Dissecting Genomic Determinants of Positive Selection with an Evolution-Guided Regression Model

Area of Science:

• Evolutionary genomics
• Population genetics
• Genomic adaptation

Background:

• Understanding the drivers of adaptive evolution is crucial in genomics.
• Existing methods like the McDonald-Kreitman (MK) test can assess adaptation but struggle to isolate the effects of individual genomic features.
• There's a need for statistical approaches to disentangle correlated genomic influences on adaptation.

Purpose of the Study:

• To develop a novel statistical model, MK regression, to independently assess the impact of multiple genomic features on adaptive evolution.
• To identify specific genomic features that drive positive selection in chimpanzees.
• To explore the relationship between gene expression levels, metabolic genes, and adaptation rates.

Main Methods:

• Augmented the McDonald-Kreitman (MK) test with a generalized linear model to create the MK regression.
• Applied the MK regression to analyze multiple genomic features simultaneously, inferring independent effects.
• Utilized the model to examine genomic features associated with positive selection in chimpanzee populations.

Main Results:

• Identified several genomic features influencing positive selection in chimpanzees, including mutation rate, residue exposure, tissue specificity, and immune genes.
• Discovered novel associations between gene expression levels, metabolic genes, and adaptation rates.
• Demonstrated that highly expressed genes and metabolic genes exhibit higher adaptation rates, despite potential counteracting selective pressures.

Conclusions:

• The MK regression is a powerful tool for dissecting the genomic underpinnings of adaptation.
• Gene expression level and metabolic function are significant factors in the rate of adaptive evolution.
• The findings provide new insights into the evolutionary dynamics of primate genomes, particularly concerning diet and gene regulation.