The data for the study "GS-Impute: a neural network framework for accurate imputation of low-density markers in across-population genomic selection"
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下载链接：
https://figshare.com/articles/dataset/The_data_for_the_study_GS-Impute_accurate_genotype_imputation_via_neural_networks_for_across-population_genomic_selection_with_low-density_markers_/29648240
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资源简介：
The data for the study "GS-Impute: a neural network framework for accurate imputation of low-density markers in across-population genomic selection"
Upon decompression, users will find:
1. Rice and maize genotype datasets with systematic and sporadic missing patterns.
2. Rice and maize genotype datasets before and after artificial missing simulation (named original_geno_file and unimputed genotype file respectively）
3. Rice and maize genotype datasets with different missing rates (10%, 30% and 50%).
4. Refined reference panels with redundant markers removed.
Note: The original reference panels are available for download via the Plant-ImputeDB platform (https://gong_lab.hzau.edu.cn/Plant_imputeDB/#!/).
创建时间：
2025-07-26
