Literature DB >> 14649335

Confounding, ascertainment bias, and the blind quest for a genetic 'fountain of youth'.

Joseph D Terwilliger1, Kenneth M Weiss.   

Abstract

Many promises have been made about the impact of the Human Genome Project on clinical practice and public health, yet despite massively funded efforts over the past decade, little headway has been made in elucidating the specific genetic factors which have major impact on the risk of developing common complex traits. There are two fundamental reasons for this abject failure as follows: 1) studies have been inadequately designed to identify such genetic risk factors; 2) the genetic factors that do exist are individually of small marginal importance, and are characterized by extensive heterogeneity. If 2) is the truth, there is little we can do about it, so we emphasize the importance of 1) in this article, while recognizing that 2) probably is not far from the truth. Genetic studies, in contrast to epidemiological studies, use confounding and ascertainment bias to help identify weak etiologic signal due to genes, since gene mapping is fundamentally a hypothesis-free science. This strategy makes it possible to identify genetic risk factors, but makes it impossible to quantify the size of their effect on risk. Classical epidemiological study designs are of minimal value for gene identification, but may be of use in estimation of the effect size of genetic risk factors once they are identified in more appropriately designed genetic studies. However, if the effects are so weak that we need this strong, systematic ascertainment bias to find them, their relevance to public health may be of questionable immediate value, raising many questions about the rhetoric and promises being made to the public as justification for 'big science' approaches to dissecting the hypothetical role of genes in complex traits.

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Year:  2003        PMID: 14649335     DOI: 10.1080/07853890310015181

Source DB:  PubMed          Journal:  Ann Med        ISSN: 0785-3890            Impact factor:   4.709


  16 in total

1.  Randomised by (your) god: robust inference from an observational study design.

Authors:  George Davey Smith
Journal:  J Epidemiol Community Health       Date:  2006-05       Impact factor: 3.710

Review 2.  Translational research in central nervous system drug discovery.

Authors:  Orest Hurko; John L Ryan
Journal:  NeuroRx       Date:  2005-10

3.  Exploring the performance of Multifactor Dimensionality Reduction in large scale SNP studies and in the presence of genetic heterogeneity among epistatic disease models.

Authors:  Todd L Edwards; Kenneth Lewis; Digna R Velez; Scott Dudek; Marylyn D Ritchie
Journal:  Hum Hered       Date:  2008-12-15       Impact factor: 0.444

Review 4.  Tilting at quixotic trait loci (QTL): an evolutionary perspective on genetic causation.

Authors:  Kenneth M Weiss
Journal:  Genetics       Date:  2008-08       Impact factor: 4.562

5.  The mathematical limits of genetic prediction for complex chronic disease.

Authors:  Katherine M Keyes; George Davey Smith; Karestan C Koenen; Sandro Galea
Journal:  J Epidemiol Community Health       Date:  2015-02-03       Impact factor: 3.710

Review 6.  Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes.

Authors:  Jason Flannick; Stefan Johansson; Pål R Njølstad
Journal:  Nat Rev Endocrinol       Date:  2016-04-15       Impact factor: 43.330

7.  On the validity of the likelihood ratio test and consistency of resulting parameter estimates in joint linkage and linkage disequilibrium analysis under improperly specified parametric models.

Authors:  Tero Hiekkalinna; Harald H H Göring; Joseph D Terwilliger
Journal:  Ann Hum Genet       Date:  2011-11-14       Impact factor: 1.670

8.  Assessing the phenotypic effects in the general population of rare variants in genes for a dominant Mendelian form of diabetes.

Authors:  Jason Flannick; Nicola L Beer; Alexander G Bick; Vineeta Agarwala; Janne Molnes; Namrata Gupta; Noël P Burtt; Jose C Florez; James B Meigs; Herman Taylor; Valeriya Lyssenko; Henrik Irgens; Ervin Fox; Frank Burslem; Stefan Johansson; M Julia Brosnan; Jeff K Trimmer; Christopher Newton-Cheh; Tiinamaija Tuomi; Anders Molven; James G Wilson; Christopher J O'Donnell; Sekar Kathiresan; Joel N Hirschhorn; Pål R Njølstad; Tim Rolph; J G Seidman; Stacey Gabriel; David R Cox; Christine E Seidman; Leif Groop; David Altshuler
Journal:  Nat Genet       Date:  2013-10-06       Impact factor: 38.330

9.  What are genes "for" or where are traits "from"? What is the question?

Authors:  Anne V Buchanan; Samuel Sholtis; Joan Richtsmeier; Kenneth M Weiss
Journal:  Bioessays       Date:  2009-02       Impact factor: 4.345

10.  Prediction and interaction in complex disease genetics: experience in type 1 diabetes.

Authors:  David G Clayton
Journal:  PLoS Genet       Date:  2009-07-03       Impact factor: 5.917

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