Inference of elevated mutation rates and variant effects using 700k exomes.

Area of Science:

• Genomics
• Population Genetics
• Bioinformatics

Background:

• Genomic sequencing is increasingly used for genetic diagnostics and newborn screening.
• Characterizing novel mutations and identifying pathogenic variants are crucial for these applications.
• Large-scale datasets like gnomAD (Genome Aggregation Database) are essential for variant analysis.

Purpose of the Study:

• To develop a method for estimating population genetics parameters using rare variants from large datasets.
• To identify genes with loss-of-function mutational hotspots and estimate selection.
• To improve the prediction of pathogenic variants for genetic diagnostics and newborn screening.

Main Methods:

• Utilized theoretical understanding of rare variant sampling properties.
• Developed and applied the Population Inferred Estimates of Selection (PIES) method.
• Integrated population genetics inference with variant effect predictors.

Main Results:

• PIES identified novel genes with loss-of-function mutational hotspots, likely due to selection in spermatogonia.
• The method efficiently estimates selection coefficients for heterozygous loss-of-function variants.
• Combined approach improved prediction of pathogenic missense mutations.

Conclusions:

• PIES offers a powerful, data-driven approach to understanding selection and identifying disease-related genes.
• This method enhances variant prioritization for genetic diagnostics and newborn screening.
• Leveraging population data is key to advancing genomic medicine.