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Estimating effective population size using RADseq: Effects of SNP selection and sample size.

Florianne Marandel1, Grégory Charrier2, Jean-Baptiste Lamy3

  • 1Ifremer Ecologie et Modèles pour l'Halieutique Nantes France.

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Estimating effective population size (Ne) using RADseq data is complex. SNP selection criteria and missing data significantly impact Ne estimates, requiring careful consideration in population genetics studies.

Keywords:
NeEstimatorRADseqeffective population sizelinkage disequilibriumskates and rays

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Area of Science:

  • Population Genetics
  • Genomics
  • Conservation Biology

Background:

  • Effective population size (Ne) is crucial for understanding evolutionary processes.
  • Estimating Ne in natural populations is challenging due to various biases.
  • Restriction site-associated DNA sequencing (RADseq) is a common genomic tool, but has inherent data limitations.

Purpose of the Study:

  • To evaluate the impact of SNP selection criteria on Ne estimates derived from RADseq data.
  • To assess how missing data and minor allele frequency thresholds affect Ne estimations.
  • To investigate these effects in the thornback ray, a nonmodel species.

Main Methods:

  • Utilized RADseq data from thornback rays.
  • Applied the Linkage Disequilibrium (LD) method for Ne estimation.
  • Analyzed the influence of missing data percentage and minor allele frequency (MAF) thresholds on Ne.

Main Results:

  • Missing data were nonrandomly distributed, correlating positively with the inbreeding coefficient (F_IS).
  • Ne estimate precision decreased with an increasing number of SNPs.
  • Increasing missing data (25% to 50%) inflated Ne estimates (82-120%).
  • Increasing MAF threshold (0.01 to 0.1) decreased Ne estimates (71-75%).

Conclusions:

  • SNP selection criteria and missing data significantly bias Ne estimates from RADseq data.
  • Careful consideration of these factors is essential for accurate Ne interpretation in empirical studies.
  • Findings highlight the need for robust data filtering and methodological awareness in population genomics.