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The 1000 Genomes Project: data management and community access.

ABSTRACT: The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enable widespread data access.

SUBMITTER: Clarke L

PROVIDER: S-EPMC3340611 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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The 1000 Genomes Project: data management and community access.

Clarke Laura L Zheng-Bradley Xiangqun X Smith Richard R Kulesha Eugene E Xiao Chunlin C Toneva Iliana I Vaughan Brendan B Preuss Don D Leinonen Rasko R Shumway Martin M Sherry Stephen S Flicek Paul P

Nature methods 20120427 5

The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enabl ...[more]

PMID: 22543379

Similar Datasets

Evaluating the quality of the 1000 genomes project data.

Project description:BackgroundData from the 1000 Genomes project is quite often used as a reference for human genomic analysis. However, its accuracy needs to be assessed to understand the quality of predictions made using this reference. We present here an assessment of the genotyping, phasing, and imputation accuracy data in the 1000 Genomes project. We compare the phased haplotype calls from the 1000 Genomes project to experimentally phased haplotypes for 28 of the same individuals sequenced using the 10X Genomics platform.ResultsWe observe that phasing and imputation for rare variants are unreliable, which likely reflects the limited sample size of the 1000 Genomes project data. Further, it appears that using a population specific reference panel does not improve the accuracy of imputation over using the entire 1000 Genomes data set as a reference panel. We also note that the error rates and trends depend on the choice of definition of error, and hence any error reporting needs to take these definitions into account.ConclusionsThe quality of the 1000 Genomes data needs to be considered while using this database for further studies. This work presents an analysis that can be used for these assessments.

| S-EPMC6696682 | biostudies-literature

1000 genomes project

Project description:Salmonella sequencing

| PRJEB56604 | ENA

Discovery of Western European R1b1a2 Y chromosome variants in 1000 genomes project data: an online community approach.

Project description:The authors have used an online community approach, and tools that were readily available via the Internet, to discover genealogically and therefore phylogenetically relevant Y-chromosome polymorphisms within core haplogroup R1b1a2-L11/S127 (rs9786076). Presented here is the analysis of 135 unrelated L11 derived samples from the 1000 Genomes Project. We were able to discover new variants and build a much more complex phylogenetic relationship for L11 sub-clades. Many of the variants were further validated using PCR amplification and Sanger sequencing. The identification of these new variants will help further the understanding of population history including patrilineal migrations in Western and Central Europe where R1b1a2 is the most frequent haplogroup. The fine-grained phylogenetic tree we present here will also help to refine historical genetic dating studies. Our findings demonstrate the power of citizen science for analysis of whole genome sequence data.

| S-EPMC3404022 | biostudies-literature

Performance of genotype imputations using data from the 1000 Genomes Project.

Project description:Genotype imputations based on 1000 Genomes (1KG) Project data have the advantage of imputing many more SNPs than imputations based on HapMap data. It also provides an opportunity to discover associations with relatively rare variants. Recent investigations are increasingly using 1KG data for genotype imputations, but only limited evaluations of the performance of this approach are available. In this paper, we empirically evaluated imputation performance using 1KG data by comparing imputation results to those using the HapMap Phase II data that have been widely used. We used three reference panels: the CEU panel consisting of 120 haplotypes from HapMap II and 1KG data (June 2010 release) and the EUR panel consisting of 566 haplotypes also from 1KG data (August 2010 release). We used Illumina 324,607 autosomal SNPs genotyped in 501 individuals of European ancestry. Our most important finding was that both 1KG reference panels provided much higher imputation yield than the HapMap II panel. There were more than twice as many successfully imputed SNPs as there were using the HapMap II panel (6.7 million vs. 2.5 million). Our second most important finding was that accuracy using both 1KG panels was high and almost identical to accuracy using the HapMap II panel. Furthermore, after removing SNPs with MACH Rsq <0.3, accuracy for both rare and low frequency SNPs was very high and almost identical to accuracy for common SNPs. We found that imputation using the 1KG-EUR panel had advantages in successfully imputing rare, low frequency and common variants. Our findings suggest that 1KG-based imputation can increase the opportunity to discover significant associations for SNPs across the allele frequency spectrum. Because the 1KG Project is still underway, we expect that later versions will provide even better imputation performance.

| S-EPMC3322630 | biostudies-literature

Open-Access Worldwide Population STR Database Constructed Using High-Coverage Massively Parallel Sequencing Data Obtained from the 1000 Genomes Project.

Project description:Achieving accurate STR genotyping by using next-generation sequencing data has been challenging. To provide the forensic genetics community with a reliable open-access STR database, we conducted a comprehensive genotyping analysis of a set of STRs of broad forensic interest obtained from 1000 Genome populations. We analyzed 22 STR markers using files of the high-coverage dataset of Phase 3 of the 1000 Genomes Project. We used HipSTR to call genotypes from 2504 samples obtained from 26 populations. We were not able to detect the D21S11 marker. The Hardy-Weinberg equilibrium analysis coupled with a comprehensive analysis of allele frequencies revealed that HipSTR was not able to identify longer alleles, which resulted in heterozygote deficiency. Nevertheless, AMOVA, a clustering analysis that uses STRUCTURE, and a Principal Coordinates Analysis showed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium. Except for larger Penta D and Penta E alleles, and two very small Penta D alleles (2.2 and 3.2) usually observed in African populations, our analyses revealed that allele frequencies and genotypes offered as an open-access database are consistent and reliable.

| S-EPMC9778533 | biostudies-literature

Copy Number Variation detection from 1000 Genomes Project exon capture sequencing data.

Project description:BackgroundDNA capture technologies combined with high-throughput sequencing now enable cost-effective, deep-coverage, targeted sequencing of complete exomes. This is well suited for SNP discovery and genotyping. However there has been little attention devoted to Copy Number Variation (CNV) detection from exome capture datasets despite the potentially high impact of CNVs in exonic regions on protein function.ResultsAs members of the 1000 Genomes Project analysis effort, we investigated 697 samples in which 931 genes were targeted and sampled with 454 or Illumina paired-end sequencing. We developed a rigorous Bayesian method to detect CNVs in the genes, based on read depth within target regions. Despite substantial variability in read coverage across samples and targeted exons, we were able to identify 107 heterozygous deletions in the dataset. The experimentally determined false discovery rate (FDR) of the cleanest dataset from the Wellcome Trust Sanger Institute is 12.5%. We were able to substantially improve the FDR in a subset of gene deletion candidates that were adjacent to another gene deletion call (17 calls). The estimated sensitivity of our call-set was 45%.ConclusionsThis study demonstrates that exonic sequencing datasets, collected both in population based and medical sequencing projects, will be a useful substrate for detecting genic CNV events, particularly deletions. Based on the number of events we found and the sensitivity of the methods in the present dataset, we estimate on average 16 genic heterozygous deletions per individual genome. Our power analysis informs ongoing and future projects about sequencing depth and uniformity of read coverage required for efficient detection.

| S-EPMC3563612 | biostudies-literature

Mycoplasma contamination in the 1000 Genomes Project.

Project description:BackgroundIn silco Biology is increasingly important and is often based on public data. While the problem of contamination is well recognised in microbiology labs the corresponding problem of database corruption has received less attention.ResultsMapping 50 billion next generation DNA sequences from The Thousand Genome Project against published genomes reveals many that match one or more Mycoplasma but are not included in the reference human genome GRCh37.p5. Many of these are of low quality but NCBI BLAST searches confirm some high quality, high entropy sequences match Mycoplasma but no human sequences.ConclusionsIt appears at least 7% of 1000G samples are contaminated.

| S-EPMC4022254 | biostudies-literature

The 1000 Genomes Project

Project description:The 1000 Genomes Project

| PRJNA28889 | ENA

A comparison of cataloged variation between International HapMap Consortium and 1000 Genomes Project data.

Project description:Since publication of the human genome in 2003, geneticists have been interested in risk variant associations to resolve the etiology of traits and complex diseases. The International HapMap Consortium undertook an effort to catalog all common variation across the genome (variants with a minor allele frequency (MAF) of at least 5% in one or more ethnic groups). HapMap along with advances in genotyping technology led to genome-wide association studies which have identified common variants associated with many traits and diseases. In 2008 the 1000 Genomes Project aimed to sequence 2500 individuals and identify rare variants and 99% of variants with a MAF of <1%.To determine whether the 1000 Genomes Project includes all the variants in HapMap, we examined the overlap between single nucleotide polymorphisms (SNPs) genotyped in the two resources using merged phase II/III HapMap data and low coverage pilot data from 1000 Genomes.Comparison of the two data sets showed that approximately 72% of HapMap SNPs were also found in 1000 Genomes Project pilot data. After filtering out HapMap variants with a MAF of <5% (separately for each population), 99% of HapMap SNPs were found in 1000 Genomes data.Not all variants cataloged in HapMap are also cataloged in 1000 Genomes. This could affect decisions about which resource to use for SNP queries, rare variant validation, or imputation. Both the HapMap and 1000 Genomes Project databases are useful resources for human genetics, but it is important to understand the assumptions made and filtering strategies employed by these projects.

| S-EPMC3277631 | biostudies-literature

Evaluation of MC1R high-throughput nucleotide sequencing data generated by the 1000 Genomes Project.

Project description:The advent of next-generation sequencing allows simultaneous processing of several genomic regions/individuals, increasing the availability and accuracy of whole-genome data. However, these new approaches may present some errors and bias due to alignment, genotype calling, and imputation methods. Despite these flaws, data obtained by next-generation sequencing can be valuable for population and evolutionary studies of specific genes, such as genes related to how pigmentation evolved among populations, one of the main topics in human evolutionary biology. Melanocortin-1 receptor (MC1R) is one of the most studied genes involved in pigmentation variation. As MC1R has already been suggested to affect melanogenesis and increase risk of developing melanoma, it constitutes one of the best models to understand how natural selection acts on pigmentation. Here we employed a locally developed pipeline to obtain genotype and haplotype data for MC1R from the raw sequencing data provided by the 1000 Genomes FTP site. We also compared such genotype data to Phase 3 VCF to evaluate its quality and discover any polymorphic sites that may have been overlooked. In conclusion, either the VCF file or one of the presently described pipelines could be used to obtain reliable and accurate genotype calling from the 1000 Genomes Phase 3 data.

| S-EPMC5488459 | biostudies-literature

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