Mycorrhiza: Genotype Assignment usingPhylogenetic Networks

TL;DR

Mycorrhiza is a machine learning method that uses phylogenetic networks and random forests to improve genotype assignment accuracy across diverse datasets, especially when traditional assumptions are violated.

Contribution

It introduces a novel phylogenetic network-based feature engineering approach for genotype assignment, outperforming existing methods in accuracy and robustness.

Findings

Outperforms STRUCTURE and Admixture in accuracy.

Provides good estimates of mixture proportions.

Excels on datasets with high FST or deviation from Hardy-Weinberg equilibrium.

Abstract

Motivation The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on. Results In this article, we introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Those features are then used as input to a Random Forests classifier. The classification accuracy was assessed on multiple published empirical SNP, microsatellite or consensus sequence datasets with wide ranges of size, geographical distribution and population structure and on simulated datasets. It compared favorably against widely used assessment tests or mixture analysis methods such as STRUCTURE and Admixture, and against another machine-learning based approach using principal component analysis for dimensionality reduction. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium. Moreover, the phylogenetic network approach estimates mixture proportions with good accuracy.