BACKGROUND: Variability in phenotypic characterization of coronary artery disease (CAD) may contribute to the heterogeneity of genetic association studies, and more consistency in phenotype definitions might improve replication of genetic associations. We assessed the extent of phenotypic heterogeneity and quantified its impact in a large literature sample of association studies. METHODS AND RESULTS: We searched for large (≥15 studies) meta-analyses of genetic associations and reviewed all studies included therein. From each primary study, we extracted phenotypic definitions, demographics, study design characteristics, and genotypic data. For each association, we assessed the magnitude and heterogeneity of genetic effects within and across CAD phenotypes, using meta-analytic methodologies. A total of 965 individual studies investigating 32 distinct variants in 22 genes were included, from which we grouped CAD phenotypes into 3 categories: acute coronary syndromes (ACS) (426 [44%] studies); angiographically documented disease (323 [34%] studies); and broad, not otherwise specified CAD (216 [22%] studies). These clinical phenotypes were overlapping. Subgroup meta-analyses by phenotype showed discordant results, but phenotypic classification generally explained small proportions of between-study heterogeneity. Differences between phenotypic groups were minimized for associations with robust statistical support. No CAD phenotype was consistently associated with larger or more homogeneous genetic effects in meta-analyses. CONCLUSIONS: Substantial phenotypic heterogeneity exists in CAD genetic associations, but differences in phenotype definition make a small contribution to between-study heterogeneity. We did not find a consistent effect in terms of the magnitude or homogeneity of summary effects for a specific phenotype to support its preferential use in genetic studies or meta-analyses for CAD.
BACKGROUND: Variability in phenotypic characterization of coronary artery disease (CAD) may contribute to the heterogeneity of genetic association studies, and more consistency in phenotype definitions might improve replication of genetic associations. We assessed the extent of phenotypic heterogeneity and quantified its impact in a large literature sample of association studies. METHODS AND RESULTS: We searched for large (≥15 studies) meta-analyses of genetic associations and reviewed all studies included therein. From each primary study, we extracted phenotypic definitions, demographics, study design characteristics, and genotypic data. For each association, we assessed the magnitude and heterogeneity of genetic effects within and across CAD phenotypes, using meta-analytic methodologies. A total of 965 individual studies investigating 32 distinct variants in 22 genes were included, from which we grouped CAD phenotypes into 3 categories: acute coronary syndromes (ACS) (426 [44%] studies); angiographically documented disease (323 [34%] studies); and broad, not otherwise specified CAD (216 [22%] studies). These clinical phenotypes were overlapping. Subgroup meta-analyses by phenotype showed discordant results, but phenotypic classification generally explained small proportions of between-study heterogeneity. Differences between phenotypic groups were minimized for associations with robust statistical support. No CAD phenotype was consistently associated with larger or more homogeneous genetic effects in meta-analyses. CONCLUSIONS: Substantial phenotypic heterogeneity exists in CAD genetic associations, but differences in phenotype definition make a small contribution to between-study heterogeneity. We did not find a consistent effect in terms of the magnitude or homogeneity of summary effects for a specific phenotype to support its preferential use in genetic studies or meta-analyses for CAD.
Authors: Katherine Hartmann; Michał Seweryn; Samuel K Handelman; Grzegorz A Rempała; Wolfgang Sadee Journal: BMC Genomics Date: 2016-09-17 Impact factor: 3.969
Authors: Nora Franceschini; Cara Carty; Petra Bůzková; Alex P Reiner; Tiana Garrett; Yi Lin; Jens-S Vöckler; Lucia A Hindorff; Shelley A Cole; Eric Boerwinkle; Dan-Yu Lin; Ebony Bookman; Lyle G Best; Jonathan N Bella; Charles Eaton; Philip Greenland; Nancy Jenny; Kari E North; Darin Taverna; Alicia M Young; Ewa Deelman; Charles Kooperberg; Bruce Psaty; Gerardo Heiss Journal: Circ Cardiovasc Genet Date: 2011-10-31
Authors: Frank Beutner; Daniel Teupser; Stephan Gielen; Lesca Miriam Holdt; Markus Scholz; Enno Boudriot; Gerhard Schuler; Joachim Thiery Journal: PLoS One Date: 2011-12-22 Impact factor: 3.240
Authors: Kenneth Chan; Riyaz S Patel; Paul Newcombe; Christopher P Nelson; Atif Qasim; Stephen E Epstein; Susan Burnett; Viola L Vaccarino; A Maziar Zafari; Svati H Shah; Jeffrey L Anderson; John F Carlquist; Jaana Hartiala; Hooman Allayee; Kunihiko Hinohara; Bok-Soo Lee; Anna Erl; Katrina L Ellis; Anuj Goel; Arne S Schaefer; Nour Eddine El Mokhtari; Benjamin A Goldstein; Mark A Hlatky; Alan S Go; Gong-Qing Shen; Yan Gong; Carl Pepine; Ross C Laxton; John C Whittaker; W H Wilson Tang; Julie A Johnson; Qing K Wang; Themistocles L Assimes; Ute Nöthlings; Martin Farrall; Hugh Watkins; A Mark Richards; Vicky A Cameron; Axel Muendlein; Heinz Drexel; Werner Koch; Jeong Euy Park; Akinori Kimura; Wei-feng Shen; Iain A Simpson; Stanley L Hazen; Benjamin D Horne; Elizabeth R Hauser; Arshed A Quyyumi; Muredach P Reilly; Nilesh J Samani; Shu Ye Journal: J Am Coll Cardiol Date: 2013-01-23 Impact factor: 24.094