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PubMed2026

Using deep learning models as a genetic architecture for the simulation of breeding schemes.

In several simulation studies, long-term selection led to the rapid depletion of genetic variance. These outcomes differ from real-life observations that we aim to replicate, thereby highlighting a fundamental limitation of current classical quantitative genetic simulation models. Deep learning (DL) models have demonstrated promising results in capturing complex interactions essential for maintaining genetic variance; thus, we hypothesize that DL-based genetic simulation models may preserve more genetic variance than classical models, because the biological pathways underlying complex traits exhibit interactions that classical models ignore. The primary objective of this study was to introduce alternative DL-based genetic simulation models and compare them with classical genetic simulation models in terms of their retention of additive genetic variance under truncation selection in a simulated full-sib pig breeding scheme using real haplotypes as founders. After 20 generations of directional truncation selection, the classical models (A, ADAA, and ADAAADDD) retained between 55% and 64% of their initial additive genetic variance. In contrast, while the DL_simple model lost all its additive variance, the DL medium retained 92% to 98% of its additive variance, and the DL_complex model's initial additive variance increased by 296% to 314%. This paper introduces DL-based genetic simulation models and concludes that their ability to retain additive genetic variance depends on the models' architectural complexity. When sufficiently complex, DL-based models exhibit greater retention of additive genetic variance because they intrinsically capture epistatic interactions that are converted into additive variance, as selection progresses, thus, affirming the role of non-additive genetic effects in maintaining long-term genetic variation.

Deep Learning↗

PubMed1976

Transplantation in miniature swine. I. Fixation of the major histocompatibility complex.

Three strains of miniature swine, each homozygous for a different allele of the major histocompatibility locus (MHC), have been developed by a selective breeding scheme based on tissue typing of the offspring of each generation. Prior to breeding, the original parents were reciprocally immunized by skin grafts and lymphocyte injections to produce lymphocytotoxic antisera. These antisera were then used to assess the MHC genotype of the offspring by an analysis based on selective absorption of cytotoxicity. Offspring inheriting the same serologically determined genotype were then bred sequentially. Subsequent mixed lymphocyte cultures showed a pattern of reactivity consistent with the serological genotyping, further confirming the homology between the MHC of these miniature swine and those of man and mouse. In addition to their usefulness as a model for large animal surgical transplantation, these animals provide an abundant homozygous source of histocompatibility antigens and of antihistocompatibility antisera for use in chemical characterization of products of the MHC.

Animals↗

PubMed2024

Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice.

Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.

Oryza↗

PubMed2025

Comparing artificial and convolutional neural networks with traditional models for Genomic prediction in wheat.

With the rapid development of sequencing technology, the application of genomic prediction has become more and more common in breeding schemes of livestocks and crops. Selecting an appropriate statistical model is of central importance to achieve high prediction accuracy. Recently, machine learning models have been expected to upgrade genomic prediction into a new era. However, the perspective still suffers from lack of evidence that machine learning models can generally outperform the traditional ones on empirical data sets. In this study, we compared two machine learning models based on artificial neural network (ANN) and convolutional neural network (CNN) with four traditional models, including genomic best linear unbiased prediction (GBLUP), Bayesian ridge regression (BRR), BayesA and BayesB, using three published data sets for grain yield in wheat. For each model, we considered two variants: modeling and ignoring the genotype-by-environment ([Formula: see text]) interaction. In the comparison, we considered two strategies of cross-validation: predicting genotypes that have not been evaluated in any environment (CV1) and predicting genotypes that have been tested in other environments (CV2). Our results showed that traditional Bayesian models (BayesA, BayesB, and BRR) outperformed GBLUP, ANN and CNN when considering [Formula: see text] interaction. The accuracies of ANN and CNN were higher than traditional models only in CV1 and when [Formula: see text] interaction was ignored. It was also found that the performance of the two machine learning models was significantly affected by the interaction between the CV strategy and the way of treating the [Formula: see text] interaction, while that of the four traditional models was only influenced by whether the [Formula: see text] interaction was considered or not. Thus, machine learning models can be a powerful complementary to the traditional ones and their superiority may depend on the prediction scenario. Among the two machine learning models, we observed that the accuracy of ANN was higher than CNN in most cases, indicating that it is still challenging to adapt complex machine learning models such as CNN to genomic prediction.

ANN↗

PubMed1978

A serological survey of accredited breeding colonies in the United Kingdom for common rodent viruses.

This paper reports the results of a general survey, the first in the United Kingdom, carried out on accredited breeding colonies of mice, rats and guinea-pigs over a period of a year. While the results show the potential usefulness of a viral accreditation grading scheme, they also show that contamination of breeding colonies with inapparent viral infections is widespread. This situation can only be improved by the continuous monitoring of animal stocks for rodent viruses, with the aim of improving the standard of animals available for research and for pharmacological, toxicological and routine diagnostic procedures.

Animals↗

PubMed2005

Animal breeding and disease.

Single-locus disorders in domesticated animals were among the first Mendelian traits to be documented after the rediscovery of Mendelism, and to be included in early linkage maps. The use of linkage maps and (increasingly) comparative genomics has been central to the identification of the causative gene for single-locus disorders of considerable practical importance. The 'score-card' in domestic animals is now more than 100 disorders for which the molecular lesion has been identified and hence for which a DNA test is available. Because of the limited lifespan of any such test, a cost-effective and hence popular means of protecting the intellectual property inherent in a DNA test is not to publish the discovery. While understandable, this practice creates a disconcerting precedent. For multifactorial disorders that are scored on an all-or-none basis or into many classes, the effectiveness of control schemes could be greatly enhanced by selection on estimated breeding values for liability. Genetic variation for resistance to pathogens and parasites is ubiquitous. Selection for resistance can therefore be successful. Because of the technical and welfare challenges inherent in the requirement to expose animals to pathogens or parasites in order to be able to select for resistance, there is a very active search for DNA markers for resistance. The first practical fruits of this research were seen in 2002, with the launch of a national scrapie control programme in the UK.

Animal Diseases↗

PubMed2026

Finlay-Wilkinson random regression for yield and yield stability prediction in cereals.

Year-to-year climate variability poses a challenge for agriculture by increasing crop yield variability; therefore, there is a need to identify genotypes that can withstand these fluctuations. With the right selection criteria, genotypes with yield stability across variable environmental conditions can be selected. Methods such as Finlay-Wilkinson random regression (FWRR) may allow us to use sparse datasets-common in plant breeding pipelines-and incorporate genomic data to leverage phenotypic information from related genotypes to predict yield stability. Our objective was to examine how the number of environments and the variance among those environments affect stability predictions. We also integrate FWRR as a genomic prediction tool for characterizing yield stability, comparing it to the traditional genomic prediction models as a reference. We used three datasets: one highly unbalanced dataset for oats (Avena sativa L.) and two completely balanced datasets with different numbers of environments for barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.). We fit standard Finlay-Wilkinson (FW) and FWRR models to estimate grain yield and stability under various scenarios. We found that the estimated stability values obtained were similar using balanced datasets for FW or FWRR. FWRR also achieved moderate predictive ability for stability using unbalanced datasets under 10-fold cross-validation (CV1) with new genotypes. In terms of environmental representation, selecting the right set of environments for inclusion in the model was more important than adding more environments. Our results suggest the possibility of using FWRR to select stable genotypes earlier in line development, as well as to design resource-efficient stability-testing schemes.

Hordeum↗

PubMed1976

Ord River arboviruses--the study site and mosquitoes.

The Ord Valey of tropical Western Australia has been studied for arbovirus activity following the development of a man-made lake of considerable size, a diversion dam and an irrigation scheme. Kununurra, the largest town in the valley, is the focus for very large populations of birds and mosquitoes. The irrigation areas have not been important as mosquito breeding areas because of the excessive use of insecticides. Lake Argyle does not support high mosquito a bird population at present. However, this may change as the ecosystem stabilizes. The mosquito fauna of the Ord Valley is dominated by Culex annulirostris.

Animals↗

PubMed2026

Combining ability and gene action for grain yield and biofortification traits in pearl millet [Pennisetum glaucum (L.) R. Br.]: implications for breeding high-yielding biofortified hybrids in arid regions.

Hybrid RIB-9184 × RIB-15131 combines high yield (18.84 g plant⁻¹) with iron (46.16 mg kg⁻¹), zinc (38.86 mg kg⁻¹), and protein (11.91%); Fe-Zn correlation (rg = 0.82) permits simultaneous biofortification. Pearl millet [Pennisetum glaucum (L.) R. Br., syn. Cenchrus americanus (L.) Morrone] is a climate-resilient cereal with inherently high micronutrient levels, making it a priority crop for biofortification. Understanding gene action for yield and nutritional traits is essential for designing effective breeding strategies. Ten diverse inbred lines were crossed in a half-diallel design (Griffing's Method 2, Model 1), and the 55 entries (45 F1 hybrids + 10 parents) were evaluated across two sowing-date environments in a randomised complete block design with three replications at Jaipur, Rajasthan, India. Biofortification traits (Fe, Zn, protein) showed predominantly additive gene action (Baker's ratio 0.71-0.91) with high heritability (0.90-0.94). G×E interaction was significant for Fe and Zn but genotypic variance was substantially larger, maintaining high heritability; protein showed no G×E interaction. Grain yield was governed largely by non-additive effects (Baker's ratio 0.54) with significant G×E interaction, favouring hybrid breeding. Among parents, RIB-9205 had the highest GCA for Fe (6.65, P < 0.001), RIB-9184 for Zn (3.85, P < 0.001) and protein (0.78, P < 0.001), and RIB-9185 was a balanced combiner for yield (1.39, P < 0.001) and micronutrients. The hybrid RIB-9184 × RIB-15131 ranked first across all five weighting schemes of the multi-trait performance index (1.31), combining grain yield of 18.84 g plant⁻1 with Fe of 46.16 mg kg⁻1, Zn of 38.86 mg kg⁻1, and protein of 11.91%. The strong Fe-Zn correlation (rg = 0.82, P < 0.01) permits simultaneous micronutrient improvement. An integrated approach combining hybrid development for yield with population improvement for micronutrient density is recommended for biofortified pearl millet cultivars in arid regions.

Pennisetum↗

PubMed1976

[Studies of climatic factors influencing the performance of cattle in the Syrian Arab Republic. 2. Assessment fo 1968/69 climatic factors].

On the basis of the values of temperature and moisture for one year, an experimental cowshed (80 cows) on the border of the Syriain semidesert was investigated using a new evaluation scheme. The assessment was made according to three ranges of physiological compatibility. Essential deficiencies were found in the construction and in the function of the cowshed. Using the relevant literature (see part 1) on this subject, the paper tries to show that such conditions must be detremental to the performance of European cattle breeds. If cattle stay for a long time in temperature regions above the physiological compatibility range, damages to the health must also be expected in non-adapted and local breeds. Suitable devices must be built into the cowsheds to enable release of warmth from the cattle kept there. Improvements in the climate of the cowshed are part of the complex measure for increasing the performance. The paper tries to stimulate more intensive studies of the climate and its effect on the performance in animal production for other regions and countries as well.

Animal Husbandry↗

PubMed1977

[Studies of climatic factors influencing the performance of cattle in the Syrian Arab Republic. 3. Assessment of the factors effective in 1969/70--comparison with those of 1968/69].

Continuing previous investigations (1968/69), the values of temperature and humidity (inside and outside the cowsheds) were determined for the period October 1969 to September 1970. They were related to the physiological compatibility ranges established for dairy cattle and discussed in connection with a cowshed scheme in the Animal Experimental Station at Deir el Hajar in the Syrian Arab Republic. The values obtained in the two years of investigation largely agreed. The means of the temperature in both years was 27.7% inside and 30.5% outside the cowsheds above the physiologically compatible range of 0 to 24 degrees C for the whole period. Only in 47.6% and 46.7%, respectively, of the overall hours the temperature inside and outside the cowsheds was within the optimum compatibility range for dairy cattle. 43.5% of the relative humidity outside the cowshed was in the too dry and 21.7% was in the too moist compatibility range.

Animals↗

PubMed1979

Genetic basis of creatine kinase isozymes in skeletal muscle of salmonid fishes.

The genetic basis of isozyme phenotypes of creatine kinase (CK) from extracts of skeletal muscle of salmonids has been resolved through breeding data including double heterozygous crosses and backcrosses of rainbow trout (Salmo gairdneri), and backcrosses of coho salmon (Oncorhynchus kisutch). The two-three-, or four-banded phenotypes of homozygous individuals and all heterozygous and hybrid phenotypes of ten salmonid species are readily explained by the following model: (1) there are no detectable heterodimers either between allelic products at a single locus or between loci: (2) each allele is represented electrophoretically by two bands, presumably a reflection of stable posttranslational modification of a single polypeptide unit; (3) CK of salmonid muscle is encoded by two loci--CK-1 and CK-2. The distance separating the paired bands reflecting each allele provides a basis for two groupings--a broad-spaced group (including all species of Oncorhynchus tested excepting O. masou) and a narrow-spaced group (including all species of Salmo tested and O. masou). The relationships among species suggested by the relative mobilities and spacings of these CK bands are consistent with taxonomic schemes inferred from morphological, cytogenetic, and other isozymic data.

Animals↗