate occurences of temperature thresholds of 5, 10, 15, 20 and 25 °C for the reference period 1961-1990 and the recent period from 1991 to 2018, at three locations - Osijek, Gospić and Zadar, covering three agricultural regions of Croatia - Pannonian, Mountain and Adriatic region, respectively, and to indicate potential climate change in selected areas through analysis of time series and calculation of trends in the beginning, end and duration period of a certain temperature. Based on the results of the calculation of temperature thresholds in the observed period from 1991 to 2018, warming occurred in all three agricultural regions and the last four years - 2015, 2016, 2017 and 2018 have been the warmest years so far. Furthermore, temperature thresholds on average appear earlier than in the period from 1961 to 1990 and often have later endings. The most significant extension of the number of days is calculated for the temperature thresholds of 5 °C and 10 °C in the area of the Pannonian and Mountain agricultural regions, and in the Adriatic region for the temperature threshold of 10 °C. Results indicate pronounced winter/ spring warming which can induce advanced start of active vegetation and extension of warm season in mountainous areas

A comparative study of three methods for detecting association of quantitative traits in samples of related subjects

We used Genetic Analysis Workshop 16 Problem 3 Framingham Heart Study simulated data set to compare methods for association analysis of quantitative traits in related individuals. More specifically, we investigated type I error and relative power of three approaches: the measured genotype, the quantitative transmission-disequilibrium test (QTDT), and the quantitative trait linkage-disequilibrium (QTLD) tests. We studied high-density lipoprotein and triglyceride (TG) lipid variables, as measured at Visit 1. Knowing the answers, we selected three true major genes for high-density lipoprotein and/or TG. Empirical distributions of the three association models were derived from the first 100 replicates. In these data, all three models were similar in error rates. Across the three association models, the power was the lowest for the functional SNP with smallest size effects (i.e., α2), and for the less heritable trait (i.e., TG). Our results showed that measured genotype outperformed the two orthogonal-based association models (QTLD, QTDT), even after accounting for population stratification. QTDT had the lowest power rates. This is consistent with the amount of marker and trait data used by each association model. While the effective sample sizes varied little across our tested variants, we observed some large power drops and marked differences in performances of the models. We found that the performances contrasted the most for the tightly linked, but not associated, functional variants

Efecto de la varianza genética aditiva generacional sobre las componentes de la respuesta a la selección en una población con generaciones superpuestas

p.221-230El objetivo de esta investigación fue comparar la respuesta a la selección usando el Modelo Animal-BLUP con grupos genéticos, utilizando la variancia genética aditiva de cada generación (s²A(C), con aquel que utiliza la variancia aditiva en la población base (s²a), mediante simulación estocastica de una población animal con generaciones superpuestas. A diferencia de otros estudios, el modelo de generación de datos incluyó efectos fijos como el sexo (variable clasificatoria) y la edad del animal a la medición del carácter (covariable), con el objeto de asemejarse a los modelos de evaluación en poblaciones reales. Los resultados corresponden a 20 años de selección, tomando el promedio de 100 réplicas. La h² original en la población fue 0,4. La pérdida de información consistió en omitir al azar relaciones de parentesco, afín de incorporar los grupos al modelo de evaluación animal. El 25 por ciento de los animales poseían ambos padres desconocidos, 25 por ciento poseían la madre desconocida, 25 por ciento el padre y el 25 por ciento restante poseían ambos padres conocidos. En las condiciones simuladas no se observaron diferencias significativas (pmayor a 0,05), en las variables estudiadas: respuesta a la selección, variancia aditiva, exactitud, intensidad de selección, consanguinidad e intervalo generacional, para los casos de información completa e incompleta con la inclusión de grupos, según se consideró las s²a ó la s²a(g

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs

Background: Most developments in quantitative genetics theory focus on the study of intra-breed/line concepts. With the availability of massive genomic information, it becomes necessary to revisit the theory for crossbred populations. We propose methods to construct genomic covariances with additive and non-additive (dominance) inheritance in the case of pure lines and crossbred populations. Results: We describe substitution effects and dominant deviations across two pure parental populations and the crossbred population. Gene effects are assumed to be independent of the origin of alleles and allelic frequencies can differ between parental populations. Based on these assumptions, the theoretical variance components (additive and dominant) are obtained as a function of marker effects and allelic frequencies. The additive genetic variance in the crossbred population includes the biological additive and dominant effects of a gene and a covariance term. Dominance variance in the crossbred population is proportional to the product of the heterozygosity coefficients of both parental populations. A genomic BLUP (best linear unbiased prediction) equivalent model is presented. We illustrate this approach by using pig data (two pure lines and their cross, including 8265 phenotyped and genotyped sows). For the total number of piglets born, the dominance variance in the crossbred population represented about 13 % of the total genetic variance. Dominance variation is only marginally important for litter size in the crossbred population. Conclusions: We present a coherent marker-based model that includes purebred and crossbred data and additive and dominant actions. Using this model, it is possible to estimate breeding values, dominant deviations and variance components in a dataset that comprises data on purebred and crossbred individuals. These methods can be exploited to plan assortative mating in pig, maize or other species, in order to generate superior crossbred individuals in terms of performance

Selecting the quality of mule duck fatty liver based on near-infrared spectroscopy

Background: "Foie gras" is produced predominantly in France and about 90% of the commercialized product is obtained from male mule ducks. The melting rate (percentage of fat released during cooking) is the main criterion used to determine the quality of "foie gras". However, up to now the melting rate could not be predicted without causing liver damage, which means that selection programs could not use this criterion. Methods: Fatty liver phenotypes were obtained for a population of over 1400 overfed male mule ducks. The phenotypes were based on two types of near-infrared spectra (on the liver surface and on ground liver) in order to predict the melting rate and liver composition (ash, dry matter, lipid and protein contents). Genetic parameters were computed in multiple traits with a "sire-dam" model and using a Gibbs sampling approach. Results: The estimates for the genetic parameters show that the measured melting rate and the predicted melting rate obtained with two near-infrared spectrometer devices are genetically the same trait: genetic correlations are very high (ranging from +0.89 to +0.97 depending on the mule duck parental line and the spectrometer) and heritabilities are comparable. The predictions based on the spectra of ground liver samples using a laboratory spectrometer correlate with those based on the surface spectra using a portable spectrometer (from +0.83 to +0.95 for dry matter, lipid and protein content) and are particularly high for the melting rate (higher than +0.95). Although less accurate than the predictions obtained using the spectra of ground liver samples, the phenotypic prediction of the melting rate based on surface spectra is sufficiently accurate to be used by "foie gras" processors. Conclusions: Near-infrared spectrometry is an efficient tool to select liver quality in breeding programs because animals can be ranked according to their liver melting rate without damaging their livers. Thus, these original results will help breeders to select ducks based on the liver melting rate, a crucial criterion that defines the quality of the liver and for which there was previously no accurate predictor. (Résumé d'auteur

SNP-based mate allocation strategies to maximize total genetic value in pigs

International audienceAbstractBackgroundMate allocation strategies that account for non-additive genetic effects can be used to maximize the overall genetic merit of future offspring. Accounting for dominance effects in genetic evaluations is easier in a genomic context, than in a classical pedigree-based context because the combinations of alleles at loci are known. The objective of our study was two-fold. First, dominance variance components were estimated for age at 100 kg (AGE), backfat depth (BD) at 140 days, and for average piglet weight at birth within litter (APWL). Second, the efficiency of mate allocation strategies that account for dominance and inbreeding depression to maximize the overall genetic merit of future offspring was explored.ResultsGenetic variance components were estimated using genomic models that included inbreeding depression with and without non-additive genetic effects (dominance). Models that included dominance effects did not fit the data better than the genomic additive model. Estimates of dominance variances, expressed as a percentage of additive genetic variance, were 20, 11, and 12% for AGE, BD, and APWL, respectively. Estimates of additive and dominance single nucleotide polymorphism effects were retrieved from the genetic variance component estimates and used to predict the outcome of matings in terms of total genetic and breeding values. Maximizing total genetic values instead of breeding values in matings gave the progeny an average advantage of − 0.79 days, − 0.04 mm, and 11.3 g for AGE, BD and APWL, respectively, but slightly reduced the expected additive genetic gain, e.g. by 1.8% for AGE.ConclusionsGenomic mate allocation accounting for non-additive genetic effects is a feasible and potential strategy to improve the performance of the offspring without dramatically compromising additive genetic gain

Genomic analysis of dominance effects on milk production and conformation traits in Fleckvieh cattle

Background Estimates of dominance variance in dairy cattle based on pedigree data vary considerably across traits and amount to up to 50% of the total genetic variance for conformation traits and up to 43% for milk production traits. Using bovine SNP (single nucleotide polymorphism) genotypes, dominance variance can be estimated both at the marker level and at the animal level using genomic dominance effect relationship matrices. Yield deviations of high-density genotyped Fleckvieh cows were used to assess cross-validation accuracy of genomic predictions with additive and dominance models. The potential use of dominance variance in planned matings was also investigated. Results Variance components of nine milk production and conformation traits were estimated with additive and dominance models using yield deviations of 1996 Fleckvieh cows and ranged from 3.3% to 50.5% of the total genetic variance. REML and Gibbs sampling estimates showed good concordance. Although standard errors of estimates of dominance variance were rather large, estimates of dominance variance for milk, fat and protein yields, somatic cell score and milkability were significantly different from 0. Cross-validation accuracy of predicted breeding values was higher with genomic models than with the pedigree model. Inclusion of dominance effects did not increase the accuracy of the predicted breeding and total genetic values. Additive and dominance SNP effects for milk yield and protein yield were estimated with a BLUP (best linear unbiased prediction) model and used to calculate expectations of breeding values and total genetic values for putative offspring. Selection on total genetic value instead of breeding value would result in a larger expected total genetic superiority in progeny, i.e. 14.8% for milk yield and 27.8% for protein yield and reduce the expected additive genetic gain only by 4.5% for milk yield and 2.6% for protein yield. Conclusions Estimated dominance variance was substantial for most of the analyzed traits. Due to small dominance effect relationships between cows, predictions of individual dominance deviations were very inaccurate and including dominance in the model did not improve prediction accuracy in the cross-validation study. Exploitation of dominance variance in assortative matings was promising and did not appear to severely compromise additive genetic gain

Linkage analysis of high myopia susceptibility locus in 26 families

Purpose: We conducted a linkage analysis in high myopia families to replicate suggestive results from chromosome 7q36 using a model of autosomal dominant inheritance and genetic heterogeneity. We also performed a genome-wide scan to identify novel loci. Methods: Twenty-six families, with at least two high-myopic subjects (ie. refractive value in the less affected eye of -5 diopters) in each family, were included. Phenotypic examination included standard autorefractometry, ultrasonographic eye length measurement, and clinical confirmation of the non-syndromic character of the refractive disorder. Nine families were collected de novo including 136 available members of whom 34 were highly myopic subjects. Twenty new subjects were added in 5 of the 17 remaining families. A total of 233 subjects were submitted to a genome scan using ABI linkage mapping set LMSv2-MD-10, additional markers in all regions where preliminary LOD scores were greater than 1.5 were used. Multipoint parametric and non-parametric analyses were conducted with the software packages Genehunter 2.0 and Merlin 1.0.1. Two autosomal recessive, two autosomal dominant, and four autosomal additive models were used in the parametric linkage analyses. Results: No linkage was found using the subset of nine newly collected families. Study of the entire population of 26 families with a parametric model did not yield a significant LOD score (>3), even for the previously suggestive locus on 7q36. A non-parametric model demonstrated significant linkage to chromosome 7p15 in the entire population (Z-NPL=4.07, p=0.00002). The interval is 7.81 centiMorgans (cM) between markers D7S2458 and D7S2515. Conclusions: The significant interval reported here needs confirmation in other cohorts. Among possible susceptibility genes in the interval, certain candidates are likely to be involved in eye growth and development