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<p>Linkage disequilibrium methods for demographic inference usually rely on panmictic population models. However, the structure of natural populations is generally complex and the quality of the genotyping data is often suboptimal. We present two software tools that implement theoretical developments to estimate the effective population size (<i>N</i><sub><i>e</i></sub>): <i>GONE2</i>, for inferring recent changes in <i>N</i><sub><i>e</i></sub> when a genetic map is available, and <i>currentNe2</i>, which estimates contemporary <i>N</i><sub><i>e</i></sub> even in the absence of genetic maps. These tools operate on SNP data from a single sample of individuals, and provide insights into population structure, including the <i>F</i><sub><i>ST</i></sub> index, migration rate, and subpopulation number. <i>GONE2</i> can also handle haploid data, genotyping errors, and low sequencing depth data. Results from simulations and laboratory populations of <i>Drosophila melanogaster</i> validated the tools in different demographic scenarios, and analysis were extended to populations of several species. These results highlight that ignoring population subdivision often leads to <i>N</i><sub><i>e</i></sub> underestimation.</p>

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Accounting for population structure and data quality in demographic inference with linkage disequilibrium methods

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月1日

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  • Enrique Santiago,
  • Carlos Köpke,
  • Armando Caballero

摘要

Linkage disequilibrium methods for demographic inference usually rely on panmictic population models. However, the structure of natural populations is generally complex and the quality of the genotyping data is often suboptimal. We present two software tools that implement theoretical developments to estimate the effective population size (Ne): GONE2, for inferring recent changes in Ne when a genetic map is available, and currentNe2, which estimates contemporary Ne even in the absence of genetic maps. These tools operate on SNP data from a single sample of individuals, and provide insights into population structure, including the FST index, migration rate, and subpopulation number. GONE2 can also handle haploid data, genotyping errors, and low sequencing depth data. Results from simulations and laboratory populations of Drosophila melanogaster validated the tools in different demographic scenarios, and analysis were extended to populations of several species. These results highlight that ignoring population subdivision often leads to Ne underestimation.