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13 results on '"Kullo, I.J."'

1. Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis

Author

Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Zhang, W, Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., Peloso, G.M., Kanoni, S., Graham, S.E., Wang, Yuxuan, Surakka, I., Ramdas, S., Zhu, X, Clarke, S.L., Bhatti, K.F., Vedantam, S., Winkler, T.W., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Zeyuan, Xue, C., Thorleifsson, G., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, Wei, Weir, D.R., Turman, C., Huang, H., Graff, M, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Zhang, W, Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Yousri, N.A., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Hwu, C.M., Hung, Y.J., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Sabater-Lleal, M., Noordam, R., Mauro, P., Galesloot, T.E., Lores-Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Graaf, J. de, Assimes, T.L., and Peloso, G.M.

Abstract

Item does not contain fulltext, BACKGROUND: Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery. RESULTS: To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism. CONCLUSIONS: Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.

Published
2022

2. A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids

Author

Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, Brown, C.D., Ramdas, S., Judd, J., Graham, S.E., Kanoni, S., Wang, Y, Surakka, I., Wenz, B., Clarke, S.L., Chesi, A., Wells, A., Bhatti, K.F., Vedantam, S., Locke, A.E., Marouli, E., Zajac, G.J.M., Wu, K.H., Ntalla, I., Hui, Q., Klarin, D., Hilliard, A.T., Wang, Z, Xue, C., Helgadottir, A., Gudbjartsson, D.F., Holm, H., Olafsson, I., Hwang, M.Y., Han, S., Akiyama, M., Sakaue, S., Terao, C., Kanai, M., Zhou, W, Brumpton, B.M., Rasheed, H., Havulinna, A.S., Veturi, Y., Pacheco, J.A., Rosenthal, E.A., Lingren, T., Feng, Q., Kullo, I.J., Narita, A., Takayama, J., Martin, H.C., Hunt, K.A., Trivedi, B., Haessler, J., Giulianini, F., Bradford, Y., Miller, J.E., Campbell, A., Lin, K., Millwood, I.Y., Rasheed, A., Hindy, G., Faul, J.D., Zhao, W, Weir, D.R., Turman, C., Huang, H., Graff, Mariaelisa, Choudhury, A., Sengupta, D., Mahajan, A., Brown, M.R., Yu, K., Schmidt, E.M., Pandit, A., Gustafsson, S., Yin, X., Luan, J, Zhao, J.H., Matsuda, F., Jang, H.M., Yoon, K., Medina-Gomez, C., Pitsillides, A., Hottenga, J.J., Wood, A.R., Ji, Y., Gao, Z, Haworth, S., Mitchell, R.E., Chai, J.F., Aadahl, M., Bjerregaard, A.A., Yao, J., Manichaikul, A., Lee, W.J., Hsiung, C.A., Warren, H.R., Ramirez, J., Bork-Jensen, J., Kårhus, L.L., Goel, A., Galesloot, T.E., Lores de Motta, L., Pauper, M., Hollander, A.I. den, Kiemeney, L.A.L.M., Zhu, Xiang, and Brown, C.D.

Abstract

Item does not contain fulltext, A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Published
2022

4. Specification of ACMG/AMP guidelines for standardized variant interpretation in familial hypercholesterolemia: On behalf of the clingen FH variant curation expert panel

Author

Iacocca, M.A., primary, Chora, J.R., additional, Freiberger, T., additional, Carrie, A., additional, Sijbrands, E.J., additional, Wand, H., additional, Williams, M., additional, Zimmermann, H., additional, Leon, A., additional, Kurtz, C.L., additional, Tichy, L., additional, Alves, A.C., additional, Wang, J., additional, Cuchel, M., additional, Humphries, S.E., additional, Defesche, J.C., additional, Mata, P., additional, Santos, R.D., additional, Kullo, I.J., additional, Brunham, L.R., additional, Hegele, R.A., additional, Knowles, J.W., additional, and Bourbon, M., additional

Published
2020
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5. Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci

Author

Jones, G.T., Tromp, G., Kuivaniemi, H., Gretarsdottir, S., Baas, A.F., Giusti, B., Strauss, E., Hof, F.N. van 't, Webb, T.R., Erdman, R., Ritchie, M.D., Elmore, J.R., Verma, A., Pendergrass, S., Kullo, I.J., Ye, Z., Peissig, P.L., Gottesman, O., Verma, S.S., Malinowski, J., Rasmussen-Torvik, L.J., Borthwick, K.M., Smelser, D.T., Crosslin, D.R., Andrade, M. de, Ryer, E.J., McCarty, C.A., Bottinger, E.P., Pacheco, J.A., Crawford, D.C., Carrell, D.S., Gerhard, G.S., Franklin, D.P., Carey, D.J., Phillips, V.L., Williams, M.J., Wei, W., Blair, R., Hill, A.A., Vasudevan, T.M., Lewis, D.R., Thomson, I.A., Krysa, J., Hill, G.B., Roake, J., Merriman, T.R., Oszkinis, G., Galora, S., Saracini, C., Abbate, R., Pulli, R., Pratesi, C., Saratzis, A., Verissimo, A.R., Bumpstead, S., Badger, S.A., Clough, R.E., Cockerill, G., Hafez, H., Scott, D.J., Futers, T.S., Romaine, S.P., Bridge, K., Griffin, K.J., Bailey, M.A., Smith, A., Thompson, M.M., Bockxmeer, F.M. van, Matthiasson, S.E., Thorleifsson, G., Thorsteinsdottir, U., Blankensteijn, J.D., Teijink, J.A., Wijmenga, C., Graaf, J. de, Kiemeney, L.A.L.M., Lindholt, J.S., Hughes, A., Bradley, D.T., Stirrups, K., Golledge, J., Norman, P.E., Powell, J.T., Humphries, S.E., Hamby, S.E., Goodall, A.H., Nelson, C.P., Sakalihasan, N., Courtois, A., Ferrell, R.E., Eriksson, P., Folkersen, L., Franco-Cereceda, A., Eicher, J.D., Johnson, A.D., Betsholtz, C., Ruusalepp, A., Franzen, O., Schadt, E.E., and Bjorkegren, J.L.

Subjects
Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], cardiovascular system, cardiovascular diseases
Abstract

Contains fulltext : 169744.pdf (Publisher’s version ) (Open Access) RATIONALE: Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA. OBJECTIVE: To identify additional AAA risk loci using data from all available genome-wide association studies. METHODS AND RESULTS: Through a meta-analysis of 6 genome-wide association study data sets and a validation study totaling 10 204 cases and 107 766 controls, we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11 (LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches, we observed no new associations between the lead AAA single nucleotide polymorphisms and coronary artery disease, blood pressure, lipids, or diabetes mellitus. Network analyses identified ERG, IL6R, and LDLR as modifiers of MMP9, with a direct interaction between ERG and MMP9. CONCLUSIONS: The 4 new risk loci for AAA seem to be specific for AAA compared with other cardiovascular diseases and related traits suggesting that traditional cardiovascular risk factor management may only have limited value in preventing the progression of aneurysmal disease.

Published
2017

6. A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease

Author

Nikpay, M. Goel, A. Won, H.-H. Hall, L.M. Willenborg, C. Kanoni, S. Saleheen, D. Kyriakou, T. Nelson, C.P. CHopewell, J. Webb, T.R. Zeng, L. Dehghan, A. Alver, M. MArmasu, S. Auro, K. Bjonnes, A. Chasman, D.I. Chen, S. Ford, I. Franceschini, N. Gieger, C. Grace, C. Gustafsson, S. Huang, J. Hwang, S.-J. Kim, Y.K. Kleber, M.E. Lau, K.W. Lu, X. Lu, Y. Lyytikäinen, L.-P. Mihailov, E. Morrison, A.C. Pervjakova, N. Qu, L. Rose, L.M. Salfati, E. Saxena, R. Scholz, M. Smith, A.V. Tikkanen, E. Uitterlinden, A. Yang, X. Zhang, W. Zhao, W. De Andrade, M. De Vries, P.S. Van Zuydam, N.R. Anand, S.S. Bertram, L. Beutner, F. Dedoussis, G. Frossard, P. Gauguier, D. Goodall, A.H. Gottesman, O. Haber, M. Han, B.-G. Huang, J. Jalilzadeh, S. Kessler, T. König, I.R. Lannfelt, L. Lieb, W. Lind, L. MLindgren, C. Lokki, M.-L. Magnusson, P.K. Mallick, N.H. Mehra, N. Meitinger, T. Memon, F.-U.-R. Morris, A.P. Nieminen, M.S. Pedersen, N.L. Peters, A. Rallidis, L.S. Rasheed, A. Samuel, M. Shah, S.H. Sinisalo, J. EStirrups, K. Trompet, S. Wang, L. Zaman, K.S. Ardissino, D. Boerwinkle, E. Borecki, I.B. Bottinger, E.P. Buring, J.E. Chambers, J.C. Collins, R. Cupples, L. Danesh, J. Demuth, I. Elosua, R. Epstein, S.E. Esko, T. Feitosa, M.F. Franco, O.H. Franzosi, M.G. Granger, C.B. Gu, D. Gudnason, V. SHall, A. Hamsten, A. Harris, T.B. LHazen, S. Hengstenberg, C. Hofman, A. Ingelsson, E. Iribarren, C. Jukema, J.W. Karhunen, P.J. Kim, B.-J. Kooner, J.S. Kullo, I.J. Lehtimäki, T. Loos, R.J.F. Melander, O. Metspalu, A. März, W. Palmer, C.N. Perola, M. Quertermous, T. Rader, D.J. Ridker, P.M. Ripatti, S. Roberts, R. Salomaa, V. Sanghera, D.K. Schwartz, S.M. Seedorf, U. Stewart, A.F. Stott, D.J. Thiery, J. Zalloua, P.A. O'Donnell, C.J. Reilly, M.P. Assimes, T.L. Thompson, J.R. Erdmann, J. Clarke, R. Watkins, H. Kathiresan, S. McPherson, R. Deloukas, P. Schunkert, H. Samani, N.J. Farrall, M.

Abstract

Existing knowledge of genetic variants affecting risk of coronary artery disease (CAD) is largely based on genome-wide association study (GWAS) analysis of common SNPs. Leveraging phased haplotypes from the 1000 Genomes Project, we report a GWAS meta-analysis of ∼185,000 CAD cases and controls, interrogating 6.7 million common (minor allele frequency (MAF) > 0.05) and 2.7 million low-frequency (0.005 < MAF < 0.05) variants. In addition to confirming most known CAD-associated loci, we identified ten new loci (eight additive and two recessive) that contain candidate causal genes newly implicating biological processes in vessel walls. We observed intralocus allelic heterogeneity but little evidence of low-frequency variants with larger effects and no evidence of synthetic association. Our analysis provides a comprehensive survey of the fine genetic architecture of CAD, showing that genetic susceptibility to this common disease is largely determined by common SNPs of small effect size. © 2015 Nature America, Inc.

Published
2015

7. Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms

Author

Hof, F.N. van 't, Ruigrok, Y.M., Lee, C.H., Ripke, S., Anderson, G., Baas, A.F., Blankensteijn, J.D., Bottinger, E.P., Bown, M.J., Broderick, J., Bijlenga, P., Carrell, D.S., Crawford, D.C., Crosslin, D.R., Ebeling, C., Eriksson, J.G., Fornage, M., Foroud, T., von Und Zu Fraunberg, M., Friedrich, C.M., Gaal, E.I., Gottesman, O., Guo, D.C., Harrison, S.C., Hernesniemi, J., Hofman, A., Inoue, I., Jaaskelainen, J.E., Jones, G.T., Kiemeney, L.A., Kivisaari, R., Ko, N., Koskinen, S., Kubo, M., Kullo, I.J., Kuivaniemi, H., Kurki, M.I., Laakso, A., Lai, D., Leal, S.M., Lehto, H., Lemaire, S.A., Low, S.K., Malinowski, J., McCarty, C.A., Milewicz, D.M., Mosley, T.H., Nakamura, Y., Nakaoka, H., Niemela, M., Pacheco, J., Peissig, P.L., Pera, J., Rasmussen-Torvik, L., Ritchie, M.D., Rivadeneira, F., Rij, A.M. van, Santos-Cortez, R.L., Saratzis, A., Slowik, A., Takahashi, A., Tromp, G., Uitterlinden, A.G., Verma, S.S., Vermeulen, S.H., Wang, G.T., Han, B., Rinkel, G.J., Bakker, P.I. de, Graaf, J. de, et al., Hof, F.N. van 't, Ruigrok, Y.M., Lee, C.H., Ripke, S., Anderson, G., Baas, A.F., Blankensteijn, J.D., Bottinger, E.P., Bown, M.J., Broderick, J., Bijlenga, P., Carrell, D.S., Crawford, D.C., Crosslin, D.R., Ebeling, C., Eriksson, J.G., Fornage, M., Foroud, T., von Und Zu Fraunberg, M., Friedrich, C.M., Gaal, E.I., Gottesman, O., Guo, D.C., Harrison, S.C., Hernesniemi, J., Hofman, A., Inoue, I., Jaaskelainen, J.E., Jones, G.T., Kiemeney, L.A., Kivisaari, R., Ko, N., Koskinen, S., Kubo, M., Kullo, I.J., Kuivaniemi, H., Kurki, M.I., Laakso, A., Lai, D., Leal, S.M., Lehto, H., Lemaire, S.A., Low, S.K., Malinowski, J., McCarty, C.A., Milewicz, D.M., Mosley, T.H., Nakamura, Y., Nakaoka, H., Niemela, M., Pacheco, J., Peissig, P.L., Pera, J., Rasmussen-Torvik, L., Ritchie, M.D., Rivadeneira, F., Rij, A.M. van, Santos-Cortez, R.L., Saratzis, A., Slowik, A., Takahashi, A., Tromp, G., Uitterlinden, A.G., Verma, S.S., Vermeulen, S.H., Wang, G.T., Han, B., Rinkel, G.J., Bakker, P.I. de, Graaf, J. de, and et al.

Abstract

Contains fulltext : 171496.pdf (publisher's version ) (Open Access), BACKGROUND: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co-occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. METHODS AND RESULTS: We performed a mega-analysis of 1000 Genomes Project-imputed genome-wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA-, AAA-, and TAA-associated SNPs and tested these scores for association to case-control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium-score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single-nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1x10(-5)) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1x10(-3)). CONCLUSIONS: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.

Published
2016

11. Association Between Chromosome 9p21 Variants and the Ankle-Brachial Index Identified by a Meta-Analysis of 21 Genome-Wide Association Studies

Author

Murabito, J.M., White, C.C., Kavousi, M., Sun, Y.V., Feitosa, M.F., Nambi, V., Lamina, C., Schillert, A., Coassin, S., Bis, J.C., Broer, L., Crawford, D.C., Franceschini, N., Frikke-Schmidt, R., Haun, M., Holewijn, S., Huffman, J.E., Hwang, S.J., Kiechl, S., Kollerits, B., Montasser, M.E., Nolte, I.M., Rudock, M.E., Senft, A., Teumer, A., van der Harst, P., Vitart, V., Waite, L.L., Wood, A.R., Wassel, C.L., Absher, D.M., Allison, M.A., Amin, N., Arnold, A., Asselbergs, F.W., Aulchenko, Y., Bandinelli, S., Barbalic, M., Boban, M., Brown-Gentry, K., Couper, D.J., Criqui, M.H., Dehghan, A., den Heijer, M., Dieplinger, B., Ding, J.Z., Dorr, M., Espinola-Klein, C., Felix, S.B., Ferrucci, L., Folsom, A.R., Fraedrich, G., Gibson, Q., Goodloe, R., Gunjaca, G., Haltmayer, M., Heiss, G., Hofman, A., Kieback, A., Kiemeney, L.A., Kolcic, I., Kullo, I.J., Kritchevsky, S.B., Lackner, K.J., Li, X.H., Lieb, W.G., Lohman, K., Meisinger, C., Melzer, D., Mohler, E.R., Mudnic, I., Mueller, T., Navis, G., Oberhollenzer, F., Olin, J.W., O'Connell, J., O'Donnell, C.J., Palmas, W., Penninx, B.W.J.H., Petersmann, A., Polasek, O., Psaty, B.M., Rantner, B., Rice, K., Rivadeneira, F., Rotter, J.I., Seldenrijk, A., Stadler, M., Summerer, M., Tanaka, T., Tybjaerg-Hansen, A., Uitterlinden, A. G., van Gilst, W.H., Vermeulen, S.H., Wild, S.H., Wild, P.S., Willeit, J., Zeller, T., Zemunik, T., Murabito, J.M., White, C.C., Kavousi, M., Sun, Y.V., Feitosa, M.F., Nambi, V., Lamina, C., Schillert, A., Coassin, S., Bis, J.C., Broer, L., Crawford, D.C., Franceschini, N., Frikke-Schmidt, R., Haun, M., Holewijn, S., Huffman, J.E., Hwang, S.J., Kiechl, S., Kollerits, B., Montasser, M.E., Nolte, I.M., Rudock, M.E., Senft, A., Teumer, A., van der Harst, P., Vitart, V., Waite, L.L., Wood, A.R., Wassel, C.L., Absher, D.M., Allison, M.A., Amin, N., Arnold, A., Asselbergs, F.W., Aulchenko, Y., Bandinelli, S., Barbalic, M., Boban, M., Brown-Gentry, K., Couper, D.J., Criqui, M.H., Dehghan, A., den Heijer, M., Dieplinger, B., Ding, J.Z., Dorr, M., Espinola-Klein, C., Felix, S.B., Ferrucci, L., Folsom, A.R., Fraedrich, G., Gibson, Q., Goodloe, R., Gunjaca, G., Haltmayer, M., Heiss, G., Hofman, A., Kieback, A., Kiemeney, L.A., Kolcic, I., Kullo, I.J., Kritchevsky, S.B., Lackner, K.J., Li, X.H., Lieb, W.G., Lohman, K., Meisinger, C., Melzer, D., Mohler, E.R., Mudnic, I., Mueller, T., Navis, G., Oberhollenzer, F., Olin, J.W., O'Connell, J., O'Donnell, C.J., Palmas, W., Penninx, B.W.J.H., Petersmann, A., Polasek, O., Psaty, B.M., Rantner, B., Rice, K., Rivadeneira, F., Rotter, J.I., Seldenrijk, A., Stadler, M., Summerer, M., Tanaka, T., Tybjaerg-Hansen, A., Uitterlinden, A. G., van Gilst, W.H., Vermeulen, S.H., Wild, S.H., Wild, P.S., Willeit, J., Zeller, T., and Zemunik, T.

Abstract

Background-Genetic determinants of peripheral arterial disease (PAD) remain largely unknown. To identify genetic variants associated with the ankle-brachial index (ABI), a noninvasive measure of PAD, we conducted a meta-analysis of genome-wide association study data from 21 population-based cohorts. Methods and Results-Continuous ABI and PAD (ABI <0.9) phenotypes adjusted for age and sex were examined. Each study conducted genotyping and imputed data to the <2.5 million single nucleotide polymorphisms (SNPs) in HapMap. Linear and logistic regression models were used to test each SNP for association with ABI and PAD using additive genetic models. Study-specific data were combined using fixed effects inverse variance weighted meta-analyses. There were a total of 41 692 participants of European ancestry (<60% women, mean ABI 1.02 to 1.19), including 3409 participants with PAD and with genome-wide association study data available. In the discovery meta-analysis, rs10757269 on chromosome 9 near CDKN2B had the strongest association with ABI (β-0.006, P=2.46×10-8). We sought replication of the 6 strongest SNP associations in 5 population-based studies and 3 clinical samples (n=16 717). The association for rs10757269 strengthened in the combined discovery and replication analysis (P=2.65×10-9). No other SNP associations for ABI or PAD achieved genome-wide significance. However, 2 previously reported candidate genes for PAD and 1 SNP associated with coronary artery disease were associated with ABI: DAB21P (rs13290547, P=3.6×10-5), CYBA (rs3794624, P=6.3×10-5), and rs1122608 (LDLR, P=0.0026). Conclusions-Genome-wide association studies in more than 40 000 individuals identified 1 genome wide significant association on chromosome 9p21 with ABI. Two candidate genes for PAD and 1 SNP for coronary artery disease are associated with ABI. © 2012 American Heart Association, Inc.

Published
2012
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13. Disclosing genetic risk for coronary heart disease: effects on perceived personal control and genetic counseling satisfaction.

Author

Robinson, C.L., Jouni, H., Kruisselbrink, T.M., Austin, E.E., Christensen, K.D., Green, R.C., and Kullo, I.J.

Subjects
GENETIC research, BIOLOGY, EMBRYOLOGY, CORONARY disease, HEART diseases
Abstract

We investigated whether disclosure of coronary heart disease ( CHD) genetic risk influences perceived personal control ( PPC) and genetic counseling satisfaction ( GCS). Participants ( n = 207, age: 45-65 years) were randomized to receive estimated 10-year risk of CHD based on a conventional risk score ( CRS) with or without a genetic risk score ( GRS). Risk estimates were disclosed by a genetic counselor who also reviewed how GRS altered risk in those randomized to CRS+ GRS. Each participant subsequently met with a physician and then completed surveys to assess PPC and GCS. Participants who received CRS+ GRS had higher PPC than those who received CRS alone although the absolute difference was small (25.2 ± 2.7 vs 24.1 ± 3.8, p = 0.04). A greater proportion of CRS+ GRS participants had higher GCS scores (17.3 ± 5.3 vs 15.9 ± 6.3, p = 0.06). In the CRS+ GRS group, PPC and GCS scores were not correlated with GRS. Within both groups, PPC and GCS scores were similar in patients with or without family history (p = NS). In conclusion, patients who received their genetic risk of CHD had higher PPC and tended to have higher GCS. Our findings suggest that disclosure of genetic risk of CHD together with conventional risk estimates is appreciated by patients. Whether this results in improved outcomes needs additional investigation. [ABSTRACT FROM AUTHOR]

Published
2016
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