Whole genome analyses reveal no pathogenetic single nucleotide or structural differences between monozygotic twins discordant for amyotrophic lateral sclerosis

2015 ◽  
Vol 16 (5-6) ◽  
pp. 385-392 ◽  
Author(s):  
Karyn Meltz Steinberg ◽  
Thomas J. Nicholas ◽  
Daniel C. Koboldt ◽  
Bing Yu ◽  
Elaine Mardis ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Monozygotic Twins ◽  
Whole Genome ◽  
Single Nucleotide ◽  
Structural Differences ◽  
Genome Analyses ◽  
Lateral Sclerosis

scholarly journals Evidence for polygenic and oligogenic basis of Australian sporadic amyotrophic lateral sclerosis

2020 ◽  
pp. jmedgenet-2020-106866 ◽  
Author(s):  
Emily P McCann ◽  
Lyndal Henden ◽  
Jennifer A Fifita ◽  
Katharine Y Zhang ◽  
Natalie Grima ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Genetic Variation ◽  
Genetic Analysis ◽  
Genetic Variants ◽  
Age Of Onset ◽  
Sporadic Amyotrophic Lateral Sclerosis ◽  
Whole Genome ◽  
Identity By Descent ◽  
Sporadic Als ◽  
Lateral Sclerosis

BackgroundAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with phenotypic and genetic heterogeneity. Approximately 10% of cases are familial, while remaining cases are classified as sporadic. To date, >30 genes and several hundred genetic variants have been implicated in ALS.MethodsSeven hundred and fifty-seven sporadic ALS cases were recruited from Australian neurology clinics. Detailed clinical data and whole genome sequencing (WGS) data were available from 567 and 616 cases, respectively, of which 426 cases had both datasets available. As part of a comprehensive genetic analysis, 853 genetic variants previously reported as ALS-linked mutations or disease-associated alleles were interrogated in sporadic ALS WGS data. Statistical analyses were performed to identify correlation between clinical variables, and between phenotype and the number of ALS-implicated variants carried by an individual. Relatedness between individuals carrying identical variants was assessed using identity-by-descent analysis.ResultsForty-three ALS-implicated variants from 18 genes, including C9orf72, ATXN2, TARDBP, SOD1, SQSTM1 and SETX, were identified in Australian sporadic ALS cases. One-third of cases carried at least one variant and 6.82% carried two or more variants, implicating a potential oligogenic or polygenic basis of ALS. Relatedness was detected between two sporadic ALS cases carrying a SOD1 p.I114T mutation, and among three cases carrying a SQSTM1 p.K238E mutation. Oligogenic/polygenic sporadic ALS cases showed earlier age of onset than those with no reported variant.ConclusionWe confirm phenotypic associations among ALS cases, and highlight the contribution of genetic variation to all forms of ALS.

scholarly journals The role of single-nucleotide variants of the energy metabolism-linked genes SIRT3, PPARGC1A and APOE in amyotrophic lateral sclerosis risk

2016 ◽  
Vol 91 (6) ◽  
pp. 301-309 ◽  
Author(s):  
Diego Albani ◽  
Elisabetta Pupillo ◽  
Elisa Bianchi ◽  
Armando Chierchia ◽  
Rosalba Martines ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Energy Metabolism ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Lateral Sclerosis

scholarly journals Monozygotic twins and triplets discordant for amyotrophic lateral sclerosis display differential methylation and gene expression

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ingrid S. Tarr ◽  
Emily P. McCann ◽  
Beben Benyamin ◽  
Timothy J. Peters ◽  
Natalie A. Twine ◽  
...  
Keyword(s):  
Gene Expression ◽  
Amyotrophic Lateral Sclerosis ◽  
Monozygotic Twins ◽  
Differential Methylation ◽  
Lateral Sclerosis

scholarly journals Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype

2019 ◽  
Vol 35 (14) ◽  
pp. i538-i547 ◽  
Author(s):  
Bojian Yin ◽  
Marleen Balvert ◽  
Rick A A van der Spek ◽  
Bas E Dutilh ◽  
Sander Bohté ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Deep Learning ◽  
Network Architecture ◽  
Linear Models ◽  
Association Studies ◽  
Supplementary Information ◽  
Whole Genome ◽  
Promoter Regions ◽  
Genome Data ◽  
Lateral Sclerosis

Abstract Motivation Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing variants have been identified, a major part of heritability remains unexplained. ALS is believed to have a complex genetic basis where non-additive combinations of variants constitute disease, which cannot be picked up using the linear models employed in classical genotype–phenotype association studies. Deep learning on the other hand is highly promising for identifying such complex relations. We therefore developed a deep-learning based approach for the classification of ALS patients versus healthy individuals from the Dutch cohort of the Project MinE dataset. Based on recent insight that regulatory regions harbor the majority of disease-associated variants, we employ a two-step approach: first promoter regions that are likely associated to ALS are identified, and second individuals are classified based on their genotype in the selected genomic regions. Both steps employ a deep convolutional neural network. The network architecture accounts for the structure of genome data by applying convolution only to parts of the data where this makes sense from a genomics perspective. Results Our approach identifies potentially ALS-associated promoter regions, and generally outperforms other classification methods. Test results support the hypothesis that non-additive combinations of variants contribute to ALS. Architectures and protocols developed are tailored toward processing population-scale, whole-genome data. We consider this a relevant first step toward deep learning assisted genotype–phenotype association in whole genome-sized data. Availability and implementation Our code will be available on Github, together with a synthetic dataset (https://github.com/byin-cwi/ALS-Deeplearning). The data used in this study is available to bona-fide researchers upon request. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Hypervirulent Klebsiella pneumoniae of Lineage ST66-K2 Caused Tonsillopharyngitis in a German Patient

2021 ◽  
Vol 9 (1) ◽  
pp. 133
Author(s):  
Kathleen Klaper ◽  
Sebastian Wendt ◽  
Christoph Lübbert ◽  
Norman Lippmann ◽  
Yvonne Pfeifer ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
Snp Analysis ◽  
Whole Genome ◽  
Single Nucleotide ◽  
German Patient ◽  
Genome Data ◽  
Genome Analyses ◽  
Clinical Awareness

Hypervirulent Klebsiella pneumoniae (hvKp) is a novel pathotype that has been rarely described in Europe. This study characterizes a hvKp isolate that caused a community-acquired infection. The hypermucoviscous Klebsiella pneumoniae (K. pneumoniae) strain 18-0005 was obtained from a German patient with tonsillopharyngitis in 2017. Antibiotic susceptibility testing was performed and the genome was sequenced by Illumina and Nanopore technology. Whole genome data were analyzed by conducting core genome multilocus sequence typing (cgMLST) and single nucleotide polymorphism (SNP) analysis. Virulence genes were predicted by applying Kleborate. Phenotypic and whole genome analyses revealed a high similarity of the study isolate 18-0005 to the recently reported antibiotic-susceptible hvKp isolate SB5881 from France and the “ancestral” strain Kp52.145; both were assigned to the ST66-K2 lineage. Comparative genomic analysis of the three plasmids showed that the 18-0005 plasmid II differs from SB5881 plasmid II by an additional 3 kb integrated fragment of plasmid I. Our findings demonstrate the genetic flexibility of hvKp and the occurrence of a strain of the clonal group CG66-K2 in Germany. Hence, it emphasizes the need to improve clinical awareness and infection monitoring of hvKp.

Sign in / Sign up

Export Citation Format

Share Document