Genetics of rearing success in four pure laying hen lines during the first 17 weeks of age

This study aimed to investigate the genetics of rearing success (RS) in laying hens. Four rearing traits: clutch size (CS), first week mortality (FWM), rearing abnormalities (RA), and natural death (ND), were included as factors determining RS. Pedigree, genotypic, and phenotypic records of 4 purebred genetic lines of White Leghorn layers were available for 23,000 rearing batches obtained between 2010 and 2020. FWM and ND showed little or no variation amongst the 4 genetic lines over the years 2010-2020, whereas an increase was observed for CS and a decrease for RA. To determine whether these traits were heritable, genetic parameters for each trait were estimated, using a Linear Mixed Model. Heritabilities within lines were low (0.05-0.19 for CS, 0.01-0.04 for FWM, 0.02-0.06 for RA, 0.02 -0.04 for ND, and 0.01-0.07 for RS). Additionally, genome wide association study was done to scan the genomes of the breeders to reveal single nucleotide polymorphisms (SNPs) associated with these traits. Manhattan plots indicated the existence of 12 differ-ent SNPs having a significant effect on RS. Thus, the identified SNPs will increase the understanding of the genetics of RS in laying hens

A Genetic Biomarker of Oxidative Stress, the Paraoxonase-1 Q192R Gene Variant, Associates with Cardiomyopathy in CKD: A Longitudinal Study

Background. Oxidative stress is a hallmark of CKD and this alteration is strongly implicated in LV hypertrophy and in LV dysfunction. Methods and Patients. We resorted to the strongest genetic biomarker of paraoxonase-1 (PON1) activity, the Q192R variant in the PON1 gene, to unbiasedly assess (Mendelian randomization) the cross-sectional and longitudinal association of this gene-variant with LV mass and function in 206 CKD patients with a 3-year follow-up. Results. The R allele of Q192R polymorphism associated with oxidative stress as assessed by plasma 8-isoPGF2α (P=0.03) and was dose-dependently related in a direct fashion to LVMI (QQ: 131.4 ± 42.6 g/m2; RQ: 147.7 ± 51.1 g/m2; RR: 167.3 ± 41.9 g/m2; P=0.001) and in an inverse fashion to systolic function (LV Ejection Fraction) (QQ: 79 ± 12%; RQ: 69 ± 9%; RR: 65 ± 10% P=0.002). On longitudinal observation, this gene variant associated with the evolution of the same echocardiographic indicators [LVMI: 13.40 g/m2 per risk allele, P=0.005; LVEF: −2.96% per risk allele, P=0.001]. Multivariate analyses did not modify these associations. Conclusion. In CKD patients, the R allele of the Q192R variant in the PON1 gene is dose-dependently related to the severity of LVH and LV dysfunction and associates with the longitudinal evolution of these cardiac alterations. These results are compatible with the hypothesis that oxidative stress is implicated in cardiomyopathy in CKD patients

The intensification of thermal extremes in west Africa

International audienceThis study aims in filling the gap in understanding the relationship between trend and extreme in diurnal and nocturnal temperatures (Tx and Tn) over the Gulf of Guinea area and the Sahel. Time-evolution and trend of Tx and Tn anomalies, extreme temperatures and heat waves are examined using regional and station-based indices over the 1900–2012 and 1950–2012 periods respectively. In investigating extreme temperature anomalies and heat waves, a percentile method is used. At the regional and local scales, rising trends in Tx and Tn anomalies, which appear more pronounced over the past 60 years, are identified over the two regions. The trends are characterized by an intensification of: i) nocturnal/Tn warming over the second half of the 20th century; and ii) diurnal/Tx warming over the post-1980s. This is the same scheme with extreme warm days and warm nights. Finally annual number of diurnal and nocturnal heat waves has increase over the Gulf of Guinea coastal regions over the second half of the 20th century, and even more substantially over the post-1980s period. Although this trend in extreme warm days and nights is always overestimated in the simulations, from the Coupled Model Intercomparison Project Phase 5 (CMIP5), those models display rising trends whatever the scenario, which are likely to be more and more pronounced over the two regions in the next 50 years

Distribution of two X-linked trinucleotide polymorphisms in Greece

Objective: To determine the distribution of allele frequencies of two X-linked trinucleotide repeat polymorphisms in five major geographic areas of Greece. Methods: We have studied the distribution of the CGG repeat in the FMR-1 gene and of the CAG repeat in the androgen receptor (AR) gene in 194 and 175 unrelated blood donors, respectively. Results: The distribution of (CGG)n was not significantly different between the four main areas of the Hellenic peninsula, with the exception of an island area in southern Greece, while the distribution of (CAG)n was similar among the groups. The mean repeat number in the FMR-1 gene is 30 and the mean in the AR gene is 21. Conclusions: The Hellenic population is rather homogeneous regarding the X-linked polymorphisms studied. Copyright © 2001 S. Karger AG, Basel

Genetics of rearing success in four pure laying hen lines during the first 17 weeks of age

This study aimed to investigate the genetics of rearing success (RS) in laying hens. Four rearing traits: clutch size (CS), first week mortality (FWM), rearing abnormalities (RA), and natural death (ND), were included as factors determining RS. Pedigree, genotypic, and phenotypic records of 4 purebred genetic lines of White Leghorn layers were available for 23,000 rearing batches obtained between 2010 and 2020. FWM and ND showed little or no variation amongst the 4 genetic lines over the years 2010–2020, whereas an increase was observed for CS and a decrease for RA. To determine whether these traits were heritable, genetic parameters for each trait were estimated, using a Linear Mixed Model. Heritabilities within lines were low (0.05–0.19 for CS, 0.01–0.04 for FWM, 0.02–0.06 for RA, 0.02–0.04 for ND, and 0.01–0.07 for RS). Additionally, genome wide association study was done to scan the genomes of the breeders to reveal single nucleotide polymorphisms (SNPs) associated with these traits. Manhattan plots indicated the existence of 12 different SNPs having a significant effect on RS. Thus, the identified SNPs will increase the understanding of the genetics of RS in laying hens

Predicting hatchability of layer breeders and identifying effects of animal related and environmental factors

In this study, a data driven approach was used by applying linear regression and machine learning methods to understand animal related and environmental factors affecting hatchability. Data was obtained from a parent stock and grand-parent stock hatchery, including 1,737 batches of eggs incubated in the years 2010–2018. Animal related factors taken into consideration were strain (white vs. brown strain), breeder age, and egg weight uniformity at the start of incubation, whereas environmental factors considered were length of egg storage before incubation, egg weight loss during incubation and season. Effects of these factors on hatchability were analyzed with 3 different models: a linear regression (LR) model, a random forest (RF) model and a gradient boosting machine (GBM) model. In part one of the study, hatchability was predicted and the performance of the models in terms of coefficient of determination (R2) and root mean square error (RMSE) was compared. The ensemble machine learning models (RF: R2 = 0.35, RMSE = 8.41; GBM: R2 = 0.31, RMSE = 8.67) appeared to be superior than the LR model (R2 = 0.27, RMSE = 8.92) as indicated by the higher R2 and lower RMSE. In part 2 of the study, effects of these factors on hatchability were investigated more into detail. Hatchability was affected by strain, breeder age, egg weight uniformity, length of egg storage and season, but egg weight loss didn't have a significant effect on hatchability. Additionally, four 2-way interactions (breeder age × egg weight uniformity, breeder age × length of egg storage, breeder age × strain, season × strain) were significant on hatchability. It can be concluded that hatchability of parent stock and grand-parent stock layer breeders is affected by several animal related and environmental factors, but the size of the predicted effects varies between the methods used. In this study, 3 models were used to predict hatchability and to analyze effects of animal related and environmental factors on hatchability. This opens new horizons for future studies on hatchery data by taking the advantage of applying machine learning methods, that can fit complex datasets better than LR and applying statistical analysis

Predicting hatchability of layer breeders and identifying effects of animal related and environmental factors

In this study, a data driven approach was used by applying linear regression and machine learning methods to understand animal related and environmental factors affecting hatchability. Data was obtained from a parent stock and grand-parent stock hatchery, including 1,737 batches of eggs incubated in the years 2010–2018. Animal related factors taken into consideration were strain (white vs. brown strain), breeder age, and egg weight uniformity at the start of incubation, whereas environmental factors considered were length of egg storage before incubation, egg weight loss during incubation and season. Effects of these factors on hatchability were analyzed with 3 different models: a linear regression (LR) model, a random forest (RF) model and a gradient boosting machine (GBM) model. In part one of the study, hatchability was predicted and the performance of the models in terms of coefficient of determination (R2) and root mean square error (RMSE) was compared. The ensemble machine learning models (RF: R2 = 0.35, RMSE = 8.41; GBM: R2 = 0.31, RMSE = 8.67) appeared to be superior than the LR model (R2 = 0.27, RMSE = 8.92) as indicated by the higher R2 and lower RMSE. In part 2 of the study, effects of these factors on hatchability were investigated more into detail. Hatchability was affected by strain, breeder age, egg weight uniformity, length of egg storage and season, but egg weight loss didn't have a significant effect on hatchability. Additionally, four 2-way interactions (breeder age × egg weight uniformity, breeder age × length of egg storage, breeder age × strain, season × strain) were significant on hatchability. It can be concluded that hatchability of parent stock and grand-parent stock layer breeders is affected by several animal related and environmental factors, but the size of the predicted effects varies between the methods used. In this study, 3 models were used to predict hatchability and to analyze effects of animal related and environmental factors on hatchability. This opens new horizons for future studies on hatchery data by taking the advantage of applying machine learning methods, that can fit complex datasets better than LR and applying statistical analysis