Genetic Variance Research Articles

Elucidation of gene action and estimation of combining ability effects for fruit yield attributes and biochemical traits in brinjal (Solanum melongena L.)

Abstract The present investigation was undertaken in brinjal (Solanum melongena L.) to estimate combining ability and gene action following a half diallel mating design using ten parents. The ratio of genetic variance components, σ2gca/σ2sca and σ2D/σ2A, showed dominant gene action, indicating the presence of non-additive genetic variance for days to 50% flowering, fruit yield per plant, total phenol, antioxidant activity and total soluble sugar. The traits fruits per plant, fruit girth, and fruit weight showed additive genetic variance. Among the parental genotypes, AB 15-08, ABSR 2 and AB 20-13 were found to be good general combiners for fruit yield, fruits per plant, and total soluble solids. The three highest-ranking hybrids were AB 15-08 × AB 20-13, AB 15-08 × AB 20-03 and AB 20-07 × GP BRJ 224. The above hybrids would be rewarding for harnessing heterosis. Keywords: Diallel cross, brinjal, combining ability, fruit yield, antioxidant activity

Genetic analysis for reproductive and longevity traits in Landlly sows

The rate of reproduction and lifetime productivity of the animals determine the success of a herd. As these features significantly impact sow productivity, welfare, and profitability, there is considerable interest in global swine breeding programmes to genetically select better sows for these attributes. In this scenario, reliable heritability estimates and genetic correlations between traits are essential for the success of genetic selection for such traits. The goals of the current study were to examine the reproductive and longevity traits and to estimate the genetic and phenotypic parameters for these traits in Landlly sows kept at a swine production farm, Izatnagar, Uttar Pradesh, India. The effect of non-genetic factors on different reproductive traits was estimated using SPSS version 16.0 and Bayesian approach which were utilized to estimate the genetic parameters for reproductive and longevity traits. The current study’s finding that the year and season of birth significantly affect reproductive traits generally suggests that there is potential for boosting productivity by optimising managemental practices. The heritability estimates were low for the reproductive and longevity traits, indicating a very little additive genetic variance in these traits, and individual selection will not be helpful for improving them. As some traits, such as LSB_FF & LSW_FF and LSB _SF & LSW_SF, have a moderate genetic correlation, indirect selection can be used to improve these two pairs of features.

7939 Impact of genetic background on chronic inflammation in a letrozole-induced mouse model of PCOS

Abstract Disclosure: N. Ujagar: None. G. De Robles: None. L.M. Velez: None. M. Seldin: None. D. Nicholas: None. The etiology behind polycystic ovary syndrome (PCOS) is elusive due to the heterogeneity of its symptoms and varying severity in patients. There are a wide variety of factors associated with PCOS, like hyperinsulinemia, hyperandrogenism, endotoxemia and chronic inflammation that have been implicated as causal factors in the condition. However mechanisms of cause and effect are difficult to establish in human cohorts. Endotoxemia, observed in humans and mouse models of PCOS, is known to trigger chronic inflammation by activating immune cells. Although correlation of chronic inflammation with PCOS has been established, the type of inflammation in PCOS patients has not been defined, in part due to the genetic variance from person to person. Using a systems immunology approach to mimic human genetic diversity, combined with a letrozole-induced PCOS mouse model, we aim to distinguish the types of inflammation present in PCOS. We analyzed the immune cells from lymph nodes and spleens of 21 different strains of PCOS induced and control mice (130 mice in total), and found that based on genetic background the reproductive and metabolic phenotypes differed- similar to how PCOS presents clinically. We were able to categorize each letrozole-induced mouse strain into the sub-phenotypes of PCOS based on the current guidelines for diagnosing PCOS published in 2023 and extensive characterization of each strain. Splenocytes from these control and PCOS mice were activated in vitro to trigger T cell and myeloid cell cytokine secretion, which was measured via luminex. Clustering analysis of these multi-analyte cytokine measurements together with flow cytometry of the immune cells allowed us to identify T-helper 17 (Th17) cells as the prominent immune cell driving inflammation in letrozole-induce mouse model of PCOS independent of genetic background. This systems immunology approach has allowed us to define PCOS associated inflammation and will be the basis for integrating human inflammation in PCOS with mouse models that appropriately recapitulate disease. The discovery of Th17 cells as key inflammatory mediators provide targets for understanding the mechanisms linking chronic inflammation to reproductive and metabolic outcomes in PCOS, and will assist in future development of anti-inflammatory therapies. Presentation: 6/2/2024

7939 Impact of genetic background on chronic inflammation in a letrozole-induced mouse model of PCOS

Abstract Disclosure: N. Ujagar: None. G. De Robles: None. L.M. Velez: None. M. Seldin: None. D. Nicholas: None. The etiology behind polycystic ovary syndrome (PCOS) is elusive due to the heterogeneity of its symptoms and varying severity in patients. There are a wide variety of factors associated with PCOS, like hyperinsulinemia, hyperandrogenism, endotoxemia and chronic inflammation that have been implicated as causal factors in the condition. However mechanisms of cause and effect are difficult to establish in human cohorts. Endotoxemia, observed in humans and mouse models of PCOS, is known to trigger chronic inflammation by activating immune cells. Although correlation of chronic inflammation with PCOS has been established, the type of inflammation in PCOS patients has not been defined, in part due to the genetic variance from person to person. Using a systems immunology approach to mimic human genetic diversity, combined with a letrozole-induced PCOS mouse model, we aim to distinguish the types of inflammation present in PCOS. We analyzed the immune cells from lymph nodes and spleens of 21 different strains of PCOS induced and control mice (130 mice in total), and found that based on genetic background the reproductive and metabolic phenotypes differed- similar to how PCOS presents clinically. We were able to categorize each letrozole-induced mouse strain into the sub-phenotypes of PCOS based on the current guidelines for diagnosing PCOS published in 2023 and extensive characterization of each strain. Splenocytes from these control and PCOS mice were activated in vitro to trigger T cell and myeloid cell cytokine secretion, which was measured via luminex. Clustering analysis of these multi-analyte cytokine measurements together with flow cytometry of the immune cells allowed us to identify T-helper 17 (Th17) cells as the prominent immune cell driving inflammation in letrozole-induce mouse model of PCOS independent of genetic background. This systems immunology approach has allowed us to define PCOS associated inflammation and will be the basis for integrating human inflammation in PCOS with mouse models that appropriately recapitulate disease. The discovery of Th17 cells as key inflammatory mediators provide targets for understanding the mechanisms linking chronic inflammation to reproductive and metabolic outcomes in PCOS, and will assist in future development of anti-inflammatory therapies. Presentation: 6/2/2024

The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions.

Observational studies have highlighted the pivotal role of inflammatory cytokines in cirrhosis progression. However, the existence of a causal link between inflammatory cytokines and cirrhosis remains uncertain. In this study, we conducted a bidirectional Mendelian randomization (MR) analysis at a summarized level to illuminate the potential causal relationship between the two variables. This study utilized genetic variance in cirrhosis and inflammatory cytokines from a genome-wide association study (GWAS) of European descent. The MR-PRESSO outlier test, Cochran's Q test, and MR-Egger regression were applied to assess outliers, heterogeneity, and pleiotropy. The inverse variance weighted method and multiple sensitivity analyses were used to evaluate causalities. Furthermore, the validation set was used for simultaneous data validation. The inflammatory cytokine monocyte chemoattractant protein 3 (MCP-3) was supposedly associated with a greater risk of cirrhosis. And cirrhosis was significantly correlated with increased levels of hepatocyte growth factor (HGF). This study suggests that MCP-3 might be associated with the etiology of cirrhosis, while several inflammatory cytokines could potentially play a role in its downstream development. Additionally, the progression of cirrhosis was associated with elevated levels of HGF, suggesting a possible role for liver repair functions.

First insight of the genome-wide association study and genomic prediction into enteritis disease (Vibrio harveyi) resistance trait in the lined seahorse (Hippocampus erectus).

Enteritis caused by Vibrio is a highly die-off disease that severely impeded substantial production in seahorse aquaculture. In the present study, challenged with LD50 of concentration of Vibrio harveyi, a total of 161 of susceptible and 166 of resistant individuals were allocated into binary survival phenotypes, thus, to firstly investigate the genetic architecture by genome-wide association study (GWAS) analysis, as well as to evaluate the feasibility of genomic selection (GS) in enteritis disease resistance trait of the lined seahorse Hippocampus erectus. Results indicated that the heritability for resistance to Vibrio harveyi was estimated to be 0.10. And a set of 10 significant/suggestive SNPs in a multiple chromosomes localization were identified, explaining 7.76% to 13.28% of genetic variance. Associated 82 of candidate genes were clustered into signal transduction, cell proliferation, response of external stress, bacteria defence, and anti-inflammatory processes. Moreover, the potential performance of genomic selection (GS) in application in selective breeding for enteritis disease resistance seahorses was assessed by genomic prediction (GP). In general, the predictive accuracy of the genomic estimated breeding value (GEBV) of BayesC exceeded the rrBLUP, BayesA, RKHS, and SVM models while with no significant difference. And the GWAS-informative SNPs was significantly superior in efficience than random selected markers by comparison of predictive performance on different selection strategies of SNPs. Overall, the genetic basis of enteritis disease resistance trait in the lined seahorse is a polygenic genetic architecture. SNPs associated with the important genes of cathepsin L1-like previously reported with respect to disease resistance are consider as potential molecular markers of genetic breeding. Furthermore, GS approach is an appropriate, effective, and less-cost application in breeding enteritis disease-resistant seahorses.

Nitrogen use efficiency of open-pollinated maize cultivars and topcrosses under low nitrogen soil

The study was conducted to evaluate the nitrogen utilization efficiency (NUE) of open-pollinated maize cultivars under low nitrogen soil conditions. It took place at the Lower Niger River Basin Development Authority in Ilorin, Nigeria, during the 2019 and 2020 cropping seasons. The nitrogen treatments (0, 45, and 90 kg N ha⁻1) were assigned as the main plots, while the genotypes were designated as sub-plots in a split-plot design. The NUE of open-pollinated varieties (OPVs) and Topcrosses significantly increased with the application of 45 kg of nitrogen per hectare compared to 0 kg. Similarly, the maize grain yield for both OPVs and Topcrosses ranged from 1.40 to 3.57 t ha⁻1 and from 1.07 to 2.64 t ha⁻1 across the two years with the application of 45 kg of nitrogen per hectare compared to 0 kg. With the exception of days to 50 % silking, all other traits tested exhibited a significant correlation with grain yield. A gene pool for tolerance to low nitrogen may have been present in the cultivars Dmr-Esr Y Cif 2 and Acr 95 Tze Comp 4C3, which combined well with other cultivars to produce high grain yield and NUE. Topcrosses, including Ev Dt-Y 2000 Str C0 × Tze-W Dt Str C4, Dmr-Esr Y Cif 2 × Acr 95 Tze Comp 4C3, Ak 95 Dmr-Esrw × Tzb Rel D4 CO-W, Dmr-Esr Y Cif 2 × Tze-W Dt Str C4, and Br 9922 Dmr Sr × Ev Dt-Y 2000 Str C0, also demonstrated strong potential for maize grain yield and NUE. This suggests that these crosses could serve as promising candidates for developing low nitrogen tolerance in maize for low nitrogen soil environments. A positive relationship between fertilizer levels, NUE, and grain yield indicates that proper nitrogen fertilizer management can enhance both NUE and the grain yield of maize genotypes. Genetic analysis revealed that both phenotypic coefficient of variation (PCV) and environmental variance (δ2p) were higher than genotypic coefficient of variation (GCV) and genetic variance (δ2g), suggesting that environmental influences were more significant than genetic factors for the tested traits. Consequently, improving these traits may be achieved by manipulating environmental conditions.

Season-specific genetic variation underlies early-life migration in a partially migratory bird.

Eco-evolutionary responses to environmentally induced selection fundamentally depend on magnitudes of genetic variation underlying traits that facilitate population persistence. Additive genetic variances and associated heritabilities can vary across environmental conditions, especially for labile phenotypic traits expressed through early life. However, short-term seasonal dynamics of genetic variances are rarely quantified in wild populations, precluding inference on eco-evolutionary outcomes in seasonally dynamic systems. This limitation applies to seasonal migration versus residence, constituting one key trait where rapid microevolution could rescue partially migratory populations from changing seasonal environments. We fitted novel quantitative genetic 'capture-recapture animal models' to multi-generational pedigree and year-round resighting data from 11 cohorts of European shags (Gulosus aristotelis), to estimate season-specific additive genetic variances in liabilities to migrate, and in resulting expression of migration, in juveniles' first autumn and winter. We demonstrate non-negligible genetic variation underlying early-life migration, with twice as large additive genetic variances and heritabilities in autumn than winter. Since early-life survival selection on migration typically occurs in winter, highest genetic variation and strongest selection are seasonally desynchronized. Our results reveal complex within- and among-year dynamics of early-life genetic and phenotypic variation, demonstrating that adequate inference of eco-evolutionary outcomes requires quantifying microevolutionary potential on appropriate scales and seasonal timeframes.

A genome-wide association study reveals candidate genes and regulatory regions associated with birth weight in pigs.

Piglet birth weight is associated with preweaning survival, and its related traits have been included in the breeding program. Thus, understanding its genetic basis is essential. This study identified four birth weight-associated genomic regions on chromosomes 2, 4, 5, and 7 through genome-wide association study analysis in 7286 pigs from three different pure breeds using the FarmCPU model. The genetic and phenotypic variance explained by the four candidate regions is 8.42% and 1.85%, respectively. Twenty-eight candidate genes were detected, of which APPL2, TGFBI, MACROH2A1, and SEC22B have been reported to affect body growth or development. In addition, 21 H3K4me3-enriched peaks overlapped with the birth weight-associated genomic regions were identified by integrating the genome-wide association study results with our previous ChIP-seq and RNA-seq data generated in the pig placenta, a fetal organ relevant to birth weight, and three of the regulatory regions influence TGFBI, MACROH2A1, and SEC22B expression. This study provides new insights into understanding the mechanisms for birth weight. Further investigating the variants in the regulatory regions would help identify the functional variants for birth weight in pigs.

Evaluation of Bread Wheat (Tritium aestivum L.) Genotype in Multi-environment Trials Using Enhanced Statistical Models

In varietal selection field trials, spatial variation and genotype by environment (GxE) interaction are frequent and present a major challenge to plant breeders comparing the genetic potential of several cultivars. To consistently select superior cultivars that increase agricultural production, bread wheat breeding studies must be evaluated using efficient statistical techniques. By modeling the interactions of geographical field trends and genotypes by environment interaction, this work aimed to forecast the genetic potential of bread wheat varieties across settings and improve selection tactics. The dataset utilized in this investigation consisted of sixteen multi-environment trials (MET) that were carried out using a randomized complete block design (RCBD), with two replications arranged in plot arrays of rows and columns. The findings showed that the factor analytical and spatial models were effective ways to analyze the data for this study under the linear mixed model. By ranking average Best Linear Unbiased Predictions (BLUPs) within clusters, the 16 bread wheat environments were grouped into three mega environments (C1, C2, and C3) based on yield. This served as a selection indicator. Ranking average BLUPs helped in the selection of superior and stable genotypes. The first cluster (C1)'s mean BLUP values were used to score the genotypes' performance; C2 and C3 were excluded because of their limited genetic variety and low genetic connection with the other trials. The genotypes with the highest potential based on this cluster were EBW192346 and EBW192347, chosen for a subsequent verification study to release a variety. The estimates for variance component parameters ranged from 0.013 to 3.024 for genetic variance and from 0.072 to 0.37 for error variance. Hence, scaling up the use of this efficient analysis method will improve the selection of superior bread wheat varieties.

Marker weighting improves single-step genomic prediction reliabilities of udder health traits in Nordic Red and Jersey dairy cattle populations

The standard single-step genomic prediction model assumes that all SNP markers explain an equal amount of genetic variance, which, however, may not be true. This is because SNPs are located in or near different genes with different functions. Therefore, it seems logical to consider SNP marker-specific weights when predicting genomic breeding values. We hypothesized that allowing differences in the amount of genetic variance explained by each SNP marker will improve prediction reliability and response to selection. To investigate this hypothesis, we first developed multi-trait standard single-step genomic models based on the current multi-trait random regression evaluation models for udder health traits of the Nordic Red (RDC) and Jersey (JER) dairy cattle populations. The models included 4 clinical mastitis (CM) traits, 3 test-day somatic cell score (SCS) traits, and the conformation traits fore udder attachment and udder depth. In the second step, we investigated the effect of applying different SNP marker weighting scenarios in the single-step genomic prediction models, for which a single-step SNP best linear unbiased prediction model was applied. We investigated the prediction reliability of the different models by forward prediction, where the last 4 years of the data were removed to estimate breeding values for validation candidates. In addition, genetic trends of the pedigree-based estimated breeding values (PEBV) and genomic enhanced breeding values (GEBV) were examined. The data sets for RDC and JER included 6.9 and 1.2 million animals of which 5.6 and 0.9 million cows had records, respectively. The number of genotyped animals was 125,789 and 64,777 for RDC and JER, respectively. Cows had repeated SCS observations but only single observations for all other traits and breeding values for all traits were modeled by one covariance function. This required modeling 12 eigenvalue breeding value coefficients for each cow and developing SNP marker weights for the principal components rather than for the biological traits. We investigated 3 SNP marker weighting scenarios: 1) a nonlinear method similar to BayesA, 2) using the classical formula 2pqû2 that accounts for allele heterozygosity, and 3) applying a mean SNP weight calculated by 2pqû2 for every 20 adjacent SNP markers. Bias, dispersion, and prediction reliability were calculated using PEBV or GEBV from the evaluation based on the full data set on those using the reduced data set. We found that the recent favorable genetic trend in CM and SCS has been accelerated since the introduction of genomic selection. The study also shows that a significant increase in prediction reliability, i.e., 0.74 vs. 0.48 for RDC and 0.72 vs. 0.41 for JER cows for CM, can be achieved with a standard single-step genomic prediction model compared with a pedigree-based prediction model. Almost all scenarios with SNP marker weighting further improved the prediction reliability between 0.5% and 12.7%. The highest improvement was achieved by weighing the SNP markers based on the 2pqû2 formula.

Identification of quantitative trait loci and candidate genes for pod shatter resistance in Brassica carinata

BackgroundUnderstanding the genetic control of pod shatter resistance and its association with pod length is crucial for breeding improved pod shatter resistance and reducing pre-harvest yield losses due to extensive shattering in cultivars of Brassica species. In this study, we evaluated a doubled haploid (DH) mapping population derived from an F1 cross between two Brassica carinata parental lines Y-BcDH64 and W-BcDH76 (YWDH), originating from Ethiopia and determined genetic bases of variation in pod length and pod shatter resistance, measured as rupture energy. The YWDH population, its parental lines and 11 controls were grown across three years for genetic analysis.ResultsBy using three quantitative trait loci (QTL) analytic approaches, we identified nine genomic regions on B02, B03, B04, B06, B07 and C01 chromosomes for rupture energy that were repeatedly detected across three growing environments. One of the QTL on chromosome B07, flanked with DArTseq markers 100,046,735 and 100,022,658, accounted for up to 27.6% of genetic variance in rupture energy. We observed no relationship between pod length and rupture energy, suggesting that pod length does not contribute to variation in pod shatter resistance. Comparative mapping identified six candidate genes; SHP1 on B6, FUL and MAN on chromosomes B07, IND and NST2 on B08, and MAN7 on C07 that mapped within 0.2 Mb from the QTL for rupture energy.ConclusionThe results suggest that favourable alleles of stable QTL on B06, B07, B08 and C01 for pod shatter resistance can be incorporated into the shatter-prone B. carinata and its related species to improve final seed yield at harvest.

Adaptive potential in the face of a transmissible cancer in Tasmanian devils.

Emerging infectious diseases (EIDs) not only cause catastrophic declines in wildlife populations but also generate selective pressures that may result in rapid evolutionary responses. One such EID is devil facial tumour disease (DFTD) in the Tasmanian devil. DFTD is almost always fatal and has reduced the average lifespan of individuals by around 2 years, likely causing strong selection for traits that reduce susceptibility to the disease, but population decline has also left Tasmanian devils vulnerable to inbreeding depression. We analysed 22 years of data from an ongoing study of a population of Tasmanian devils on Freycinet Peninsula, Tasmania, to (1) identify whether DFTD may be causing selection on body size, by estimating phenotypic and genetic correlations between DFTD and size traits, (2) estimate the additive genetic variance of susceptibility to DFTD, and (3) investigate whether size traits or susceptibility to DFTD were under inbreeding depression. We found a positive phenotypic relationship between head width and susceptibility to DFTD, but this was not underpinned by a genetic correlation. Conversely, we found a negative phenotypic relationship between body weight and susceptibility to DFTD, and there was evidence for a negative genetic correlation between susceptibility to DFTD and body weight. There was additive genetic variance in susceptibility to DFTD, head width and body weight, but there was no evidence for inbreeding depression in any of these traits. These results suggest that Tasmanian devils have the potential to respond adaptively to DFTD, although the realised evolutionary response will critically further depend on the evolution of DFTD itself.

Multi Environment Trials and Adaption of Advanced Bread Wheat (<i>Triticum aestivum </i>L.) Genotypes in Low Moisture Stress Areas of Ethiopia

To predict bread wheat genetic potential across environments and adaption in low moisture stress wheat growing areas of Ethiopia. Multi-location trials were conducted in Ethiopia from 2020 to 2021 in main seasons. A total of advanced genotypes including the checks were arranged in randomized complete block design in a rectangular (row x column) array of plots with two replications. The results showed that, under the linear mixed model, the spatial and factor analytic models were efficient methods of data analysis for this study. By ranking average best linear unbiased prediction (BLUPs) within clusters, the 13 bread wheat environments were clustered into three mega environments (C1, C2, and C3) for the trait grain yield. This method used as a selection indicator, assisting in the selection of superior and adaptable types. The predicted performance of genotypes based on BLUP values averaged across correlated settings of C1 and C2, eliminating C3 due to low genetic correlation with the other trials and low genetic variation. Based on these clusters, the genotypes with the highest potential EBW192350 and EBW192369 were selected for a subsequent verification study that might potentially use them as a released variety. For genetic variance, the estimates for variance component parameters ranged from 0.069 to 2.896 and error variance, they ranged from 0.175 to 1.002. Therefore, increasing the application of this efficient analysis method will improve the selection of superior bread wheat varieties. The two genotypes can be further verified using national performance trials/ or verified in farmers’ fields for registration and commercialization.

Meat tenderness in Australian lamb: Data editing, environmental variation and their effects in genetic parameter estimation

Breeding for meat quality increases the value of lambs and requires reliable genetic parameters to achieve balanced genetic progress. Meat tenderness, accomplished by selecting for lower shear force, is an important eating quality trait because of its relationship with consumer satisfaction. Factors influencing shear force, include the pH and temperature decline post-mortem which can contribute towards higher shear force values and increased variation across contemporary groups. This study explored if genetic parameters for shear force change when post slaughter covariates and heterogeneous variance are corrected for, using data from 32,223 animals from different sheep breeds. Results showed that removing extreme individuals and contemporary groups with high mean shear force values reduced residual variance, followed by a smaller reduction in additive genetic variance and little effect on heritability. Results show that edited data performed better at predicting progeny performance and reduced potential bias introduced in the genetic evaluation due to data quality. The effect of including post-slaughter covariates in the genetic analysis was tested by estimating different model predictability through regression of estimated breeding values against offspring performance, showing that the model including hot carcass weight performed better followed by the one including both carcass weight and C-site fat depth. Our results highlight that historic and current in-plant recording practices do not provide the capacity to account for non-genetic factors associated with abattoir environment that might be impacting the ability to accurately calculate shear force breeding values. In that sense, genetic evaluation can be improved by applying more rigorous data editing.

Evaluating genotype by environment interaction for growth traits in Limousine cattle

Environmental conditions affect the growth and health of animals, making it crucial to quantify heat stress and the genetic basis of heat tolerance in animal breeding. The main objective of this study was to evaluate heat stress on beef cattle growth and investigate the genetic background of tolerance to harsh environmental conditions in the Italian Limousine beef cattle. Three growth traits were analysed: average daily gain (ADG), weaning weight (WW), and yearling weight (YW). Data were collected from animals raised between 1991 and 2022 and combined with 14 environmental covariates. Records for ADG, WW, and YW encompassed 108 205, 100 058, and 24 939 individuals, respectively, with 4 617, 4 670, and 2 048 genotyped individuals. Climatic variables were compared for inclusion in a linear mixed model using the Deviance Information Criterion. Multiple-trait models and genomic information incorporated environmental conditions with the largest impact on the studied traits Genotype by environment interaction (G × E) was detected in all the studied traits, showing substantial heterogeneity of the variance components across the different environments (Env). Heritability for WW remains constant among Env; instead, for ADG and YW decreased under uncomfortable environmental conditions. YW showed the lowest genetic correlation (0.28) between divergent conditions (Env 2 and Env 5,) for ADG and WW correlations dropped below 0.50 among Env. The values of genetic correlations indicate that growth traits are moderately to strongly affected by G × E. Eigenvalue decomposition of the additive genetic (co)variance matrix for ADG, WW, and YW indicated that three components accounted for over 0.80 of the proportion of the variance explained, suggesting different animal performances across Env. Spearman rank correlations showed potential re-ranking of genotyped sires, because ADG, WW, and YW showed correlations between Env below 0.80. The accuracy of single-step genomic EBV was higher compared to EBV for al traits. Overall, the result confirms the existence of G × E for growth traits in the Italian Limousine population. Including G × E in the model allows for more environment-aware predictions, helping breeders understand how different genetic bases respond to varying conditions. Genomic predictions incorporating G × E could accelerate genetic gains and improve response to selection for heat tolerance.

Analysis of Component of Genetic Variance, Combining Ability, Gene Action and Heterotic Response in Rice (Oryza sativa. L)

Rice (Oryza sativa L.) is an important staple crop, particularly in Asia, where it feeds more than half the world's population. The analysis of variance (ANOVA) for nine traits in a rice breeding experiment with seven lines, three testers, 24 F1 hybrids, and two checks indicated significant variations between treatments, showing significant genetic diversity. General combining ability (GCA) effects revealed that line MTU 1010 and tester NDR 2026 had favorable GCA effects on grain yield per plant, but NDR 2064 and Barani Deep had negative effects. Three crosses, showed considerable favorable benefits on grain yield due to specific combining ability (SCA). Heterosis analysis identified promising crosses, including Gopal Bhag × Prakhar and NDR-2064 × NDR-2026, that demonstrated considerable positive heterosis above the parent and standard varieties Shushk Samrat and Ashwani. These findings emphasize the possibility of specific crosses to improve grain production in rice breeding efforts.

Density dependent survival drives variation in density dependent population growth of an insect pest

Several ecological processes – from population dynamics to species co‐existence – are driven by density dependence (DD) in population growth rate. Thus, to predict and manage ecological outcomes, we need a deep understanding of which factors and demographic traits drive variation in DD. In the insect pest Tribolium castaneum, we found large variation in DD across habitats but not across source populations. We modeled DD in population growth as the product of DD in fecundity and survival, experimentally estimating each parameter. Across habitats, survival parameters varied more than fecundity, including in simulations with varying parameter values. Thus, DD in survival drives variation in density‐dependent population growth. Hence, under strong density‐dependent selection, we expect evolutionary change in density‐dependent survival, provided sufficient genetic variance. Our general framework combining detailed experiments and simulations with a simple model can be used for other species to better understand the causes and consequences of density dependent population growth.

Antler size in red deer: declining selection and increasing genetic variance with age, but little senescence.

While senescence is a common occurrence in wild populations, not all traits decline with age simultaneously and some do not show any senescence. A lack of senescence in secondary sexual traits is thought to be due to their importance for reproductive success. However, if reproductive success senesces, why would secondary sexual traits apparently not senesce? Here, we explored this question in a wild population of red deer (Cervus elaphus) using antler form (number of points), a secondary sexual trait which shows little senescence, despite the occurrence of reproductive senescence. In line with expectations for traits that senesce, genetic variance in antler form increased with age and selection weakened with age. Therefore, there was no indication that a stronger selection on individuals that survived to older ages was countering the dilution of selection due to fewer individuals being alive. Furthermore, the effect of selective disappearance masking a slight decline in antler form in the oldest years was small. Interestingly, although genetic variance and positive selection of antler form were found, there was no evidence of a response to selection, supporting a genetic decoupling of antler senescence and reproductive senescence. Finally, a positive genetic covariance in antler form among age classes provides a possible explanation for the lack of senescence. These findings suggest that the antler form is under a genetic constraint that prevents it from senescing, providing an interesting evolutionary explanation for negligible senescence in a secondary sexual trait, and consequently, the existence of asynchrony in senescence among traits within populations.

Influence of variance component estimates on genomic predictions for growth and reproductive-related traits in Nellore cattle.

This study aimed to estimate variance components (VCs) for growth and reproductive traits in Nellore cattle using two relationship matrices (pedigree relationship A matrix and pedigree plus genomic relationship H matrix), and records collected before and after genomic selection (GS) implementation. The study also evaluated how genomic breeding values (GEBV) are affected by variance components and discarding old records. The analysed traits were weight at 120 days (W120), weight and scrotal circumference at 450 days (W450 and SC450, respectively). Three datasets were used to estimate VCs, including all phenotypic information (All) or records for animals born before or after GS implementation (Before or After datasets, respectively). Both relationship matrices were considered for VC estimation, the A matrix was used in all three datasets and VC from each combination were named as A_Before, A_After, and A_All). The H was used in two datasets: H_All and H_After. Different VCs were used for GEBV prediction through ssGBLUP. This step used two possible Datasets, using all available phenotypic data (Dataset 1) or just records collected since GS implementation (Dataset 2). Validation was conducted using accuracy, bias and dispersion according to the LR method and prediction accuracy from corrected phenotypes. The heritability of all traits increased from A_Before to A_After, while estimates for A_All were intermediary. In the previous order, the estimates were 0.16, 0.17, and 0.15 for W120; 0.31, 0.39, and 0.35 for W450; 0.35, 0.47, and 0.41 for SC. For W450 and SC, using the H matrix reduced the heritability (0.33 and 0.32 for W450; 0.41 and 0.38 for SC for H_After and H_All, respectively). For W120, Dataset1 and VCs from A_After showed the highest accuracy for direct and maternal GEBV (0.953 and 0.868). For W450, Dataset 1 and VC from H_After allowed the highest accuracy (0.854) but use Dataset 2 and same VC source yield similar value (0.846). For SC, Dataset 2 with VC from H_After showed the highest accuracy (0.925). To use Dataset 2 does not cause important changes in bias or dispersion with respect to Dataset 1. The VC and genetic parameters changed for W120, W450, and SC450, using records before or after the GS implementation. For W450 and SC450, genetic variance and heritability estimates increased with the use of GS. For W120, genomic predictions were more accurate using A for VC estimation. Accuracy gains were observed for W450 and SC450 using H in VC estimation and/or discarding records before GS. It is possible to discard phenotypic records before GS implementation without generating bias or dispersion in the GEBV of young candidates.