Citations 14
Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms
Hof, Femke N. G. van ’t, Ruigrok, Ynte M. , Lee, Cue Hyunkyu , Ripke, Stephan , Anderson, Graig, Baas, Annette F., Blankensteijn, Jan D. , Böttinger, Erwin P. , Bown, Matthew J. , Broderick, Joseph P., Bijlenga, Philippe , Crosslin, David R. , Ebeling, Christian , Eriksson, Johan G. , Fornage, Myriam , Foroud, Tatiana, Fraunberg, Mikael von und zu , Friedrich, Christoph M. , Gaál, Emília Ilona, Gottesman, Omri, Guo, Dongchuan , Harrison, Seamus C. , Hernesniemi, Juha , Hofman, Albert , Inoue, Ituro , Jääskeläinen, Juha E. , Kiemeney, Lambertus A. , Kivisaari, Riku , Ko, Nerissa , Koskinen, Seppo, Kubo, Michiaki , Kuivaniemi, Helena , Laakso, Aki , Lehto, Hanna, LeMaire, Scott A. , Malinowski, Jennifer , McCarty, Catherine A. , Mosley, Thomas H., Nakaoka, Hirofumi , Peissig, Peggy , Pera, Joanna , Rasmussen‐Torvik, Laura J. , Ritchie, Marylyn D. , Rivadeneira, Fernando , Rij, André M. van , Santos‐Cortez, Regie Lyn P., Saratzis, Athanasios , Słowik, Agnieszka , Tromp, Gerard , Uitterlinden, André G. , Vermeulen, Sita H. , Wang, Gao T., Han, Buhm , Bakker, Paul I. W. de , Verissimo, Ana Raquel, Wright, Benjamin J., Bumpstead, Suzannah, Grétarsdóttir, Sólveig , Badger, Stephen A., Child, Anne H. , Cockerill, Gillian, Hafez, Hany , Futers, Simon , Sohrabi, Soroush, Smith, Alberto, Thompson, Matthew M., Bockxmeer, Frank M. van, Matthíasson, Stefán E., Þorsteinsdóttir, Unnur , Graaf, Jacqueline de , Palmen, Jutta, Lindholt, Jes S. , Bradley, Declan , Waltham, Matthew, Gwilliam, Rhian, Golledge, Jonathan , Eriksson, Per , Stefánsson, Kāri , Thompson, John R., Humphries, Steve E. , Sayers, Robert D., Deloukas, Panos , Samani, Nilesh J. , Phillip, L. Victoria, Hill, Geraldine B., Williams, Michael , Thomson, Ian , Krysa, Jo, Wilkins, Gerard , Vasudevan, Thodor M., Lewis, David R. , Blair, Ross D., Hill, Andrew A., de Andrade, Mariza, Ripke, Stephan , Andrade, Mariza de , Broderick, Joseph, Carrell, David , Crawford, Dana C. , Jones, Gregory T. , Kullo, Iftikhar J. , Kurki, Mitja , Lai, Dongbing , Leal, Suzanne M. , Low, Siew‐Kee , Milewicz, Dianna M. , Nakamura, Yusuke , Niemela, Mika R. , Pacheco, Jennifer A. , Takahashi, Atsushi , Verma, Shefali S. , Rinkel, Gabriël J.E. , Clough, Rachel E. , Scott, D. Julian A., Þorleifsson, Guðmar , Teijink, Joep A.W. , Wijmenga, Cisca , Smith, Andrew J P , Edkins, Sarah , Hunt, Sarah E , Potter, Simon , Norman, Paul E , Powell, Janet T. , Merriman, Tony Raymond , Kiemeney, Lambertus A , Bijlenga, Philippe , Lindholt, Jes Sanddal , Malinowski, Jennnifer , Rasmussen-Torvik, Laura J , Eriksson, Per
Journal of the American Heart Association | July 15, 2016 | Volume: 5 | Issue: 7 | Pages: No page available
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 SNP s 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 RBBP 8 to AAA (odds ratio [ OR ]=1.11; P =4.1×10 −5 ) and for TAA risk locus 15q21 near FBN 1 to AAA ( OR =1.07; P =1.1×10 −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.
The phenotypic legacy of admixture between modern humans and Neandertals
Simonti, Corinne , Vernot, Benjamin , Bastarache, Lisa , Böttinger, Erwin P. , Chisholm, Rex L. , Crosslin, David R. , Hebbring, Scott J. , Jarvik, Gail P. , Li, Rongling, Pathak, Jyotishman , Ritchie, Marylyn D. , Roden, Dan M. , Tromp, Gerard , Prato, Jeffrey D., Bush, William S. , Akey, Joshua M. , Denny, Joshua C. , Capra, John A. , Carrell, David , Kullo, Iftikhar J. , Roden, Dan M. , Verma, Shefali S. , Denny, Joshua C , Jarvik, Gail P , Bush, William S
Science | February 12, 2016 | Volume: 351 | Issue: 6274 | Pages: 737-741
The legacy of human-Neandertal interbreeding Non-African humans are estimated to have inherited on average 1.5 to 4% of their genomes from Neandertals. However, how this genetic legacy affects human traits is unknown. Simonti et al. combined genotyping data with electronic health records. Individual Neandertal alleles were correlated with clinically relevant phenotypes in individuals of European descent. These archaic genetic variants were associated with medical conditions affecting the skin, the blood, and the risk of depression. Science , this issue p. 737
Association of Arrhythmia-Related Genetic Variants With Phenotypes Documented in Electronic Medical Records
Stallings, Sarah , Bush, William S. , Gordon, Allan , Nickerson, Deborah A., Kim, Jerry H., Crosslin, David R. , Jarvik, Gail P. , Ralston, James D. , Larson, Eric B. , Bielinski, Suzette J. , Ye, Zi , Abul‐Husn, Noura S. , Scott, Stuart A. , Böttinger, Erwin P. , Almoguera, Berta , Connolly, John J. , Chiavacci, Rosetta, Håkonarson, Håkon , Rasmussen‐Torvik, Laura J. , Pan, Vivian , Smith, Maureen E. , Chisholm, Rex L. , Kitchner, Terrie, He, Max M. , Brilliant, Murray H. , Wallace, John R. , Doheny, Kimberly F. , Shoemaker, M. Benjamin , Li, Rongling, Manolio, Teri A. , Macaya, Daniela , Carey, David J. , Kapplinger, Jamie D., Ackerman, Michael J. , Ritchie, Marylyn D. , Denny, Joshua C. , Roden, Dan M. , Ralston, James D. , Driest, Sara L. Van , Wells, Quinn S. , Carrell, David , Olson, Janet E. , Kullo, Iftikhar J. , Persell, Stephen D. , Callis, Thomas E. , Williams, Marc S , Roden, Dan M. , Hakonarson, Hakon , Denny, Joshua C , Jarvik, Gail P , Bielinski, Suzette , Gordon, Adam S , Connolly, John J , Scott, Stuart A , Rasmussen-Torvik, Laura J , Bush, William S
JAMA | January 11, 2016 | Volume: 315 | Issue: 1 | Pages: 47
Biology‐Driven Gene‐Gene Interaction Analysis of Age‐Related Cataract in the eMERGE Network
Hall, Molly A. , Wallace, John R. , Lucas, Anastasia, Berg, Richard L. , Connolly, John J. , Crawford, Dana C. , Crosslin, David R. , Doheny, Kimberly F. , Haines, Jonathan L. , Harley, John B. , Jarvik, Gail P. , Kitchner, Terrie, Kuivaniemi, Helena , Larson, Eric B. , Tromp, Gerard , Vrabec, Tamara R. , Pendergrass, Sarah A. , McCarty, Catherine A. , Ritchie, Marylyn D. , de Andrade, Mariza, Verma, Shefali S. , Andrade, Mariza de , Carrell, David , Jarvik, Gail P , Connolly, John J
Genetic Epidemiology | May 18, 2015 | Volume: 39 | Issue: 5 | Pages: 376-384
ABSTRACTBioinformatics approaches to examine gene‐gene models provide a means to discover interactions between multiple genes that underlie complex disease. Extensive computational demands and adjusting for multiple testing make uncovering genetic interactions a challenge. Here, we address these issues using our knowledge‐driven filtering method, Biofilter, to identify putative single nucleotide polymorphism (SNP) interaction models for cataract susceptibility, thereby reducing the number of models for analysis. Models were evaluated in 3,377 European Americans (1,185 controls, 2,192 cases) from the Marshfield Clinic, a study site of the Electronic Medical Records and Genomics (eMERGE) Network, using logistic regression. All statistically significant models from the Marshfield Clinic were then evaluated in an independent dataset of 4,311 individuals (742 controls, 3,569 cases), using independent samples from additional study sites in the eMERGE Network: Mayo Clinic, Group Health/University of Washington, Vanderbilt University Medical Center, and Geisinger Health System. Eighty‐three SNP‐SNP models replicated in the independent dataset at likelihood ratio test P < 0.05. Among the most significant replicating models was rs12597188 (intron of CDH1)–rs11564445 (intron of CTNNB1). These genes are known to be involved in processes that include: cell‐to‐cell adhesion signaling, cell‐cell junction organization, and cell‐cell communication. Further Biofilter analysis of all replicating models revealed a number of common functions among the genes harboring the 83 replicating SNP‐SNP models, which included signal transduction and PI3K‐Akt signaling pathway. These findings demonstrate the utility of Biofilter as a biology‐driven method, applicable for any genome‐wide association study dataset.