Biology subjects

Beyter, D.

Publications and source records attributed to Beyter, D..

6 recordsLinked to original sources

bioRxiv2021

The sequences of 150,119 genomes in the UK biobank

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.

genetics↗

bioRxiv2020

Benchmarking small variant detection with ONT reveals high performance in challenging regions

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 as STRC and CFC1B. 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.

bioinformatics↗

bioRxiv2020

Ratatosk - Hybrid error correction of long reads enables accurate variant calling and assembly

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/Ratatosk Contactguillaume.holley@decode.is

bioinformatics↗

bioRxiv2019

Long read sequencing of 1,817 Icelanders provides insight into the role of structural variants in human disease

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 of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0{middle dot}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 of ACAN. 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{middle dot}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.

genetics↗

bioRxiv2019

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

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, PopDels 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.

bioinformatics↗

bioRxiv2017

Strelka2: Fast and accurate variant calling for clinical sequencing applications

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.

bioinformatics↗