PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Niehus, Sebastian; Schönberger, Janina; Björnsson, Eyþór; Beyter, Doruk; Sulem, Patrick; Stefánsson, Kāri; Halldórsson, Bjarni V.; Kehr, Birte

doi:10.1038/s41467-020-20850-5

Cited by 10 publications

(12 citation statements)

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“…S12 ) as a sanity check that our variant calls can be used to clearly distinguish samples from different continental groups. Analogously to previous call set evaluations ( Niehus et al , 2021 ), we converted the genotypes into a variant-sample matrix containing NRS variant allele counts and filtered uninformative NRS variants and those in linkage disequilibrium. As a result, the principal component analysis was calculated on 1787 variants.…”

Section: Resultsmentioning

confidence: 99%

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Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

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Motivation With the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across tens of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data, the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. Results We introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the PopIns2 workflow and highlight our novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets. Availability The source code of PopIns2 is available from https://github.com/kehrlab/PopIns2. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

“…For both PopIns and PopIns2, we calculated the Mendelian inheritance error rate and transmission rate as in Niehus et al (2021) . The Mendelian inheritance error rate is a measure to assess the plausibility of variant genotypes in related individuals.…”

Section: Resultsmentioning

confidence: 99%

Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

et al. 2021

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“…In addition, we performed a principal component analysis (Supplement Figure 12) as a sanity check that our variant calls can be used to clearly distinguish samples from different continental groups. Analogously to previous call set evaluations [Niehus et al , 2021], we converted the genotypes into a variant-sample matrix containing NRS variant allele counts and filtered uninformative NRS variants and those in linkage disequilibrium. As a result, the principal component analysis was calculated on 1787 variants.…”

Section: Resultsmentioning

confidence: 99%

“…We previously noted its similarity to a classical genome assembly problem [Kehr et al, 2016]. Classical genome assembly for short read data is commonly based on de Bruijn graphs (DBG) [Pevzner et al, 2001, Compeau et al, 2011. The tool Cortex [Iqbal et al, 2012] augmented DBGs with colors in order to simultaneously process several genomes.…”

Section: Introductionmentioning

confidence: 99%

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

Krannich

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Niehus

et al. 2021

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With the increasing throughput of sequencing technologies, structural variant (SV) detection has become possible across ten of thousands of genomes. Non-reference sequence (NRS) variants have drawn less attention compared to other types of SVs due to the computational complexity of detecting them. When using short-read data the detection of NRS variants inevitably involves a de novo assembly which requires high-quality sequence data at high coverage. Previous studies have demonstrated how sequence data of multiple genomes can be combined for the reliable detection of NRS variants. However, the algorithms proposed in these studies have limited scalability to larger sets of genomes. We introduce PopIns2, a tool to discover and characterize NRS variants in many genomes, which scales to considerably larger numbers of genomes than its predecessor PopIns. In this article, we briefly outline the workflow of PopIns and highlight the novel algorithmic contributions. We developed an entirely new approach for merging contig assemblies of unaligned reads from many genomes into a single set of NRS using a colored de Bruijn graph. Our tests on simulated data indicate that the new merging algorithm ranks among the best approaches in terms of quality and reliability and that PopIns2 shows the best precision for a growing number of genomes processed. Results on the Polaris Diversity Cohort and a set of 1000 Icelandic human genomes demonstrate unmatched scalability for the application on population-scale datasets.

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“…We attempted replication for each of the 33 trait-associated SVs using a combination of short-read and long-read WGS data and genotype imputation. We utilized independent datasets composed of Icelandic (deCODE genetics) [17][18][19] and multi-ancestry (UK Biobank, UKBB) 20 participants. Note that the SV calling and genotyping algorithms used in replication datasets (described under Methods) are different from the Parliament2 pipeline used for SV discovery in TOPMed.…”

Section: Replication Of Significant Sv-blood Cell Trait Associationsmentioning

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Whole genome sequencing identifies common and rare structural variants contributing to hematologic traits in the NHLBI TOPMed program

Wheeler

Stilp

Rao

et al. 2021

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Genome-wide association studies (GWAS) have identified thousands of single nucleotide variants and small indels that contribute to the genetic architecture of hematologic traits. While structural variants (SVs) are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of SVs to quantitative blood cell trait variation is unknown. Here we utilized SVs detected from whole genome sequencing (WGS) in ancestrally diverse participants of the NHLBI TOPMed program (N=50,675). Using single variant tests, we assessed the association of common and rare SVs with red cell-, white cell-, and platelet-related quantitative traits. The results show 33 independent SVs (23 common and 10 rare) reaching genome-wide significance. The majority of significant association signals (N=27) replicated in independent datasets from deCODE genetics and the UK BioBank. Moreover, most trait-associated SVs (N=24) are within 1Mb of previously-reported GWAS loci. SV analyses additionally discovered an association between a complex structural variant on 17p11.2 and white blood cell-related phenotypes. Based on functional annotation, the majority of significant SVs are located in non-coding regions (N=26) and predicted to impact regulatory elements and/or local chromatin domain boundaries in blood cells. We predict that several trait-associated SVs represent the causal variant. This is supported by genome-editing experiments which provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.

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PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Cited by 10 publications

References 54 publications

Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

Population-scale detection of non-reference sequence variants using colored de Bruijn graphs

Population-scale detection of non-reference sequence variants using colored de Bruijn Graphs

Whole genome sequencing identifies common and rare structural variants contributing to hematologic traits in the NHLBI TOPMed program

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