Biology subjects

Lek, M.

Publications and source records attributed to Lek, M..

5 recordsLinked to original sources

bioRxiv2018

The Genetic Landscape of Diamond-Blackfan Anemia

Diamond-Blackfan anemia (DBA) is a rare bone marrow failure disorder that affects 1 in 100,000 to 200,000 live births and has been associated with mutations in components of the ribosome. In order to characterize the genetic landscape of this genetically heterogeneous disorder, we recruited a cohort of 472 individuals with a clinical diagnosis of DBA and performed whole exome sequencing (WES). Overall, we identified rare and predicted damaging mutations in likely causal genes for 78% of individuals. The majority of mutations were singletons, absent from population databases, predicted to cause loss of function, and in one of 19 previously reported genes encoding for a diverse set of ribosomal proteins (RPs). Using WES exon coverage estimates, we were able to identify and validate 31 deletions in DBA associated genes. We also observed an enrichment for extended splice site mutations and validated the diverse effects of these mutations using RNA sequencing in patientderived cell lines. Leveraging the size of our cohort, we observed several robust genotype-phenotype associations with congenital abnormalities and treatment outcomes. In addition to comprehensively identifying mutations in known genes, we further identified rare mutations in 7 previously unreported RP genes that may cause DBA. We also identified several distinct disorders that appear to phenocopy DBA, including 9 individuals with biallelic CECR1 mutations that result in deficiency of ADA2. However, no new genes were identified at exome-wide significance, suggesting that there are no unidentified genes containing mutations readily identified by WES that explain > 5% of DBA cases. Overall, this comprehensive report should not only inform clinical practice for DBA patients, but also the design and analysis of future rare variant studies for heterogeneous Mendelian disorders.

genetics↗

bioRxiv2017

Resolving the Full Spectrum of Human Genome Variation using Linked-Reads

Large-scale population based analyses coupled with advances in technology have demonstrated that the human genome is more diverse than originally thought. To date, this diversity has largely been uncovered using short read whole genome sequencing. However, standard short-read approaches, used primarily due to accuracy, throughput and costs, fail to give a complete picture of a genome. They struggle to identify large, balanced structural events, cannot access repetitive regions of the genome and fail to resolve the human genome into its two haplotypes. Here we describe an approach that retains long range information while harnessing the advantages of short reads. Starting from only [~]1ng of DNA, we produce barcoded short read libraries. The use of novel informatic approaches allows for the barcoded short reads to be associated with the long molecules of origin producing a novel datatype known as Linked-Reads. This approach allows for simultaneous detection of small and large variants from a single Linked-Read library. We have previously demonstrated the utility of whole genome Linked-Reads (lrWGS) for performing diploid, de novo assembly of individual genomes (Weisenfeld et al. 2017). In this manuscript, we show the advantages of Linked-Reads over standard short read approaches for reference based analysis. We demonstrate the ability of Linked-Reads to reconstruct megabase scale haplotypes and to recover parts of the genome that are typically inaccessible to short reads, including phenotypically important genes such as STRC, SMN1 and SMN2. We demonstrate the ability of both lrWGS and Linked-Read Whole Exome Sequencing (lrWES) to identify complex structural variations, including balanced events, single exon deletions, and single exon duplications. The data presented here show that Linked-Reads provide a scalable approach for comprehensive genome analysis that is not possible using short reads alone.

genomics↗

bioRxiv2017

Scaling accurate genetic variant discovery to tens of thousands of samples

Comprehensive disease gene discovery in both common and rare diseases will require the efficient and accurate detection of all classes of genetic variation across tens to hundreds of thousands of human samples. We describe here a novel assembly-based approach to variant calling, the GATK HaplotypeCaller (HC) and Reference Confidence Model (RCM), that determines genotype likelihoods independently per-sample but performs joint calling across all samples within a project simultaneously. We show by calling over 90,000 samples from the Exome Aggregation Consortium (ExAC) that, in contrast to other algorithms, the HC-RCM scales efficiently to very large sample sizes without loss in accuracy; and that the accuracy of indel variant calling is superior in comparison to other algorithms. More importantly, the HC-RCM produces a fully squared-off matrix of genotypes across all samples at every genomic position being investigated. The HC-RCM is a novel, scalable, assembly-based algorithm with abundant applications for population genetics and clinical studies.

genomics↗

bioRxiv2017

Contribution of structural and intronic mutations to RPGRIP1-mediated inherited retinal dystrophies.

PurposeWith the advent of gene therapies for inherited retinal degenerations (IRDs), genetic diagnostics will have an increasing role in clinical decision-making. Yet the genetic cause of disease cannot be identified using exon-based sequencing for a significant portion of patients. We hypothesized that non-coding mutations contribute significantly to the genetic causality of IRDs and evaluated patients with single coding mutations in RPGRIP1 to test this hypothesis.\n\nMethodsIRD families underwent targeted panel sequencing. Unsolved cases were explored by whole exome and genome sequencing looking for additional mutations. Candidate mutations were then validated by Sanger sequencing, quantitative PCR, and in vitro splicing assays in two cell lines analyzed through amplicon sequencing.\n\nResultsAmong 1722 families, three had biallelic loss of function mutations in RPGRIP1 while seven had a single disruptive coding mutation. Whole exome and genome sequencing revealed potential non-coding mutations in these seven families. In six, the non-coding mutations were shown to lead to loss of function in vitro.\n\nConclusionNon-coding mutations were identified in 6 of 7 families with single coding mutations in RPGRIP1. The results suggest that non-coding mutations contribute significantly to the genetic causality of IRDs and RPGRIP1-mediated IRDs are more common than previously thought.

genomics↗

bioRxiv2017

STRetch: detecting and discovering pathogenic short tandem repeats expansions

Short tandem repeat (STR) expansions have been identified as the causal DNA mutation in dozens of Mendelian diseases. Historically, pathogenic STR expansions could only be detected by single locus techniques, such as PCR and electrophoresis. The ability to use short read sequencing data to screen for STR expansions has the potential to reduce both the time and cost to reaching diagnosis and enable the discovery of new causal STR loci. Most existing tools detect STR variation within the read length, and so are unable to detect the majority of pathogenic expansions. Those tools that can detect large expansions are limited to a set of known disease loci and as yet no new disease causing STR expansions have been identified with high-throughput sequencing technologies.\n\nHere we address this by presenting STRetch, a new genome-wide method to detect STR expansions at all loci across the human genome. We demonstrate the use of STRetch for detecting pathogenic STR expansions in short-read whole genome sequencing data with a very low false discovery rate. We further demonstrate the application of STRetch to solve cases of patients with undiagnosed disease and apply STRetch to the analysis of 97 whole genomes to reveal variation at STR loci. STRetch assesses expansions at all STR loci in the genome and allows screening for novel disease-causing STRs.\n\nSTRetch is open source software, available from github.com/Oshlack/STRetch.

bioinformatics↗