Literature DB >> 23095857

Assessment of systematic effects of methodological characteristics on candidate genetic associations.

Badr Aljasir1, John P A Ioannidis, Alex Yurkiewich, David Moher, Julian P T Higgins, Paul Arora, Julian Little.   

Abstract

Candidate genetic association studies have been found to have a low replication rate in the past. Here, we aimed to assess whether aspects of reported methodological characteristics in genetic association studies may be related to the magnitude of effects observed. An observational, literature-based investigation of 511 case-control studies of genetic association studies indexed in 2007, was undertaken. Meta-regression analyses were used to assess the relationship between 23 reported methodological characteristics and the magnitude of genetic associations. The 511 studies had been conducted in 52 countries and were published in 220 journals (median impact factor 5.1). The multivariate meta-regression model of methodological characteristics plus disease category accounted for 17.2 % of the between-study variance in the magnitude of the reported genetic associations. Our findings are consistent with the view that better conducted and better reported genetic association research may lead to less inflated results.

Mesh:

Year:  2012        PMID: 23095857     DOI: 10.1007/s00439-012-1237-4

Source DB:  PubMed          Journal:  Hum Genet        ISSN: 0340-6717            Impact factor:   4.132


  75 in total

Review 1.  Candidate-gene approaches for studying complex genetic traits: practical considerations.

Authors:  Holly K Tabor; Neil J Risch; Richard M Myers
Journal:  Nat Rev Genet       Date:  2002-05       Impact factor: 53.242

2.  Quantifying heterogeneity in a meta-analysis.

Authors:  Julian P T Higgins; Simon G Thompson
Journal:  Stat Med       Date:  2002-06-15       Impact factor: 2.373

Review 3.  Meta-analyses of molecular association studies: methodologic lessons for genetic epidemiology.

Authors:  John Attia; Ammarin Thakkinstian; Catherine D'Este
Journal:  J Clin Epidemiol       Date:  2003-04       Impact factor: 6.437

Review 4.  A compendium of genome-wide associations for cancer: critical synopsis and reappraisal.

Authors:  John P A Ioannidis; Peter Castaldi; Evangelos Evangelou
Journal:  J Natl Cancer Inst       Date:  2010-05-26       Impact factor: 13.506

5.  Relative citation impact of various study designs in the health sciences.

Authors:  Nikolaos A Patsopoulos; Apostolos A Analatos; John P A Ioannidis
Journal:  JAMA       Date:  2005-05-18       Impact factor: 56.272

6.  Language of publication restrictions in systematic reviews gave different results depending on whether the intervention was conventional or complementary.

Authors:  Ba' Pham; Terry P Klassen; Margaret L Lawson; David Moher
Journal:  J Clin Epidemiol       Date:  2005-08       Impact factor: 6.437

7.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.

Authors:  Lucia A Hindorff; Praveen Sethupathy; Heather A Junkins; Erin M Ramos; Jayashri P Mehta; Francis S Collins; Teri A Manolio
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-27       Impact factor: 11.205

Review 8.  Genetic diversity and new therapeutic concepts.

Authors:  Barkur S Shastry
Journal:  J Hum Genet       Date:  2005-07-23       Impact factor: 3.172

9.  Moving genetics into clinical cancer care: examples from BRCA gene testing and telemedicine.

Authors:  James Mackay; Ailsa Taylor
Journal:  Breast       Date:  2006-12       Impact factor: 4.380

Review 10.  The A1 allele at the D2 dopamine receptor gene and alcoholism. A reappraisal.

Authors:  J Gelernter; D Goldman; N Risch
Journal:  JAMA       Date:  1993-04-07       Impact factor: 56.272

View more
  2 in total

1.  Special issue on 'Genetic epidemiology of complex diseases: impact of population history and modelling assumptions'.

Authors:  Amke Caliebe; Michael Nothnagel
Journal:  Hum Genet       Date:  2020-01       Impact factor: 4.132

2.  How Genetic and Other Biological Factors Interact with Smoking Decisions.

Authors:  Laura Bierut; David Cesarini
Journal:  Big Data       Date:  2015-09-01       Impact factor: 2.128

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.