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.

scholarly journals Efficient analysis of large datasets and sex bias with ADMIXTURE

2016 ◽  
Author(s):  
Suyash S. Shringarpure ◽  
Carlos D. Bustamante ◽  
Kenneth L. Lange ◽  
David H. Alexander
Keyword(s):  
Large Datasets ◽  
Allele Frequencies ◽  
Sex Bias ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Males And Females ◽  
Related Individuals ◽  
Using Data ◽  
Human Ancestry ◽  
Individual Ancestry

Background: A number of large genomic datasets are being generated for studies of human ancestry and diseases. The ADMIXTURE program is commonly used to infer individual ancestry from genomic data. Results: We describe two improvements to the ADMIXTURE software. The first enables ADMIXTURE to infer ancestry for a new set of individuals using cluster allele frequencies from a reference set of individuals. Using data from the 1000 Genomes Project, we show that this allows ADMIXTURE to infer ancestry for 10,920 individuals in a few hours (a 5x speedup). This mode also allows ADMIXTURE to correctly estimate individual ancestry and allele frequencies from a set of related individuals. The second modification allows ADMIXTURE to correctly handle X-chromosome (and other haploid) data from both males and females. We demonstrate increased power to detect sex-biased admixture in African-American individuals from the 1000 Genomes project using this extension. Conclusions: These modifications make ADMIXTURE more efficient and versatile, allowing users to extract more information from large genomic datasets.

scholarly journals PyVar: An Extensible Framework for Variant Annotator Comparison

2016 ◽  
Author(s):  
Julie Wertz ◽  
Qianli Liao ◽  
Thomas B Bair ◽  
Michael S Chimenti
Keyword(s):  
Gene Expression ◽  
Clinical Significance ◽  
Protein Function ◽  
State Of The Art ◽  
Research Community ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
New Methods ◽  
Downstream Effects ◽  
Functional Consequences

AbstractModern genomics projects are generating millions of variant calls that must be annotated for predicted functional consequences at the level of gene expression and protein function. Many of these variants are of interest owing to their potential clinical significance. Unfortunately, state-of-the-art methods do not always agree on downstream effects for any given variant. Here we present a readily extensible python framework (PyVar) for comparing the output of variant annotator methods in order to aid the research community in quickly assessing differences between methods and benchmarking new methods as they are developed. We also apply our framework to assess the annotation performance of ANNOVAR, VEP, and SnpEff when annotating 81 million variants from the ‘1000 Genomes Project’ against both RefSeq and Ensembl human transcript sets.

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 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 Comparative Analysis of rs12979860 SNP of the IFNL3 Gene in Children with Hepatitis C and Ethnic Matched Controls Using 1000 Genomes Project Data

PLoS ONE ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. e85899 ◽  
Author(s):  
Giuseppe Indolfi ◽  
Giusi Mangone ◽  
Elisa Bartolini ◽  
Gabriella Nebbia ◽  
Pier Luigi Calvo ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Hepatitis C ◽  
1000 Genomes Project ◽  
1000 Genomes ◽  
Project Data

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.

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