Literature DB >> 23701538

Development and evaluation of a genetic risk score for obesity.

Daniel W Belsky1, Terrie E Moffitt, Karen Sugden, Benjamin Williams, Renate Houts, Jeanette McCarthy, Avshalom Caspi.   

Abstract

Multi-locus profiles of genetic risk, so-called "genetic risk scores," can be used to translate discoveries from genome-wide association studies into tools for population health research. We developed a genetic risk score for obesity from results of 16 published genome-wide association studies of obesity phenotypes in European-descent samples. We then evaluated this genetic risk score using data from the Atherosclerosis Risk in Communities (ARIC) cohort GWAS sample (N = 10,745, 55% female, 77% white, 23% African American). Our 32-locus GRS was a statistically significant predictor of body mass index (BMI) and obesity among ARIC whites [for BMI, r = 0.13, p<1 × 10(-30); for obesity, area under the receiver operating characteristic curve (AUC) = 0.57 (95% CI 0.55-0.58)]. The GRS predicted differences in obesity risk net of demographic, geographic, and socioeconomic information. The GRS performed less well among African Americans. The genetic risk score we derived from GWAS provides a molecular measurement of genetic predisposition to elevated BMI and obesity.[Supplemental materials are available for this article. Go to the publisher's online edition of Biodemography and Social Biology for the following resource: Supplement to Development &amp; Evaluation of a Genetic Risk Score for Obesity.].

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Year:  2013        PMID: 23701538      PMCID: PMC3671353          DOI: 10.1080/19485565.2013.774628

Source DB:  PubMed          Journal:  Biodemography Soc Biol        ISSN: 1948-5565


  52 in total

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Journal:  Hum Genet       Date:  2010-08-20       Impact factor: 4.132

2.  Principal components analysis corrects for stratification in genome-wide association studies.

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Review 3.  Obesity, diets, and social inequalities.

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Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-27       Impact factor: 11.205

5.  Prevalence of overweight and obesity in the United States, 1999-2004.

Authors:  Cynthia L Ogden; Margaret D Carroll; Lester R Curtin; Margaret A McDowell; Carolyn J Tabak; Katherine M Flegal
Journal:  JAMA       Date:  2006-04-05       Impact factor: 56.272

6.  Evidence for substantial effect modification by gender in a large-scale genetic association study of the metabolic syndrome among coronary heart disease patients.

Authors:  Jeanette J McCarthy; Joanne Meyer; David J Moliterno; L Kristin Newby; William J Rogers; Eric J Topol
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7.  Genome-wide association study and follow-up analysis of adiposity traits in Hispanic Americans: the IRAS Family Study.

Authors:  Jill M Norris; Carl D Langefeld; Matthew E Talbert; Maria R Wing; Talin Haritunians; Tasha E Fingerlin; Anthony J G Hanley; Julie T Ziegler; Kent D Taylor; Steven M Haffner; Yii-Der I Chen; Donald W Bowden; Lynne E Wagenknecht
Journal:  Obesity (Silver Spring)       Date:  2009-05-21       Impact factor: 5.002

8.  Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium: Design of prospective meta-analyses of genome-wide association studies from 5 cohorts.

Authors:  Bruce M Psaty; Christopher J O'Donnell; Vilmundur Gudnason; Kathryn L Lunetta; Aaron R Folsom; Jerome I Rotter; André G Uitterlinden; Tamara B Harris; Jacqueline C M Witteman; Eric Boerwinkle
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9.  The genomic applications in practice and prevention network.

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10.  Population structure and eigenanalysis.

Authors:  Nick Patterson; Alkes L Price; David Reich
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  64 in total

1.  Genetic determinants of BMI from early childhood to adolescence: the Santiago Longitudinal Study.

Authors:  A E Justice; G Chittoor; E Blanco; M Graff; Y Wang; C Albala; J L Santos; B Angel; B Lozoff; V S Voruganti; K E North; S Gahagan
Journal:  Pediatr Obes       Date:  2018-12-04       Impact factor: 4.000

Review 2.  CRISPR/Cas9, the Powerful New Genome-Editing Tool for Putative Therapeutics in Obesity.

Authors:  María José Franco-Tormo; Mireille Salas-Crisostomo; Nuno Barbosa Rocha; Henning Budde; Sérgio Machado; Eric Murillo-Rodríguez
Journal:  J Mol Neurosci       Date:  2018-05-07       Impact factor: 3.444

3.  The mathematical limits of genetic prediction for complex chronic disease.

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Journal:  J Epidemiol Community Health       Date:  2015-02-03       Impact factor: 3.710

4.  The Geographic Distribution of Genetic Risk as Compared to Social Risk for Chronic Diseases in the United States.

Authors:  David H Rehkopf; Benjamin W Domingue; Mark R Cullen
Journal:  Biodemography Soc Biol       Date:  2016

5.  A genetic risk tool for obesity predisposition assessment and personalized nutrition implementation based on macronutrient intake.

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Journal:  Genes Nutr       Date:  2014-11-28       Impact factor: 5.523

6.  Big data challenges in bone research: genome-wide association studies and next-generation sequencing.

Authors:  Nerea Alonso; Gavin Lucas; Pirro Hysi
Journal:  Bonekey Rep       Date:  2015-02-11

Review 7.  Clinical use of current polygenic risk scores may exacerbate health disparities.

Authors:  Alicia R Martin; Masahiro Kanai; Yoichiro Kamatani; Yukinori Okada; Benjamin M Neale; Mark J Daly
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8.  Causal Effect of Genetic Variants Associated With Body Mass Index on Multiple Sclerosis Susceptibility.

Authors:  Milena A Gianfrancesco; M Maria Glymour; Stefan Walter; Brooke Rhead; Xiaorong Shao; Ling Shen; Hong Quach; Alan Hubbard; Ingileif Jónsdóttir; Kári Stefánsson; Pernilla Strid; Jan Hillert; Anna Hedström; Tomas Olsson; Ingrid Kockum; Catherine Schaefer; Lars Alfredsson; Lisa F Barcellos
Journal:  Am J Epidemiol       Date:  2017-02-01       Impact factor: 4.897

9.  Abruptio placentae risk and genetic variations in mitochondrial biogenesis and oxidative phosphorylation: replication of a candidate gene association study.

Authors:  Tsegaselassie Workalemahu; Daniel A Enquobahrie; Bizu Gelaye; Timothy A Thornton; Fasil Tekola-Ayele; Sixto E Sanchez; Pedro J Garcia; Henry G Palomino; Anjum Hajat; Roberto Romero; Cande V Ananth; Michelle A Williams
Journal:  Am J Obstet Gynecol       Date:  2018-09-05       Impact factor: 8.661

10.  Prediction of fetal hemoglobin in sickle cell anemia using an ensemble of genetic risk prediction models.

Authors:  Jacqueline N Milton; Victor R Gordeuk; James G Taylor; Mark T Gladwin; Martin H Steinberg; Paola Sebastiani
Journal:  Circ Cardiovasc Genet       Date:  2014-03-01
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