Browsing by Author "Sedlazeck, Fritz J"

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    Genome-Wide Analysis of Structural Variants in Parkinson Disease
    (Wiley, 2023) Billingsley, Kimberley J.; Ding, Jinhui; Jerez, Pilar Alvarez; Illarionova, Anastasia; Levine, Kristin; Grenn, Francis P.; Makarious, Mary B.; Moore, Anni; Vitale, Daniel; Reed, Xylena; Hernandez, Dena; Torkamani, Ali; Ryten, Mina; Hardy, John; Consortium (UKBEC), UK Brain Expression; Chia, Ruth; Scholz, Sonja W.; Traynor, Bryan J.; Dalgard, Clifton L.; Ehrlich, Debra J.; Tanaka, Toshiko; Ferrucci, Luigi; Beach, Thomas G.; Serrano, Geidy E.; Quinn, John P.; Bubb, Vivien J.; Collins, Ryan L; Zhao, Xuefang; Walker, Mark; Pierce-Hoffman, Emma; Brand, Harrison; Talkowski, Michael E.; Casey, Bradford; Cookson, Mark R; Markham, Androo; Nalls, Mike A.; Mahmoud, Medhat; Sedlazeck, Fritz J; Blauwendraat, Cornelis; Gibbs, J. Raphael; Singleton, Andrew B.
    Objective Identification of genetic risk factors for Parkinson disease (PD) has to date been primarily limited to the study of single nucleotide variants, which only represent a small fraction of the genetic variation in the human genome. Consequently, causal variants for most PD risk are not known. Here we focused on structural variants (SVs), which represent a major source of genetic variation in the human genome. We aimed to discover SVs associated with PD risk by performing the first large-scale characterization of SVs in PD. Methods We leveraged a recently developed computational pipeline to detect and genotype SVs from 7,772 Illumina short-read whole genome sequencing samples. Using this set of SV variants, we performed a genome-wide association study using 2,585 cases and 2,779 controls and identified SVs associated with PD risk. Furthermore, to validate the presence of these variants, we generated a subset of matched whole-genome long-read sequencing data. Results We genotyped and tested 3,154 common SVs, representing over 412 million nucleotides of previously uncatalogued genetic variation. Using long-read sequencing data, we validated the presence of three novel deletion SVs that are associated with risk of PD from our initial association analysis, including a 2 kb intronic deletion within the gene LRRN4. Interpretation We identified three SVs associated with genetic risk of PD. This study represents the most comprehensive assessment of the contribution of SVs to the genetic risk of PD to date. ANN NEUROL 2023;93:1012–1022
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    Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment
    (Oxford University Press, 2021) Fu, Yilei; Mahmoud, Medhat; Muraliraman, Viginesh Vaibhav; Sedlazeck, Fritz J; Treangen, Todd J
    Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection.We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone.Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.