wQFM: highly accurate genome-scale species tree estimation from weighted quartets

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Species tree estimation is complex due to gene tree-species tree discordance, often caused by incomplete lineage sorting (ILS).
• Quartet-based summary methods offer accuracy and statistical guarantees under ILS for species tree inference.
• Handling weighted quartets, crucial for accurate species tree reconstruction, presents a significant challenge.

Purpose of the Study:

• To introduce wQFM, a novel and highly accurate method for species tree estimation from multi-locus genetic data.
• To extend the existing quartet FM (QFM) algorithm to effectively handle weighted quartets.
• To provide a statistically consistent approach for inferring species trees by amalgamating weighted quartets.

Main Methods:

• The study proposes wQFM, an extension of the quartet FM (QFM) algorithm, adapted for a weighted quartet setting.
• wQFM is designed to infer species trees by amalgamating weighted quartets, addressing challenges in handling their relative importance.
• The method was evaluated using both simulated and real biological datasets, including a large avian phylogenomic dataset.

Main Results:

• wQFM demonstrated high accuracy in species tree estimation across tested datasets.
• Comparisons with existing methods, including wQMC and ASTRAL, showed that wQFM matches or surpasses their accuracy.
• The performance was validated on complex datasets, confirming wQFM's effectiveness.

Conclusions:

• wQFM offers a robust and accurate solution for species tree estimation, particularly in the presence of ILS.
• The method provides a valuable tool for evolutionary biologists studying complex phylogenetic relationships.
• wQFM represents an advancement in handling weighted quartets for more reliable phylogenetic inference.