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Your search keyword '"Jaquish, Cashell E"' showing total 19 results
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19 results on '"Jaquish, Cashell E"'

1. A System for Phenotype Harmonization in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) Program

Author

Stilp, Adrienne M, Emery, Leslie S, Broome, Jai G, Buth, Erin J, Khan, Alyna T, Laurie, Cecelia A, Wang, Fei Fei, Wong, Quenna, Chen, Dongquan, D'Augustine, Catherine M, Heard-Costa, Nancy L, Hohensee, Chancellor R, Johnson, William Craig, Juarez, Lucia D, Liu, Jingmin, Mutalik, Karen M, Raffield, Laura M, Wiggins, Kerri L, de Vries, Paul S, Kelly, Tanika N, Kooperberg, Charles, Natarajan, Pradeep, Peloso, Gina M, Peyser, Patricia A, Reiner, Alex P, Arnett, Donna K, Aslibekyan, Stella, Barnes, Kathleen C, Bielak, Lawrence F, Bis, Joshua C, Cade, Brian E, Chen, Ming-Huei, Correa, Adolfo, Cupples, L Adrienne, de Andrade, Mariza, Ellinor, Patrick T, Fornage, Myriam, Franceschini, Nora, Gan, Weiniu, Ganesh, Santhi K, Graffelman, Jan, Grove, Megan L, Guo, Xiuqing, Hawley, Nicola L, Hsu, Wan-Ling, Jackson, Rebecca D, Jaquish, Cashell E, Johnson, Andrew D, Kardia, Sharon LR, Kelly, Shannon, Lee, Jiwon, Mathias, Rasika A, McGarvey, Stephen T, Mitchell, Braxton D, Montasser, May E, Morrison, Alanna C, North, Kari E, Nouraie, Seyed Mehdi, Oelsner, Elizabeth C, Pankratz, Nathan, Rich, Stephen S, Rotter, Jerome I, Smith, Jennifer A, Taylor, Kent D, Vasan, Ramachandran S, Weeks, Daniel E, Weiss, Scott T, Wilson, Carla G, Yanek, Lisa R, Psaty, Bruce M, Heckbert, Susan R, and Laurie, Cathy C

Subjects
Epidemiology, Health Sciences, Human Genome, Lung, Genetics, Clinical Research, Cardiovascular, Good Health and Well Being, Data Aggregation, Genetic Association Studies, Humans, Information Dissemination, National Heart, Lung, and Blood Institute (U.S.), Phenomics, Phenotype, Precision Medicine, Program Evaluation, United States, cardiovascular disease, common data elements, hematologic disease, information dissemination, lung diseases, phenotypes, sleep-wake disorders, Mathematical Sciences, Medical and Health Sciences
Abstract

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948-2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Published
2021

2. Genomic integrity of human induced pluripotent stem cells across nine studies in the NHLBI NextGen program

Author

Kanchan, Kanika, Iyer, Kruthika, Yanek, Lisa R, Carcamo-Orive, Ivan, Taub, Margaret A, Malley, Claire, Baldwin, Kristin, Becker, Lewis C, Broeckel, Ulrich, Cheng, Linzhao, Cowan, Chad, D'Antonio, Matteo, Frazer, Kelly A, Quertermous, Thomas, Mostoslavsky, Gustavo, Murphy, George, Rabinovitch, Marlene, Rader, Daniel J, Steinberg, Martin H, Topol, Eric, Yang, Wenli, Knowles, Joshua W, Jaquish, Cashell E, Ruczinski, Ingo, and Mathias, Rasika A

Subjects
Biological Sciences, Genetics, Stem Cell Research - Induced Pluripotent Stem Cell - Human, Stem Cell Research - Induced Pluripotent Stem Cell, Biotechnology, Human Genome, Cancer, Stem Cell Research, 2.1 Biological and endogenous factors, Aetiology, Cell Differentiation, DNA Copy Number Variations, DNA-Binding Proteins, Genomic Instability, Genomics, Humans, Induced Pluripotent Stem Cells, National Heart, Lung, and Blood Institute (U.S.), United States, hiPSCs, Structural integrity, GWAS, VanillaICE, Oncogenes, Tumor suppressor genes, Medical and Health Sciences, Developmental Biology, Medical biotechnology, Oncology and carcinogenesis
Abstract

Human induced pluripotent stem cell (hiPSC) lines have previously been generated through the NHLBI sponsored NextGen program at nine individual study sites. Here, we examined the structural integrity of 506 hiPSC lines as determined by copy number variations (CNVs). We observed that 149 hiPSC lines acquired 258 CNVs relative to donor DNA. We identified six recurrent regions of CNVs on chromosomes 1, 2, 3, 16 and 20 that overlapped with cancer associated genes. Furthermore, the genes mapping to regions of acquired CNVs show an enrichment in cancer related biological processes (IL6 production) and signaling cascades (JNK cascade & NFκB cascade). The genomic region of instability on chr20 (chr20q11.2) includes transcriptomic signatures for cancer associated genes such as ID1, BCL2L1, TPX2, PDRG1 and HCK. Of these HCK shows statistically significant differential expression between carrier and non-carrier hiPSC lines. Overall, while a low level of genomic instability was observed in the NextGen generated hiPSC lines, the observation of structural instability in regions with known cancer associated genes substantiates the importance of systematic evaluation of genetic variations in hiPSCs before using them as disease/research models.

Published
2020

3. Analysis commons, a team approach to discovery in a big-data environment for genetic epidemiology

Author

Brody, Jennifer A, Morrison, Alanna C, Bis, Joshua C, O'Connell, Jeffrey R, Brown, Michael R, Huffman, Jennifer E, Ames, Darren C, Carroll, Andrew, Conomos, Matthew P, Gabriel, Stacey, Gibbs, Richard A, Gogarten, Stephanie M, Gupta, Namrata, Jaquish, Cashell E, Johnson, Andrew D, Lewis, Joshua P, Liu, Xiaoming, Manning, Alisa K, Papanicolaou, George J, Pitsillides, Achilleas N, Rice, Kenneth M, Salerno, William, Sitlani, Colleen M, Smith, Nicholas L, Heckbert, Susan R, Laurie, Cathy C, Mitchell, Braxton D, Vasan, Ramachandran S, Rich, Stephen S, Rotter, Jerome I, Wilson, James G, Boerwinkle, Eric, Psaty, Bruce M, and Cupples, L Adrienne

Subjects
Biological Sciences, Genetics, Biotechnology, Networking and Information Technology R&D (NITRD), Lung, Human Genome, 2.5 Research design and methodologies (aetiology), Aetiology, Generic health relevance, Good Health and Well Being, Big Data, Fibrinogen, Genetics, Population, Genome, Humans, Information Dissemination, Mobile Applications, Molecular Epidemiology, Regression Analysis, Software, Workflow, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, TOPMed Hematology and Hemostasis Working Group, CHARGE Analysis and Bioinformatics Working Group, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

The exploding volume of whole-genome sequence (WGS) and multi-omics data requires new approaches for analysis. As one solution, we have created a cloud-based Analysis Commons, which brings together genotype and phenotype data from multiple studies in a setting that is accessible by multiple investigators. This framework addresses many of the challenges of multi-center WGS analyses, including data sharing mechanisms, phenotype harmonization, integrated multi-omics analyses, annotation, and computational flexibility. In this setting, the computational pipeline facilitates a sequence-to-discovery analysis workflow illustrated here by an analysis of plasma fibrinogen levels in 3996 individuals from the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) WGS program. The Analysis Commons represents a novel model for transforming WGS resources from a massive quantity of phenotypic and genomic data into knowledge of the determinants of health and disease risk in diverse human populations.

Published
2017

6. The Framingham Heart Study, on its way to becoming the gold standard for Cardiovascular Genetic Epidemiology?

Author

Jaquish Cashell E

Subjects
Internal medicine, RC31-1245, Genetics, QH426-470
Abstract

Abstract The Framingham Heart Study, founded in 1948 to examine the epidemiology of cardiovascular disease in a small town outside of Boston, has become the worldwide standard for cardiovascular epidemiology. It is among the longest running, most comprehensively characterized multi-generational studies in the world. Such seminal findings as the effects of smoking and high cholesterol on heart disease came from the Framingham Heart Study. At the time of publication these were novel cardiovascular disease (CVD) risk factors, now they are the basis of treatment and prevention in the US. Is the Framingham study now on it's way to becoming the gold standard for genetic epidemiology of CVD? Will the novel genetic findings of today become the health care standards of tomorrow? The accompanying articles summarizing the results of genome-wide association studies (GWAS) give the reader a first glimpse into the possibilities.

Published
2007
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7. Sex and age specific effects of chromosomal regions linked to body mass index in the Framingham Study

Author

Fox Caroline S, Heard-Costa Nancy L, Atwood Larry D, Jaquish Cashell E, and Cupples L Adrienne

Subjects
Genetics, QH426-470
Abstract

Abstract Background Previously, we reported significant linkage of body mass index (BMI) to chromosomes 6 and 11 across six examinations, covering 28 years, of the Framingham Heart Study. These results were on all individuals available at each exam, thus the sample size varied from exam to exam. To remove any effect of sample size variation we have now constructed six subsets; for each exam individuals were only included if they were measured at every exam, i.e. for each exam, included individuals comprise the intersection of the original six exams. This strategy preferentially removed older individuals who died before reaching the sixth exam, thus the intersection datasets are smaller (n = 1114) and significantly younger than the full datasets. We performed variance components linkage analysis on these intersection datasets and on their sex-specific subsets. Results Results from the sex-specific genome scans revealed 11 regions in which a sex-specific maximum lodscore was at least 2.0 for at least one dataset. Randomization tests indicated that all 11 regions had significant (p < 0.05) differences in sex-specific maximum lodscores for at least three datasets. The strongest sex-specific linkage was for men on chromosome 16 with maximum lodscores 2.70, 3.00, 3.42, 3.61, 2.56 and 1.93 for datasets 1–6 respectively. Results from the full genome scans revealed that linked regions on chromosomes 6 and 11 remained significantly and consistently linked in the intersection datasets. Surprisingly, the maximum lodscore on chromosome 10 for dataset 1 increased from 0.97 in the older original dataset to 4.23 in the younger smaller intersection dataset. This difference in maximum lodscores was highly significant (p < 0.0001), implying that the effect of this chromosome may vary with age. Age effects may also exist for the linked regions on chromosomes 6 and 11. Conclusion Sex specific effects of chromosomal regions on BMI are common in the Framingham study. Some evidence also exists for age-specific effects of chromosomal regions.

Published
2006
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8. The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study

Author

Winkler, Thomas W, Justice, Anne E, Graff, Mariaelisa, Barata, Llilda, Feitosa, Mary F, Chu, Su, Czajkowski, Jacek, Esko, Tõnu, Fall, Tove, Kilpeläinen, Tuomas O, Lu, Yingchang, Mägi, Reedik, Mihailov, Evelin, Pers, Tune H, Rüeger, Sina, Teumer, Alexander, Ehret, Georg B, Ferreira, Teresa, Heard-Costa, Nancy L, Karjalainen, Juha, Lagou, Vasiliki, Mahajan, Anubha, Neinast, Michael D, Prokopenko, Inga, Simino, Jeannette, Teslovich, Tanya M, Jansen, Rick, Westra, Harm-Jan, White, Charles C, Absher, Devin, Ahluwalia, Tarunveer S, Ahmad, Shafqat, Albrecht, Eva, Alves, Alexessander Couto, Bragg-Gresham, Jennifer L, de Craen, Anton JM, Bis, Joshua C, Bonnefond, Amélie, Boucher, Gabrielle, Cadby, Gemma, Cheng, Yu-Ching, Chiang, Charleston WK, Delgado, Graciela, Demirkan, Ayse, Dueker, Nicole, Eklund, Niina, Eiriksdottir, Gudny, Eriksson, Joel, Feenstra, Bjarke, Fischer, Krista, Frau, Francesca, Galesloot, Tessel E, Geller, Frank, Goel, Anuj, Gorski, Mathias, Grammer, Tanja B, Gustafsson, Stefan, Haitjema, Saskia, Hottenga, Jouke-Jan, Huffman, Jennifer E, Jackson, Anne U, Jacobs, Kevin B, Johansson, Åsa, Kaakinen, Marika, Kleber, Marcus E, Lahti, Jari, Mateo Leach, Irene, Lehne, Benjamin, Liu, Youfang, Lo, Ken Sin, Lorentzon, Mattias, Luan, Jian'an, Madden, Pamela AF, Mangino, Massimo, McKnight, Barbara, Medina-Gomez, Carolina, Monda, Keri L, Montasser, May E, Müller, Gabriele, Müller-Nurasyid, Martina, Nolte, Ilja M, Panoutsopoulou, Kalliope, Pascoe, Laura, Paternoster, Lavinia, Rayner, Nigel W, Renström, Frida, Rizzi, Federica, Rose, Lynda M, Ryan, Kathy A, Salo, Perttu, Sanna, Serena, Scharnagl, Hubert, Shi, Jianxin, Smith, Albert Vernon, Southam, Lorraine, Stančáková, Alena, Steinthorsdottir, Valgerdur, Strawbridge, Rona J, Sung, Yun Ju, Tachmazidou, Ioanna, Tanaka, Toshiko, Thorleifsson, Gudmar, Trompet, Stella, Pervjakova, Natalia, Tyrer, Jonathan P, Vandenput, Liesbeth, van der Laan, Sander W, van der Velde, Nathalie, van Setten, Jessica, van Vliet-Ostaptchouk, Jana V, Verweij, Niek, Vlachopoulou, Efthymia, Waite, Lindsay L, Wang, Sophie R, Wang, Zhaoming, Wild, Sarah H, Willenborg, Christina, Wilson, James F, Wong, Andrew, Yang, Jian, Yengo, Loïc, Yerges-Armstrong, Laura M, Yu, Lei, Zhang, Weihua, Zhao, Jing Hua, Andersson, Ehm A, Bakker, Stephan JL, Baldassarre, Damiano, Banasik, Karina, Barcella, Matteo, Barlassina, Cristina, Bellis, Claire, Benaglio, Paola, Blangero, John, Blüher, Matthias, Bonnet, Fabrice, Bonnycastle, Lori L, Boyd, Heather A, Bruinenberg, Marcel, Buchman, Aron S, Campbell, Harry, Chen, Yii-Der Ida, Chines, Peter S, Claudi-Boehm, Simone, Cole, John, Collins, Francis S, de Geus, Eco JC, de Groot, Lisette CPGM, Dimitriou, Maria, Duan, Jubao, Enroth, Stefan, Eury, Elodie, Farmaki, Aliki-Eleni, Forouhi, Nita G, Friedrich, Nele, Gejman, Pablo V, Gigante, Bruna, Glorioso, Nicola, Go, Alan S, Gottesman, Omri, Gräßler, Jürgen, Grallert, Harald, Grarup, Niels, Gu, Yu-Mei, Broer, Linda, Ham, Annelies C, Hansen, Torben, Harris, Tamara B, Hartman, Catharina A, Hassinen, Maija, Hastie, Nicholas, Hattersley, Andrew T, Heath, Andrew C, Henders, Anjali K, Hernandez, Dena, Hillege, Hans, Holmen, Oddgeir, Hovingh, Kees G, Hui, Jennie, Husemoen, Lise L, Hutri-Kähönen, Nina, Hysi, Pirro G, Illig, Thomas, De Jager, Philip L, Jalilzadeh, Shapour, Jørgensen, Torben, Jukema, J Wouter, Juonala, Markus, Kanoni, Stavroula, Karaleftheri, Maria, Khaw, Kay Tee, Kinnunen, Leena, Kittner, Steven J, Koenig, Wolfgang, Kolcic, Ivana, Kovacs, Peter, Krarup, Nikolaj T, Kratzer, Wolfgang, Krüger, Janine, Kuh, Diana, Kumari, Meena, Kyriakou, Theodosios, Langenberg, Claudia, Lannfelt, Lars, Lanzani, Chiara, Lotay, Vaneet, Launer, Lenore J, Leander, Karin, Lindström, Jaana, Linneberg, Allan, Liu, Yan-Ping, Lobbens, Stéphane, Luben, Robert, Lyssenko, Valeriya, Männistö, Satu, Magnusson, Patrik K, McArdle, Wendy L, Menni, Cristina, Merger, Sigrun, Milani, Lili, Montgomery, Grant W, Morris, Andrew P, Narisu, Narisu, Nelis, Mari, Ong, Ken K, Palotie, Aarno, Pérusse, Louis, Pichler, Irene, Pilia, Maria G, Pouta, Anneli, Rheinberger, Myriam, Ribel-Madsen, Rasmus, Richards, Marcus, Rice, Kenneth M, Rice, Treva K, Rivolta, Carlo, Salomaa, Veikko, Sanders, Alan R, Sarzynski, Mark A, Scholtens, Salome, Scott, Robert A, Scott, William R, Sebert, Sylvain, Sengupta, Sebanti, Sennblad, Bengt, Seufferlein, Thomas, Silveira, Angela, Slagboom, P Eline, Smit, Jan H, Sparsø, Thomas H, Stirrups, Kathleen, Stolk, Ronald P, Stringham, Heather M, Swertz, Morris A, Swift, Amy J, Syvänen, Ann-Christine, Tan, Sian-Tsung, Thorand, Barbara, Tönjes, Anke, Tremblay, Angelo, Tsafantakis, Emmanouil, van der Most, Peter J, Völker, Uwe, Vohl, Marie-Claude, Vonk, Judith M, Waldenberger, Melanie, Walker, Ryan W, Wennauer, Roman, Widén, Elisabeth, Willemsen, Gonneke, Wilsgaard, Tom, Wright, Alan F, Zillikens, M Carola, van Dijk, Suzanne C, van Schoor, Natasja M, Asselbergs, Folkert W, de Bakker, Paul IW, Beckmann, Jacques S, Beilby, John, Bennett, David A, Bergman, Richard N, Bergmann, Sven, Böger, Carsten A, Boehm, Bernhard O, Boerwinkle, Eric, Boomsma, Dorret I, Bornstein, Stefan R, Bottinger, Erwin P, Bouchard, Claude, Chambers, John C, Chanock, Stephen J, Chasman, Daniel I, Cucca, Francesco, Cusi, Daniele, Dedoussis, George, Erdmann, Jeanette, Eriksson, Johan G, Evans, Denis A, de Faire, Ulf, Farrall, Martin, Ferrucci, Luigi, Ford, Ian, Franke, Lude, Franks, Paul W, Froguel, Philippe, Gansevoort, Ron T, Gieger, Christian, Grönberg, Henrik, Gudnason, Vilmundur, Gyllensten, Ulf, Hall, Per, Hamsten, Anders, van der Harst, Pim, Hayward, Caroline, Heliövaara, Markku, Hengstenberg, Christian, Hicks, Andrew A, Hingorani, Aroon, Hofman, Albert, Hu, Frank, Huikuri, Heikki V, Hveem, Kristian, James, Alan L, Jordan, Joanne M, Jula, Antti, Kähönen, Mika, Kajantie, Eero, Kathiresan, Sekar, Kiemeney, Lambertus ALM, Kivimaki, Mika, Knekt, Paul B, Koistinen, Heikki A, Kooner, Jaspal S, Koskinen, Seppo, Kuusisto, Johanna, Maerz, Winfried, Martin, Nicholas G, Laakso, Markku, Lakka, Timo A, Lehtimäki, Terho, Lettre, Guillaume, Levinson, Douglas F, Lind, Lars, Lokki, Marja-Liisa, Mäntyselkä, Pekka, Melbye, Mads, Metspalu, Andres, Mitchell, Braxton D, Moll, Frans L, Murray, Jeffrey C, Musk, Arthur W, Nieminen, Markku S, Njølstad, Inger, Ohlsson, Claes, Oldehinkel, Albertine J, Oostra, Ben A, Palmer, Lyle J, Pankow, James S, Pasterkamp, Gerard, Pedersen, Nancy L, Pedersen, Oluf, Penninx, Brenda W, Perola, Markus, Peters, Annette, Polašek, Ozren, Pramstaller, Peter P, Psaty, Bruce M, Qi, Lu, Quertermous, Thomas, Raitakari, Olli T, Rankinen, Tuomo, Rauramaa, Rainer, Ridker, Paul M, Rioux, John D, Rivadeneira, Fernando, Rotter, Jerome I, Rudan, Igor, den Ruijter, Hester M, Saltevo, Juha, Sattar, Naveed, Schunkert, Heribert, Schwarz, Peter EH, Shuldiner, Alan R, Sinisalo, Juha, Snieder, Harold, Sørensen, Thorkild IA, Spector, Tim D, Staessen, Jan A, Stefania, Bandinelli, Thorsteinsdottir, Unnur, Stumvoll, Michael, Tardif, Jean-Claude, Tremoli, Elena, Tuomilehto, Jaakko, Uitterlinden, André G, Uusitupa, Matti, Verbeek, André LM, Vermeulen, Sita H, Viikari, Jorma S, Vitart, Veronique, Völzke, Henry, Vollenweider, Peter, Waeber, Gérard, Walker, Mark, Wallaschofski, Henri, Wareham, Nicholas J, Watkins, Hugh, Zeggini, Eleftheria, arcOGEN Consortium, CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Chakravarti, Aravinda, Clegg, Deborah J, Cupples, L Adrienne, Gordon-Larsen, Penny, Jaquish, Cashell E, Rao, DC, Abecasis, Goncalo R, Assimes, Themistocles L, Barroso, Inês, Berndt, Sonja I, Boehnke, Michael, Deloukas, Panos, Fox, Caroline S, Groop, Leif C, Hunter, David J, Ingelsson, Erik, Kaplan, Robert C, McCarthy, Mark I, Mohlke, Karen L, O'Connell, Jeffrey R, Schlessinger, David, Strachan, David P, Stefansson, Kari, van Duijn, Cornelia M, Hirschhorn, Joel N, Lindgren, Cecilia M, Heid, Iris M, North, Kari E, Borecki, Ingrid B, Kutalik, Zoltán, Loos, Ruth JF, Division of Statistical Genomics, Washington University School of Medicine, Institut Pasteur du Cambodge, Réseau International des Instituts Pasteur (RIIP), Estonian Genome and Medicine, University of Tartu, Institute of Molecular and Cell Biology, Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], Institute of Metabolic Science, MRC, Brown University, Center for Biological Sequence Analysis [Lyngby], Technical University of Denmark [Lyngby] (DTU), King‘s College London, Groningen Bioinformatics Centre, GBB, University of Groningen [Groningen], University of Queensland [Brisbane], National Institut of Food Science and Technology, University of Agriculture, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, Department of neurology, Leiden University Medical Center (LUMC), University of Washington [Seattle], Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (GI3M), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Genetic Epidemiology Unit, Erasmus University Medical Center [Rotterdam] (Erasmus MC), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Limnology, Ecology, Department of Ecology and Evolutionary Biology, University of California, Core Genotyping Facility, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH)-National Institutes of Health [Bethesda] (NIH), Division of Cancer Epidemiology and Genetics, Institute of Health Sciences and Biocenter Oulu, University of Oulu, Department of Chemical Engineering Taiwan (DCET - NTHU), National Tsing Hua University [Hsinchu] (NTHU), University of Oxford [Oxford], Department of Epidemiology, University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Human Genetics, The Wellcome Trust Sanger Institute [Cambridge], Departments of Epidemiology and Nutrition, Harvard School of Public Health, Department of Clinical Sciences, Lund University [Lund]-Lund University Diabetes Centre, Genetic Epidemiology and Clinical Research Group, Umea University Hospital, Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Department of Medical Sciences, Department of Educational Psychology and Counseling, National Taiwan Normal University (NTNU), Laboratory for Cardiovascular Genomics and Informatics [Yokohama] (RIKEN IMS), RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN)-RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), deCODE Genetics, deCODE genetics [Reykjavik], Interuniversity Cardiology Institute Netherlands, Molecular Genetics Section, University of Groningen [Groningen]-University Medical Centre Groningen, Kidney Center, University Medical Center Groningen [Groningen] (UMCG), The University of Texas Health Science Center at Houston (UTHealth), Texas Biomedical Research Institute [San Antonio, TX], Medical Department III, Universität Leipzig [Leipzig], Foie, métabolismes et cancer, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], GNS Science, Blackett Laboratory, Imperial College London, Medstar Research Institute, University of Rochester [USA], MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, Institute of Epidemiology [Neuherberg] (EPI), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Novo Nordisk Foundation Center for Basic Metabolic Research (CBMR), Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU), Dept Biochem & Mol Biol, Pennsylvania State University (Penn State), Penn State System-Penn State System, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Epidemiology, University Medical Centre Groningen, Unit for Molecular Epidemiology, German Research Center for Environmental Health-Helmholtz-Zentrum München (HZM), Program in Translational NeuroPsychiatric Genomics, Brigham and Women's Hospital [Boston], Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts, USA, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston]-Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Research Centre for Prevention and Health (RCPH), Department of Public Health [Copenhagen], University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (KU)-University of Copenhagen = Københavns Universitet (KU)-Capital Region of Denmark, University of Copenhagen = Københavns Universitet (KU), Faculty of Medicine, Institute of Public Health, Aalborg University [Denmark] (AAU), Department of Nutrition-Dietetics, Harokopio University of Athens, Department of Medical Statistics, Epidemiology and Medical Informatics, University of Zagreb, MRC National Survey of Health and Development, MRC Unit for Lifelong Health and Ageing, Department of Epidemiology and Public Health, University College of London [London] (UCL), Department of Public health and Caring Sciences, Rudbeck Laboratory, Uppsala University, Uppsala, Sweden, Research Center for Prevention and Health, Glostrup Hospital, Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Cambridge Institute of Public Health, University of Cambridge [UK] (CAM), Chronic Disease Epidemiology and Prevention Unit, National Institute for Health and Welfare [Helsinki], Queensland Institute of Medical Research, Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Head of Medical Sequencing, Department of Biostatistics, University of Washington, Department of Chronic Disease Prevention, Molecular Epidemiology, Department of Genetics, Université Laval [Québec] (ULaval), Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, Netherlands Genomics Initiative, Netherlands Consortium for Healthy Aging [Leiden, Netherlands] (NCHA), Department of Internal Medicine, Université de Lausanne (UNIL), Carl Gustav Carus University Hospital, Loughborough University, Genomics and Bioinformatics Platform, Fondazione Filarete, Department of Medicine, Surgery, and Dentistry, University of Milano, Medizinische Klinik II, Universität zu Lübeck [Lübeck], Department of Genomic Medicine, University of Manchester [Manchester], The Wellcome Trust Centre for Human Genetics [Oxford], National Institutes of Health [Bethesda] (NIH), Department of Nephrology, University Medical Center, University of Groningen, Helmholtz-Zentrum München (HZM), Icelandic Heart Association, Kopavogur, Iceland., Faculty of Medicine, University of Iceland [Reykjavik], Genetics and Pathology, Department of child and adolescent psychiatry, Universität Duisburg-Essen [Essen], Department of Internal Medicine II, Division of Respirology, University of Regensburg, Regensburg, Germany, Universität Regensburg (UR), Franz-Volhard-Centrum für Klinische Forschung, ECRC, Department of Clinical Physiology, University of Tampere [Finland]-Tampere University Hospital, Finnish Institute of Occupational Health of Helsinki, Department of Clinical Chemistry, Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Department of Oncology, University of Tampere Medical School, University of Tampere, Department of Physiology, University of Eastern Finland-Institute of Biomedicine, Department of Medicine, Montreal, Developmental Brain and Behaviour Unit, University of Southampton, Division of Gastroenterology and Hepatology, Department of Immunology, Mayo Clinic, Centre for Bone and Arthritis Research, Institute of Medicine-University of Gothenburg (GU), Interdisciplinary Center for Psychiatric Epidemiology, Experimental Cardiology Laboratory, University Medical Center [Utrecht], Peter MacCallum Cancer Center, Faculty of Health Sciences, Aarhus University [Aarhus], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Department of Health Services, University of Washington, Group Health Research Institute, Group Health Cooperative, Department of Epidemiology, University of Washington, Department of Medicine, University of Washington, Cardiovascular Health Research Unit, University of Washington, The Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku University Hospital (TYKS), Erasmus Medical Centre, Cedars-Sinai Medical Center, Department of Pathological Biochemistry, Royal Infirmary, Unit of Genetic Epidemiology and Bioinformatics, Department of Epidemiology, University Medical Center Groningen, Department of Pediatrics, Augusta University - Medical College of Georgia, University System of Georgia (USG)-University System of Georgia (USG), Institute of Preventive Medicine, Copenhagen University Hospital-Bispebjerg and Frederiksberg Hospitals, Maastricht University [Maastricht], Department of Public Health, South Ostrobothnia Central Hospital, Department of Clinical and Preventive Medicine, Danube-University Krems, Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands, Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Department of Epidemiology & Biostatistics, Radboud University Medical Center [Nijmegen], Institute for Community Medicine, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), MRC epidemiology Unit, Framingham Heart Study, National Heart, Lung, and Blood Institute [Bethesda] (NHLBI)-Boston University [Boston] (BU), Metabolic Disease Group, University of Cambridge Metabolic Research Laboratories, Department of Genetics and Biotechnology, Wellcome Trust, Divisions of Genetics and Endocrinology and Program in Genomics, Boston Children's Hospital, Metabolism Initiative and Program in Medical and Population Genetics, Endocrinology and Metabolism, The Churchill Hospital-Oxford Centre for Diabetes, Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, University of North Carolina System (UNC)-University of North Carolina System (UNC)-UNC Gillings School of Global Public Health-Carolina Center for Genome Sciences, Department of Medical Genetics, Epidemiology Unit, Addenbrooke's Hospital-Medical Research Council (MRC), P30 AG010161/AG/NIA NIH HHS/United States, Psychiatry, Epidemiology and Data Science, NCA - Neurobiology of mental health, EMGO - Lifestyle, overweight and diabetes, APH - Amsterdam Public Health, AMS - Amsterdam Movement Sciences, Geriatrics, ACS - Amsterdam Cardiovascular Sciences, Vascular Medicine, Other departments, Biological Psychology, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, EMGO+ - Lifestyle, Overweight and Diabetes, RS: CARIM - R3 - Vascular biology, MUMC+: DA BV AIOS Radiologie (9), Epidemiologie, Orthopedie, Institute for Molecular Medicine Finland, Helsinki Collegium for Advanced Studies, Behavioural Sciences, University of Helsinki, Transplantation Laboratory, Medicum, Research Programs Unit, Research Programme of Molecular Medicine, Aarno Palotie / Principal Investigator, Elisabeth Ingrid Maria Widen / Principal Investigator, Clinicum, Johan Eriksson / Principal Investigator, Department of General Practice and Primary Health Care, Children's Hospital, Lastentautien yksikkö, Marja-Liisa Lokki / Principal Investigator, Kardiologian yksikkö, Leif Groop Research Group, Quantitative Genetics, Developmental Psychology Research Group, Genomics of Neurological and Neuropsychiatric Disorders, Genomic Discoveries and Clinical Translation, Ehret, Georg Benedikt, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Universiteit Leiden-Universiteit Leiden, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), University of California (UC), University of Oxford, Lund University [Lund], Universität Leipzig, Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH), Helmholtz Zentrum München = German Research Center for Environmental Health, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Faculty of Health and Medical Sciences, University of Copenhagen = Københavns Universitet (UCPH)-University of Copenhagen = Københavns Universitet (UCPH)-Capital Region of Denmark, University of Copenhagen = Københavns Universitet (UCPH), Université de Lausanne = University of Lausanne (UNIL), Universität zu Lübeck = University of Lübeck [Lübeck], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of Gothenburg (GU)-Institute of Medicine, Boston University [Boston] (BU)-National Heart, Lung, and Blood Institute [Bethesda] (NHLBI), Luan, Jian'an [0000-0003-3137-6337], Tyrer, Jonathan [0000-0003-3724-4757], Forouhi, Nita [0000-0002-5041-248X], Khaw, Kay-Tee [0000-0002-8802-2903], Langenberg, Claudia [0000-0002-5017-7344], Luben, Robert [0000-0002-5088-6343], Ong, Kenneth [0000-0003-4689-7530], Johnson, Kathleen [0000-0002-6823-3252], Wareham, Nicholas [0000-0003-1422-2993], Barroso, Ines [0000-0001-5800-4520], Apollo - University of Cambridge Repository, CHARGE Consortium, DIAGRAM Consortium, GLGC Consortium, Global-BPGen Consortium, ICBP Consortium, MAGIC Consortium, Biochemistry, Surgery, Internal Medicine, Public Health, Medical Oncology, Pathology, Erasmus MC other, Child and Adolescent Psychiatry / Psychology, and Clinical Genetics

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Male, Cancer Research, endocrine system diseases, Biological pathways, QH426-470, Genome, Body Mass Index, Body Size, Genetics (clinical), ddc:616, Genetics & Heredity, Sex Characteristics, Loci, MAGIC Consortium, Mass index, Age Factors, Chromosome Mapping, Middle Aged, Genealogy, Self-reported height, Peripheral-blood, Scale (social sciences), ICBP Consortium, Female, Life Sciences & Biomedicine, Medical Genetics, Research Article, arcOGEN Consortium, musculoskeletal diseases, Adult, European Continental Ancestry Group, Natural menopause, Aged, Body Size/genetics, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Polymorphism, Single Nucleotide, Waist-Hip Ratio, Biology, Body size, DIAGRAM Consortium, Age and sex, White People, Fat distribution, GLGC Consortium, 03 medical and health sciences, Life-course, Genetics, Weight-gain, Molecular Biology, Ecology, Evolution, Behavior and Systematics, Medicinsk genetik, [SDV.GEN]Life Sciences [q-bio]/Genetics, 0604 Genetics, Science & Technology, nutritional and metabolic diseases, Correction, 030104 developmental biology, GWAS meta-analysis, Global-BPGen Consortium, Common SNPS, CHARGE Consortium, 3111 Biomedicine, Developmental Biology
Abstract

Genome-wide association studies (GWAS) have identified more than 100 genetic variants contributing to BMI, a measure of body size, or waist-to-hip ratio (adjusted for BMI, WHRadjBMI), a measure of body shape. Body size and shape change as people grow older and these changes differ substantially between men and women. To systematically screen for age- and/or sex-specific effects of genetic variants on BMI and WHRadjBMI, we performed meta-analyses of 114 studies (up to 320,485 individuals of European descent) with genome-wide chip and/or Metabochip data by the Genetic Investigation of Anthropometric Traits (GIANT) Consortium. Each study tested the association of up to ~2.8M SNPs with BMI and WHRadjBMI in four strata (men ≤50y, men >50y, women ≤50y, women >50y) and summary statistics were combined in stratum-specific meta-analyses. We then screened for variants that showed age-specific effects (G x AGE), sex-specific effects (G x SEX) or age-specific effects that differed between men and women (G x AGE x SEX). For BMI, we identified 15 loci (11 previously established for main effects, four novel) that showed significant (FDR, Author Summary Adult body size and body shape differ substantially between men and women and change over time. More than 100 genetic variants that influence body mass index (measure of body size) or waist-to-hip ratio (measure of body shape) have been identified. While there is evidence that some genetic loci affect body shape differently in men than in women, little is known about whether genetic effects differ in older compared to younger adults, and whether such changes differ between men and women. Therefore, we conducted a systematic genome-wide search, including 114 studies (>320,000 individuals), to specifically identify genetic loci with age- and or sex-dependent effects on body size and shape. We identified 15 loci of which the effect on BMI was different in older compared to younger adults, whereas we found no evidence for loci with different effects in men compared to women. The opposite was seen for body shape as we identified 44 loci of which the effect on waist-to-hip ratio differed between men and women, but no difference between younger and older adults were observed. Our observations may provide new insights into the biology that underlies weight change with age or the sexual dimorphism of body shape.

Published
2014

9. Variation in Genes that Regulate Blood Pressure Are Associated with Glomerular Filtration Rate in Chinese.

Author

Montasser, May E., Shimmin, Lawrence C., Gu, Dongfeng, Chen, Jing, Gu, Charles, Kelly, Tanika N., Jaquish, Cashell E., Rice, Treva K., Rao, Dabeeru C., Cao, Jie, Chen, Jichun, Liu, De-Pei, Whelton, Paul K., Hamm, Lotuce Lee, He, Jiang, and Hixson, James E.

Subjects
REGULATION of blood pressure, GLOMERULAR filtration rate, HEALTH of Chinese people, HUMAN genetic variation, CHRONIC kidney failure, DIABETES, HYPERTENSION, IMMUNOLOGIC diseases
Abstract

Chronic kidney disease (CKD) can be a consequence of diabetes, hypertension, immunologic disorders, and other exposures, as well as genetic factors that are still largely unknown. Glomerular filtration rate (GFR), which is widely used to measure kidney function, has a heritability ranging from 25% to 75%, but only 1.5% of this heritability is explained by genetic loci that have been identified to date. In this study we tested for associations between GFR and 234 SNPs in 26 genes from pathways of blood pressure regulation in 3,025 rural Chinese participants of the “Genetic Epidemiology Network of Salt Sensitivity” (GenSalt) study. We estimated GFR (eGFR) using baseline serum creatinine measurements obtained prior to dietary intervention. We identified significant associations between eGFR and 12 SNPs in 6 genes (ACE, ADD1, AGT, GRK4, HSD11B1, and SCNN1G). The cumulative effect of the protective alleles was an increase in mean eGFR of 4 mL/min per 1.73 m2, while the cumulative effect of the risk alleles was a decrease in mean eGFR of 3 mL/min per 1.73 m2. In addition, we identified a significant interaction between SNPs in CYP11B1 and ADRB2. We have identified common variants in genes from pathways that regulate blood pressure and influence kidney function as measured by eGFR, providing new insights into the genetic determinants of kidney function. Complex genetic effects on kidney function likely involve interactions among genes as we observed for CYP11B1 and ADRB2. [ABSTRACT FROM AUTHOR]

Published
2014
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11. Interactions of Genetic Variants With Physical Activity Are Associated With Blood Pressure in Chinese: The GenSalt Study.

Author

Montasser, May E., Donfeng Gu, Jing Chen, Shimmin, Lawrence C., Gu, Charles, Kelly, Tanika N., Jaquish, Cashell E., Rice, Treva, Rao, Dabeeru C., Jie Cao, Jichun Chen, Liu, De-pei, Whelton, Paul K., Jiang He, and Hixson, James E.

Subjects
HUMAN genetic variation, BLOOD pressure, HYPERTENSION genetics, PHYSICAL activity, HOMEOSTASIS, EPIDEMIOLOGY
Abstract

BackgroundBlood pressure (BP) homeostasis involves complex interactions among genetic and nongenetic factors, providing major challenges to dissection of the genetic components that influence BP and hypertension. In this study, we examine the effects of interaction of genetic variants with physical activity on BP in a relatively genetically homogenous cohort of rural Chinese villagers.MethodsGeneralized estimating equations analysis was used to test for associations of systolic blood pressure (SBP) and diastolic blood pressure (DBP) with variants in 24 genes in BP pathways (196 single-nucleotide polymorphisms (SNPs)) among 3,142 Chinese participants divided according to physical activity (active vs. inactive groups).ResultsIn the physically active group, two SNPs in NR3C2 were significantly associated with lower SBP, and a SNP in SCNN1B was significantly associated with lower SBP and DBP. In the physically inactive group, a SNP in APLNR was associated with lower SBP, a SNP in GNB3 (guanine nucleotide binding protein, β polypeptide 3) was associated with higher SBP and DBP, and a SNP in BDKRB2 (bradykinin receptor B2) was associated with lower DBP. Cumulative effects in carriers of minor alleles of these SNPs showed reductions of SBP and DBP as large as 8 and 5 mm Hg, respectively, in the active individuals compared to inactive individuals carrying the same number of minor alleles.ConclusionsWe found that physical activity modifies the effects of genetic variants on BP. However, our results also show that active individuals with specific genotypes always have lower BP than inactive individuals with the same genotypes, demonstrating the overall beneficial effects of physical activity on BP.American Journal of Hypertension (2011). doi:10.1038/ajh.2011.97 [ABSTRACT FROM AUTHOR]

Published
2011
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12. Genome-Wide Linkage and Positional Candidate Gene Study of Blood Pressure Response to Dietary Potassium Intervention.

Author

Kelly, Tanika N., Hixson, James E., Rao, Dabeeru C., Hao Mei, Rice, Treva K., Jaquish, Cashell E., Shimmin, Lawrence C., Schwander, Karen, Chung-Shuian Chen, Depei Liu, Jichun Chen, Bormans, Concetta, Shukla, Pramila, Farhana, Naveed, Stuart, Colin, Whelton, Paul K., Jiang He, and Dongfeng Gu

Subjects
GENETIC epidemiology, POTASSIUM in the body, BLOOD pressure, HYPERTENSION, INFECTIOUS disease transmission
Abstract

The article presents information on the Genetic Epidemiology Network of Salt Sensitivity (GenSalt) study. The study shows that increase in potassium intake reduces blood pressure (BP) among hypertensive and normotensive people. According to the author genetic variant AGTR1 gene identified with BP responses to potassium intake.

Published
2010
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13. Genome-wide linkage scans for loci affecting total cholesterol, HDL-C, and triglycerides: the Family Blood Pressure Program.

Author

Bielinski, Suzette J., Tang, Weihong, Pankow, James S., Miller, Michael B., Mosley, Thomas H., Boerwinkle, Eric, Olshen, Richard A., Curb, J. David, Jaquish, Cashell E., Rao, D. C., Weder, Alan, and Arnett, Donna K.

Subjects
CARDIOVASCULAR diseases, ATHEROSCLEROSIS, CORONARY disease, LIPID metabolism disorders, HUMAN genome, DIAGNOSIS, GENETICS
Abstract

Atherosclerosis accounts for 75% of all deaths from cardiovascular disease and includes coronary heart disease (CHD), stroke, and other diseases of the arteries. More than half of all CHD is attributable to abnormalities in levels and metabolism of lipids. To locate genes that affect total cholesterol, high density lipoprotein cholesterol (HDL-C), and triglycerides, genome-wide linkage scans for quantitative trait loci were performed using variance components methods as implemented in SOLAR on a large diverse sample recruited as part of the Family Blood Pressure Program. Phenotype and genetic marker data were available for 9,299 subjects in 2,953 families for total cholesterol, 8,668 subjects in 2,736 families for HDL, and 7,760 subjects in 2,499 families for triglycerides. Mean lipid levels were adjusted for the effects of sex, age, age2, age-by-sex interaction, body mass index, smoking status, and field center. HDL-C and triglycerides were further adjusted for average total alcoholic drinks per week and estrogen use. Significant linkage was found for total cholesterol on chromosome 2 (LOD = 3.1 at 43 cM) in Hispanics and for HDL-C on chromosome 3 (LOD = 3.0 at 182 cM) and 12 (LOD = 3.5 at 124 cM) in Asians. In addition, there were 13 regions that showed suggestive linkage (LOD ≥ 2.0); 7 for total cholesterol, 4 for HDL, and 2 for triglycerides. The identification of these loci affecting lipid phenotypes and the apparent congruence with previous linkage results provides increased support that these regions contain genes influencing lipid levels. [ABSTRACT FROM AUTHOR]

Published
2006
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14. Sex and age specific effects of chromosomal regions linked to body mass index in the Framingham Study.

Author

Atwood, Larry D, Heard-Costa, Nancy L, Fox, Caroline S, Jaquish, Cashell E, and Cupples, L Adrienne

Subjects
BODY mass index, GENETICS, GENOMICS, CHROMOSOMES, HUMAN body composition
Abstract

Background: Previously, we reported significant linkage of body mass index (BMI) to chromosomes 6 and 11 across six examinations, covering 28 years, of the Framingham Heart Study. These results were on all individuals available at each exam, thus the sample size varied from exam to exam. To remove any effect of sample size variation we have now constructed six subsets; for each exam individuals were only included if they were measured at every exam, i.e. for each exam, included individuals comprise the intersection of the original six exams. This strategy preferentially removed older individuals who died before reaching the sixth exam, thus the intersection datasets are smaller (n = 1114) and significantly younger than the full datasets. We performed variance components linkage analysis on these intersection datasets and on their sex-specific subsets. Results: Results from the sex-specific genome scans revealed 11 regions in which a sex-specific maximum lodscore was at least 2.0 for at least one dataset. Randomization tests indicated that all 11 regions had significant (p < 0.05) differences in sex-specific maximum lodscores for at least three datasets. The strongest sex-specific linkage was for men on chromosome 16 with maximum lodscores 2.70, 3.00, 3.42, 3.61, 2.56 and 1.93 for datasets 1-6 respectively. Results from the full genome scans revealed that linked regions on chromosomes 6 and 11 remained significantly and consistently linked in the intersection datasets. Surprisingly, the maximum lodscore on chromosome 10 for dataset 1 increased from 0.97 in the older original dataset to 4.23 in the younger smaller intersection dataset. This difference in maximum lodscores was highly significant (p < 0.0001), implying that the effect of this chromosome may vary with age. Age effects may also exist for the linked regions on chromosomes 6 and 11. Conclusion: Sex specific effects of chromosomal regions on BMI are common in the Framingham study. Some evidence also exists for age-specific effects of chromosomal regions. [ABSTRACT FROM AUTHOR]

Published
2006

15. Genetics of adult body mass and maintenance of adult body mass in captive baboons (Papio hamadryas subspecies ).

Author

Jaquish, Cashell E., Dyer, Tom, Williams-Blangero, Sarah, Dyke, Bennett, Leland, Michelle, and Blangero, John

Subjects
*BABOONS, *PRIMATES, *BODY weight, *GENETICS, *PHENOTYPES, *FERTILITY
Abstract

Adult body mass and changes in mass during an individual's life are important indicators of general health and reproductive fitness. Therefore, characterization of the factors that influence normal variation in body mass has important implications for colony management and husbandry. The main objective of this study was to quantify the genetic contribution to adult body mass and its maintenance in baboons. Intra-individual mean and variance in body mass were calculated from multiple weight measures available for each of 1,614 animals at least 10 years of age. Heritabilities were estimated using maximum likelihood methods. Mean adult body mass had a significant heritability (50%) as did variance in adult body mass (12%). The sexes differed in several respects: on average females were smaller than males and had greater variability in adult body mass; mean and variance in body mass increased with age in females only; and number of offspring showed a significant positive relationship with body mass in females only. There were significant differences between subspecies in body mass as well as ability to maintain body mass. These results indicate that there is a significant genetic influence on body mass and its maintenance, and suggest that different factors influence changes in body mass with age as well as body mass maintenance in male and female baboons. Am. J. Primatol. 42:281–288, 1997. © 1997 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]

Published
1997
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16. Genome-wide Linkage and Positional Association Study of Blood Pressure Response to Dietary Sodium Intervention.

Author

Mei, Hao, Gu, Dongfeng, Hixson, James E., Rice, Treva K., Chen, Jing, Shimmin, Lawrence C., Schwander, Karen, Kelly, Tanika N., Liu, De-Pei, Chen, Shufeng, Huang, Jian-Feng, Jaquish, Cashell E., Rao, Dabeeru C., and He, Jiang

Subjects
ALLERGIES, CHROMOSOME classification, ARTERIES, BLOOD pressure, STATISTICAL correlation, EPIDEMIOLOGY, EXPERIMENTAL design, SODIUM content of food, GENES, GENETIC polymorphisms, HUMAN genome, INGESTION, INTERGENERATIONAL relations, LONGITUDINAL method, QUESTIONNAIRES, REGRESSION analysis, RESEARCH funding, GENETIC markers, DATA analysis, REPEATED measures design, DESCRIPTIVE statistics, SEQUENCE analysis, GENETICS
Abstract

The authors conducted a genome-wide linkage scan and positional association analysis to identify the genetic determinants of salt sensitivity of blood pressure (BP) in a large family-based, dietary-feeding study. The dietary intervention was conducted among 1,906 participants in rural China (2003–2005). A 7-day low-sodium intervention was followed by a 7-day high-sodium intervention. Salt sensitivity was defined as BP responses to low- and high-sodium interventions. Signals of the logarithm of the odds to the base 10 (LOD ≥ 3) were detected at 33–42 centimorgans of chromosome 2 (2p24.3-2p24.1), with a maximum LOD score of 3.33 for diastolic blood pressure responses to high-sodium intervention. LOD scores were 2.35–2.91 for mean arterial pressure (MAP) and 0.80–1.49 for systolic blood pressure responses in this region, respectively. Correcting for multiple tests, single nucleotide polymorphism (SNP) rs11674786 (2.7 kilobases upstream of the family with sequence similarity 84, member A, gene (FAM84A)) in the linkage region was significantly associated with diastolic blood pressure (P = 0.0007) and MAP responses (P = 0.0007), and SNP rs16983422 (2.8 kilobases upstream of the visinin-like 1 gene (VSNL1)) was marginally associated with diastolic blood pressure (P = 0.005) and MAP responses (P = 0.005). An additive interaction between SNPs rs11674786 and rs16983422 was observed, with P = 7.00 × 10−5 and P = 7.23 × 10−5 for diastolic blood pressure and MAP responses, respectively. The authors concluded that genetic region 2p24.3-2p24.1 might harbor functional variants for the salt sensitivity of BP. [ABSTRACT FROM PUBLISHER]

Published
2012
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17. An APOO Pseudogene on Chromosome 5q Is Associated With Low-Density Lipoprotein Cholesterol Levels.

Author

Montasser, May E., O'Hare, Elizabeth A., Wang, Xiaochun, Howard, Alicia D., McFarland, Rebecca, Perry, James A., Ryan, Kathleen A., Rice, Kenneth, Jaquish, Cashell E., Shuldiner, Alan R., Miller, Michael, Mitchell, Braxton D., Zaghloul, Norann A., Chang, Yen-Pei C., Abe, Namiko, Abecasis, Goncalo, Albert, Christine, Allred, Nicholette P., Almasy, Laura, and Alonso, Alvaro

Subjects
*PSEUDOGENES, *CHROMOSOMES, *LOW density lipoproteins, *CHOLESTEROL, *ATHEROSCLEROTIC plaque, *IN vitro studies, *LABORATORY zebrafish, *AMISH, *ANIMAL experimentation, *ATHEROSCLEROSIS, *COMPARATIVE studies, *DISEASE susceptibility, *FISHES, *GENES, *GENETICS, *GENETIC techniques, *HYPERLIPIDEMIA, *RESEARCH methodology, *MEDICAL cooperation, *RESEARCH, *TRANSGENIC animals, *PHENOTYPES, *EVALUATION research, *HAPLOTYPES
Abstract

Background: Elevated levels of low-density lipoprotein cholesterol (LDL-C) are a major risk factor for cardiovascular disease via its contribution to the development and progression of atherosclerotic lesions. Although the genetic basis of LDL-C has been studied extensively, currently known genetic variants account for only ≈20% of the variation in LDL-C levels.Methods: Through an array-based association analysis in 1102 Amish subjects, we identified a variant strongly associated with LDL-C levels. Using a combination of genetic analyses, zebrafish models, and in vitro experiments, we sought to identify the causal gene driving this association.Results: We identified a founder haplotype associated with a 15 mg/dL increase in LDL-C on chromosome 5. After recombination mapping, the associated region contained 8 candidate genes. Using a zebrafish model to evaluate the relevance of these genes to cholesterol metabolism, we found that expression of the transcribed pseudogene, APOOP1, increased LDL-C and vascular plaque formation.Conclusions: Based on these data, we propose that APOOP1 regulates levels of LDL-C in humans, thus identifying a novel mechanism of lipid homeostasis. [ABSTRACT FROM AUTHOR]

Published
2018
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18. Linkage analysis incorporating gene–age interactions identifies seven novel lipid loci: The Family Blood Pressure Program.

Author

Simino, Jeannette, Kume, Rezart, Kraja, Aldi T., Turner, Stephen T., Hanis, Craig L., Sheu, Wayne H.-H., Chen, Yii-Der Ida, Jaquish, Cashell E., Cooper, Richard S., Chakravarti, Aravinda, Quertermous, Thomas, Boerwinkle, Eric, Hunt, Steven C., and Rao, D.C.

Subjects
*BLOOD pressure, *GENOTYPE-environment interaction, *PHYSIOLOGICAL effects of lipids, *PHYSIOLOGICAL effects of cholesterol, *ESTROGEN replacement therapy, *BODY mass index
Abstract

Abstract: Objective: To detect novel loci with age-dependent effects on fasting (≥8 h) levels of total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides using 3600 African Americans, 1283 Asians, 3218 European Americans, and 2026 Mexican Americans from the Family Blood Pressure Program (FBPP). Methods: Within each subgroup (defined by network, race, and sex), we employed stepwise linear regression (retention p ≤ 0.05) to adjust lipid levels for age, age-squared, age-cubed, body-mass-index, current smoking status, current drinking status, field center, estrogen therapy (females only), as well as antidiabetic, antihypertensive, and antilipidemic medication use. For each trait, we pooled the standardized male and female residuals within each network and race and fit a generalized variance components model that incorporated gene–age interactions. We conducted FBPP-wide and race-specific meta-analyses by combining the p-values of each linkage marker across subgroups using a modified Fisher's method. Results: We identified seven novel loci with age-dependent effects; four total cholesterol loci from the meta-analysis of Mexican Americans (on chromosomes 2q24.1, 4q21.21, 8q22.2, and 12p11.23) and three high-density lipoprotein loci from the meta-analysis of all FBPP subgroups (on chromosomes 1p12, 14q11.2, and 21q21.1). These loci lacked significant genome-wide linkage or association evidence in the literature and had logarithm of odds (LOD) score ≥ 3 in the meta-analysis with LOD ≥ 1 in at least two network and race subgroups (exclusively of non-European descent). Conclusion: Incorporating gene–age interactions into the analysis of lipids using multi-ethnic cohorts can enhance gene discovery. These interaction loci can guide the selection of families for sequencing studies of lipid-associated variants. [Copyright &y& Elsevier]

Published
2014
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19. Cerivastatin, genetic variants, and the risk of rhabdomyolysis.

Author

Marciante, Kristin D., Durda, Jon P., Heckbert, Susan R., Lumley, Thomas, Rice, Ken, Mcknight, Barbara, Totah, Rheem A., Tamraz, Bani, Kroetz, Deanna L., Fukushima, Hisayo, Kaspera, Rüdiger, Bis, Joshua C., Glazer, Nicole L., Li, Guo, Austin, Thomas R., Taylor, Kent D., Rotter, Jerome I., Jaquish, Cashell E., Kwok, Pui-Yan, and Tracy, Russell P.

Abstract

The withdrawal of cerivastatin involved an uncommon but serious adverse reaction, rhabdomyolysis. The bimodal response, rhabdomyolysis in a small proportion of users, points to genetic factors as a potential cause. We conducted a case-control study to evaluate genetic markers for cerivastatin-associated rhabdomyolysis.This study had two components: a candidate gene study to evaluate variants in CYP2C8, UGT1A1, UGT1A3, and SLCO1B1; and a genome-wide association study to identify risk factors in other regions of the genome. A total of 185 rhabdomyolysis cases were frequency matched to statin-using controls from the Cardiovascular Health Study (n=374) and the Heart and Vascular Health Study (n=358). Validation relied on functional studies.Permutation test results suggested an association between cerivastatin-associated rhabdomyolysis and variants in SLCO1B1 (P=0.002), but not variants in CYP2C8 (P=0.073) or UGTs (P=0.523). An additional copy of the minor allele of SLCO1B1 rs4149056 (p.Val174Ala) was associated with the risk of rhabdomyolysis (odds ratio: 1.89; 95% confidence interval: 1.40-2.56). In transfected cells, this variant reduced cerivastatin transport by 40% compared with the reference transporter (P<0.001). The genome-wide association study identified an intronic variant (rs2819742) in the ryanodine receptor 2 gene (RYR2) as significant (P=1.74E-07). An additional copy of the minor allele of the RYR2 variant was associated with a reduced risk of rhabdomyolysis (odds ratio: 0.48; 95% confidence interval: 0.36-0.63).We identified modest genetic risk factors for an extreme response to cerivastatin. Disabling genetic variants in the candidate genes were not responsible for the bimodal response to cerivastatin. [ABSTRACT FROM AUTHOR]

Published
2011
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