Simulated genotype data from: Effective population size estimation in large marine populations: Considering current challenges and opportunities when simulating large datasets with high-density genomic information

Next-generation sequencing has broadened perspectives regarding the estimation of the effective population size (Ne) by providing high-density genomic information. These technologies have expanded data collection and analytical tools in population genetics, increasing understanding of populations with high abundance, such as marine species with high commercial or conservation priority. Several common methods for estimating Ne are based on allele frequency spectra or linkage disequilibrium between loci. However, their specific constraints make it difficult to apply them to large populations, especially with confounding factors such as migration rates, complex sampling schemes, or non-independence between loci. Computer simulations have long represented invaluable tools to explore the influence of biological or logistical factors on Ne estimation and to assess the robustness of dedicated methods. Here, we outline several Ne estimation methods and their foundational principles, requirement..., , # Simulated genotype data from: Effective population size estimation in large marine populations: Considering current challenges and opportunities when simulating large datasets with high-density genomic information

Dataset DOI: [10.5061/dryad.6wwpzgn9w](10.5061/dryad.6wwpzgn9w)

## Description of the data and file structure

This repository contains genotype data, in the \"genepop\" format, obtained from the simulation framework described in the related article \"Effective population size estimation in large marine populations: Considering current challenges and opportunities when simulating large datasets with high-density genomic information\" published in *Evolutionary Applications*.

A total of 108 genotypic data subsets (9 folders X 12 data files) were derived from 3 independent simulations conducted with gene flows '*m*' between 2 populations set at 0.01, 0.05, or 0.10, respectively, each simulation repeated 3 times (leading to 3 replicates for each value of '*m*'). 

Each .zip file...,

下一代测序（Next-generation sequencing）通过提供高密度基因组信息，拓展了有效种群数量（effective population size, Ne）估算的研究视角。相关技术推动了种群遗传学领域的数据收集与分析工具的发展，加深了学界对高丰度种群的认知，例如具备高商业价值或保护优先级的海洋物种。目前主流的Ne估算方法多基于等位基因频率谱（allele frequency spectra）或位点间的连锁不平衡（linkage disequilibrium），但这类方法存在固有局限，难以应用于大型种群——当存在迁移率、复杂采样方案或位点间非独立性等混杂因素时，应用难度进一步提升。长期以来，计算机模拟（computer simulations）都是探究生物学或后勤因素对Ne估算的影响、评估专用方法稳健性的宝贵工具。在此，我们概述了多种Ne估算方法及其基本原理与要求…… # 模拟基因型数据源自：《大型海洋种群有效种群数量估算：基于高密度基因组信息的大型数据集模拟中的当前挑战与机遇》
数据集DOI：[10.5061/dryad.6wwpzgn9w]

## 数据与文件结构说明
本数据集仓库包含以genepop格式存储的基因型数据，数据源自发表于"Evolutionary Applications"的相关论文《大型海洋种群有效种群数量估算：基于高密度基因组信息的大型数据集模拟中的当前挑战与机遇》中所描述的模拟框架。
本数据集共包含108个基因型数据子集（9个文件夹×12个数据文件），源自3组独立模拟实验：两组种群间的基因流（gene flows）'m'分别设置为0.01、0.05或0.10，每组参数设置下的模拟均重复3次（即每个'm'值对应3次重复实验（replicates））。
每个.zip文件……