Literature DB >> 19482860

Twin studies and the heritability of MS: a conclusion.

C H Hawkes1, A J Macgregor.   

Abstract

OBJECTIVE: The classical twin study has the potential to evaluate the relative contribution of genes and environment and guide further research strategies, provided the sampling and methods of analysis are correct. We wish to review all the more informative twin studies on multiple sclerosis (MS).
METHODS: We examined six large population-based twin studies in MS and calculated indices of heritability (h(2)), which is the traditional method of assessing genetic contribution to disease and to allow comparison between studies.
RESULTS: This index was found to vary widely from 0.25 to 0.76 with large confidence intervals that reflect small sample size and prevent robust interpretation.
CONCLUSION: Overall the studies support a genetic contribution to disease; however, the imprecision of the heritability estimates and potential biases that they contain mean that very little inference can be drawn its exact size. Given that the magnitude of genetic effect cannot be measured because of the relative infrequency of MS; the consequent difficulty in collecting an informative sample; and in many countries, the lack of a comprehensive twin register, we suggest that further twin prevalence surveys should not be undertaken. Twin studies could be used more effectively in other ways, such as the co-twin case-control approach.

Entities:  

Mesh:

Year:  2009        PMID: 19482860     DOI: 10.1177/1352458509104592

Source DB:  PubMed          Journal:  Mult Scler        ISSN: 1352-4585            Impact factor:   6.312


  27 in total

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Review 2.  Multiple sclerosis genetics--is the glass half full, or half empty?

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3.  Inflammation in the spotlight-clinical relevance of genetic variants affecting nuclear factor κB and tumor necrosis factor receptor 1.

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Authors:  Jenny van Dongen; P Eline Slagboom; Harmen H M Draisma; Nicholas G Martin; Dorret I Boomsma
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5.  Annotation of functional variation within non-MHC MS susceptibility loci through bioinformatics analysis.

Authors:  F B S Briggs; L J Leung; L F Barcellos
Journal:  Genes Immun       Date:  2014-07-17       Impact factor: 2.676

Review 6.  Autoimmune diseases - connecting risk alleles with molecular traits of the immune system.

Authors:  Maria Gutierrez-Arcelus; Stephen S Rich; Soumya Raychaudhuri
Journal:  Nat Rev Genet       Date:  2016-02-15       Impact factor: 53.242

7.  Interaction of HLA-DRB1*1501 and TNF-Alpha in a Population-based Case-control Study of Multiple Sclerosis.

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Journal:  Immunol Infect Dis       Date:  2013-09

Review 8.  An integrated approach to design novel therapeutic interventions for demyelinating disorders.

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Journal:  Eur J Neurosci       Date:  2012-06       Impact factor: 3.386

Review 9.  The intelligent use and clinical benefits of electronic medical records in multiple sclerosis.

Authors:  Mary F Davis; Jonathan L Haines
Journal:  Expert Rev Clin Immunol       Date:  2014-12-11       Impact factor: 4.473

10.  A transcription factor map as revealed by a genome-wide gene expression analysis of whole-blood mRNA transcriptome in multiple sclerosis.

Authors:  Carlos Riveros; Drew Mellor; Kaushal S Gandhi; Fiona C McKay; Mathew B Cox; Regina Berretta; S Yahya Vaezpour; Mario Inostroza-Ponta; Simon A Broadley; Robert N Heard; Stephen Vucic; Graeme J Stewart; David W Williams; Rodney J Scott; Jeanette Lechner-Scott; David R Booth; Pablo Moscato
Journal:  PLoS One       Date:  2010-12-01       Impact factor: 3.240

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