An ABC Method for Whole-Genome Sequence Data: Inferring Paleolithic and Neolithic Human Expansions

Area of Science:

• Population Genetics
• Genomics
• Bioinformatics

Background:

• Species exhibit complex demographic histories with significant population size fluctuations.
• Genome-wide sequencing data offer rich information for reconstructing these histories.
• Extracting demographic insights from large genomic datasets presents a significant challenge.

Purpose of the Study:

• To develop a novel computational approach for inferring past demographic events from moderate numbers of sequenced genomes.
• To identify the most fitting demographic scenario and estimate its parameters using simulation-based statistics.
• To validate the method's accuracy in inferring complex demographic histories, including population bottlenecks and expansions.

Main Methods:

• Utilized Approximate Bayesian Computation (ABC), a simulation-based statistical framework.
• Employed a cross-validation approach to combine haplotype sharing and linkage disequilibrium decay with classical statistics (heterozygosity, Tajima's D).
• Incorporated simultaneous estimation of genotyping error rates for improved accuracy.

Main Results:

• Demonstrated that combining various summary statistics accurately infers complex demographic scenarios.
• Showcased the importance of accounting for genotyping error rates in demographic inference.
• Applied the method to human genome data, identifying a bottleneck followed by Paleolithic and Neolithic expansions as the most relevant model for Eurasian populations.

Conclusions:

• The developed ABC approach effectively reconstructs demographic history from genome-wide data.
• The study provides a robust model for Eurasian population history, including key expansion periods.
• This method offers a powerful tool for analyzing genomic data to understand evolutionary pasts.