Literature DB >> 20568294

Risk categorization for complex disorders according to genotype relative risk and precision in parameter estimates.

Graham H M Goddard1, Cathryn M Lewis.   

Abstract

PURPOSE: To develop a method of genetic risk categorization based on the risk conferred by genetic variants and the precision with which risks are known.
METHODS: We develop a method for risk assignment based on an "average" member of the population and their genotype, deriving empirical confidence intervals encompassing all relevant sources of variation in disease risk. An individual with risk confidence interval that does not overlap with that of the "average" individual is categorized as having higher or lower disease risk. The method is applied to data sets in Crohn's disease and type 2 diabetes.
RESULTS: The proportion of the population assigned to the average risk category depends on genotype relative risk, allele frequency and sample size of the study used to estimate these parameters. For low genotype relative risks or minor allele frequency, little resolution into different risk categories may be possible.
CONCLUSION: The utility of a genetic risk variant for risk categorization depends on both the magnitude of the genotype relative risk and the accuracy with which this, and other elements of risk calculation, are known. Genetic risk calculations should include an assessment of the accuracy of the risk estimation. (c) 2010 Wiley-Liss, Inc.

Entities:  

Mesh:

Year:  2010        PMID: 20568294     DOI: 10.1002/gepi.20519

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  6 in total

1.  REGENT: a risk assessment and classification algorithm for genetic and environmental factors.

Authors:  Daniel J M Crouch; Graham H M Goddard; Cathryn M Lewis
Journal:  Eur J Hum Genet       Date:  2012-06-06       Impact factor: 4.246

2.  Genetic risk models: Influence of model size on risk estimates and precision.

Authors:  Ying Shan; Gerard Tromp; Helena Kuivaniemi; Diane T Smelser; Shefali S Verma; Marylyn D Ritchie; James R Elmore; David J Carey; Yvette P Conley; Michael B Gorin; Daniel E Weeks
Journal:  Genet Epidemiol       Date:  2017-02-15       Impact factor: 2.135

3.  The refinement of genetic predictors of multiple sclerosis.

Authors:  Giulio Disanto; Ruth Dobson; Julia Pakpoor; Ramyiadarsini I Elangovan; Rocco Adiutori; Jens Kuhle; Gavin Giovannoni
Journal:  PLoS One       Date:  2014-05-02       Impact factor: 3.240

4.  Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking.

Authors:  Ian C Scott; Seth D Seegobin; Sophia Steer; Rachael Tan; Paola Forabosco; Anne Hinks; Stephen Eyre; Ann W Morgan; Anthony G Wilson; Lynne J Hocking; Paul Wordsworth; Anne Barton; Jane Worthington; Andrew P Cope; Cathryn M Lewis
Journal:  PLoS Genet       Date:  2013-09-19       Impact factor: 5.917

Review 5.  Genetic-based prediction of disease traits: prediction is very difficult, especially about the future.

Authors:  Steven J Schrodi; Shubhabrata Mukherjee; Ying Shan; Gerard Tromp; John J Sninsky; Amy P Callear; Tonia C Carter; Zhan Ye; Jonathan L Haines; Murray H Brilliant; Paul K Crane; Diane T Smelser; Robert C Elston; Daniel E Weeks
Journal:  Front Genet       Date:  2014-06-02       Impact factor: 4.599

6.  Population-Wide Genetic Risk Prediction of Complex Diseases: A Pilot Feasibility Study in Macau Population for Precision Public Healthcare Planning.

Authors:  Nancy B Y Tsui; Gregory Cheng; Teresa Chung; Christopher W K Lam; Anita Yee; Peter K C Chung; Tsz-Ki Kwan; Elaine Ko; Daihai He; Wing-Tak Wong; Johnson Y N Lau; Lok Ting Lau; Manson Fok
Journal:  Sci Rep       Date:  2018-01-30       Impact factor: 4.379

  6 in total

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