PURPOSE: Phenotype-disease odds ratios calculated from the effect of a genotype on its phenotype and on disease risk ("Mendelian triangulation") can be used as a standard to assess bias on the corresponding odds ratio from nongenetic studies. Statistical tests are commonly used to compare these odds ratios. We propose a method to estimate the magnitude of the bias and judge the validity of the phenotype-disease association. METHODS: For four published examples, we obtained 10,000 random values from distributions of the odds ratios from both genetic and nongenetic studies. A range of values compatible with an unbiased odds ratio was then calculated from the empirical distribution of the differences between both odds ratios. RESULTS: We show that estimating a range of likely values for an unbiased odds ratio is useful to judge the effect of the phenotype and identify cases for which information from genetic studies adds little to the evaluation of the phenotype-disease association. Conversely, statistical tests could be misleading. CONCLUSIONS: Estimating a range of values for an unbiased odds ratio is more informative and appropriate than statistical tests when using the Mendelian triangulation approach for assessment of bias in phenotype-disease association studies.
PURPOSE: Phenotype-disease odds ratios calculated from the effect of a genotype on its phenotype and on disease risk ("Mendelian triangulation") can be used as a standard to assess bias on the corresponding odds ratio from nongenetic studies. Statistical tests are commonly used to compare these odds ratios. We propose a method to estimate the magnitude of the bias and judge the validity of the phenotype-disease association. METHODS: For four published examples, we obtained 10,000 random values from distributions of the odds ratios from both genetic and nongenetic studies. A range of values compatible with an unbiased odds ratio was then calculated from the empirical distribution of the differences between both odds ratios. RESULTS: We show that estimating a range of likely values for an unbiased odds ratio is useful to judge the effect of the phenotype and identify cases for which information from genetic studies adds little to the evaluation of the phenotype-disease association. Conversely, statistical tests could be misleading. CONCLUSIONS: Estimating a range of values for an unbiased odds ratio is more informative and appropriate than statistical tests when using the Mendelian triangulation approach for assessment of bias in phenotype-disease association studies.
Authors: Duncan C Thomas; David V Conti; James Baurley; Frederik Nijhout; Michael Reed; Cornelia M Ulrich Journal: Hum Genomics Date: 2009-10 Impact factor: 4.639
Authors: Reecha Sofat; Aroon D Hingorani; Liam Smeeth; Steve E Humphries; Philippa J Talmud; Jackie Cooper; Tina Shah; Manjinder S Sandhu; Sally L Ricketts; S Matthijs Boekholdt; Nicholas Wareham; Kay Tee Khaw; Meena Kumari; Mika Kivimaki; Michael Marmot; Folkert W Asselbergs; Pim van der Harst; Robin P F Dullaart; Gerjan Navis; Dirk J van Veldhuisen; Wiek H Van Gilst; John F Thompson; Pamela McCaskie; Lyle J Palmer; Marcello Arca; Fabiana Quagliarini; Carlo Gaudio; François Cambien; Viviane Nicaud; Odette Poirer; Vilmundur Gudnason; Aaron Isaacs; Jacqueline C M Witteman; Cornelia M van Duijn; Michael Pencina; Ramachandran S Vasan; Ralph B D'Agostino; Jose Ordovas; Tricia Y Li; Sakari Kakko; Heikki Kauma; Markku J Savolainen; Y Antero Kesäniemi; Anton Sandhofer; Bernhard Paulweber; Jose V Sorli; Akimoto Goto; Shinji Yokoyama; Kenji Okumura; Benjamin D Horne; Chris Packard; Dilys Freeman; Ian Ford; Naveed Sattar; Valerie McCormack; Debbie A Lawlor; Shah Ebrahim; George Davey Smith; John J P Kastelein; John Deanfield; Juan P Casas Journal: Circulation Date: 2009-12-21 Impact factor: 29.690
Authors: Tom M Palmer; Debbie A Lawlor; Roger M Harbord; Nuala A Sheehan; Jon H Tobias; Nicholas J Timpson; George Davey Smith; Jonathan A C Sterne Journal: Stat Methods Med Res Date: 2011-01-07 Impact factor: 3.021
Authors: Irene Pichler; Fabiola Del Greco M; Martin Gögele; Christina M Lill; Lars Bertram; Chuong B Do; Nicholas Eriksson; Tatiana Foroud; Richard H Myers; Michael Nalls; Margaux F Keller; Beben Benyamin; John B Whitfield; Peter P Pramstaller; Andrew A Hicks; John R Thompson; Cosetta Minelli Journal: PLoS Med Date: 2013-06-04 Impact factor: 11.069
Authors: Patrick Linsel-Nitschke; Anika Götz; Jeanette Erdmann; Ingrid Braenne; Peter Braund; Christian Hengstenberg; Klaus Stark; Marcus Fischer; Stefan Schreiber; Nour Eddine El Mokhtari; Arne Schaefer; Jürgen Schrezenmeir; Jürgen Schrezenmeier; Diana Rubin; Anke Hinney; Thomas Reinehr; Christian Roth; Jan Ortlepp; Peter Hanrath; Alistair S Hall; Massimo Mangino; Wolfgang Lieb; Claudia Lamina; Iris M Heid; Angela Doering; Christian Gieger; Annette Peters; Thomas Meitinger; H-Erich Wichmann; Inke R König; Andreas Ziegler; Florian Kronenberg; Nilesh J Samani; Heribert Schunkert Journal: PLoS One Date: 2008-08-20 Impact factor: 3.240
Authors: J Jaime Miranda; Victor M Herrera; Julio A Chirinos; Luis F Gómez; Pablo Perel; Rafael Pichardo; Angel González; José R Sánchez; Catterina Ferreccio; Ximena Aguilera; Eglé Silva; Myriam Oróstegui; Josefina Medina-Lezama; Cynthia M Pérez; Erick Suárez; Ana P Ortiz; Luis Rosero; Noberto Schapochnik; Zulma Ortiz; Daniel Ferrante; Juan P Casas; Leonelo E Bautista Journal: PLoS One Date: 2013-01-17 Impact factor: 3.240