Inferring the mode and strength of ongoing selection.

Area of Science:

• Genetics
• Bioinformatics
• Evolutionary Biology

Background:

• Genome sequencing is rapidly expanding, with large datasets like the UK Biobank containing hundreds of thousands of individual genomes.
• Existing population genetic methods are insufficient for analyzing the vast, interconnected data from large, closely related populations.
• The increasing availability of population-level genomic data necessitates advanced analytical approaches for human health and agriculture.

Purpose of the Study:

• To develop a novel computational method for inferring natural selection from large-scale genomic data.
• To address the limitations of current methods in analyzing family-based genomic datasets.
• To enable new avenues for studying evolutionary processes in populations.

Main Methods:

• Development of the trio-based inference of dominance and selection (TIDES) method.
• Utilizing data from tens of thousands of family trios for analysis.
• TIDES makes no assumptions regarding demography, linkage, or dominance.

Main Results:

• TIDES enables robust inference of natural selection acting within a single generation.
• The method is optimized for large datasets with thousands of closely related individuals.
• Demonstrates a significant advancement over existing population genetic analysis tools.

Conclusions:

• TIDES provides a powerful new approach for studying natural selection using population-scale genomic data.
• This method facilitates deeper insights into evolutionary dynamics in both humans and other species.
• Paves the way for novel research angles in population genetics and evolutionary studies.