scholarly journals The International Genome Sample Resource (IGSR) collection of open human genomic variation resources

2019 ◽  
Vol 48 (D1) ◽  
pp. D941-D947 ◽  
Author(s):  
Susan Fairley ◽  
Ernesto Lowy-Gallego ◽  
Emily Perry ◽  
Paul Flicek
Keyword(s):  
Sequence Data ◽  
Genomic Variation ◽  
1000 Genomes Project ◽  
High Coverage ◽  
Web Based ◽  
1000 Genomes ◽  
Open Consent ◽  
Unified View ◽  
Human Genomic ◽  
Project Data

Abstract To sustain and develop the largest fully open human genomic resources the International Genome Sample Resource (IGSR) (https://www.internationalgenome.org) was established. It is built on the foundation of the 1000 Genomes Project, which created the largest openly accessible catalogue of human genomic variation developed from samples spanning five continents. IGSR (i) maintains access to 1000 Genomes Project resources, (ii) updates 1000 Genomes Project resources to the GRCh38 human reference assembly, (iii) adds new data generated on 1000 Genomes Project cell lines, (iv) shares data from samples with a similarly open consent to increase the number of samples and populations represented in the resources and (v) provides support to users of these resources. Among recent updates are the release of variation calls from 1000 Genomes Project data calculated directly on GRCh38 and the addition of high coverage sequence data for the 2504 samples in the 1000 Genomes Project phase three panel. The data portal, which facilitates web-based exploration of the IGSR resources, has been updated to include samples which were not part of the 1000 Genomes Project and now presents a unified view of data and samples across almost 5000 samples from multiple studies. All data is fully open and publicly accessible.

scholarly journals High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios

2021 ◽  
Author(s):  
Marta Byrska-Bishop ◽  
Uday S. Evani ◽  
Xuefang Zhao ◽  
Anna O. Basile ◽  
Haley J. Abel ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Sequence Data ◽  
Whole Genome ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
High Coverage ◽  
Entire Cohort ◽  
1000 Genomes ◽  
Low Coverage

ABSTRACTThe 1000 Genomes Project (1kGP), launched in 2008, is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final (phase 3) 2015 release of 1kGP included 2,504 unrelated samples from 26 populations, representing five continental regions of the world and was based on a combination of technologies including low coverage WGS (mean depth 7.4X), high coverage whole exome sequencing (mean depth 65.7X), and microarray genotyping. Here, we present a new, high coverage WGS resource encompassing the original 2,504 1kGP samples, as well as an additional 698 related samples that result in 602 complete trios in the 1kGP cohort. We sequenced this expanded 1kGP cohort of 3,202 samples to a targeted depth of 30X using Illumina NovaSeq 6000 instruments. We performed SNV/INDEL calling against the GRCh38 reference using GATK’s HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model, upgrading the 1kGP dataset to current state-of-the-art standards. Using this strategy, we defined over 111 million SNVs, 14 million INDELs, and ∼170 thousand SVs across the entire cohort of 3,202 samples with estimated false discovery rate (FDR) of 0.3%, 1.0%, and 1.8%, respectively. By comparison to the low-coverage phase 3 callset, we observed substantial improvements in variant discovery and estimated FDR that were facilitated by high coverage re-sequencing and expansion of the cohort. Specifically, we called 7% more SNVs, 59% more INDELs, and 170% more SVs per genome than the phase 3 callset. Moreover, we leveraged the presence of families in the cohort to achieve superior haplotype phasing accuracy and we demonstrate improvements that the high coverage panel brings especially for INDEL imputation. We make all the data generated as part of this project publicly available and we envision this updated version of the 1kGP callset to become the new de facto public resource for the worldwide scientific community working on genomics and genetics.

scholarly journals Genome diversity in Ukraine

GigaScience ◽  
2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Taras K Oleksyk ◽  
Walter W Wolfsberger ◽  
Alexandra M Weber ◽  
Khrystyna Shchubelka ◽  
Olga T Oleksyk ◽  
...  
Keyword(s):  
Sequence Data ◽  
Copy Number Variations ◽  
Genomic Variation ◽  
High Coverage ◽  
Genome Data ◽  
New Information ◽  
Genome Wide ◽  
Public Data ◽  
Genome Wide Data ◽  
Multiple Samples

Abstract Background The main goal of this collaborative effort is to provide genome-wide data for the previously underrepresented population in Eastern Europe, and to provide cross-validation of the data from genome sequences and genotypes of the same individuals acquired by different technologies. We collected 97 genome-grade DNA samples from consented individuals representing major regions of Ukraine that were consented for public data release. BGISEQ-500 sequence data and genotypes by an Illumina GWAS chip were cross-validated on multiple samples and additionally referenced to 1 sample that has been resequenced by Illumina NovaSeq6000 S4 at high coverage. Results The genome data have been searched for genomic variation represented in this population, and a number of variants have been reported: large structural variants, indels, copy number variations, single-nucletide polymorphisms, and microsatellites. To our knowledge, this study provides the largest to-date survey of genetic variation in Ukraine, creating a public reference resource aiming to provide data for medical research in a large understudied population. Conclusions Our results indicate that the genetic diversity of the Ukrainian population is uniquely shaped by evolutionary and demographic forces and cannot be ignored in future genetic and biomedical studies. These data will contribute a wealth of new information bringing forth a wealth of novel, endemic and medically related alleles.

scholarly journals Ancestral Spectrum Analysis With Population-Specific Variants

2021 ◽  
Vol 12 ◽  
Author(s):  
Gang Shi ◽  
Qingmin Kuang
Keyword(s):  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
1000 Genomes Project ◽  
Specific Population ◽  
High Coverage ◽  
Single Nucleotide ◽  
Target Populations ◽  
1000 Genomes ◽  
Sequencing Studies ◽  
Best Linear Unbiased

With the advance of sequencing technology, an increasing number of populations have been sequenced to study the histories of worldwide populations, including their divergence, admixtures, migration, and effective sizes. The variants detected in sequencing studies are largely rare and mostly population specific. Population-specific variants are often recent mutations and are informative for revealing substructures and admixtures in populations; however, computational methods and tools to analyze them are still lacking. In this work, we propose using reference populations and single nucleotide polymorphisms (SNPs) specific to the reference populations. Ancestral information, the best linear unbiased estimator (BLUE) of the ancestral proportion, is proposed, which can be used to infer ancestral proportions in recently admixed target populations and measure the extent to which reference populations serve as good proxies for the admixing sources. Based on the same panel of SNPs, the ancestral information is comparable across samples from different studies and is not affected by genetic outliers, related samples, or the sample sizes of the admixed target populations. In addition, ancestral spectrum is useful for detecting genetic outliers or exploring co-ancestry between study samples and the reference populations. The methods are implemented in a program, Ancestral Spectrum Analyzer (ASA), and are applied in analyzing high-coverage sequencing data from the 1000 Genomes Project and the Human Genome Diversity Project (HGDP). In the analyses of American populations from the 1000 Genomes Project, we demonstrate that recent admixtures can be dissected from ancient admixtures by comparing ancestral spectra with and without indigenous Americans being included in the reference populations.

scholarly journals Open-Access STRS Database Of Populations From The 1000 Genomes Project Using High Coverage Phase 3 Data

2021 ◽  
Author(s):  
Tamara Soledad Frontanilla ◽  
Guilherme Valle Silva ◽  
Jesus Ayala ◽  
Celso Teixeira Mendes
Keyword(s):  
Open Access ◽  
Forensic Genetics ◽  
Allele Frequencies ◽  
Equilibrium Analysis ◽  
1000 Genomes Project ◽  
Phase 3 ◽  
Principal Coordinates Analysis ◽  
High Coverage ◽  
1000 Genomes ◽  
Principal Coordinates

Accurate STR genotyping from next-generation sequencing (NGS) data has been challenging. Haplotype inference and phasing for STRs (HipSTR) was specifically developed to deal with genotyping errors and obtain reliable STR genotypes from whole-genome sequencing datasets. The objective of this investigation was to perform a comprehensive genotyping analysis of a set of STRs of broad forensic interest from the 1000 Genomes populations and release a reliable open-access STR database to the forensic genetics community. A set of 22 STR markers were analyzed using the CRAM files of the 1000 Genomes Project Phase 3 high-coverage (30x) dataset generated by the New York Genome Center (NYGC). HipSTR was used to call genotypes from 2,504 samples from 26 populations organized into five groups: African, East Asian, European, South Asian, and admixed American. The D21S11 marker could not be detected in the present study. Moreover, the Hardy-Weinberg equilibrium analysis, coupled with a comprehensive analysis of allele frequencies, revealed that HipSTR could not identify longer Penta E (and Penta D at a lesser extent) alleles. This issue is probably due to the limited length of sequencing reads available for genotype calling, resulting in heterozygote deficiency. Notwithstanding that, AMOVA, a clustering analysis using STRUCTURE, and a Principal Coordinates Analysis revealed a clear-cut separation between the four major ancestries sampled by the 1000 Genomes Consortium (AFR, EUR, EAS, SAS). Meanwhile, the AMOVA results corroborated previous reports that most of the variance is (97.12%) observed within populations. This set of analyses revealed that except for larger Penta D and Penta E alleles, allele frequencies and genotypes defined by HipSTR from the 1000 Genomes Project phase 3 data and offered as an open-access database are consistent and highly reliable.

scholarly journals The 1000 Genomes Project: data management and community access

2012 ◽  
Vol 9 (5) ◽  
pp. 459-462 ◽  
Author(s):  
Laura Clarke ◽  
◽  
Xiangqun Zheng-Bradley ◽  
Richard Smith ◽  
Eugene Kulesha ◽  
...  
Keyword(s):  
Data Management ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Project Data ◽  
Community Access

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

2012 ◽  
Vol 19 (2) ◽  
pp. 289-294 ◽  
Author(s):  
Carrie C Buchanan ◽  
Eric S Torstenson ◽  
William S Bush ◽  
Marylyn D Ritchie
Keyword(s):  
1000 Genomes Project ◽  
1000 Genomes ◽  
International Hapmap Consortium ◽  
Project Data

scholarly journals Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project

2019 ◽  
Vol 4 ◽  
pp. 50 ◽  
Author(s):  
Ernesto Lowy-Gallego ◽  
Susan Fairley ◽  
Xiangqun Zheng-Bradley ◽  
Magali Ruffier ◽  
Laura Clarke ◽  
...  
Keyword(s):  
De Novo ◽  
Variant Calling ◽  
Final Phase ◽  
1000 Genomes Project ◽  
Data Set ◽  
1000 Genomes ◽  
Project Data

We present a set of biallelic SNVs and INDELs, from 2,548 samples spanning 26 populations from the 1000 Genomes Project, called de novo on GRCh38. We believe this will be a useful reference resource for those using GRCh38. It represents an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date by encompassing all of the GRCh38 primary assembly autosomes and pseudo-autosomal regions, including novel, medically relevant loci. Here, we describe how the data set was created and benchmark our call set against that produced by the final phase of the 1000 Genomes Project on GRCh37 and the lift-over of that data to GRCh38.

scholarly journals Variant calling on the GRCh38 assembly with the data from phase three of the 1000 Genomes Project

2019 ◽  
Vol 4 ◽  
pp. 50 ◽  
Author(s):  
Ernesto Lowy-Gallego ◽  
Susan Fairley ◽  
Xiangqun Zheng-Bradley ◽  
Magali Ruffier ◽  
Laura Clarke ◽  
...  
Keyword(s):  
Variant Calling ◽  
Final Phase ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Project Data

We present biallelic SNVs called from 2,548 samples across 26 populations from the 1000 Genomes Project, called directly on GRCh38. We believe this will be a useful reference resource for those using GRCh38, representing an improvement over the “lift-overs” of the 1000 Genomes Project data that have been available to date and providing a resource necessary for the full adoption of GRCh38 by the community. Here, we describe how the call set was created and provide benchmarking data describing how our call set compares to that produced by the final phase of the 1000 Genomes Project on GRCh37.

scholarly journals Inference of recent admixture using genotype data

2020 ◽  
Author(s):  
Peter Pfaffelhuber ◽  
Elisabeth Sester-Huss ◽  
Franz Baumdicker ◽  
Jana Naue ◽  
Sabine Lutz-Bonengel ◽  
...  
Keyword(s):  
State Of The Art ◽  
Forensic Genetics ◽  
Statistical Test ◽  
Genotype Data ◽  
1000 Genomes Project ◽  
Additional Information ◽  
1000 Genomes ◽  
Project Data ◽  
Ancestry Proportions ◽  
Individual Ancestry

AbstractThe inference of biogeographic ancestry (BGA) has become a focus of forensic genetics. Mis-inference of BGA can have profound unwanted consequences for investigations and society. We show that recent admixture can lead to misclassification and erroneous inference of ancestry proportions, using state of the art analysis tools with (i) simulations, (ii) 1000 genomes project data, and (iii) two individuals analyzed using the ForenSeq DNA Signature Prep Kit. Subsequently, we extend existing tools for estimation of individual ancestry (IA) by allowing for different IA in both parents, leading to estimates of parental individual ancestry (PIA), and a statistical test for recent admixture. Estimation of PIA outperforms IA in most scenarios of recent admixture. Furthermore, additional information about parental ancestry can be acquired with PIA that may guide casework.

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