Publications by authors named "O E Gaggiotti"
How emerging adaptive variants interact is an important factor in the evolution of wild populations, but the opportunity to empirically study this interaction is rare. We recently documented the emergence of an adaptive phenotype "curly-wing" in Hawaiian populations of field crickets (). Curly-wing inhibits males' ability to sing, protecting them from eavesdropping parasitoid flies ().
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- In coastal British Columbia, humpback and fin whales have faced exploitation and are now recovering, but still face threats from resource development and climate change.
- Researchers are evaluating a non-intrusive method of collecting whale DNA by capturing exhaled breath samples using drones, minimizing disruption to the whales' behavior.
- The study found high success rates in extracting DNA from the breath samples, supporting the use of this method for understanding whale genetic diversity and individual identification for conservation efforts.
Article Synopsis
- Parallel evolution shows how species adapt to different environments through natural selection, but there's still debate about how and when it happens.
- Using paleogenomics, researchers analyzed genomes from ancient bottlenose dolphins to see how closely linked they are to today's coastal populations.
- They discovered that more recent genomes contain genetic variants associated with coastal habitats, revealing a shared genetic history that helped these dolphins adapt quickly to changing environments.
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets.
View Article and Find Full Text PDFIdentifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning-based framework, DeepGenomeScan, which can detect signatures of spatially varying selection.
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