PGG Han

PGG Han provides a centralized repository of genomic data from 114,783 individuals (Han100K), including 319 high-coverage whole-genome sequences (WGS), 11,878 low-coverage WGS, and high-density single-nucleotide variant (SNV) data with >8 million variants genotyped or partially imputed across 102,586 samples, to characterize fine-scale genetic diversity and support population-genetic and association analyses of Han Chinese sub-populations across 33 Chinese administrative divisions and Singapore.

Key Features:

• Han100K dataset composition: Aggregates genomic data from 114,783 individuals comprising 319 high-coverage WGS, 11,878 low-coverage WGS, and SNV data across 102,586 samples with >8 million variants.
• Geographic coverage: Captures genetic samples from Han Chinese sub-populations spanning 33 of China's 34 administrative divisions and Singapore.
• Population structure analysis: Provides data and analyses to resolve fine-scale genetic structure within Han Chinese sub-populations.
• Ancestry inference: Implements nested panels of ancestry informative markers (AIMs) for individual ancestry inference and control of population stratification.
• Genotype imputation: Includes a Han-Chinese-specific reference panel to improve genotype imputation accuracy for Han Chinese samples.
• GWAS and association resources: Supplies genome-wide allele frequencies for hierarchical sub-populations and population-structure-aware shared control data for genome-wide association studies and genotype-phenotype analyses.

Scientific Applications:

• Population genetics: Characterizing fine-scale structure and genetic diversity within and among Han Chinese sub-populations.
• Ancestry and stratification control: Inferring individual ancestry and mitigating population stratification in genetic association studies using nested AIM panels.
• Genotype imputation: Improving imputation accuracy for Han Chinese cohorts using a population-specific reference panel.
• GWAS and genotype-phenotype associations: Enabling association studies with hierarchical allele-frequency references and population-structure-aware shared controls.
• Allele frequency reference: Providing hierarchical sub-population allele frequencies for variant interpretation and comparative analyses.

Methodology:

Data comprise high-coverage WGS, low-coverage WGS, and high-density SNV genotyping with partial imputation; ancestry inference uses nested AIM panels; genotype imputation employs a Han-Chinese-specific reference panel.