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Träfflista för sökning "WFRF:(Goldman S. A.) "

Search: WFRF:(Goldman S. A.)

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1.
  • Campbell, PJ, et al. (author)
  • Pan-cancer analysis of whole genomes
  • 2020
  • In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
  • Journal article (peer-reviewed)abstract
    • Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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  • Munn-Chernoff, M. A., et al. (author)
  • Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies
  • 2021
  • In: Addiction Biology. - : Wiley. - 1355-6215 .- 1369-1600. ; 26:1
  • Journal article (peer-reviewed)abstract
    • Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r(g)], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from similar to 2400 to similar to 537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r(g) = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r(g) = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r(g) = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r(gs) = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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  • Fresard, Laure, et al. (author)
  • Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts
  • 2019
  • In: Nature Medicine. - : NATURE PUBLISHING GROUP. - 1078-8956 .- 1546-170X. ; 25:6, s. 911-919
  • Journal article (peer-reviewed)abstract
    • It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene(1). The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches(2-5). For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases(6-8). This includes muscle biopsies from patients with undiagnosed rare muscle disorders(6,9), and cultured fibroblasts from patients with mitochondrial disorders(7). However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution.
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  • Escartin, C., et al. (author)
  • Reactive astrocyte nomenclature, definitions, and future directions
  • 2021
  • In: Nature Neuroscience. - : Springer Science and Business Media LLC. - 1097-6256 .- 1546-1726. ; 24, s. 312-325
  • Journal article (peer-reviewed)abstract
    • Reactive astrocytes are astrocytes undergoing morphological, molecular, and functional remodeling in response to injury, disease, or infection of the CNS. Although this remodeling was first described over a century ago, uncertainties and controversies remain regarding the contribution of reactive astrocytes to CNS diseases, repair, and aging. It is also unclear whether fixed categories of reactive astrocytes exist and, if so, how to identify them. We point out the shortcomings of binary divisions of reactive astrocytes into good-vs-bad, neurotoxic-vs-neuroprotective or A1-vs-A2. We advocate, instead, that research on reactive astrocytes include assessment of multiple molecular and functional parameters-preferably in vivo-plus multivariate statistics and determination of impact on pathological hallmarks in relevant models. These guidelines may spur the discovery of astrocyte-based biomarkers as well as astrocyte-targeting therapies that abrogate detrimental actions of reactive astrocytes, potentiate their neuro- and glioprotective actions, and restore or augment their homeostatic, modulatory, and defensive functions. Good-bad binary classifications fail to describe reactive astrocytes in CNS disorders. Here, 81 researchers reach consensus on widespread misconceptions and provide definitions and recommendations for future research on reactive astrocytes.
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10.
  • Weinstein, John N., et al. (author)
  • The cancer genome atlas pan-cancer analysis project
  • 2013
  • In: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 45:10, s. 1113-1120
  • Research review (peer-reviewed)abstract
    • The Cancer Genome Atlas (TCGA) Research Network has profiled and analyzed large numbers of human tumors to discover molecular aberrations at the DNA, RNA, protein and epigenetic levels. The resulting rich data provide a major opportunity to develop an integrated picture of commonalities, differences and emergent themes across tumor lineages. The Pan-Cancer initiative compares the first 12 tumor types profiled by TCGA. Analysis of the molecular aberrations and their functional roles across tumor types will teach us how to extend therapies effective in one cancer type to others with a similar genomic profile. © 2013 Nature America, Inc. All rights reserved.
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  • Result 1-10 of 200
Type of publication
journal article (173)
conference paper (23)
research review (3)
other publication (1)
Type of content
peer-reviewed (174)
other academic/artistic (26)
Author/Editor
Ramsey-Goldman, R (56)
Petri, M. (49)
Ramsey-Goldman, Rosa ... (48)
Nived, Ola (45)
Alarcón, Graciela S. (44)
Manzi, S (44)
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Bae, Sang-Cheol (42)
Kamen, Diane L. (40)
Gordon, C. (40)
Merrill, Joan T. (39)
Aranow, C (38)
Steinsson, K (36)
Bernatsky, S (36)
Petri, Michelle (36)
Manzi, Susan (36)
Aranow, Cynthia (35)
Bernatsky, Sasha (35)
Sanchez-Guerrero, J (35)
Rahman, Anisur (34)
Ruiz-Irastorza, Guil ... (34)
Gordon, Caroline (34)
Sanchez-Guerrero, Jo ... (34)
Bruce, Ian N. (34)
Wallace, Daniel J. (34)
Rahman, A. (33)
Romero-Diaz, Juanita (33)
Gladman, Dafna D. (33)
Hanly, John G. (32)
Inanc, Murat (32)
Bae, SC (30)
Fortin, Paul R. (30)
Sturfelt, Gunnar (29)
Ginzler, Ellen M. (29)
Zoma, A. (29)
Urowitz, Murray B. (29)
van Vollenhoven, Ron ... (27)
Wallace, DJ (27)
Jacobsen, S (27)
Hanly, JG (26)
Ruiz-Irastorza, G (26)
Isenberg, David A. (26)
Dooley, Mary Anne (25)
Askanase, Anca (24)
Steinsson, Kristjan (23)
Ramos-Casals, M. (23)
Alarcon, GS (23)
Kamen, DL (23)
van Vollenhoven, RF (23)
Romero-Diaz, J (23)
Askanase, A (23)
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University
Karolinska Institutet (130)
Lund University (63)
Uppsala University (41)
Royal Institute of Technology (13)
University of Gothenburg (10)
Umeå University (10)
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Linköping University (10)
Chalmers University of Technology (5)
Stockholm University (3)
Stockholm School of Economics (2)
Mälardalen University (1)
Örebro University (1)
Linnaeus University (1)
Swedish University of Agricultural Sciences (1)
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Language
English (199)
Undefined language (1)
Research subject (UKÄ/SCB)
Medical and Health Sciences (91)
Natural sciences (27)
Engineering and Technology (1)
Social Sciences (1)
Humanities (1)

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