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Experimental support for genomic prediction of climate maladaptation using the machine learning approach Gradient
Matthew C Fitzpatrick1, Vikram E Chhatre2,3, Raju Y Soolanayakanahally4
1Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, USA.
Molecular Ecology Resources
|March 8, 2021
Summary
Gradient Forests (GF) accurately predicts population maladaptation to climate change using genomic offsets. This machine learning approach shows promise for identifying climate-adaptive SNPs in species like balsam poplar.
Area of Science:
- Ecology
- Genetics
- Machine Learning
Background:
- Gradient Forests (GF) is increasingly used to study environmental drivers of genomic variation and climate change impacts.
- Genomic offsets quantify climate maladaptation, predicting organismal responses to environmental shifts.
Purpose of the Study:
- To experimentally evaluate genomic offsets derived from GF for predicting organismal responses to environmental change.
- To explore GF's utility in identifying single nucleotide polymorphisms (SNPs) associated with climate adaptation.
Main Methods:
- Utilized high-throughput sequencing and genome scans in balsam poplar (Populus balsamifera L.).
- Applied Gradient Forests to link candidate loci with environmental gradients and predict genetic offsets.
- Compared predicted genetic offsets with common garden performance data.
Main Results:
- A significant inverse relationship was found between predicted genetic offset and population performance.
- Genomic offsets predicted performance better than naive climate transfer distances.
- Randomly selected SNPs outperformed candidate SNPs in predicting performance.
Conclusions:
- Genomic offsets provide a reliable first-order estimate of expected maladaptation under rapid environmental change.
- Gradient Forests show potential for identifying candidate SNPs related to climate adaptation.
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