Biology subjects

Vourlaki, I.-T.

Publications and source records attributed to Vourlaki, I.-T..

3 recordsLinked to original sources

bioRxiv2026

Ruminosignatures associated with methane emissions and feed efficiency across geographies and cattle breeds

The cattle rumen microbiota represents a highly complex and dynamic ecosystem, whose organization and connection to host phenotypes are of the highest importance to food security and the environment. In this study, we analyzed the rumen microbiota, from 2,492 cattle belonging to five different breeds and production systems across five countries, categorizing them into microbial co-abundance groups referred to as Ruminosignatures. We identified twelve distinct Ruminosignatures, including two that were consistently observed across all populations and were dominated by the genus Prevotella and UBA2810. Additional Ruminosignatures showed breed-and diet-specific patterns and collectively explained 96-99% of the variance in rumen microbial composition. The abundances of several Ruminosignatures were associated with methane emissions and feed efficiency, and were influenced by host genetics, with heritability estimates ranging from 0.09 to 0.51. The Ruminosignature dominated by UAB2810 was negatively associated with methane emissions across all datasets and positively linked to feed efficiency in Holstein from Italy and crossbred from Ireland. Additionally, the type of production system affects both the occurrence of Ruminosignatures and their impact on host phenotypes, emphasizing the need for context-specific approaches to modulate the rumen microbiome. Overall, our results offer new perspectives on the assembly of ruminal microbes and underscore the potential of the Ruminosignatures framework for microbiome-informed precision agriculture and breeding initiatives aimed at enhancing feed efficiency and minimizing the environmental impact of cattle farming.

microbiology↗

bioRxiv2024

Evaluation of Deep Learning for predicting rice traits using structural and single-nucleotide genomic variants

Structural variants (SVs) such as deletions, inversions, duplications, and Transposable Element (TE) Insertion Polymorphisms (TIPs) are prevalent in plant genomes and have played an important role in evolution and domestication, as they constitute a significant source of genomic and phenotypic variability. Nevertheless, most methods in quantitative genetics focusing on crop improvement, such as genomic prediction, consider Single Nucleotide Polymorphisms (SNPs) as the only type of genetic marker. Here, we used rice to investigate whether combining the structural and nucleotide genome-wide variation can improve prediction ability of traits when compared to using only SNPs. Moreover, we also examine the potential advantage of Deep Learning (DL) networks over Bayesian Linear models, which have been widely applied in genomic prediction. Specifically, the performance of BayesC and a Bayesian Reproducible Kernel Hilbert space regressions were compared to two different DL architectures, the Multilayer Perceptron, and the Convolution Neural Network. We further explore their prediction ability by using various marker input strategies and found that exploiting structural and nucleotide variation improves prediction ability on complex traits in rice. Also, DL models outperformed Bayesian models in 75% of the studied cases. Finally, DL systematically improved prediction ability of binary traits against the Bayesian models.

genomics↗

bioRxiv2022

Detection of Domestication Signals through the Analysis of the Full Distribution of Fitness Effects using Forward Simulations and Polygenic Adaptation

Domestication is a process marked by complex interactions between demographic changes and selective pressures, which together shape genetic diversity. While the phenotypic outcomes of domestication are well documented, its genetic basis--particularly the dynamics of selection-- remain less well understood. To investigate these dynamics, we performed simulations designed to approximate the demographic history of large domestic mammals. These simulations used selection coefficients as a modeling tool to represent changes in selection pressures, recognizing that such coefficients are abstractions rather than direct representations of biological reality. Specifically, we analyzed site frequency spectra (SFS) under varying distributions of fitness effects (DFE) and proportions of mutations with divergent selective pressures. Our results show that the discretized deleterious DFE can be reliably inferred from the SFS of a single population, but reconstructing the beneficial DFE and demographic history remains challenging, even when using the joint SFS of both populations. We further developed a novel joint DFE inference model to estimate the proportion of mutations with divergent selection coefficients (pc), although we found that signals of classic hard sweeps can mimic increases in pc, complicating interpretation. These findings underscore both the utility and limitations of DFE inference and highlight the need for caution when interpreting demographic histories in domesticated populations based on such modeling assumptions.

evolutionary biology↗