PGG.Population database documents 7122 genomes representing 356 global populations from countries, and provides necessary information for researchers and medical doctors to understand genomic diversity and genetic ancestry of human populations. We included miscellaneous functions and a friendly graphical user interface to visualize genomic diversity, population relationship (genetic affinity), ancestral makeup, footprints of natural selection, and population history etc. Moreover, PGG.Population provides a dynamic feature for users to analyze and compare their data to population samples in the database which are updated timely when new data are available. The long-term aim of the PGG.Population, together with the joint efforts from other researchers who publish or re-publish their data and visualize results in a dynamic style via online illustration in our database, is to bridge evolutionary genetic studies to future precision medicine.
Data Collection We manually searched for information of each enrolled population online or from literatures (Figure right). Genome-wide genotyping data or NGS data of whole-genomes were collected for each human population. These genomic data covered not only general populations studied by international projects such as the HapMap , HGDP , the 1000 Genomes Project , the HUGO Pan-Asia SNP Project , the Human Origin data set , and Simons Genomic Diversity Project , but also the indigenous/isolated populations contributed by regional sequencing efforts such as Tibetans , Sherpas , Xingjiang’s Uyghurs  and ethnic groups with genomes deposited in Estonian Biocentre (http://evolbio.ut.ee/). The list of populations and genomes will be updated once new data are published. In the current version of PGG.Population, all the genome information were based on the Human Genome Build 37 positions.
Data integration Different genotyping data sets from diverse platforms were assembled for further analysis (Figure right). For individual data genotyped on the same platform, such as Illumina arrays, these were directly pooled and then that of respective individuals were extracted with PLINK using filters that will be described in the next section
The NGS data were combined flexibly with (DC1 in Figure 1) or without genotyping data (DC3 in Figure right), depending on the requirements of downstream analyses. When NGS data were combined with genotyping data, strand information was determined from the whole-genome sequence data based on the Human Genome Build 37 positions and strand was flipped to match that of the sequenced data. Only intersections of SNPs among NGS and genotyping data were retained for further analysis.
NGS data of high coverage (≥ 20 ×) were integrated from bam files for further analysis (DC3 in Figure right). However, NGS data of low coverage (< 20 ×) were not combined considering the VCF files were generated from data of different read depth, coverage and variant calling process. NGS reads were mapped to the human reference genome (Build 37) using Burrows-Wheeler Algorithm . SNP calling and raw variants filtering were carried out using the HaplotypeCaller module and the variant quality score recalibration (VQSR) module in GATK [11, 12], respectively.
These steps thus generate different pooled data sets (‘Illumina’ data sets, ‘Affymetrix’ data sets, ‘Illumina-Affymetrix’ data sets, and ‘NGS’ data sets), which were flexible for reconstructing histories of diverse populations. We selected the latest and most representative data set for one group when the given population is covered by different datasets. A distinguished example is the Xinjiang’s Uyghurs, where the data published by Feng et al  were included as the best representative data set, as it consisted of around 1,000 samples from diverse geographical regions.
Quality control We filtered each combined data set that was assembled at both the single nucleotide polymorphism (SNP) and individual levels (QC1 in Figure right). At the SNP level, we removed SNPs with call rate of < 90% (across all individuals). At the individual level, we required at least 90% genotyping completeness for each individual (across all SNPs). We also removed recently related individuals by filtering one individual from all pairs when identity by descent (IBD) was > 35%. All of the analyses were performed with PLINK v1.07 . Only biallelic variants were used for downstream analysis. Outliers were removed based on principal components analysis (PCA) for each data set. For each “NGS” data set, only nucleotide sites passed universal filters were retained, as variant calling can be challenging in complex genomic regions  (QC2 in Figure right).
To test batch effect for each merged data set, PCA and FST-based analysis were performed (QC1 in Figure right). Population samples from the same group and genotyped on different platforms were particularly used for examining any potential batch effects. These population samples are expected to show close genetic affinity (FST <0.004) and cluster together in PCA plot  given there is no considerable batch effect. Data sets with significant batch effect were excluded from further analysis.
Analysis of genomic diversity and inference of genetic ancestry Y chromosomal haplogroups were determined on the basis of key mutations commonly used for nomenclature of human paternal lineages. We developed an algorithm to search all possible combinations of the key mutations used for nomenclature from our sequence data to determine the fine-scale paternal haplogroup that was affiliated with each sample. mtDNA haplogroups were defined as described by Weissensteiner et al . To estimate genetic affinities, FST between each pair of populations was calculated following Weir and Cockerham . To investigate fine-scale population structures, we performed a series of PCA using EIGENSOFT . We applied ADMIXTURE v1.30  for unsupervised clustering analysis. Because the model in ADMIXTURE does not take linkage disequilibrium (LD) into consideration, we pruned each dataset using an r2 cutoff of 0.1 in each continuous window of 50 SNPs, and advanced by 10 SNPs (--indep-pairwise 50 10 0.1) with PLINK v1.07. We ran ADMIXTURE with random seeds for the datasets from K = 2 to K = 20 with default parameters (--cv = 5) in 10 replicates for each K for each data set. We used runs of homozygosity (ROH) to measure genetic diversity for each population. Natural selection analysis was performed only for NGS data sets using SelScan .
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We provided several interactive website elements for each figure to enhance user experience. Here we use the ADMIXTURE part (Figure attached below) on our website as an example to provide an introduction of the interactive events, and how they can be controlled to obtain the best solution scheme.
(1). Mouse click event: Clicking on the color bars (or figure legend) will hide components represented by the corresponding color in the plot, and re-clicking on the inactive color (shown in grey) will enable it show again on the plot.
(2). Mouse hover event: Hovering on an element in a plot will trigger an information box containing detailed data about this element. For instance, by hovering a bar in the ADMIXTURE plot, users can find which
(3). Mouse wheel scroll: In ADMIXTURE and ROH plots, scrolling will zoom in/out the resolution of a specific plot, ranging from minimal 1 individual to maximal all samples.
(4). Data view and figure download toolbox: For each plot we prepared a toolbox for users to check numerical data and to download the adjusted figures. The buttons for these functions can be found upright each plot, of which the page-shaped button will open a new box containing Tab-seperated data, and the download button will convert the current plot into JPEG format and provided the download option.
(5). Option menus: In ADMIXTURE and ROH parts, we provided option menus by which users could change the ancestry (K) numbers in Admixture and the ROH length in descent. The options will change the level of analysis results and presentation.
(6). Data download button. We provided download button to each plot so that users can obtain the corresponding data underlying the figure.
The following table lists the data resources and their platforms, so that users interested in the population may have a chance to access the original data and know the background of the original study.