Literature DB >> 20623818

Evaluating diagnostic accuracy of genetic profiles in affected offspring families.

Jerome Carayol1, Frédéric Tores, Inke R König, Jörg Hager, Andreas Ziegler.   

Abstract

Diagnostic accuracy of a genetic test involving multiple disease genes is evaluated using sensitivity and specificity. For estimation, data from both affected and unaffected subjects are required. For early onset diseases, such as autism spectrum disorder (ASD), only data from families with affected offspring are available. To enable estimation of specificity when no data for unaffected offspring are available (single affected offspring, SAO, data), we combine the pseudocontrol method of Cordell and Clayton (Am. J. Hum. Genet. 2002; 70:124-141) with the approach of DeLong et al. (Biometrics 1985; 41:947-958) in a logistic regression model for disease outcome with a risk score (RS) constructed from genotype information as prognostic variable. The area under the receiver operating characteristic curve (AUC) is then computed using the non-parametric Mann-Whitney method. Extensive simulation studies show that, analogous to other approaches utilizing pseudocontrols, the resulting estimates of AUC using SAO data are slightly conservative when compared with the estimates computed using the full population-based data. The method is illustrated using data from a study of ASD.

Entities:  

Mesh:

Year:  2010        PMID: 20623818      PMCID: PMC2939926          DOI: 10.1002/sim.4006

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  36 in total

Review 1.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

2.  General score tests for associations of genetic markers with disease using cases and their parents.

Authors:  D J Schaid
Journal:  Genet Epidemiol       Date:  1996       Impact factor: 2.135

3.  A simulation study of the number of events per variable in logistic regression analysis.

Authors:  P Peduzzi; J Concato; E Kemper; T R Holford; A R Feinstein
Journal:  J Clin Epidemiol       Date:  1996-12       Impact factor: 6.437

Review 4.  Development of a clinical prediction model for an ordinal outcome: the World Health Organization Multicentre Study of Clinical Signs and Etiological agents of Pneumonia, Sepsis and Meningitis in Young Infants. WHO/ARI Young Infant Multicentre Study Group.

Authors:  F E Harrell; P A Margolis; S Gove; K E Mason; E K Mulholland; D Lehmann; L Muhe; S Gatchalian; H F Eichenwald
Journal:  Stat Med       Date:  1998-04-30       Impact factor: 2.373

5.  The haplotype-relative-risk (HRR) method for analysis of association in nuclear families.

Authors:  M Knapp; S A Seuchter; M P Baur
Journal:  Am J Hum Genet       Date:  1993-06       Impact factor: 11.025

6.  Comparison of statistics for candidate-gene association studies using cases and parents.

Authors:  D J Schaid; S S Sommer
Journal:  Am J Hum Genet       Date:  1994-08       Impact factor: 11.025

7.  Jackknife estimators of variance for parameter estimates from estimating equations with applications to clustered survival data.

Authors:  S R Lipsitz; K B Dear; L Zhao
Journal:  Biometrics       Date:  1994-09       Impact factor: 2.571

8.  Sensitivity and specificity of a monitoring test.

Authors:  E R DeLong; W B Vernon; R R Bollinger
Journal:  Biometrics       Date:  1985-12       Impact factor: 2.571

Review 9.  Evidence-based medicine: answering questions of diagnosis.

Authors:  Laura Zakowski; Christine Seibert; Selma VanEyck
Journal:  Clin Med Res       Date:  2004-02

10.  Impact of common type 2 diabetes risk polymorphisms in the DESIR prospective study.

Authors:  Martine Vaxillaire; Jacques Veslot; Christian Dina; Christine Proença; Stéphane Cauchi; Guillaume Charpentier; Jean Tichet; Frédéric Fumeron; Michel Marre; David Meyre; Beverley Balkau; Philippe Froguel
Journal:  Diabetes       Date:  2007-10-31       Impact factor: 9.461

View more
  6 in total

1.  A new explained-variance based genetic risk score for predictive modeling of disease risk.

Authors:  Ronglin Che; Alison A Motsinger-Reif
Journal:  Stat Appl Genet Mol Biol       Date:  2012-09-25

Review 2.  Risk estimation and risk prediction using machine-learning methods.

Authors:  Jochen Kruppa; Andreas Ziegler; Inke R König
Journal:  Hum Genet       Date:  2012-07-03       Impact factor: 4.132

3.  Autism risk assessment in siblings of affected children using sex-specific genetic scores.

Authors:  Jerome Carayol; Gerard D Schellenberg; Beth Dombroski; Emmanuelle Genin; Francis Rousseau; Geraldine Dawson
Journal:  Mol Autism       Date:  2011-10-21       Impact factor: 7.509

4.  Evaluation of genetic risk score models in the presence of interaction and linkage disequilibrium.

Authors:  Ronglin Che; Alison A Motsinger-Reif
Journal:  Front Genet       Date:  2013-07-23       Impact factor: 4.599

5.  Allelic interaction effects of DNA damage and repair genes on the predisposition to age-related cataract.

Authors:  Mei Yang; Junfang Zhang; Shu Su; Bai Qin; Lihua Kang; Rongrong Zhu; Huaijin Guan
Journal:  PLoS One       Date:  2018-04-24       Impact factor: 3.240

6.  Development of a Genetic Risk Score to predict the risk of overweight and obesity in European adolescents from the HELENA study.

Authors:  Miguel Seral-Cortes; Sergio Sabroso-Lasa; Pilar De Miguel-Etayo; Marcela Gonzalez-Gross; Eva Gesteiro; Cristina Molina-Hidalgo; Stefaan De Henauw; Frederic Gottrand; Christina Mavrogianni; Yannis Manios; Maria Plada; Kurt Widhalm; Anthony Kafatos; Éva Erhardt; Aline Meirhaeghe; Diego Salazar-Tortosa; Jonatan Ruiz; Luis A Moreno; Luis Mariano Esteban; Idoia Labayen
Journal:  Sci Rep       Date:  2021-02-04       Impact factor: 4.379

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.