Biology subjects

Ashkenazy, N.

Publications and source records attributed to Ashkenazy, N..

2 recordsLinked to original sources

bioRxiv2024

Transposable elements are entangled in rapid adaptation to climate change

Biodiversity conservation is urged at biodiversity hotspots that are under constant threat from anthropogenic development, yet a careful examination of the adaptive potential is a prerequisite for action. The Levant is considered a biodiversity hotspot and the distribution edge for many species, including important crop wild relatives. This region is under accelerated desertification and constantly disturbed by human activities, thus urging intervenient action. We collected and sequenced 300 wild barley plants along an eco-geographic gradient following a unique ecological-genetic sampling design. This scheme enabled to overcome the tight correlation between environmental and geographic distances. Phenotypic data was collected from 3600 progeny plants over three years and enabled to identify adaptive haplotype blocks comprised of phenological regulating genes tightly linked to drought and heat responsive genes. These haplotype blocks were highly enriched for transposable elements insertions, likely regulating genetic variation around adaptive genes, especially in stressed populations. Ecological and evolutionary models using over 2600 observations were combined to predict maladaptive risk, indicating that populations will be funneled into higher water availability refugia habitats while increasing isolation. Our findings highlight the main factors affecting rapid local adaptation and provide important recommendations for biodiversity management and conservation.

plant biology↗

bioRxiv2022

GWANN: Implementing deep learning in genome wide association studies

MotivationGenome wide association studies (GWAS) are extensively used across species to identify genes that underlie important traits. Most GWAS methods apply modifications and extensions to a linear regression model in order to detect significant associations between genetic variation and a trait. Despite their popularity, these statistical models tend to suffer from high false positive rates, especially when utilized on large variant datasets or complex demographic scenarios. To overcome this, aggressive statistical corrections are applied which frequently diminish true associations. ResultsHere we consider a deep learning approach, and present an implementation of a convolutional neural network (CNN) to identify genetic variation that is associated with a trait of interest. To exploit the strength of CNNs in visual recognition, the genotype information is represented as an image, which enables the model to correctly classify genetic variants with respect to the trait, even when a population structure is present. Our proposed approach was implemented in a package called GWANN which exhibited solid performance. Overall, GWANN outperformed popular GWAS tools on both simulated and real datasets, and enabled the identification of association signals with increased sensitivity and speed. Availability and implementationThe package is available at: https://github.com/hubner-lab/GWANN

bioinformatics↗