Author
Jennifer C. Schymick
Other affiliations: University of Oxford
Bio: Jennifer C. Schymick is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Amyotrophic lateral sclerosis & Population. The author has an hindex of 19, co-authored 21 publications receiving 8025 citations. Previous affiliations of Jennifer C. Schymick include University of Oxford.
Papers
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National Institutes of Health1, Cardiff University2, Erasmus University Rotterdam3, VU University Amsterdam4, University of Manchester5, University College London6, University of Helsinki7, University of Oulu8, Johns Hopkins University9, Georgetown University10, Illumina11, University Hospital of Wales12, University of Eastern Finland13, University of Miami14, University of Turin15, University of Cagliari16, The Catholic University of America17, Microsoft18, University of Toronto19, University of Würzburg20, University of Washington21, Aneurin Bevan University Health Board22
TL;DR: The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases, and a large hexanucleotide repeat expansion in the first intron of C9ORF72 is shown.
3,784 citations
University of Modena and Reggio Emilia1, Emory University2, University of Pennsylvania3, University College London4, National Institutes of Health5, Johns Hopkins University6, The Catholic University of America7, University of Turin8, Seconda Università degli Studi di Napoli9, University of Siena10, University of Palermo11, University of Cagliari12, Georgetown University13
TL;DR: Exome sequencing data broaden the phenotype of IBMPFD to include motor neuron degeneration, suggest that VCP mutations may account for ∼1%-2% of familial ALS, and provide evidence directly implicating defects in the ubiquitination/protein degradation pathway in motor neurons degeneration.
1,040 citations
TL;DR: The analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations produces new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human- genetic studies in diverse worldwide populations.
Abstract: Genome-wide patterns of variation across individuals provide a powerful source of data for uncovering the history of migration, range expansion, and adaptation of the human species. However, high-resolution surveys of variation in genotype, haplotype and copy number have generally focused on a small number of population groups. Here we report the analysis of high-quality genotypes at 525,910 single-nucleotide polymorphisms (SNPs) and 396 copy-number-variable loci in a worldwide sample of 29 populations. Analysis of SNP genotypes yields strongly supported fine-scale inferences about population structure. Increasing linkage disequilibrium is observed with increasing geographic distance from Africa, as expected under a serial founder effect for the out-of-Africa spread of human populations. New approaches for haplotype analysis produce inferences about population structure that complement results based on unphased SNPs. Despite a difference from SNPs in the frequency spectrum of the copy-number variants (CNVs) detected--including a comparatively large number of CNVs in previously unexamined populations from Oceania and the Americas--the global distribution of CNVs largely accords with population structure analyses for SNP data sets of similar size. Our results produce new inferences about inter-population variation, support the utility of CNVs in human population-genetic research, and serve as a genomic resource for human-genetic studies in diverse worldwide populations.
872 citations
TL;DR: The ITALSGEN Consortium is a network of around-the-world experts, academics, and practitioners working together to provide real-time information about the human brain’s response to ALS, and to provide a scaffolding for future research.
651 citations
TL;DR: This work generated publicly available genotype data for Parkinson's disease patients and controls so that these data can be mined and augmented by other researchers to identify common genetic variability that results in minor and moderate risk for disease.
Abstract: Summary Background Several genes underlying rare monogenic forms of Parkinson's disease have been identified over the past decade. Despite evidence for a role for genetics in sporadic Parkinson's disease, few common genetic variants have been unequivocally linked to this disorder. We sought to identify any common genetic variability exerting a large effect in risk for Parkinson's disease in a population cohort and to produce publicly available genome-wide genotype data that can be openly mined by interested researchers and readily augmented by genotyping of additional repository subjects. Methods We did genome-wide, single-nucleotide-polymorphism (SNP) genotyping of publicly available samples from a cohort of Parkinson's disease patients (n=267) and neurologically normal controls (n=270). More than 408 000 unique SNPs were used from the Illumina Infinium I and HumanHap300 assays. Findings We have produced around 220 million genotypes in 537 participants. This raw genotype data has been publicly posted and as such is the first publicly accessible high-density SNP data outside of the International HapMap Project. We also provide here the results of genotype and allele association tests. Interpretation We generated publicly available genotype data for Parkinson's disease patients and controls so that these data can be mined and augmented by other researchers to identify common genetic variability that results in minor and moderate risk for disease.
408 citations
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TL;DR: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI’s website.
Abstract: In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's website. NCBI resources include Entrez, PubMed, PubMed Central, LocusLink, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SARS Coronavirus Resource, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi.nlm.nih.gov.
9,604 citations
TL;DR: The results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
Abstract: Population stratification has long been recognized as a confounding factor in genetic association studies. Estimated ancestries, derived from multi-locus genotype data, can be used to perform a statistical correction for population stratification. One popular technique for estimation of ancestry is the model-based approach embodied by the widely applied program structure. Another approach, implemented in the program EIGENSTRAT, relies on Principal Component Analysis rather than model-based estimation and does not directly deliver admixture fractions. EIGENSTRAT has gained in popularity in part owing to its remarkable speed in comparison to structure. We present a new algorithm and a program, ADMIXTURE, for model-based estimation of ancestry in unrelated individuals. ADMIXTURE adopts the likelihood model embedded in structure. However, ADMIXTURE runs considerably faster, solving problems in minutes that take structure hours. In many of our experiments, we have found that ADMIXTURE is almost as fast as EIGENSTRAT. The runtime improvements of ADMIXTURE rely on a fast block relaxation scheme using sequential quadratic programming for block updates, coupled with a novel quasi-Newton acceleration of convergence. Our algorithm also runs faster and with greater accuracy than the implementation of an Expectation-Maximization (EM) algorithm incorporated in the program FRAPPE. Our simulations show that ADMIXTURE's maximum likelihood estimates of the underlying admixture coefficients and ancestral allele frequencies are as accurate as structure's Bayesian estimates. On real-world data sets, ADMIXTURE's estimates are directly comparable to those from structure and EIGENSTRAT. Taken together, our results show that ADMIXTURE's computational speed opens up the possibility of using a much larger set of markers in model-based ancestry estimation and that its estimates are suitable for use in correcting for population stratification in association studies.
5,846 citations
TL;DR: It is found that repeat expansion in C9ORF72 is a major cause of both FTD and ALS, suggesting multiple disease mechanisms.
4,153 citations
National Institutes of Health1, Cardiff University2, Erasmus University Rotterdam3, VU University Amsterdam4, University of Manchester5, University College London6, University of Helsinki7, University of Oulu8, Georgetown University9, Johns Hopkins University10, Illumina11, University Hospital of Wales12, University of Eastern Finland13, University of Miami14, University of Turin15, University of Cagliari16, The Catholic University of America17, Microsoft18, University of Toronto19, University of Würzburg20, University of Washington21, Aneurin Bevan University Health Board22
TL;DR: The chromosome 9p21 amyotrophic lateral sclerosis-frontotemporal dementia (ALS-FTD) locus contains one of the last major unidentified autosomal-dominant genes underlying these common neurodegenerative diseases, and a large hexanucleotide repeat expansion in the first intron of C9ORF72 is shown.
3,784 citations
TL;DR: Clumpak, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology by automating the postprocessing of results of model‐based population structure analyses.
Abstract: The identification of the genetic structure of populations from multilocus genotype data has become a central component of modern population-genetic data analysis. Application of model-based clustering programs often entails a number of steps, in which the user considers different modelling assumptions, compares results across different predetermined values of the number of assumed clusters (a parameter typically denoted K), examines multiple independent runs for each fixed value of K, and distinguishes among runs belonging to substantially distinct clustering solutions. Here, we present CLUMPAK (Cluster Markov Packager Across K), a method that automates the postprocessing of results of model-based population structure analyses. For analysing multiple independent runs at a single K value, CLUMPAK identifies sets of highly similar runs, separating distinct groups of runs that represent distinct modes in the space of possible solutions. This procedure, which generates a consensus solution for each distinct mode, is performed by the use of a Markov clustering algorithm that relies on a similarity matrix between replicate runs, as computed by the software CLUMPP. Next, CLUMPAK identifies an optimal alignment of inferred clusters across different values of K, extending a similar approach implemented for a fixed K in CLUMPP and simplifying the comparison of clustering results across different K values. CLUMPAK incorporates additional features, such as implementations of methods for choosing K and comparing solutions obtained by different programs, models, or data subsets. CLUMPAK, available at http://clumpak.tau.ac.il, simplifies the use of model-based analyses of population structure in population genetics and molecular ecology.
2,252 citations