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Modeling SNP array ascertainment with Approximate Bayesian Computation for demographic inference.

Consuelo D Quinto-Cortés1, August E Woerner2, Joseph C Watkins3

  • 1National Laboratory of Genomics for Biodiversity (LANGEBIO), CINVESTAV, Irapuato, 36821, Mexico, Mexico. consuelo.quinto@cinvestav.mx.

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Summary
This summary is machine-generated.

This study introduces a new method to correct for biases in genetic data from commercial arrays. This improves the accuracy of population genetic inferences, especially for underrepresented populations.

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Area of Science:

  • Population Genetics
  • Genomic Data Analysis
  • Bioinformatics

Background:

  • Commercial SNP arrays often use data from limited populations, leading to ascertainment bias.
  • This bias distorts nucleotide diversity patterns and impacts population genetic inferences.

Purpose of the Study:

  • To develop a demographic inference pipeline that accounts for SNP discovery protocols.
  • To improve the accuracy of population genetic inferences using SNP array data.

Main Methods:

  • Implemented an Approximate Bayesian Computation (ABC) framework to model SNP discovery.
  • Simulated genomic regions and recreated SNP distributions from commercial arrays.
  • Inferred ascertainment and demographic parameters using summary statistics from ascertained and genomic data.

Main Results:

  • The developed pipeline successfully estimated demographic and ascertainment parameters.
  • Applied to Mexican population admixture, the method provided a more historically accurate admixture time estimate.
  • Demonstrated the pipeline's utility for demographic inference with SNP array data.

Conclusions:

  • The proposed ABC framework effectively corrects for ascertainment bias in SNP array data.
  • This approach enhances the reliability of demographic inferences, particularly for populations with limited genomic data.
  • The pipeline is valuable for studying population history using SNP array data from underrepresented regions.