Genetic Progress Research Articles

Genetic Variability Studies in Chickpea (Cicer arietinum L.) for Yield and Contributing Traits Through Half-diallel Mating Strategy under Late-sown Conditions

Background: One of the oldest cultivated legumes, chickpeas (Cicer arietinum L.), are essential for world nutrition, especially with climate change. Chickpea output is poor despite rising demand for its nutritional value. Improved genetics can enhance yields to fulfil local and global demands. By selecting better parents and potential hybrids utilising half-diallel mating, breeders can hasten the development of high-yielding, durable cultivars. Methods: This study examined 55 half-diallel mating genotypes, comprising 10 parental lines and 45 F1 hybrids. At the Acharya Narendra Deva University of Agriculture and Technology Genetics and Plant Breeding Farm in Ayodhya, U.P., these were evaluated using a randomised block design with three replications under late-sown circumstances in 2022-23 rabi. Normal agricultural procedures were used. A four-meter row of each genotype was planted with 30 cm between rows and 10 cm between plants. Genetic indices like GCV, PCV, heritability, genetic progress, correlation coefficient and path analysis were evaluated. Result: ANOVA revealed significant genetic variability across all traits. Biological yield plant-1 had the highest PCV and GCV, followed by seed yield plant-1. Heritability and genetic progress were highest for days to 50% flowering and biological yield plant-1, indicating strong genetic control. Days to 50% flowering and maturity inversely linked with most yield-related characteristics. Seed yield was closely associated with primary and secondary branches plant-1, plant height, pods per plant, seeds pod-1, 100-seed weight and biological yield. Path coefficient study showed that all characteristics except days to 50% flowering, days to maturity and plant height influenced grain yield.

Novel antiseizure medications in the development pipeline: promising candidates and recent failures

AbstractIn the past decade, we have observed a paradigm shift in the discovery of novel antiseizure medications (ASMs), which is a consequence of significant progress in epilepsy genetics, the availability of novel disease models, drug-screening technologies, the discovery of new mechanisms of action, expanding existing drugs into new populations, or new routes of delivery. This resulted in a very rich pipeline of potential future treatments for epilepsy, including gene-modifying treatments for rare genetic epilepsies and mechanistically guided precision treatments. Hopes are high that this pipeline may bring therapeutic breakthroughs similar to those recently achieved with cenobamate for the common drug-resistant focal epilepsy and fenfluramine for seizures in Dravet syndrome. Furthermore, we stand at the beginning of a new era of epilepsy treatment expansion, from traditional seizure suppression to disease prevention and modification. This review discusses the most advanced and promising candidates in the clinical development pipeline but also describes recent failures in the development of novel ASMs.

Identification of genetic fingerprint of type I interferon therapy in visceral metastases of melanoma

Malignant melanoma is a difficult-to-treat skin cancer with increasing incidence worldwide. Although type-I interferon (IFN) is no longer part of guidelines, several melanoma patients are treated with type-I interferon (IFN) at some point of the disease, potentially affecting its genetic progression. We run genome-wide copy number variation (CNV) analysis on previously type-I IFN-treated (n = 17) and control (n = 11) visceral metastases of melanoma patients. Results were completed with data from the TCGA and MM500 databases. We identified metastasis- and brain metastasis-specific gene signatures mostly affected by CN gains. Some cases were genetically resistant to IFN showing characteristic gene alterations (e.g. ABCA4 or ZEB2 gain and alterations of DNA repair genes). Analysis of a previously identified type-I IFN resistance gene set indicates that only a proportion of these genes was exclusive for the IFN-treated metastases reflecting a possible selective genomic pressure of endogenous IFNs during progression. Our data suggest that previous type-I IFN treatment and/or endogenous IFN production by immune response affect genomic progression of melanoma which may have clinical relevance, potentially influence immune checkpoint regulation in the tumor microenvironment.

Wheat genetic progress in biomass allocation and yield components: A global perspective

BackgroundWheat is an essential food source and is subjected to intense breeding efforts for increased grain yield, but stagnation in grain yield improvements has been reported in many regions. The identification of genetically linked factors impeding further progress in wheat grain yield improvement is therefore urgently required. MethodA comparative meta-analysis of data from 66 publicly available field experiments involving multiple wheat genotypes was performed to identify traits altered in breeding programs, their relationship with grain yield, and their past and current impact on grain yield increases. ResultsWheat grain yield can be increased by increasing either the aboveground biomass (ABM) or the harvest index (HI). However, there was no correlation between these traits since a reduction in plant height can occur with increases in the HI and overall grain yield, but with no reduction in the ABM. The combined data from 32 global datasets revealed a substantial increase in wheat grain yield from 1860 to 2017, accompanied by improvement in HI and yield components. When considering only the genotypes introduced from the 1960s to 2017, there was a linear increase observed in both grain yield and HI until the mid-1980s. However, genetic progress in HI and GY has slowed down since then. Before the mid-1980s, there was a decreasing trend observed in plant height which remained relatively static thereafter. While ABM did not exhibit significant increases during this period. After the mid-1980s, significant improvements have been observed in ABM; however, no obvious increase were observed in other yield components. ConclusionsSince the 1980s, there has been an increase in the aboveground biomass of wheat, while grain weigth and grains per m2 increases trending slowly, and both harvest index and grain yield have almost stagnated. Therefore, increasing grains per m2 and/or grain weight should be the major research direction to further improve the wheat harvest index and grain yield in the future. ImplicationsThe systematic study of changes in wheat traits in past breeding efforts for improved grain yields has provided useful indicators for the direction of wheat breeding in the future.

Genetic Studies on Yield and Its Components in Pigeonpea (Cajanus cajan L. Millsp.)

A field trial was carried out at the International Crop Research Institute for the Semi-Arid Tropics, Patancheru, Hyderabad during Kharif, 2023-24, with the aim of studying genetic parameters for yield and its components among pigeonpea genotypes which provides in developing an apt selection index to be included in the breeding programs. This research was conducted on 200 genotypes of Indian pigeon pea on seven traits. Data were analysed to calculate analysis of variance (ANOVA), phenotypic coefficient of variation (PCV), genetic coefficient of variation (GCV), broad sense heritability (h2bs) and estimated genetic progression as percentage mean using R software v 4.3.1. The results of the analysis of variance showed that there was a significant difference for days to 50% flowering, plant height (cm), hundred seed weight and seed yield per plant among the genotypes. The genotypic coefficient of variance was lower than the genotypic coefficient of variance for all the traits under selection. Higher extent of genotypic coefficient of variation is manifested by days to 50% flowering, plant height, seeds per pod, hundred seed weight and seed yield per plant. High heritability coupled with genetic advance as per cent mean was observed in seed yield per plant, number of days to 50% flowering and plant height. The results concluded that seed yield per plant, number of days to flowering up to 50%, and plant height are the traits that should be emphasized as selection indicator in Indian pigeon pea breeding programs.

Transforming estimated breeding values from observed to probability scale: how to make categorical data analyses more efficient.

Threshold models are often used in genetic analysis of categorical data, such as calving ease. Solutions in the liability scale are easily transformed into probabilities; therefore, estimated breeding values are published as the probability of expressing the category of main interest and are the industry's gold standard because they are easy to interpret and use for selection. However, because threshold models involve non-linear equations and probability functions, implementing such a method is complex. Challenges include long computing time and convergence issues, intensified by including genomic data. Linear models are an alternative to overcome those challenges. Estimated breeding values computed using linear models are highly correlated (≥ 0.96) with those from threshold models; however, the lack of a transformation from the observed to the probability scale limits the use of linear models. The objective of this study was to propose transformations from observed to probability scale analogous to the transformation from liability to probability scale. We assessed computing time, peak memory use, correlations between estimated breeding values, and estimated genetic trends from linear and threshold models. With 11M animals in the pedigree and almost 965k genotyped animals, linear models were 5x faster to converge than threshold models (32 vs. 145h), and peak memory use was the same (189 GB). The transformations proposed provided highly correlated probabilities from linear and threshold models. Correlations between direct (maternal) estimated breeding values from linear and threshold models and transformed to probabilities were ≥ 0.99 (0.97) for all animals in the pedigree, sires with/without progeny records, or animals with phenotypic records; therefore, estimated genetic trends were analogous, suggesting no loss of genetic progress in breeding programs that would adopt linear instead of threshold models. Furthermore, linear models reduced computing time by five-fold compared to the threshold models; this enables weekly genetic evaluations and opens the possibility of using multi-trait models for categorical traits to improve selection effectiveness.

Age-Related Macular Degeneration (AMD): Pathophysiology, Drug Targeting Approaches, and Recent Developments in Nanotherapeutics

The most prevalent reason for vision impairment in aging inhabitants is age-related macular degeneration (AMD), a posterior ocular disease with a poor understanding of the anatomic, genetic, and pathophysiological progression of the disease. Recently, new insights exploring the role of atrophic changes in the retinal pigment epithelium, extracellular drusen deposits, lysosomal lipofuscin, and various genes have been investigated in the progression of AMD. Hence, this review explores the incidence and risk factors for AMD, such as oxidative stress, inflammation, the complement system, and the involvement of bioactive lipids and their role in angiogenesis. In addition to intravitreal anti-vascular endothelial growth factor (VEGF) therapy and other therapeutic interventions such as oral kinase inhibitors, photodynamic, gene, and antioxidant therapy, as well as their benefits and drawbacks as AMD treatment options, strategic drug delivery methods, including drug delivery routes with a focus on intravitreal pharmacokinetics, are investigated. Further, the recent advancements in nanoformulations such as polymeric and lipid nanocarriers, liposomes, etc., intended for ocular drug delivery with pros and cons are too summarized. Therefore, the purpose of this review is to give new researchers an understanding of AMD pathophysiology, with an emphasis on angiogenesis, inflammation, the function of bioactive lipids, and therapy options. Additionally, drug delivery options that focus on the development of drug delivery system(s) via several routes of delivery can aid in the advancement of therapeutic choices.

An efficient and easy-to-use protocol for induction of haploids in cucumber through parthenogenic embryo development

BackgroundCucumber (Cucumis sativus L.) is a model crop to study cell biology, including the development of haploids and doubled haploids in vegetable crops. In plant breeding, haploid and doubled haploids are valuable tools for developing pure homozygous inbred lines and accelerating genetic progress by reducing the time required for breeding cycles. Besides, the haploids are also valuable in genomic studies. We are reporting the induction of haploids in cucumber involving gynoecious and parthenocarpic genotypes for the first time. This study aimed to assess the efficient induction of haploids through pollination with gamma-irradiated pollen in cucumber. The effect of gamma irradiation dose on pollen viability and germination, fruit setting percentage, seed development, and haploid embryo development in cucumber hybrid genotypes were studied in detail. The goal was to utilize this information to produce haploid plants for genomics and transformation works in this model vegetable crop.ResultsPollination was done on six cucumber genotypes using varying doses of gamma rays (100, 200, 300, 400, and 500 Gy). Genotypes, doses of irradiation, and embryo developmental stage influenced the successful generation of in-vitro haploid plants. The optimal timeframe for embryo rescue was found to be 25 to 30 days after pollination. Haploid embryos were effectively induced using irradiated pollen at 400 to 500 Gy doses. Parthenogenetic plantlets were analyzed, and their ploidy level was confirmed through stomatal physiology, cytology (mitosis), and flow cytometry methods.ConclusionThrough parthenogenic embryo development, it is possible to induce a large number of haploids in cucumber. This technique’s power lies in its ability to streamline the breeding process, enhance genetic gain, and produce superior cultivars that contribute to sustainable agriculture and food security.

Genetic mechanism and exploration progress of global deep alkane gases and small molecule gases (H2, He)

From the perspective of global oil and gas exploration trends, deep to ultra-deep gas exploration has emerged as a focal point in the past decade, shaping the future landscape of fossil energy exploration. Specifically, China has undertaken oil and gas exploration ventures reaching depths of 10,000 m, drawing significant attention to the genesis of gas accumulation at such extreme depths. An analysis of crucial geological factors including basin formation, hydrocarbon generation, storage and accumulation reveals that kerogen cracking gas, crude oil cracking gas, and coal cracking gas served as vital gas sources for ultra-deep paraffin gas in highly over-mature oil-bearing basins, establishing a model for the formation, evolution, and accumulation of natural gas. With the continuous expansion of hydrocarbon genesis theory and the increasing global demand for clean energy, hydrogen, as an important link between inorganic and organic hydrocarbon generation theory, and as a promising clean energy, has gradually aroused extensive attention in the academic community. The genetic types of natural hydrogen are mainly generated from inorganic genetic mechanisms, including earth degassing, water-rock interaction, and water radiolysis. The lithology of the reservoir and the sealing property of the cap layer control the accumulation of natural hydrogen, with the salt-rock cap layer beneficial to the large-scale preservation of natural hydrogen reservoir. Helium, as a constituent resource of natural gas, represents the primary target for exploration among deep small molecule gases. Helium within natural gas reservoirs can be categorized into atmospheric, crust-source and mantle-source varieties based on their origins. Helium source rocks exhibit significant disparities in helium generation capacity attributed to variations in rock types, mineral compositions, and ages, resulting in a relatively complex mechanism governing helium reservoir release, migration and accumulation. Typically, helium-rich gas reservoirs lack optimal cap sealing performance, as excessive sealing inhibits the formation of pressure relief conduits, thereby impeding the supply of helium-rich fluid to the reservoir. Through an analysis of genetic types, formation and evolution models, and reservoir preservation mechanisms pertinent to deep alkane gases and small molecule gases, it is revealed that the large-scale pool-forming geologic units and giant hydrocarbon enrichment zones in ultra-deep strata are key and promising prospects for delivering successive discoveries, and the energy significance and development trend of natural hydrogen and helium in the future were pointed out, in order to provide references for promoting the transition from high carbon to low carbon and carbon-free energy in the energy field.

Genetic Variability in the Physicochemical Characteristics of Cultivated Coffea canephora Genotypes.

The objective of this study was to characterize the genetic divergence and selection gains of the physicochemical grains traits of 68 genotypes of C. canephora most cultivated in the Western Amazon. For this purpose, the following characteristics were evaluated over two harvests: aqueous extract, ash, acidity, pH, protein, ether extract, soluble solids, phenolic compounds, soluble sugars, reducing sugars, and non-reducing sugars. The genotype × measurement interaction effect was significant for all characteristics, with a predominant simple interaction, resulting in smaller changes in the ranking of genotypes. Out of a total of 45 genotypic correlation estimates, 8 were significant, of which 5 were related to acidity. The dispersion of the first two components associated with reference points shows that the genotypes BRS3193, AS1, AS2, AS3, N16, CA1, and AS7 were closest to the ideal type of higher performance. Selection for the main characteristic of soluble sugars resulted in estimates of genetic progress lower than those observed using selection indices. The genetic materials present high genetic diversity, allowing the selection of reference plants with high levels of sugars (BRS3193, AS3, GJ25, and LB30), proteins (BRS2357), lipids (GJ30), and phenolic compounds in their green beans (BRS3193) and high water solubility (AS2).

Environmental and additive genetic factors that affect artificial insemination success in Latxa dairy sheep breed

In the Latxa breed, flocks within the breeding program make use of high genetic value rams to inseminate their ewes once per year. Despite the relevance of the insemination results in the genetic progress and on farms’ productivity, external and genetic factors that affect artificial insemination (AI) success have not been up to now explored in this sheep population. For that, 135,351 edited AI records from 63,480 inseminated Latxa Cara Negra from Euskadi ewes, using 853 service rams, collected between 2000 and 2021, were used. The outcome of an AI event was treated as a binary response of either success or failure in becoming pregnant. To identify the environmental factors influencing the AI result, a multiple logistic regression was first calculated on a selection of variables related to the ewes, to the ram, and to other non-sex-specific aspects. With relevant variables detected, a threshold model, including the pedigree information of the inseminated ewes and the service rams, was used to estimate the genetic components of the trait in both sexes. Findings show that the AI success is higher in ewes who had the previous parturition from an AI event, in those with a lambing-AI interval longer than 210d, with 3 years and 2 lambings, with high prolificacy and in those having their first parturition at 1 year of age rather than at 2 or 3 years of age. In counterpart, the higher the milk produced in the nearest record before AI date, the poorer the AI results. Furthermore, relevant variability was linked to the year of insemination, the herd and the technician in charge of the insemination procedure. Regarding genetic parameters, heritability values, on the observable scale, and repeatability were 0.057 ± 0.004 and 0.204±0.007 in females and 0.009 ± 0.037 and 0.032 ± 0.002 in males, respectively. These results evidence that the AI success is under moderately low additive genetic control, with the surrounding environmental variables being the strongest controlling factor. An effort to enhance some farm management practices should be encouraged to efficiently improve reproductive results. Although genetic selection on AI success is viable, the genetic progress may be scarce. An option to improve reproduction by means of selective breeding might be the development of a multi-trait index.

Correcting for Observation Bias in Cancer Progression Modeling.

Tumor progression is driven by the accumulation of genetic alterations, including both point mutations and copy number changes. Understanding the temporal sequence of these events is crucial for comprehending the disease but is not directly discernible from cross-sectional genomic data. Cancer progression models, including Mutual Hazard Networks (MHNs), aim to reconstruct the dynamics of tumor progression by learning the causal interactions between genetic events based on their co-occurrence patterns in cross-sectional data. Here, we highlight a commonly overlooked bias in cross-sectional datasets that can distort progression modeling. Tumors become clinically detectable when they cause symptoms or are identified through imaging or tests. Detection factors, such as size, inflammation (fever, fatigue), and elevated biochemical markers, are influenced by genomic alterations. Ignoring these effects leads to "conditioning on a collider" bias, where events making the tumor more observable appear anticorrelated, creating false suppressive effects or masking promoting effects among genetic events. We enhance MHNs by incorporating the effects of genetic progression events on the inclusion of a tumor in a dataset, thus correcting for collider bias. We derive an efficient tensor formula for the likelihood function and apply it to two datasets from the MSK-IMPACT study. In colon adenocarcinoma, we observe a significantly higher rate of clinical detection for TP53-positive tumors, while in lung adenocarcinoma, the same is true for EGFR-positive tumors. Compared to classical MHNs, this approach eliminates several spurious suppressive interactions and uncovers multiple promoting effects.

Genomic prediction and validation strategies for reproductive traits in Holstein cattle across different Chinese regions and climatic conditions

Accurate genomic predictions of breeding values for traits included in the selection indexes are paramount for optimizing genetic progress in populations under selection. The size of the reference populations is a major factor influencing the accuracy of genomic predictions, which is even more important for lowly-heritable traits such as fertility and reproduction indicators. Combining data from different geographical regions or countries can be beneficial for genomic prediction of these lowly heritable traits. Therefore, the objectives of this study were to: 1) evaluate the benefits of performing across-regional genomic evaluations for reproduction traits in Chinese Holstein cattle; and, 2) assess the feasibility of validating genomic predictions across environments based on reaction norm models (RNM) and the Linear Regression (LR) method after accounting for genotype-by-environment interactions. Phenotypic records from 194,574 cows collected across 47 farms located in 2 regions of China were used for this study. The reference and validation populations were defined based on birth year for applying the LR validation method. The traits evaluated included: interval from first to last insemination (IFL), conception rate at the first insemination (CR_f), and number of inseminations (NS) recorded in heifers and first-parity cows. The results indicated that combining data from different regions resulted in greater genomic prediction accuracies compared with using data from single regions, with increases ranging from 2.74% to 93.81%. This improvement was particularly notable for the region with the least amount of available data, where the increases ranged from 26.49% to 93.81%. Furthermore, the predictive abilities could be validated for all studied traits based on the LR method across different environments when fitting RNM. The prediction accuracies and bias of genomic breeding values based on RNM were better than regular single-trait animal models in extreme climatic conditions for IFL and NS, whereas limited increases in predictive abilities were observed for CR_f. Across-regional genomic prediction by RNM can account for genotype-by-environment interactions, potentially increase the accuracy of genomic prediction, and predict the performances of individuals in the environments with limited phenotypic data available.

Bayesian approach to estimate variance components and genetic parameters of average daily gains and Kleiber ratios in crossbred cattle.

The present study aimed to estimate variance components and genetic parameters of the average daily gains from birth to 3-months (ADG1), 3- to 6-months (ADG2), 6- to 9-months (ADG3) and 9- to 12-months (ADG4) of age and corresponding Kleiber ratios (KR1, KR2, KR3 and KR4, respectively) in Jersey crossbred calves. Data for this study were collected from National Dairy Research Institute, Eastern Regional Station, Kalyani, West Bengal, India during 2013 to 2021. Genetic parameters for the traits were estimated using Bayesian procedure through Gibbs sampling by fitting six animal models. The deviance information criterion (DIC) was employed to determine the most appropriate model for each trait under investigation. Direct heritability estimates for ADGs and KRs ranged from 0.19 (ADG3) to 0.59 (ADG2) and 0.14 (KR3) to 0.72 (KR1), respectively. Significant maternal heritability ( ) was observed for average daily gains (17-24%) and Kleiber ratios (13-18%) at different age intervals under the best model. Estimates of permanent environmental effects ( ) of dam for ADG1, ADG2, KR2 and KR4 were only 1-4% of the total phenotypic variance in this study. Large negative estimates of correlations, ranging from -0.87 to -0.98, between direct and maternal ) effects for ADGs and KRs (except KR3) at different age intervals were observed. Total heritability ( ) and maternal repeatability ) of the studied traits ranged 0.05-0.28 and 0.01-0.14, respectively. The moderate to high heritability estimates for all traits in this study indicate the possibility of genetic progress for these traits through selective breeding program.

Evaluating animal models comprising additive genetic and maternal effects on growth traits in German Angora rabbit

This investigation was carried out to estimate the genetic parameters for German Angora rabbits using most appropriate animal model comprising additive and maternal effects. The pedigree information and data on growth traits were collected from 5221 rabbits, which descended from 699 does and 294 bucks over a period of 21 yr (2001–2021) kept at Northern Temperate Regional Station, Garsa, Kullu (Himachal Pradesh), India. The average daily gain (ADG) and Kleiber ratio (KR) were calculated at different age intervals. Analyses were carried out by restricted maximum likelihood procedure fitting six animal models with various combinations of direct and maternal effects. The best model was evaluated on the basis of a likelihood ratio test. Analysis revealed that the model including both direct and maternal genetic effects along with permanent effect of the dam was most suitable for all traits except for body weight (BW) at 6th wk of age, ADG from 6th to 12th wk of age and KR associated to ADG from 12th to 18th wk of age. The direct heritability estimates from the best model were ranged from, 0.34±0.05 to 0.49±0.05 for BW; 0.19±0.04, to 0.46±0.06 for ADG and 0.21±0.04 to 0.41±0.05 for KR, respectively. Direct heritability estimates were overestimated when maternal effects were ignored. Maternal effects on BW declined from 0.49±0.04 at weaning to 0.06±0.03 at 12th wk of age and 0.09±0.04 at 18th wk of age. Correlations between direct and maternal effects ranged from −0.44±0.15 to−0.52±0.14 for body weights, indicating biological antagonism between these effects. Genetic correlations among various growth traits were positive and high, indicating scope for correlated response in later expressed traits. Analysis revealed that maternal additive influences were only important until weaning, whereas permanent environmental maternal influences were present in all growth traits considered in this study. The moderate estimates of heritability for growth traits and Kleiber ratio of rabbit in this study indicate that rates of genetic progress may be possible for these traits by selection under the standard management system.

Frontal Lobe Epilepsy: Bermuda's Triangle.

Despite great progress in imaging, genetics, surgery, and therapeutics, frontal lobe epilepsy (FLE) continues to be a challenge for neurologists and epileptologists. This manuscript summarizes the latest advancements in FLE discussed at the 2023 Epilepsy Specialist Symposium during the American Epilepsy Society Annual meeting. Correlation between stereoelectroencephalography and clinical symptoms has reinvigorated symptomatology literature in FLE, allowing for more precise aura anatomical localization. Neuropsychological assessments permit the identification of different FLE cognitive phenotypes, with language being the most prominent domain-specific impairment. These tests can help develop psychotherapeutic and cognitive support systems for these patients. Genetic and molecular studies have uncovered specific genes associated with FLE susceptibility, offering prospects for targeted therapies. Advanced neuroimaging techniques such as high field magnetic resonance imaging (MRI), functional MRI (fMRI), magnetoencephalography and colocalization of multiple imaging techniques have led to more precise localization of the epileptogenic zone providing insights into the dynamic neural networks underlying frontal lobe seizures. This has facilitated guided therapeutic surgical interventions that can be employed around the world, expanding access of these technologies to multiple populations. Despite many advances, prognosis of FLE remains poor for some patients. The biggest determinant for poor prognosis continues to be nonlesional FLE. Newer technological advancements aim to pass these barriers and offer FLE patients a better quality of life with lower seizure burden and higher cognitive outcomes.

Bayesian approach for assessment of co-variances and genetic parameters of production and reproduction traits in Sahiwal cattle.

Precise as well as unbiased estimates of genetic parameters of economically important traits is important for framing breeding programmes meant for genetic progress of dairy animals. Present work was carried out to assess the co-variances along with genetic parameters of production and reproduction traits in Sahiwal cattle with Bayesian approach. Records of Sahiwal cattle kept at ICAR-National Dairy Research Institute, Karnal, Haryana for the period 1988-2016 were used. Heritability estimates ± posterior standard deviation for 305 days' milk yield (305DMY), lactation milk yield (LMY), lactation length (LL), peak yield (PY), days to attain peak yield (DPY), dry period (DP), gestation period (GP), calving to first insemination interval (CFI), days open (DO) and calving interval (CI) as obtained were 0.23 ± 0.03, 0.26 ± 0.03, 0.18 ± 0.03, 0.18 ± 0.03, 0.07 ± 0.02, 0.08 ± 0.02, 0.08 ± 0.02, 0.07 ± 0.02, 0.09 ± 0.02 and 0.02 ± 0.02, respectively and estimates of repeatability for the corresponding traits were 0.23 ± 0.03, 0.26 ± 0.03, 0.21 ± 0.03, 0.21 ± 0.03, 0.12 ± 0.02, 0.14 ± 0.03, 0.16 ± 0.03, 0.16 ± 0.03, 0.17 ± 0.03 and 0.17 ± 0.03. Moderate heritability values for 305DMY and LMY implies that sufficient additive genetic variability is available to obtain selection response. Repeatability estimates for 305DMY, LMY and PY were moderate, pointing towards the possibility of early appraisal of Sahiwal cattle for selection. Antagonistic genetic associations were observed between reproduction and production traits suggesting that reproduction traits need due attention while selecting high producing Sahiwal animals.

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.

Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.

Gait scores are widely used in the genetic evaluation of horses. However, the nature of such measurement may limit genetic progress since there is subjectivity in phenotypic information. This study aimed to assess the application of machine learning techniques in the prediction of breeding values for five visual gait scores in Campolina horses: dissociation, comfort, style, regularity, and development. The dataset contained over 5000 phenotypic records with 107,951 horses (14 generations) in the pedigree. A fixed model was used to estimate least-square solutions for fixed effects and adjusted phenotypes. Variance components and breeding values (EBV) were obtained via a multiple-trait model (MTM). Adjusted phenotypes and fixed effects solutions were used to train machine learning models (using the EBV from MTM as target variable): artificial neural network (ANN), random forest regression (RFR) and support vector regression (SVR). To validate the models, the linear regression method was used. Accuracy was comparable across all models (but it was slightly higher for ANN). The highest bias was observed for ANN, followed by MTM. Dispersion varied according to the trait; it was higher for ANN and the lowest for MTM. Machine learning is a feasible alternative to EBV prediction; however, this method will be slightly biased and over-dispersed for young animals.

PSX-4 Exploring the impact of age and body condition score on the genetic analysis of mature cow weight in American Angus cattle

Abstract Selecting sires with optimal genetic merit for mature weight (MWT) is crucial for profitability of beef producers, ensuring cost-effective production and increased economic returns per animal. The main objective of this study was to determine how different statistical approaches [e.g., including the fixed effects (FE) of age and body condition score (BCS) in the statistical model and/or pre-adjusting the weights for these effects] affect variance components estimation and genetic parameters for MWT, and their impact in the posterior selection decisions. The dataset provided by the American Angus Association (AAA) comprised 80,296 MWT records from 37,934 cows aged 2 to 15 yr. Contemporary groups (CG) were defined by concatenating herd, year, and season of measurement. Model 1 analyzed unadjusted MWT with CG and BCS as categorical FE and age as a covariable (quadratic). Model 2 was model 1 without BCS, model 3 was model 1 without age, and model 4 considered CG as the only FE. In model 5, MWT was pre-adjusted for 6 yr of age and BCS of 6, and CG was the only FE included in the statistical model. Additive genetic and permanent environment (pe) random effects were included in all statistical models except model 5, which did not include pe (only one adjusted record per animal). Variance components were calculated using REML implemented in the BLUPf90+ family of programs. Sires with at least 10 offspring (n = 1,483) were ranked according to their breeding values (EBVs) predicted for MWT, and all and the top 10% sires were used to estimate the rank correlation across models. The heritability (± SE) estimated for MWT was 0.43 (± 0.011), 0.42 (± 0.011), 0.70 (± 0.008), 0.72 (± 0.082), and 0.37 (± 0.013) for models 1, 2, 3, 4, and 5, respectively. Repeatability values were 0.64, 0.67, 0.72, and 0.76 for models 1, 2, 3, and 4, respectively. The EBV ranks were highly correlated between models 1 and 2 (Top 10%: 0.96; All: 0.99), and models 3 and 4 (Top 10%: 0.99; All: 0.99). Inflated heritabilities in models 3 and 4 suggest that the effect of age is confounded with the additive genetic effect, which will not translate to greater genetic progress. Therefore, disregarding age in the genetic evaluation of MWT is not recommended. Similarities between models 1 and 2 suggest that BCS has a relatively small impact on MWT, probably related to the relatively low genetic correlation between these traits. Models 1 and 5 cannot be directly compared, as they use slightly different response variables (body weight vs. pre-adjusted weight) and may lead to slightly different selection decisions (correlation between Top 10%: 0.85; All: 0.95). Further investigations in this field are warranted.