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Long-read sequencing (LRS) promises to improve the characterization of structural variants (SVs). We generated LRS data from 3,622 Icelanders and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions). We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association of a rare deletion in PCSK9 with lower low-density lipoprotein (LDL) cholesterol levels, compared to the population average. We also discovered an association of a multiallelic SV in ACAN with height; we found 11 alleles that differed in the number of a 57-bp-motif repeat and observed a linear relationship between the number of repeats carried and height. These results show that SVs can be accurately characterized at the population scale using LRS data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

Microsatellites are polymorphic tracts of short tandem repeats (STRs) with one to six base-pair (bp) motifs and are some of the most polymorphic markers in the genome. Using 6,084 Icelandic parent-offspring trios we estimate 63.7 (95% CI: 61.9–65.4) microsatellite de novo mutations (mDNMs) per offspring per generation. Paternal mDNMs occur at longer repeats, while maternal mDNMs affect more bp. mDNMs increase by 0.97 (95% CI: 0.90–1.04) and 0.31 (95% CI: 0.25–0.37) per year of father‘s and mother‘s age at conception, respectively. We found two independent coding variants associated with an increased number of mDNMs transmitted to offspring; A missense variant (allele frequency (AF) = 1.9%) in MSH2, a mismatch repair gene, increases transmitted mDNMs from both parents (effect: 13.1 paternal and 7.8 maternal mDNMs). A synonymous variant (AF = 20.3%) in NEIL2, a DNA damage repair gene, increases paternally transmitted mDNMs (effect: 4.4 mDNMs). Thus, the microsatellite mutation rate in humans is in part under genetic control.

Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies., Identifying structural variants (SVs) from whole genome sequence data has been a significant bioinformatic challenge. Here, the authors describe PopDel, which uses a joint SV detection approach to reliably and efficiently identify 500-10,000 bp deletions across large population cohorts.

Despite the important role that monozygotic twins have played in genetics research, little is known about their genomic differences. Here we show that monozygotic twins differ on average by 5.2 early developmental mutations and that approximately 15% of monozygotic twins have a substantial number of these early developmental mutations specific to one of them. Using the parents and offspring of twins, we identified pre-twinning mutations. We observed instances where a twin was formed from a single cell lineage in the pre-twinning cell mass and instances where a twin was formed from several cell lineages. CpG>TpG mutations increased in frequency with embryonic development, coinciding with an increase in DNA methylation. Our results indicate that allocations of cells during development shapes genomic differences between monozygotic twins.

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.

We describe the analysis of whole genome sequences (WGS) of 150,119 individuals from the UK biobank (UKB). This constitutes a set of high quality variants, including 585,040,410 SNPs, representing 7.0% of all possible human SNPs, and 58,707,036 indels. The large set of variants allows us to characterize selection based on sequence variation within a population through a Depletion Rank (DR) score for windows along the genome. DR analysis shows that coding exons represent a small fraction of regions in the genome subject to strong sequence conservation. We define three cohorts within the UKB, a large British Irish cohort (XBI) and smaller African (XAF) and South Asian (XSA) cohorts. A haplotype reference panel is provided that allows reliable imputation of most variants carried by three or more sequenced individuals. We identified 895,055 structural variants and 2,536,688 microsatellites, groups of variants typically excluded from large scale WGS studies. Using this formidable new resource, we provide several examples of trait associations for rare variants with large effects not found previously through studies based on exome sequencing and/or imputation.

Detailed knowledge of how diversity in the sequence of the human genome affects phenotypic diversity depends on a comprehensive and reliable characterization of both sequences and phenotypic variation. Over the past decade, insights into this relationship have been obtained from whole-exome sequencing or whole-genome sequencing of large cohorts with rich phenotypic data

BackgroundThe development of long read sequencing (LRS) has led to greater access to the human genome. LRS produces long read lengths at the cost of high error rates and has shown to be more useful in calling structural variants than short read sequencing (SRS) data. In this paper we evaluate how to use LRS data from Oxford Nanopore Technologies (ONT) to call small variants in regions in- and outside the reach of SRS.ResultsCalling single nucleotide polymorphisms (SNPs) with ONT data has comparable accuracy to Illumina when evaluating against the Genome in a Bottle truth set v4.2. In the major histocompatibility complex (MHC) and regions where mapping short reads is difficult, the F-measure of ONT calls exceeds those of short reads by 2-4% when sequence coverage is 20X or greater.We develop recommendations for how to perform small variant calling with LRS data and improve current approaches to the difficult regions by re-genotyping variants to increase the F-measure from 97.24% to 98.78%. Furthermore, we show how LRS can call variants in genomic regions inaccessible to SRS, including medically relevant genes such asSTRCandCFC1B.ConclusionsAlthough small variant calling in LRS data is still immature, current methods are clearly useful in difficult and inaccessible regions of the genome, enabling variant calling in medically relevant genes not accessible to SRS.

A major challenge to long read sequencing data is their high error rate of up to 15%. We present Ratatosk, a method to correct long reads with short read data. We demonstrate on 5 human genome trios that Ratatosk reduces the error rate of long reads 6-fold on average with a median error rate as low as 0.22 %. SNP calls in Ratatosk corrected reads are nearly 99 % accurate and indel calls accuracy is increased by up to 37 %. An assembly of Ratatosk corrected reads from an Ashkenazi individual yields a contig N50 of 45 Mbp and less misassemblies than a PacBio HiFi reads assembly. Supplementary Information The online version contains supplementary material available at (10.1186/s13059-020-02244-4).

MotivationLong Read Sequencing (LRS) technologies are becoming essential to complement Short Read Sequencing (SRS) technologies for routine whole genome sequencing. LRS platforms produce DNA fragment reads, from 103 to 106 bases, allowing the resolution of numerous uncertainties left by SRS reads for genome reconstruction and analysis. In particular, LRS characterizes long and complex structural variants undetected by SRS due to short read length. Furthermore, assemblies produced with LRS reads are considerably more contiguous than with SRS while spanning previously inaccessible telomeric and centromeric regions. However, a major challenge to LRS reads adoption is their much higher error rate than SRS of up to 15%, introducing obstacles in downstream analysis pipelines.ResultsWe present Ratatosk, a new error correction method for erroneous long reads based on a compacted and colored de Bruijn graph built from accurate short reads. Short and long reads color paths in the graph while vertices are annotated with candidate Single Nucleotide Polymorphisms. Long reads are subsequently anchored to the graph using exact and inexact fc-mer matches to find paths corresponding to corrected sequences. We demonstrate that Ratatosk can reduce the raw error rate of Oxford Nanopore reads 6-fold on average with a median error rate as low as 0.28%. Ratatosk corrected data maintain nearly 99% accurate SNP calls and increase indel call accuracy by up to about 40% compared to the raw data. An assembly of the Ashkenazi individual HG002 created from Ratatosk corrected Oxford Nanopore reads yields a contig N50 of 43.22 Mbp and less misassemblies than an assembly created from PacBio HiFi reads.Availabilityhttps://github.com/DecodeGenetics/RatatoskContactguillaume.holley@decode.is

Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome. We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon ofPCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0·10−20). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon ofACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2·10−18) between the number of repeats carried and height. These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

Long-read sequencing (LRS) promises to improve the characterization of structural variants (SVs). We generated LRS data from 3,622 Icelanders and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions). We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association of a rare deletion in PCSK9 with lower low-density lipoprotein (LDL) cholesterol levels, compared to the population average. We also discovered an association of a multiallelic SV in ACAN with height; we found 11 alleles that differed in the number of a 57-bp-motif repeat and observed a linear relationship between the number of repeats carried and height. These results show that SVs can be accurately characterized at the population scale using LRS data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.

Despite the important role that monozygotic twins have played in genetics research, little is known about their genomic differences. Here we show that monozygotic twins differ on average by 5.2 early developmental mutations and that approximately 15% of monozygotic twins have a substantial number of these early developmental mutations specific to one of them. Using the parents and offspring of twins, we identified pre-twinning mutations. We observed instances where a twin was formed from a single cell lineage in the pre-twinning cell mass and instances where a twin was formed from several cell lineages. CpGTpG mutations increased in frequency with embryonic development, coinciding with an increase in DNA methylation. Our results indicate that allocations of cells during development shapes genomic differences between monozygotic twins.

Thousands of genomic structural variants segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. We describe a novel approach, PopDel, which jointly identifies deletions of about 500 to at least 10,000 bp in length in many genomes together. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6,794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

We describe Strelka2 ( https://github.com/Illumina/strelka ), an open-source small-variant-calling method for research and clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model-based estimation of insertion/deletion error parameters from each sample, an efficient tiered haplotype-modeling strategy, and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperformed the current leading tools in terms of both variant-calling accuracy and computing cost.

We describe Strelka2 (https://github.com/Illumina/strelka), an open-source small variant calling method for clinical germline and somatic sequencing applications. Strelka2 introduces a novel mixture-model based estimation of indel error parameters from each sample, an efficient tiered haplotype modeling strategy and a normal sample contamination model to improve liquid tumor analysis. For both germline and somatic calling, Strelka2 substantially outperforms current leading tools on both variant calling accuracy and compute cost.

Human cells have twenty-three pairs of chromosomes. In cancer, however, genes can be amplified in chromosomes or in circular extrachromosomal DNA (ecDNA), although the frequency and functional importance of ecDNA are not understood. We performed whole-genome sequencing, structural modelling and cytogenetic analyses of 17 different cancer types, including analysis of the structure and function of chromosomes during metaphase of 2,572 dividing cells, and developed a software package called ECdetect to conduct unbiased, integrated ecDNA detection and analysis. Here we show that ecDNA was found in nearly half of human cancers; its frequency varied by tumour type, but it was almost never found in normal cells. Driver oncogenes were amplified most commonly in ecDNA, thereby increasing transcript level. Mathematical modelling predicted that ecDNA amplification would increase oncogene copy number and intratumoural heterogeneity more effectively than chromosomal amplification. We validated these predictions by quantitative analyses of cancer samples. The results presented here suggest that ecDNA contributes to accelerated evolution in cancer.

Managing ecosystems to maintain biodiversity may be one approach to ensuring their dynamic stability, productivity, and delivery of vital services. The applicability of this approach to industrial ecosystems that harness the metabolic activities of microbes has been proposed but has never been tested at relevant scales. We used a tag-sequencing approach with bacterial small subunit rRNA (16S) genes and eukaryotic internal transcribed spacer 2 (ITS2) to measuring the taxonomic composition and diversity of bacteria and eukaryotes in an open pond managed for bioenergy production by microalgae over a year. Periods of high eukaryotic diversity were associated with high and more-stable biomass productivity. In addition, bacterial diversity and eukaryotic diversity were inversely correlated over time, possibly due to their opposite responses to temperature. The results indicate that maintaining diverse communities may be essential to engineering stable and productive bioenergy ecosystems using microorganisms.

Shotgun metaproteomics has potential to reveal the functional landscape of microbial communities, but lacks appropriate methods for complex samples with unknown compositions. In the absence of prior taxonomic information, tandem mass spectra would be searched against large pan-microbial databases, which requires heavy computational workload and reduces sensitivity. We present ProteoStorm, an efficient database search framework for large-scale metaproteomics studies, which identifies high-confidence peptide-spectrum matches (PSMs) while achieving a two to three orders-of-magnitude speedup over popular tools. A reanalysis of a urinary tract infection (UTI) dataset of 110 individuals revealed a complex pattern of polymicrobial expression, including sub-types of urinary tract infections, cases of bacterial vaginosis, and evidence of no underlying disease. Importantly, compared to the initial UTI study that restricted the search database to a manually-curated list of 20 genera, ProteoStorm identified additional genera that were previously unreported, including a case of infection with the rare pathogen Propionimicrobium.