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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

Sampling strategies for frequency spectrum-based population genomic inference.

John D Robinson^1,2, Alec J Coffman³, Michael J Hickerson^4,5,6

¹Department of Biology, City College of New York, New York, NY, 10031, USA. RobinsonJ@dnr.sc.gov.

BMC Evolutionary Biology

|December 5, 2014

Summary

Accurate population genomics inference using the allele frequency spectrum (AFS) requires larger sample sizes for recent demographic events. Genome-wide AFS analysis provides valuable insights into demographic history, guiding future population sampling strategies.

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Area of Science:

Population genomics
Evolutionary biology
Bioinformatics

Background:

The allele frequency spectrum (AFS) quantifies single nucleotide polymorphism (SNP) variant frequencies within a sample.
Advanced methods enable parameter estimation and model likelihood calculations using joint AFS from multiple populations.

Purpose of the Study:

To evaluate the accuracy of parameter estimation and model selection using the δaδi Python module.
To compare performance across different sample sizes and demographic models (one- and two-population).

Main Methods:

Conducted simulation studies using the δaδi Python module.
Analyzed data based on varying sample sizes and demographic model complexities.
Quantified model selection uncertainty and parameter estimation accuracy/precision.

Main Results:

Accurate parameter estimates and model selection were achieved for ancient demographic events with relatively small sample sizes.
Recent demographic events require larger sample sizes for comparable accuracy and precision.
Performance was assessed as a function of demographic event timing, sample size, and model complexity.

Conclusions:

The genome-wide AFS is a powerful tool for inferring demographic history.
Larger sample sizes enhance the power for model selection and parameter estimation, especially for recent events.
Recommendations are provided to guide sampling strategies in population genomics studies.