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MrIML: Multi-response interpretable machine learning to model genomic landscapes.

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A new R package, MrIML (Multi-response Interpretable Machine Learning), quantifies genomic relationships and identifies adaptation loci across environments. It handles complex genetic variation and environmental interactions, advancing landscape genetics research.

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artificial intelligencecommunity ecologyecological geneticsgradient boosting modelslandscape geneticsrandom forest

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Area of Science:

  • Ecology
  • Genetics
  • Computational Biology

Background:

  • Traditional landscape genetics struggles with nonlinear and interactive relationships between genetic variation and environment.
  • Extending single-locus models to multilocus analysis is challenging.

Purpose of the Study:

  • Introduce the MrIML R package for multilocus genomic analysis.
  • Provide a framework to quantify genomic relationships and identify loci related to environmental adaptation.
  • Address complex, nonlinear interactions in landscape genetics.

Main Methods:

  • Developed the MrIML R package implementing machine learning models.
  • Applied the package to simulated genomic data to test relationship recovery.
  • Utilized MrIML for two empirical case studies: balsam poplar and bobcat FIV.

Main Results:

  • MrIML successfully recovered landscape relationships from simulated data.
  • Modeled genetic variation in balsam poplar across environmental gradients.
  • Identified landscape and host drivers of feline immunodeficiency virus genetic variation in bobcats.

Conclusions:

  • MrIML offers a transformative framework for analyzing thousands of loci collectively.
  • The package facilitates comparison of diverse models, from linear regression to extreme gradient boosting.
  • MrIML is extendable beyond genetic data, applicable to microbiomes and coinfection dynamics.