TY - JOUR
T1 - Pleiotropy among common genetic loci identified for cardiometabolic disorders and C-reactive protein
AU - CHARGE Inflammation working group
AU - Ligthart, Symen
AU - De Vries, Paul S.
AU - Uitterlinden, André G.
AU - Hofman, Albert
AU - Franco, Oscar H.
AU - Chasman, Daniel I.
AU - Dehghan, Abbas
AU - Dupuis, Josée
AU - Barbalic, Maja
AU - Bis, Joshua C.
AU - Eiriksdottir, Gudny
AU - Lu, Chen
AU - Pellikka, Niina
AU - Wallaschofski, Henri
AU - Kettunen, Johannes
AU - Henneman, Peter
AU - Baumert, Jens
AU - Strachan, David P.
AU - Fuchsberger, Christian
AU - Vitart, Veronique
AU - Wilson, James F.
AU - Paré, Guillaume
AU - Naitza, Silvia
AU - Rudock, Megan E.
AU - Surakka, Ida
AU - De Geus, Eco J C
AU - Alizadeh, Behrooz Z.
AU - Guralnik, Jack M D
AU - Shuldiner, Alan
AU - Tanaka, Toshiko
AU - Zee, Robert Y L
AU - Schnabel, Renate B.
AU - Nambi, Vijay
AU - Kavousi, Maryam
AU - Ripatti, Samuli
AU - Nauck, Matthias
AU - Smith, Nicholas L.
AU - Smith, Albert V.
AU - Sundvall, Jouko
AU - Scheet, Paul
AU - Liu, Yongmei
AU - Ruokonen, Aimo
AU - Rose, Lynda M.
AU - Larson, Martin G.
AU - Hoogeveen, Ron C.
AU - Freimer, Nelson B.
AU - Teumer, Alexander
AU - Rudan, Igor
AU - Campbell, Harry
AU - Hayward, Caroline
PY - 2015/3/13
Y1 - 2015/3/13
N2 - Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.
AB - Pleiotropic genetic variants have independent effects on different phenotypes. C-reactive protein (CRP) is associated with several cardiometabolic phenotypes. Shared genetic backgrounds may partially underlie these associations. We conducted a genome-wide analysis to identify the shared genetic background of inflammation and cardiometabolic phenotypes using published genome-wide association studies (GWAS). We also evaluated whether the pleiotropic effects of such loci were biological or mediated in nature. First, we examined whether 283 common variants identified for 10 cardiometabolic phenotypes in GWAS are associated with CRP level. Second, we tested whether 18 variants identified for serum CRP are associated with 10 cardiometabolic phenotypes. We used a Bonferroni corrected p-value of 1.1×10-04 (0.05/463) as a threshold of significance. We evaluated the independent pleiotropic effect on both phenotypes using individual level data from the Women Genome Health Study. Evaluating the genetic overlap between inflammation and cardiometabolic phenotypes, we found 13 pleiotropic regions. Additional analyses showed that 6 regions (APOC1, HNF1A, IL6R, PPP1R3B, HNF4A and IL1F10) appeared to have a pleiotropic effect on CRP independent of the effects on the cardiometabolic phenotypes. These included loci where individuals carrying the risk allele for CRP encounter higher lipid levels and risk of type 2 diabetes. In addition, 5 regions (GCKR, PABPC4, BCL7B, FTO and TMEM18) had an effect on CRP largely mediated through the cardiometabolic phenotypes. In conclusion, our results show genetic pleiotropy among inflammation and cardiometabolic phenotypes. In addition to reverse causation, our data suggests that pleiotropic genetic variants partially underlie the association between CRP and cardiometabolic phenotypes.
UR - http://www.scopus.com/inward/record.url?scp=84928981861&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0118859
DO - 10.1371/journal.pone.0118859
M3 - Article
AN - SCOPUS:84928981861
SN - 1932-6203
VL - 10
JO - PLoS ONE
JF - PLoS ONE
IS - 3
M1 - e0118859
ER -