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1. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry

Author

Mary Cushman, Shad B. Smith, Patricia A. Peyser, Jorge L. Rodriguez-Gil, Struan F.A. Grant, Regina G. Ziegler, Timothy D. Howard, Imran O. Morhason-Bello, Mariaelisa Graff, Christopher S. Carlson, Cameron D. Palmer, Charleston W. K. Chiang, Evandine Rampersaud, Lisa Chu, Uma Nayak, Curtis A. Pettaway, Ye Feng, Jing Hua Zhao, Barbara V. Howard, Joanne M. Jordan, Gregory L. Burke, Melinda C. Aldrich, Dezheng Huo, Omri Gottesman, Richard S. Cooper, Sonja I. Berndt, Donna K. Arnett, Gary S. Gilkeson, M. Cristina Leske, Ulrich Broeckel, Edmond K. Kabagambe, Stephen J. Chanock, Michael J. Thun, John S. Witte, William Maixner, Adebowale Adeyemo, Oladosu Ojengbede, Sarah J. Nyante, William J. Blot, Nicholette D. Palmer, Jyotika K. Fernandes, Laura J. Rasmussen-Torvik, Julie R. Palmer, Ida J. Spruill, Wei Zheng, Ruth J. F. Loos, Guo Li, Robert C. Millikan, Donald W. Bowden, Ellen W. Demerath, Michèle M. Sale, Neil A. Zakai, Zhaoming Wang, Fang Chen, George J. Papanicolaou, Gary K. Chen, Talin Haritunians, Rajiv Nadukuru, James J. Yang, Brian E. Henderson, Sandra Deming-Halverson, Adesola Ogunniyi, David Van Den Berg, Diane L. Kamen, Phyllis J. Goodman, Eric A. Klein, Yingchang Lu, Thomas W. Winkler, Marguerite R. Irvin, Badri Padhukasahasram, Benjamin A. Rybicki, Yan V. Sun, Yii-Der Ida Chen, Temidayo O. Ogundiran, Andrew B. Singleton, Babatunde L. Salako, Vaneet Lotay, Christine B. Ambrosone, Karen C. Johnson, Michael F. Press, Neil E. Caporaso, Guillaume Lettre, Ingrid B. Borecki, Sue A. Ingles, Yonglan Zheng, Jennifer J. Hu, Leslie Bernstein, Keri L. Monda, Kristine R. Monroe, L. Keoki Williams, Thomas H. Mosley, Shamika Ketkar, Ryan W. Driver, Margaret A. Tucker, Josyf C. Mychaleckyj, Ann W. Hsing, Xiuqing Guo, Lewis H. Kuller, Patricia M. Dubbert, Kelly J. Hunt, JoAnn E. Manson, Joel N. Hirschhorn, Mara Z. Vitolins, Tamara B. Harris, Matthew A. Allison, Abdullah Kutlar, Leslie A. Lange, Yongmei Liu, Ulrike Peters, Hakon Hakonarson, Mary K. Wojczynski, Xiaofeng Zhu, Edward A. Ruiz-Narváez, Todd L. Edwards, Heather M. Ochs-Balcom, Jingzhong Ding, Albert M. Levin, W. Timothy Garvey, Digna R. Velez Edwards, Sun J. Kang, Jie Zhou, Barry I. Freedman, Sanjay R. Patel, Suh Yuh Wu, Sylvia Wassertheil-Smoller, Simin Liu, Daniel Shriner, Elizabeth K. Speliotes, Larry D. Atwood, Graham Casey, Lisa R. Yanek, Susan Redline, Lisa B. Signorello, Wei Zhao, Scott M. Williams, Bamidele O. Tayo, Kira C. Taylor, Angela Britton, Xifeng Wu, Erwin P. Bottinger, Charles N. Rotimi, Mary F. Feitosa, Joseph M. Zmuda, Curtis C. Harris, Christine Neslund-Dudas, Bruce M. Psaty, Alexander P. Reiner, Helen N. Lyon, Youfang Liu, Krista A. Zanetti, Jonathan P. Bradfield, Esther M. John, Ann G. Schwartz, Elizabeth M. Gillanders, Mike A. Nalls, Suzanne Kolb, Cathryn H. Bock, Alan B. Zonderman, David Duggan, Gerhard A. Coetzee, Charles Kooperberg, Sharon L.R. Kardia, Olufunmilayo I. Olopade, Suhn K. Rhie, John D. Carpten, Maggie C.Y. Ng, Kari E. North, Christopher A. Haiman, Barbara Nemesure, Loic Le Marchand, Christopher I. Amos, Adeyinka Ademola, Lara Sucheston, Pamela J. Schreiner, Taylor Young, Sara S. Strom, Carl D. Langefeld, Jason H. Moore, Marian L. Neuhouser, Lawrence F. Bielak, Diane M. Becker, Margaret Wrensch, Janet L. Stanford, Laurence N. Kolonel, Yan A. Meng, Margaret R. Spitz, Brendan J. Keating, Kurt Lohman, Virginia J. Howard, Guanjie Chen, Elisa V. Bandera, Quiyin Cai, Amidou N'Diaye, Stefan Ambs, Adam B. Murphy, Eric E. Schadt, Anselm Hennis, Rick A. Kittles, Caroline S. Fox, John K. Wiencke, Katherine L. Nathanson, Barbara McKnight, Michele K. Evans, Wei-Min Chen, Dena G. Hernandez, Herman A. Taylor, David S. Siscovick, and Lorna H. McNeill

Subjects
Genetics, Single-nucleotide polymorphism, Genome-wide association study, Locus (genetics), Biology, Polymorphism, Single Nucleotide, FTO gene, Linkage Disequilibrium, Article, Body Mass Index, Black or African American, Gene Frequency, Genetic Loci, Case-Control Studies, Meta-analysis, Genetic variation, Humans, Genetic Predisposition to Disease, Obesity, Body mass index, Genome-Wide Association Study, Genetic association
Abstract

Genome-wide association studies (GWAS) have identified 36 loci associated with body mass index (BMI), predominantly in populations of European ancestry. We conducted a meta-analysis to examine the association of3.2 million SNPs with BMI in 39,144 men and women of African ancestry and followed up the most significant associations in an additional 32,268 individuals of African ancestry. We identified one new locus at 5q33 (GALNT10, rs7708584, P = 3.4 × 10(-11)) and another at 7p15 when we included data from the GIANT consortium (MIR148A-NFE2L3, rs10261878, P = 1.2 × 10(-10)). We also found suggestive evidence of an association at a third locus at 6q16 in the African-ancestry sample (KLHL32, rs974417, P = 6.9 × 10(-8)). Thirty-two of the 36 previously established BMI variants showed directionally consistent effect estimates in our GWAS (binomial P = 9.7 × 10(-7)), five of which reached genome-wide significance. These findings provide strong support for shared BMI loci across populations, as well as for the utility of studying ancestrally diverse populations.

Published
2013
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2. Hundreds of variants clustered in genomic loci and biological pathways affect human height

Author

Vilmundur Gudnason, Robert J. Weyant, Robert C. Kaplan, Jeffrey R. O'Connell, Panos Deloukas, Barbara McKnight, Alessandro De Grandi, Gert-Jan B. van Ommen, Eric Boerwinkle, Liming Liang, Douglas F. Levinson, Richard B. Hayes, Mark I. McCarthy, Andrew P. Morris, Joel N. Hirschhorn, Jaakko Kaprio, Nilesh J. Samani, Inke R. König, Frank B. Hu, Nicholas J. Wareham, Ozren Polasek, Michael C. Turchin, Daniel F. Gudbjartsson, Wilmar Igl, Vincent Mooser, Aki S. Havulinna, Themistocles L. Assimes, Cameron D. Palmer, Lachlan J. M. Coin, Eva Albrecht, Hana Lango Allen, Inês Barroso, Eco J. C. de Geus, H.-Erich Wichmann, Michael A. Province, Sarah H. Wild, Patricia B. Munroe, Jeanette Erdmann, Mattias Lorentzon, Michael Preuss, Nelson B. Freimer, Mika Kähönen, Tushar Bhangale, Nicole L. Glazer, Markku S. Nieminen, Andrew A. Hicks, Jonathan Tyrer, Grant W. Montgomery, Ruth J. F. Loos, Shaun Purcell, Manfred Kayser, Marjo-Riitta Järvelin, Guillaume Paré, Terho Lehtimäki, Irene Pichler, Ida Surakka, Tõnu Esko, Peter Kraft, Talin Haritunians, Juha Sinisalo, Mari Nelis, Veronique Vitart, Alan R. Sanders, Alistair S. Hall, Carlos Iribarren, Mark E. Cooper, André G. Uitterlinden, Peter Kovacs, David Schlessinger, Florian Ernst, Marja-Liisa Lokki, M. Juhani Junttila, Martin Ridderstråle, Jorma Viikari, Inga Prokopenko, Christopher J. O'Donnell, Jennie Hui, Aila Rissanen, Gonçalo R. Abecasis, Jouke-Jan Hottenga, Tomi Pastinen, Kevin B. Jacobs, Jan Smit, Kristin G. Ardlie, Niina Pellikka, Neil R. Robertson, Martina Müller, John F. Peden, Mary F. Feitosa, Thomas W. Winkler, Kari Stefansson, Jianjun Liu, Jaana Laitinen, Shen Haiqing, Martin Farrall, Nilanjan Chatterjee, Eleanor Wheeler, Tamara B. Harris, Åsa Johansson, Anders Hamsten, Elizabeth K. Speliotes, Larry D. Atwood, Valgerdur Steinthorsdottir, Elin Grundberg, Nicole Soranzo, Anna-Liisa Hartikainen, Devin Absher, Cecilia M. Lindgren, Rany M. Salem, Lorena Citterio, Karol Estrada, Sven Bergmann, Tony Kwan, Thomas Meitinger, Alan F. Wright, Richard N. Bergman, Sailaja Vedantam, Kirsi H. Pietiläinen, Lina Zgaga, Anne U. Jackson, Stefan Schreiber, Ju-Hyun Park, Nicola Glorioso, Jianxin Shi, Najaf Amin, Dale R. Nyholt, Lenore J. Launer, Heribert Schunkert, Thomas Quertermous, Mari Liis Tammesoo, Dorret I. Boomsma, Harry Campbell, Albert V. Smith, Andrew R. Wood, Caroline S. Fox, Paavo Zitting, Gabrielle Boucher, Alex N. Parker, Jennifer G. Sambrook, Joyce B. J. van Meurs, Heather M. Stringham, Marjolein J. Peters, Sekar Kathiresan, Elisabeth Widen, Caroline Hayward, Tim D. Spector, Dawn M. Waterworth, Carlo Rivolta, Antti Jula, Albert Hofman, Hugh Watkins, Wendy L. McArdle, Roberto Elosua, Markku Koiranen, Paul M. Ridker, Iris M. Heid, Lee M. Kaplan, Brenda W.J.H. Penninx, Peter M. Visscher, Claes Ohlsson, Per Hall, Benjamin F. Voight, Ben A. Oostra, Anna F. Dominiczak, Martin den Heijer, Andrea Maschio, Quince Gibson, Peter P. Pramstaller, Nigel W. Rayner, Carsten Oliver Schmidt, Toby Johnson, Henrik Grönberg, M. Carola Zillikens, Alan R. Shuldiner, Cornelia M. van Duijn, Samuli Ripatti, Erik Ingelsson, John R. Thompson, Stephen J. Chanock, Johannes Kettunen, David J. Hunter, Lyle J. Palmer, Ulf Gyllensten, Jianfeng Xu, Astrid Petersmann, Johan G. Eriksson, Guillaume Lettre, Teresa Ferreira, Fredrik Wiklund, Andreas Ziegler, Andres Metspalu, Jerome I. Rotter, Suzanne Rafelt, Julius S. Ngwa, Francis S. Collins, Michael N. Weedon, Thomas Illig, Wolfgang Hoffman, Nancy L. Heard-Costa, Anke Tönjes, Daniel I. Chasman, Alice M. Arnold, Olli T. Raitakari, Anna L. Dixon, Lu Qi, Michael E. Goddard, Anneli Pouta, Mark J. Caulfield, Liesbeth Vandenput, Christian Gieger, L. Adrienne Cupples, Veikko Salomaa, Elizabeth L. Altmaier, Nicholas G. Martin, Thomas D. Kocher, Jubao Duan, David S. Siscovick, Heikki V. Huikuri, Willem H. Ouwehand, Kari E. North, Christian Hengstenberg, Jacques S. Beckmann, Constance Chen, John Perry, Ana Marušić, Olle Melander, John D. Rioux, Erika Salvi, Andrew T. Hattersley, Paul Elliott, Leif Groop, Cristen J. Willer, Manuela Uda, Gudmar Thorleifsson, Eric E. Schadt, Charles C. White, Pablo V. Gejman, Serena Sanna, Ulla Sovio, Robert W. Lawrence, Unnur Thorsteinsdottir, Ayellet V. Segrè, Daniele Cusi, G. Bragi Walters, Ken Sin Lo, Ivana Kolcic, Igor Rudan, Sonja I. Berndt, Keri L. Monda, Soumya Raychaudhuri, Jian'an Luan, Joshua C. Randall, Anthony J. Balmforth, Yurii S. Aulchenko, Peter Almgren, Karen L. Mohlke, Timothy M. Frayling, Shamika Ketkar, Thomas H. Mosley, Markus Perola, Henry Völzke, Laura Zagato, Leena Peltonen, Seppo Koskinen, Massimo Mangino, Ke Hao, Tsegaselassie Workalemahu, Lambertus A. Kiemeney, Helene Alavere, Jaakko Tuomilehto, Reedik Mägi, James F. Wilson, Ingrid B. Borecki, Zoltán Kutalik, Michael Stumvoll, Dominique J. Verlaan, Arthur W. Musk, Eero Kajantie, Jian Yang, Joshua W. Knowles, Tuomas O. Kilpeläinen, Michael Boehnke, G. Mark Lathrop, John Beilby, Fernando Rivadeneira, Gudny Eiriksdottir, Gonneke Willemsen, Andrew C. Heath, Antonella Mulas, Katja K.H. Aben, David P. Strachan, Thor Aspelund, Jing Hua Zhao, Psychiatry, NCA - Anxiety & Depression, EMGO - Mental health, Internal Medicine, Epidemiology, Intensive Care, Clinical Genetics, Genetic Identification, Medical Research Council (MRC), Biological Psychology, Neuroscience Campus Amsterdam - Anxiety & Depression, EMGO+ - Mental Health, Genetics of Complex Traits, University of Exeter, Department of Epidemiology, Erasmus Medical Centre, Netherlands Genomics Initiative (NGI), Netherlands Genomics Initiative, Department of Medicine, Montreal, National Cancer Institute [Bethesda] (NCI-NIH), National Institutes of Health [Bethesda] (NIH), Department of Biostatistics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, deCODE Genetics, deCODE genetics [Reykjavik], Divisions of Genetics and Endocrinology and Program in Genomics, Boston Children's Hospital, Metabolism Initiative and Program in Medical and Population Genetics, 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], Institut de Génomique d'Evry (IG), Institut de Biologie François JACOB (JACOB), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, the Genetic Investigation of ANthropocentric Traits (GIANT) Consortium, Université Paris-Saclay-Institut de Biologie François JACOB (JACOB), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)

Subjects
Netherlands Twin Register (NTR), Multifactorial Inheritance, [SDV]Life Sciences [q-bio], Genome-wide association study, Aetiology, screening and detection [ONCOL 5], 0302 clinical medicine, POPULATION, SNPS, Genetics, 0303 health sciences, Multidisciplinary, HERITABILITY, COMMON VARIANTS, Phenotype, DISEASES, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Science & Technology - Other Topics, GROWTH, Allelic heterogeneity, Chromosomes, Human, Pair 3, Metabolic Networks and Pathways, TRAITS, Common disease-common variant, General Science & Technology, Locus (genetics), Biology, Polymorphism, Single Nucleotide, Article, Molecular epidemiology [NCEBP 1], 03 medical and health sciences, SDG 3 - Good Health and Well-being, ADULT, STRATIFICATION, MD Multidisciplinary, Humans, Genetic Predisposition to Disease, Genetic variability, 030304 developmental biology, Science & Technology, MULTIDISCIPLINARY SCIENCES, Genome, Human, Hormonal regulation [IGMD 6], Body Height, Genetic architecture, wide association analysis common variants heritability population adult stratification diseases traits growth snps, WIDE ASSOCIATION ANALYSIS, Genetic Loci, Evaluation of complex medical interventions [NCEBP 2], Polygene, Expression quantitative trait loci, 030217 neurology & neurosurgery, Genome-Wide Association Study
Abstract

Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits1, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait2,3. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways ( P=0.016) and that underlie skeletal growth defects ( P

Published
2010
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3. Evidence for a gene influencing heart rate on chromosome 4 among hypertensives

Author

Richard H. Myers, Yuqing Zhang, Curtis R. Ellison, Paul N. Hopkins, Jemma B. Wilk, Cora E. Lewis, and Larry D. Atwood

Subjects
Male, Genotype, Genetic Linkage, Quantitative Trait Loci, Black People, Physiology, Locus (genetics), Quantitative trait locus, Biology, White People, Heart Rate, Genetic linkage, Heart rate, Genetics, Humans, Allele frequency, Genetics (clinical), Genome, Human, Chromosome Mapping, Confounding Factors, Epidemiologic, Middle Aged, Chromosome 4, Hypertension, Microsatellite, Female, Chromosomes, Human, Pair 4, Lod Score, Body mass index
Abstract

While mechanisms are poorly understood, resting heart rate has been shown to be a strong predictor of the risk of cardiovascular disease, cancer, and all-cause mortality. We performed a genome scan for quantitative trait loci influencing the resting heart rate among 962 Caucasians and 1,124 African-Americans in the Hypertension Genetic Epidemiology Network (HyperGEN), a multi-center study of genetic and environmental factors related to hypertension. The NHLBI Mammalian Genotyping Service typed a total of 391 anonymous microsatellite markers, spaced roughly equally throughout the genome. Within each race and sex, heart rate was adjusted for covariates, including age, age(2), study center, body mass index, beta-blocker use, alcohol consumption, smoking, number of city blocks walked per day, and number of hours watching television. Genome scans were performed using variance component linkage analysis as implemented by GENEHUNTER (version 2) for each race, using race-specific marker allele frequencies derived from random samples. The highest lod score detected in Caucasians was 2.14 on chromosome 4 (at 195.06 cM); a lod score of 1.14 was found at the same locus among the African-Americans, and a lod score of 3.18 resulted when the two racial groups were combined. Evidence was also found on chromosome 10 to support a recent report of an association between heart rate and the beta1 adrenergic receptor. The suggestive evidence for linkage found on chromosome 4 in both Caucasian and African-American hypertensive sib pairs indicates that further investigation on that region may be warranted to locate a gene influencing variability in resting heart rate.

Published
2002
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4. Segregation analysis of serum uric acid in the NHLBI Family Heart Study

Author

Stephen S. Rich, Ingrid B. Borecki, Steven C. Hunt, Jemma B. Wilk, John H. Eckfeldt, Luc Djoussé, Richard H. Myers, Dabeeru C. Rao, Donna K. Arnett, and Larry D. Atwood

Subjects
medicine.medical_specialty, Creatinine, Aspirin, Heritability, Biology, medicine.disease, Major gene, chemistry.chemical_compound, Endocrinology, Insulin resistance, chemistry, Internal medicine, Genetics, medicine, Uric acid, Body mass index, Nuclear family, Genetics (clinical), medicine.drug
Abstract

Segregation analysis was performed on the serum uric acid measurements from 523 randomly ascertained Caucasian families from the NHLBI Family Heart Study. Gender-specific standardized residuals were used as the phenotypic variable in both familial correlation and segregation analysis. Uric acid residuals were adjusted for age, age2, age3, body mass index (kg/m2), creatinine level, aspirin use (yes/no), total drinks (per week), HOMA insulin resistance index [(glucose * insulin)/22.5], diuretic use (yes/no), and triglyceride level. Sibling correlations (r=0.193) and parent-offspring correlations (r=0.217) were significantly different from zero, but these two familial correlations were not significantly different from one another. After adjustment for covariates, the heritability estimate for serum uric acid was 0.399. Segregation analysis rejected the "no major gene" model but was unable to discriminate between an "environmental" and a "Mendelian major gene" model. These results support the hypothesis that uric acid is a multifactorial trait possibly influenced by more than one major gene, modifying genes, and environmental factors.

Published
2000
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5. Genetics Analysis Workshop 16 Problem 2: the Framingham Heart Study data

Author

Monica Lee, Nancy L. Heard-Costa, Larry D. Atwood, and L. Adrienne Cupples

Subjects
lcsh:Medicine, Disease, Bioinformatics, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Framingham Heart Study, Diabetes mellitus, Genotype, Medicine, lcsh:Science, Prospective cohort study, 030304 developmental biology, 0303 health sciences, business.industry, Incidence (epidemiology), lcsh:R, General Medicine, medicine.disease, 3. Good health, Proceedings, Blood pressure, lcsh:Q, Observational study, business, 030217 neurology & neurosurgery, Demography
Abstract

Genetic Analysis Workshop 16 (GAW16) Problem 2 presented data from the Framingham Heart Study (FHS), an observational, prospective study of risk factors for cardiovascular disease begun in 1948. Data have been collected in three generations of family participants in the study and the data presented for GAW16 included phenotype data from all three generations, with four examinations of data collected repeatedly for the first two generations. The trait data consisted of information on blood pressure, hypertension treatment, lipid levels, diabetes and blood glucose, smoking, alcohol consumed, weight, and coronary heart disease incidence. Additionally, genotype data obtained through a genome-wide scan (FHS SHARe) of 550,000 single-nucleotide polymorphisms from Affymetrix chips were included with the GAW16 data. The genotype data were also used for GAW16 Problem 3, where simulated phenotypes were generated using the actual FHS genotypes. These data served to provide investigators with a rich resource to study the behavior of genome-wide scans with longitudinally collected family data and to develop and apply new procedures

Published
2009
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6. The Framingham Heart Study 100K SNP genome-wide association study resource: overview of 17 phenotype working group reports

Author

Zhen Y Wang, Douglas P. Kiel, Nancy L. Heard-Costa, Chao-Yu Guo, James B. Meigs, Josée Dupuis, Daniel J. Gottlieb, Joanne M. Murabito, Martin G. Larson, Mona Pandey, Kathryn L. Lunetta, Caroline S. Fox, Chunyu Liu, Anita L. DeStefano, Christopher Newton-Cheh, Matthew D. Mailman, Larry D. Atwood, Alisa K. Manning, Kathleen Falls, Sudha Seshadri, Shih-Jen Hwang, Diddahally R. Govindaraju, Philip A. Wolf, George T. O'Connor, Qiong Yang, Jason M. Laramie, Emelia J. Benjamin, Sekar Kathiresan, Serkalem Demissie, Ramachandran S. Vasan, Ralph B. D'Agostino, Daniel Levy, L. Adrienne Cupples, Heather T Arruda, Christopher J. O'Donnell, and Jemma B. Wilk

Subjects
Adult, Genetic Markers, Male, Single-nucleotide polymorphism, Genome-wide association study, Disease, Biology, Polymorphism, Single Nucleotide, Cohort Studies, 03 medical and health sciences, Framingham Heart Study, Genetics, Humans, SNP, Genetics(clinical), Genetics (clinical), 030304 developmental biology, Introduction, 0303 health sciences, Genome, Human, 030305 genetics & heredity, Middle Aged, Human genetics, 3. Good health, Phenotype, Cardiovascular Diseases, Cohort, Female, Disease Susceptibility, Cohort study
Abstract

The Framingham Heart Study (FHS), founded in 1948 to examine the epidemiology of cardiovascular disease, is among the most comprehensively characterized multi-generational studies in the world. Many collected phenotypes have substantial genetic contributors; yet most genetic determinants remain to be identified. Using single nucleotide polymorphisms (SNPs) from a 100K genome-wide scan, we examine the associations of common polymorphisms with phenotypic variation in this community-based cohort and provide a full-disclosure, web-based resource of results for future replication studies.Adult participants (n = 1345) of the largest 310 pedigrees in the FHS, many biologically related, were genotyped with the 100K Affymetrix GeneChip. These genotypes were used to assess their contribution to 987 phenotypes collected in FHS over 56 years of follow up, including: cardiovascular risk factors and biomarkers; subclinical and clinical cardiovascular disease; cancer and longevity traits; and traits in pulmonary, sleep, neurology, renal, and bone domains. We conducted genome-wide variance components linkage and population-based and family-based association tests.The participants were white of European descent and from the FHS Original and Offspring Cohorts (examination 1 Offspring mean age 32 +/- 9 years, 54% women). This overview summarizes the methods, selected findings and limitations of the results presented in the accompanying series of 17 manuscripts. The presented association results are based on 70,897 autosomal SNPs meeting the following criteria: minor allele frequencyor + 10%, genotype call rateor = 80%, Hardy-Weinberg equilibrium p-valueor = 0.001, and satisfying Mendelian consistency. Linkage analyses are based on 11,200 SNPs and short-tandem repeats. Results of phenotype-genotype linkages and associations for all autosomal SNPs are posted on the NCBI dbGaP website at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.We have created a full-disclosure resource of results, posted on the dbGaP website, from a genome-wide association study in the FHS. Because we used three analytical approaches to examine the association and linkage of 987 phenotypes with thousands of SNPs, our results must be considered hypothesis-generating and need to be replicated. Results from the FHS 100K project with NCBI web posting provides a resource for investigators to identify high priority findings for replication.

Published
2007
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7. Genetic analyses of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach

Author

Jing Cui, Chao-Yu Guo, Serkalem Demissie, Martin G. Larson, Qiong Yang, Larry D. Atwood, Irmarie Chazaro, L. Adrienne Cupples, and Anita L. DeStefano

Subjects
Adult, Male, Multivariate statistics, Multivariate analysis, lcsh:QH426-470, Quantitative Trait Loci, Quantitative trait locus, Biology, Bioinformatics, Cohort Studies, 03 medical and health sciences, Statistics, Genetics, Humans, Genetics(clinical), Longitudinal Studies, Genetics (clinical), 030304 developmental biology, Linkage (software), Analysis of Variance, 0303 health sciences, 0402 animal and dairy science, Univariate, 04 agricultural and veterinary sciences, Middle Aged, Heritability, Random effects model, 040201 dairy & animal science, lcsh:Genetics, Proceedings, Cholesterol, Phenotype, Cardiovascular Diseases, Data Interpretation, Statistical, Multivariate Analysis, Adult Children, Female, Analysis of variance, Software
Abstract

Background We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two were univariate approaches and used 1) baseline measure at exam one or 2) summary measures such as mean and slope from multiple exams. The third method was a multivariate approach that directly models multiple measurements on a subject. A variance components model (SOLAR) was employed in the univariate approaches. A mixed regression model with polynomials was employed in the multivariate approach and implemented in SAS/IML. Results Using the baseline measure at exam 1, we detected all baseline or slope genes contributing a substantial amount (0.08) of variance (LOD > 3). Compared to the baseline measure, the mean measures yielded slightly higher LOD at the slope genes, and a lower LOD at the baseline genes. The slope measure produced a somewhat lower LOD for the slope gene than did the mean measure. Descriptive information on the pattern of changes in gene effects with age was estimated for three linked loci by the third approach. Conclusion We found simple univariate methods may be effective to detect genes affecting longitudinal phenotypes but may not fully reveal temporal trends in gene effects. The relative efficiency of the univariate methods to detect genes depends heavily on the underlying model. Compared with the univariate approaches, the multivariate approach provided more information on temporal trends in gene effects at the cost of more complicated modelling and more intense computations.

Published
2003
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