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

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.

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

Wellcome Open Research ◽

10.12688/wellcomeopenres.15126.1 ◽

2019 ◽

Vol 4 ◽

pp. 50 ◽

Cited By ~ 2

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.

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Evaluating the quality of the 1000 Genomes Project data

10.1101/383950 ◽

2018 ◽

Cited By ~ 2

Author(s):

Saurabh Belsare ◽

Michal Sakin-Levy ◽

Yulia Mostovoy ◽

Steffen Durinck ◽

Subhra Chaudhry ◽

...

Keyword(s):

Imputation Accuracy ◽

Genomic Analysis ◽

Error Rates ◽

Reference Panel ◽

Specific Reference ◽

1000 Genomes Project ◽

Data Set ◽

1000 Genomes ◽

Project Data

ABSTRACTData 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 genotype, 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. We 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.

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Empirical estimation of genome-wide significance thresholds based on the 1000 Genomes Project data set

Journal of Human Genetics ◽

10.1038/jhg.2016.72 ◽

2016 ◽

Vol 61 (10) ◽

pp. 861-866 ◽

Cited By ~ 36

Author(s):

Masahiro Kanai ◽

Toshihiro Tanaka ◽

Yukinori Okada

Keyword(s):

Empirical Estimation ◽

1000 Genomes Project ◽

Data Set ◽

1000 Genomes ◽

Genome Wide ◽

Project Data ◽

Genome Wide Significance

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de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

10.1101/2021.05.27.445979 ◽

2021 ◽

Author(s):

Jeffrey K. Ng ◽

Pankaj Vats ◽

Elyn Fritz-Waters ◽

Evin M. Padhi ◽

Zachary L. Payne ◽

...

Keyword(s):

Cell Line ◽

De Novo ◽

Variant Calling ◽

B Cell Lymphoma ◽

Bimodal Distribution ◽

1000 Genomes Project ◽

1000 Genomes ◽

Age Related ◽

Detailed Assessment ◽

Paternal Parent

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to call DNVs, using 602 trios from the 1000 Genomes Project as a control. We detected 445,711 DNVs, having a bimodal distribution, with peaks at 200 and 2000 DNVs. The excess DNVs are cell line artifacts that are increasing with cell passage. Reduction in DNVs at CpG sites and in percent of DNVs with a paternal parent-of-origin with increasing number of DNVs supports this finding. Detailed assessment of individual NA12878 across multiple genome datasets from 2012 to 2020 reveals increasing number of DNVs over time. Mutation signature analysis across the set revealed individuals had either 1) age-related, 2) B-cell lymphoma, or 3) no prominent signatures. Our approach provides an important advancement for DNV detection and shows cell line artifacts present in lymphoblastoid cell lines are not always random.

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Evaluation of serverless computing for scalable execution of a joint variant calling workflow

PLoS ONE ◽

10.1371/journal.pone.0254363 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254363

Author(s):

Aji John ◽

Kathleen Muenzen ◽

Kristiina Ausmees

Keyword(s):

Genetic Information ◽

Best Practice ◽

Workflow Management ◽

Variant Calling ◽

Phase Iii ◽

1000 Genomes Project ◽

1000 Genomes ◽

Genomics Research ◽

The Cost ◽

Analysis Of Performance

Advances in whole-genome sequencing have greatly reduced the cost and time of obtaining raw genetic information, but the computational requirements of analysis remain a challenge. Serverless computing has emerged as an alternative to using dedicated compute resources, but its utility has not been widely evaluated for standardized genomic workflows. In this study, we define and execute a best-practice joint variant calling workflow using the SWEEP workflow management system. We present an analysis of performance and scalability, and discuss the utility of the serverless paradigm for executing workflows in the field of genomics research. The GATK best-practice short germline joint variant calling pipeline was implemented as a SWEEP workflow comprising 18 tasks. The workflow was executed on Illumina paired-end read samples from the European and African super populations of the 1000 Genomes project phase III. Cost and runtime increased linearly with increasing sample size, although runtime was driven primarily by a single task for larger problem sizes. Execution took a minimum of around 3 hours for 2 samples, up to nearly 13 hours for 62 samples, with costs ranging from $2 to $70.

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A unified haplotype-based method for accurate and comprehensive variant calling

10.1101/456103 ◽

2018 ◽

Cited By ~ 3

Author(s):

Daniel P Cooke ◽

David C Wedge ◽

Gerton Lunter

Keyword(s):

De Novo ◽

Variant Calling ◽

Normal Sample ◽

Sequencing Data ◽

Somatic Variation ◽

Data Set ◽

Small Complex ◽

Physical Linkage ◽

Germline Variation ◽

Almost All

Haplotype-based variant callers, which consider physical linkage between variant sites, are currently among the best tools for germline variation discovery and genotyping from short-read sequencing data. However, almost all such tools were designed specifically for detecting common germline variation in diploid populations, and give sub-optimal results in other scenarios. Here we present Octopus, a versatile haplotype-based variant caller that uses a polymorphic Bayesian genotyping model capable of modeling sequencing data from a range of experimental designs within a unified haplotype-aware framework. We show that Octopus accurately calls de novo mutations in parent-offspring trios and germline variants in individuals, including SNVs, indels, and small complex replacements such as microinversions. In addition, using a carefully designed synthetic-tumour data set derived from clean sequencing data from a sample with known germline haplotypes, and observed mutations in large cohort of tumour samples, we show that Octopus accurately characterizes germline and somatic variation in tumours, both with and without a paired normal sample. Sequencing reads and prior information are combined to phase called genotypes of arbitrary ploidy, including those with somatic mutations. Octopus also outputs realigned evidence BAMs to aid validation and interpretation.

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High level of inbreeding in final phase of 1000 Genomes Project

Scientific Reports ◽

10.1038/srep17453 ◽

2015 ◽

Vol 5 (1) ◽

Cited By ~ 44

Author(s):

Steven Gazal ◽

Mourad Sahbatou ◽

Marie-Claude Babron ◽

Emmanuelle Génin ◽

Anne-Louise Leutenegger

Keyword(s):

Final Phase ◽

1000 Genomes Project ◽

1000 Genomes ◽

High Level

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Accurate, scalable cohort variant calls using DeepVariant and GLnexus

10.1101/2020.02.10.942086 ◽

2020 ◽

Cited By ~ 4

Author(s):

Taedong Yun ◽

Helen Li ◽

Pi-Chuan Chang ◽

Michael F. Lin ◽

Andrew Carroll ◽

...

Keyword(s):

Genetic Variation ◽

Best Practices ◽

Open Source ◽

Variant Calling ◽

Cost Savings ◽

Quality Improvements ◽

1000 Genomes Project ◽

Genetic Analyses ◽

1000 Genomes ◽

Population Scale

AbstractPopulation-scale sequenced cohorts are foundational resources for genetic analyses, but processing raw reads into analysis-ready variants remains challenging. Here we introduce an open-source cohort variant-calling method using the highly-accurate caller DeepVariant and scalable merging tool GLnexus. We optimized callset quality based on benchmark samples and Mendelian consistency across many sample sizes and sequencing specifications, resulting in substantial quality improvements and cost savings over existing best practices. We further evaluated our pipeline in the 1000 Genomes Project (1KGP) samples, showing superior quality metrics and imputation performance. We publicly release the 1KGP callset to foster development of broad studies of genetic variation.

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