Population-Scale Sequencing Data Enable Precise Estimates of Y-STR Mutation Rates.

Area of Science:

• Genetics
• Genomics
• Population Genetics

Background:

• Short tandem repeats (STRs) are abundant in the human genome, but their mutation rates are largely unknown.
• Previous studies relied on limited methods, hindering comprehensive mutation rate estimation for most STRs.

Purpose of the Study:

• To estimate mutation rates for Y chromosome STRs (Y-STRs) using whole-genome sequencing data.
• To develop and validate a novel algorithm (MUTEA) for inferring STR mutation rates from population-scale data.
• To provide the largest collection of Y-STR mutation rates and identify factors influencing them.

Main Methods:

• Genotyped 4,500 Y-STRs using data from the 1000 Genomes Project and Simons Genome Diversity Project.
• Developed MUTEA, an algorithm inferring STR mutation rates from population-scale data via SNP-based phylogeny.
• Validated MUTEA and applied it to 702 polymorphic STRs across 222,000 meioses.

Main Results:

• Generated the largest dataset of Y-STR mutation rates to date.
• Identified determinants of STR mutation rates and developed a predictive model.
• Estimated a de novo STR mutation load of at least 75 per generation, comparable to other variant types.
• Identified Y-STRs applicable to forensics and genetic genealogy, and improved Y-chromosome differentiation.

Conclusions:

• Whole-genome sequencing and MUTEA provide a powerful approach for estimating STR mutation rates.
• The high de novo STR mutation load has significant implications for human genetic variation and evolution.
• Y-STRs have considerable utility in forensic science and genetic genealogy applications.