Genome-Wide Association Study of Peripheral Artery Disease
Natalie R. van Zuydam, Alexander Stiby, Moustafa Abdalla, Erin Austin, Emma H. Dahlström, Stela McLachlan, Efthymia Vlachopoulou, Emma Ahlqvist, Chen Di Liao, Niina Sandholm, Carol Forsblom, Anubha Mahajan, Neil R. Robertson, Nigel W. Rayner, Eero Lindholm, Juha Sinisalo, Markus Perola, Milla Kallio, Emily Weiss, Jackie F. Price, Andrew D. Paterson, Barbara E.K. Klein, Veikko Salomaa, Colin N.A. Palmer, Per‐Henrik Groop, Leif Groop, Mark I. McCarthy, Mariza de Andrade, Andrew P. Morris, Jemma C. Hopewell, Helen M. Colhoun, Iftikhar J. Kullo, Sólveig Grétarsdóttir, Guðmar Þorleifsson, Unnur Þorsteinsdóttir, Kāri Stefánsson, Mark Michael, Timo Kanninen, Barbara Thorand, Giuseppe Remuzzi, David B. Dunger, Angela C. Shore, Ulf Smith, Seppo Ylä‐Herttuala, Claudio Cobelli, Riccardo Bellazzi, Ele Ferrannini, Carlo Patrono, Pirjo Nuutila, Paul McKeague, Birgit Steckel-Hamann, Li‐Ming Gan, Everson Nogoceke, Piero Tortoli, Bernd Jablonka, Mary-Julia Brosnan
Abstract
Background: Peripheral artery disease (PAD) affects >200 million people worldwide and is associated with high mortality and morbidity. We sought to identify genomic variants associated with PAD overall and in the contexts of diabetes and smoking status. Methods: We identified genetic variants associated with PAD and then meta-analyzed with published summary statistics from the Million Veterans Program and UK Biobank to replicate their findings. Next, we ran stratified genome-wide association analysis in ever smokers, never smokers, individuals with diabetes, and individuals with no history of diabetes and corresponding interaction analyses, to identify variants that modify the risk of PAD by diabetic or smoking status. Results: We identified 5 genome-wide significant ( P association ≤5×10 −8 ) associations with PAD in 449 548 (N cases =12 086) individuals of European ancestry near LPA (lipoprotein [a]), CDKN2BAS1 (CDKN2B antisense RNA 1), SH2B3 (SH2B adaptor protein 3) - PTPN11 (protein tyrosine phosphatase non-receptor type 11), HDAC9 (histone deacetylase 9), and CHRNA3 (cholinergic receptor nicotinic alpha 3 subunit ) loci (which overlapped previously reported associations). Meta-analysis with variants previously associated with PAD showed that 18 of 19 published variants remained genome-wide significant. In individuals with diabetes, rs116405693 at the CCSER1 (coiled-coil serine rich protein 1 ) locus was associated with PAD (odds ratio [95% CI], 1.51 [1.32–1.74], P diabetes =2.5×10 −9 , P interactionwithdiabetes =5.3×10 −7 ). Furthermore, in smokers, rs12910984 at the CHRNA3 locus was associated with PAD (odds ratio [95% CI], 1.15 [1.11–1.19], P smokers =9.3×10 −10 , P interactionwithsmoking =3.9×10 −5 ). Conclusions: Our analyses confirm the published genetic associations with PAD and identify novel variants that may influence susceptibility to PAD in the context of diabetes or smoking status.