Simulated and Real Genetic Data on Close Relatives in Population Structure Analysis

This dataset contains simulated and real genetic data used to evaluate the effect of close relatives on unsupervised Bayesian clustering algorithms. The research by Silvia T. Rodríguez-Ramilo demonstrates that including relatives can bias population genetic structure inference, and their removal improves analysis accuracy. The data supports investigations into Hardy-Weinberg and linkage disequilibrium caused by family structure within subpopulations.

Use Cases

• Assess the impact of close relatives on Hardy-Weinberg equilibrium assumptions in STRUCTURE software analyses.
• Validate pedigree reconstruction approaches for identifying and removing related individuals from subpopulation samples.
• Investigate the bias introduced by family structure on the detection of linkage disequilibrium in genetic markers.
• Benchmark the accuracy of population genetic structure analysis before and after filtering closely related individuals.

Strengths

• Includes both simulated and real genetic data for method validation.
• Data is associated with a peer-reviewed study published in 2020.
• Released under a permissive CC0 1.0 public domain license.

Limitations

• The exact size, format, and specific columns of the dataset are unknown.
• The real data's geographic origin and temporal coverage are not specified.
• Focus on close relatives may limit direct applicability to studies of unrelated individuals.

Provenance

Source

Dryad digital repository.

Collection Method

Data generated from simulations and sourced from real genetic studies, as described in the accompanying research.

Freshness

Last updated in June 2020.