Literature DB >> 31396954

Genetic model of MS severity predicts future accumulation of disability.

Kayla C Jackson1, Katherine Sun1, Christopher Barbour1,2, Dena Hernandez3, Peter Kosa1, Makoto Tanigawa1, Ann Marie Weideman1, Bibiana Bielekova1.   

Abstract

No genetic modifiers of multiple sclerosis (MS) severity have been independently validated, leading to a lack of insight into genetic determinants of the rate of disability progression. We investigated genetic modifiers of MS severity in prospectively acquired training (N = 205) and validation (N = 94) cohorts, using the following advances: (1) We focused on 113 genetic variants previously identified as related to MS severity; (2) We used a novel, sensitive outcome: MS Disease Severity Scale (MS-DSS); (3) Instead of validating individual alleles, we used a machine learning technique (random forest) that captures linear and complex nonlinear effects between alleles to derive a single Genetic Model of MS Severity (GeM-MSS). The GeM-MSS consists of 19 variants located in vicinity of 12 genes implicated in regulating cytotoxicity of immune cells, complement activation, neuronal functions, and fibrosis. GeM-MSS correlates with MS-DSS (r = 0.214; p = 0.043) in a validation cohort that was not used in the modeling steps. The recognized biology identifies novel therapeutic targets for inhibiting MS disability progression. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Entities:  

Keywords:  machine learning; multiple sclerosis; severity; single-nucleotide polymorphism

Year:  2019        PMID: 31396954      PMCID: PMC6898742          DOI: 10.1111/ahg.12342

Source DB:  PubMed          Journal:  Ann Hum Genet        ISSN: 0003-4800            Impact factor:   1.670


  31 in total

1.  AUC-RF: a new strategy for genomic profiling with random forest.

Authors:  M Luz Calle; Victor Urrea; Anne-Laure Boulesteix; Nuria Malats
Journal:  Hum Hered       Date:  2011-10-11       Impact factor: 0.444

2.  Natural history of multiple sclerosis: a unifying concept.

Authors:  Christian Confavreux; Sandra Vukusic
Journal:  Brain       Date:  2006-01-16       Impact factor: 13.501

Review 3.  The paired receptors TIGIT and DNAM-1 as targets for therapeutic antibodies.

Authors:  Natan Stein; Pinchas Tsukerman; Ofer Mandelboim
Journal:  Hum Antibodies       Date:  2017

4.  SLAMF7 is critical for phagocytosis of haematopoietic tumour cells via Mac-1 integrin.

Authors:  Jun Chen; Ming-Chao Zhong; Huaijian Guo; Dominique Davidson; Sabrin Mishel; Yan Lu; Inmoo Rhee; Luis-Alberto Pérez-Quintero; Shaohua Zhang; Mario-Ernesto Cruz-Munoz; Ning Wu; Donald C Vinh; Meenal Sinha; Virginie Calderon; Clifford A Lowell; Jayne S Danska; André Veillette
Journal:  Nature       Date:  2017-04-19       Impact factor: 49.962

5.  Genetic modifiers of multiple sclerosis progression, severity and onset.

Authors:  A Dessa Sadovnick; Anthony L Traboulsee; Yinshan Zhao; Cecily Q Bernales; Mary Encarnacion; Jay P Ross; Irene M Yee; Maria G Criscuoli; Carles Vilariño-Güell
Journal:  Clin Immunol       Date:  2017-05-10       Impact factor: 3.969

6.  Association of HLA Genetic Risk Burden With Disease Phenotypes in Multiple Sclerosis.

Authors:  Noriko Isobe; Anisha Keshavan; Pierre-Antoine Gourraud; Alyssa H Zhu; Esha Datta; Regina Schlaeger; Stacy J Caillier; Adam Santaniello; Antoine Lizée; Daniel S Himmelstein; Sergio E Baranzini; Jill Hollenbach; Bruce A C Cree; Stephen L Hauser; Jorge R Oksenberg; Roland G Henry
Journal:  JAMA Neurol       Date:  2016-07-01       Impact factor: 18.302

7.  Role of genetic susceptibility variants in predicting clinical course in multiple sclerosis: a cohort study.

Authors:  Gongbu Pan; Steve Simpson; Ingrid van der Mei; Jac C Charlesworth; Robyn Lucas; Anne-Louise Ponsonby; Yuan Zhou; Feitong Wu; Bruce V Taylor
Journal:  J Neurol Neurosurg Psychiatry       Date:  2016-08-24       Impact factor: 10.154

8.  CSMD1 is a novel multiple domain complement-regulatory protein highly expressed in the central nervous system and epithelial tissues.

Authors:  Damian M Kraus; Gary S Elliott; Hilary Chute; Thomas Horan; Karl H Pfenninger; Staci D Sanford; Stephen Foster; Sheila Scully; Andrew A Welcher; V Michael Holers
Journal:  J Immunol       Date:  2006-04-01       Impact factor: 5.422

9.  Phenotype-Genotype Integrator (PheGenI): synthesizing genome-wide association study (GWAS) data with existing genomic resources.

Authors:  Erin M Ramos; Douglas Hoffman; Heather A Junkins; Donna Maglott; Lon Phan; Stephen T Sherry; Mike Feolo; Lucia A Hindorff
Journal:  Eur J Hum Genet       Date:  2013-05-22       Impact factor: 4.246

10.  NeuroX, a fast and efficient genotyping platform for investigation of neurodegenerative diseases.

Authors:  Mike A Nalls; Jose Bras; Dena G Hernandez; Margaux F Keller; Elisa Majounie; Alan E Renton; Mohamad Saad; Iris Jansen; Rita Guerreiro; Steven Lubbe; Vincent Plagnol; J Raphael Gibbs; Claudia Schulte; Nathan Pankratz; Margaret Sutherland; Lars Bertram; Christina M Lill; Anita L DeStefano; Tatiana Faroud; Nicholas Eriksson; Joyce Y Tung; Connor Edsall; Noah Nichols; Janet Brooks; Sampath Arepalli; Hannah Pliner; Chris Letson; Peter Heutink; Maria Martinez; Thomas Gasser; Bryan J Traynor; Nick Wood; John Hardy; Andrew B Singleton
Journal:  Neurobiol Aging       Date:  2014-08-04       Impact factor: 4.673

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  6 in total

1.  Current Status and Future Opportunities in Modeling Clinical Characteristics of Multiple Sclerosis.

Authors:  Joshua Liu; Erin Kelly; Bibiana Bielekova
Journal:  Front Neurol       Date:  2022-05-27       Impact factor: 4.086

Review 2.  Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.

Authors:  Ruggiero Seccia; Silvia Romano; Marco Salvetti; Andrea Crisanti; Laura Palagi; Francesca Grassi
Journal:  Life (Basel)       Date:  2021-02-05

3.  Multiple sclerosis diagnosis and phenotype identification by multivariate classification of in vivo frontal cortex metabolite profiles.

Authors:  Kelley M Swanberg; Abhinav V Kurada; Hetty Prinsen; Christoph Juchem
Journal:  Sci Rep       Date:  2022-08-16       Impact factor: 4.996

4.  Role of Multiple Vitamin D-Related Polymorphisms in Multiple Sclerosis Severity: Preliminary Findings.

Authors:  Luisa Agnello; Concetta Scazzone; Bruna Lo Sasso; Matteo Vidali; Rosaria Vincenza Giglio; Anna Maria Ciaccio; Paolo Ragonese; Giuseppe Salemi; Marcello Ciaccio
Journal:  Genes (Basel)       Date:  2022-07-22       Impact factor: 4.141

5.  The role of machine learning in developing non-magnetic resonance imaging based biomarkers for multiple sclerosis: a systematic review.

Authors:  Md Zakir Hossain; Elena Daskalaki; Anne Brüstle; Jane Desborough; Christian J Lueck; Hanna Suominen
Journal:  BMC Med Inform Decis Mak       Date:  2022-09-15       Impact factor: 3.298

6.  Developing a clinical-environmental-genotypic prognostic index for relapsing-onset multiple sclerosis and clinically isolated syndrome.

Authors:  Valery Fuh-Ngwa; Yuan Zhou; Jac C Charlesworth; Anne-Louise Ponsonby; Steve Simpson-Yap; Jeannette Lechner-Scott; Bruce V Taylor
Journal:  Brain Commun       Date:  2021-12-04
  6 in total

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