Biology subjects

Palaiokostas, C.

Publications and source records attributed to Palaiokostas, C..

4 recordsLinked to original sources

bioRxiv2026

Two-tower models for genomic prediction of reproductive outcomes and sex-specific fertility liabilities: simulation insights

Many biological characteristics arise by interactions between more than one biological organism or unit. Fertilization success in sexually reproducing species represents such an extended phenotype where both mates are required to be fertile for a successful outcome. Consequently, predictive models should account for the joint nature of reproductive performance while offering interpretable estimates for individual mate contributions. Recent advances in genomics and machine learning (ML) provide standardized, high-dimensional genetic information on one hand and computational tools capable of modeling complex biological systems on the other. Here, we construct and evaluate two-tower (TT) machine learning architectures for genomic prediction of binary reproductive outcomes and recovery of sex-specific fertility liabilities. Simulated datasets, generated under a range of genetic architectures, were utilized to compare multilayer perceptron (TT-MLP), convolutional neural network (TT-CNN), and L1-regularized linear (TT-LASSO) two-tower models. Simulation scenarios varied sex-specific heritabilities, genetic correlations, infertility prevalence, mating structure, and sex-specific infertility rates. Models were evaluated with regard to their ability to predict reproductive success at pair level and also recover true underlying genetic values for male and female fertility. Prediction accuracy increased with the underlying heritable component as expected, while sex-specific tower-scores successfully recovered latent fertility liabilities despite models being trained only on observed joint outcomes. TT-LASSO achieved the highest overall classification performance, whereas TT-MLP provided more balanced and consistent recovery of sex-specific genetic values across scenarios. An additional simulation, incorporating genotype-dependent mate compatibility demonstrated advantages of fully-connected neural networks for capturing non-additive interactions. These results indicate that two-tower frameworks provide a powerful approach for modeling reproductive traits, enabling simultaneous prediction of aggregate reproductive outcomes and sex-specific fertility liabilities from genotypic information.

genetics↗

bioRxiv2026

Genomic insights into bacterial kidney disease resistance in Arctic charr (Salvelinus alpinus) via a 72k SNP array

Selection for disease resistance forms one of the most highlighted areas of aquaculture breeding. A breeding program for Arctic charr has been operating in Sweden for over 40 years, making it the oldest of its kind worldwide for this species. However, the lack of available genomic resources prevented selection for any disease-resistance traits. A 72k Axiom SNP array was produced in this study and used to assess the potential to select for charr resistant to bacterial kidney disease (BKD), which is currently a major threat to the industry. Following a challenge experiment with Renibacterium salmoninarum, the causative agent of BKD, relevant phenotypic proxies were collected from approximately 2,000 charr. Thereafter, those animals were genotyped with the new 72k SNP array. The magnitude of the estimated variance components suggested potential for breeding for BKD resistance in charr, with relevant heritabilities ranging from 0.05 to 0.56 depending on the resistance proxy used. In addition, GWAS suggested that BKD resistance is a polygenic trait. Furthermore, genomic prediction approaches indicated that BKD-resistant animals can be identified using their SNP genotypes. Accuracies, expressed as Pearson correlation coefficients, when BKD resistance was analysed as a continuous trait, ranged from 0.42 to 0.52. In the scenario where BKD resistance was treated as a binary trait, the efficiency of genomic prediction was assessed using ROC curves, with an area under the curve of 0.72. Finally, no unfavourable correlations were found with growth traits. The developed 72k SNP array has the potential of being a pivotal tool for the Swedish Arctic charr breeding program. Moreover, our data support the use of genomic prediction in breeding BKD-resistant Arctic charr. As a critical next step, further validations in actual industry conditions would be required.

genetics↗

bioRxiv2025

Genetic parameters and sex-specific architecture of observed and latent fertility phenotypes in a closed breeding nucleus of an Arctic salmonid

Successful reproduction is a key factor for efficient breeding schemes and sustainable animal farming. Aquaculture breeding programs rely heavily on small fractions of selected breeders to yield large production stocks, given the high fecundity typically observed in these species. In Sweden, Arctic charr (Salvelinus alpinus) is a salmonid with notable commercial potential, with a selective breeding program operating for ten generations under a growth-rate focused breeding goal. Despite significant gains, the nucleus faces challenges with low and fluctuating fertility impeding expansion efforts. In this study, we estimate genetic parameters for charr milt quality phenotypes measured with specialized cytometry and Computer-Assisted Sperm Analysis (CASA). At the same time, we assess the sex-specific architecture governing egg count and sperm concentration along body size. Finally, we propose a novel analytical framework for the analysis of realized fertilization success rates by considering a multiplicative system of latent maternal and paternal contributions. Low to moderate heritability estimates and genetic correlations were obtained from multi-trait modelling for traits reflecting sperm quality along with high estimates for fork length. Genetic correlations among sperm kinematic parameters appeared strong, while the same traits showed weak positive and weak negative correlations with sperm concentration and fork length, respectively. Furthermore, a negative genetic correlation between sperm concentration and both male body size and egg count suggests a complex interplay of a possible trade-off and sexual antagonism. Our latent fertility analytical approach returned low to moderate heritability estimates depending on the modelling configuration. Overall, our study demonstrated the complexity characterizing the heritable portions of reproductive traits in Arctic charr and tested alternative tools that have the potential for integration into selective breeding programs.

genetics↗

bioRxiv2019

Optimizing genomic prediction of host resistance to koi herpesvirus disease in carp

Genomic selection (GS) is increasingly applied in breeding programmes of major aquaculture species, enabling improved prediction accuracy and genetic gain compared to pedigree-based approaches. Koi Herpesvirus disease (KHVD) is notifiable by the World Organisation for Animal Health and the European Union, causing major economic losses to carp production. Genomic selection has potential to breed carp with improved resistance to KHVD, thereby contributing to disease control. In the current study, Restriction-site Associated DNA sequencing (RAD-seq) was applied on a population of 1,425 common carp juveniles which had been challenged with Koi herpes virus, followed by sampling of survivors and mortalities. Genomic selection (GS) was tested on a wide range of scenarios by varying both SNP densities and the genetic relationships between training and validation sets. The accuracy of correctly identifying KHVD resistant animals using genomic selection was between 8 and 18 % higher than pedigree best linear unbiased predictor (pBLUP) depending on the tested scenario. Furthermore, minor decreases in prediction accuracy were observed with decreased SNP density. However, the genetic relationship between the training and validation sets was a key factor in the efficacy of genomic prediction of KHVD resistance in carp, with substantially lower prediction accuracy when the relationships between the training and validation sets did not contain close relatives.

genetics↗