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Machine learning approaches to identify genetic markers for goat climatic adaptation
Chandana Sree Chinnareddyvari1,2, C A Dharamshaw1,2, S R Prashanthini1,2
1Division of Animal Genetics, ICAR-National Bureau of Animal Genetic Resources, Karnal, Haryana 132001 India.
3 Biotech
|June 29, 2026
Summary
Researchers identified key genetic markers in goats linked to adaptation to hot and cold climates. These ancestry informative markers (AIMs) and specific gene variants can aid in developing targeted breeding strategies for climate resilience.
Area of Science:
- Genomics and Animal Breeding
- Climate Adaptation in Livestock
Background:
- Understanding genetic basis of adaptation is crucial for livestock resilience.
- Indigenous and exotic goat populations exhibit varying climate adaptations.
- Identifying specific genetic markers can facilitate marker-assisted selection.
Purpose of the Study:
- To identify ancestry informative markers (AIMs) associated with climatic adaptation in goats.
- To leverage machine learning for refining AIMs and classifying climatic adaptation.
- To functionally annotate identified genetic variants and assess their impact on protein structure and function.
Main Methods:
- Whole-genome SNP data analysis from 109 goats (indigenous Indian and exotic).
- Selection of AIMs using FST, In, and delta statistics, followed by consensus identification.
- Admixture and Principal Component Analysis (PCA) for population structure and climatic group separation.
- Machine learning models (Random Forest, SVM, Logistic Regression, k-NN, XG-Boost) for AIM refinement and classification.
- Functional annotation and protein structural analysis of selected variants.
Main Results:
- Identified 4,040 AIMs, with 1,728 unique SNPs after machine learning refinement.
- Machine learning models achieved high accuracy (up to 100%) in classifying hot- and cold-adapted goats.
- Functional annotation highlighted three key genes (MSH5, PAPSS2, SINHCAF) with climatically relevant variants.
- Specific missense variants in MSH5 and PAPSS2, and a synonymous variant in SINHCAF, showed distinct allele frequencies and potential functional impacts.
Conclusions:
- The study successfully identified functionally important genetic variants associated with goat climatic adaptation.
- Integration of AIMs with machine learning provides an effective strategy for identifying reduced marker sets.
- Findings support the development of cost-effective SNP panels for assessing and improving climate adaptation in goats through marker-assisted selection.
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