Development and Evaluation of a Genetic Risk Score for Obesity

被引:116
作者
Belsky, Daniel W. [1 ,2 ,3 ]
Moffitt, Terrie E. [2 ,4 ,5 ,6 ,7 ]
Sugden, Karen [2 ,4 ,5 ,6 ,7 ]
Williams, Benjamin [2 ,4 ,5 ,6 ,7 ]
Houts, Renate [4 ,5 ,6 ]
McCarthy, Jeanette [8 ]
Caspi, Avshalom [2 ,4 ,5 ,6 ,7 ]
机构
[1] Univ N Carolina, Dept Hlth Policy & Management, Gillings Sch Global Publ Hlth, Chapel Hill, NC USA
[2] Duke Univ, Inst Genome Sci & Policy, Durham, NC USA
[3] Duke Univ, Med Ctr, Ctr Study Aging & Human Dev, Durham, NC 27710 USA
[4] Duke Univ, Med Ctr, Dept Psychol & Neurosci, Durham, NC USA
[5] Duke Univ, Med Ctr, Dept Psychiat, Durham, NC 27710 USA
[6] Duke Univ, Med Ctr, Dept Behav Sci, Durham, NC USA
[7] Kings Coll London, Social Genet & Dev Psychiat Ctr, Inst Psychiat, London, England
[8] Univ Calif San Francisco, Sch Med, Dept Epidemiol & Biostat, San Francisco, CA USA
基金
英国医学研究理事会;
关键词
GENOME-WIDE ASSOCIATION; CORONARY-HEART-DISEASE; ATHEROSCLEROSIS RISK; PREDICTION; OVERWEIGHT; LOCI; CLASSIFICATION; EPIDEMIOLOGY; VARIANTS; AREA;
D O I
10.1080/19485565.2013.774628
中图分类号
C921 [人口统计学];
学科分类号
摘要
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<1x10(30); for obesity, area under the receiver operating characteristic curve (AUC) = 0.57 (95% CI 0.550.58)]. 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 & Evaluation of a Genetic Risk Score for Obesity.]
引用
收藏
页码:85 / 100
页数:16
相关论文
共 50 条
  • [1] Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents
    Viljakainen, Heli
    Dahlstrom, Emma
    Figueiredo, Rejane
    Sandholm, Niina
    Rounge, Trine B.
    Weiderpass, Elisabete
    CLINICAL OBESITY, 2019, 9 (06)
  • [2] Genetic Risk Score Does Not Predict the Outcome of Obesity Surgery
    Kakela, P.
    Jaaskelainen, T.
    Torpstrom, J.
    Ilves, I.
    Venesmaa, S.
    Paakkonen, M.
    Gylling, H.
    Paajanen, H.
    Uusitupa, M.
    Pihlajamaki, J.
    OBESITY SURGERY, 2014, 24 (01) : 128 - 133
  • [3] Weighting approaches for a genetic risk score and an oxidative stress score for predicting the incidence of obesity
    Park, Seonmin
    Yoo, Hye Jin
    Jee, Sun Ha
    Lee, Jong Ho
    Kim, Minjoo
    DIABETES-METABOLISM RESEARCH AND REVIEWS, 2020, 36 (02)
  • [4] Development of a Polygenic Risk Score for BMI to Assess the Genetic Susceptibility to Obesity and Related Diseases in the Korean Population
    Yoon, Nara
    Cho, Yoon Shin
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2023, 24 (14)
  • [5] An obesity genetic risk score is associated with metabolic syndrome in Chinese children
    Zhao, Xiaoyuan
    Xi, Bo
    Shen, Yue
    Wu, Lijun
    Hou, Dongqing
    Cheng, Hong
    Mi, Jie
    GENE, 2014, 535 (02) : 299 - 302
  • [6] Polygenic risk score for genetic evaluation of prostate cancer risk in Asian populations: A narrative review
    Song, Sang Hun
    Byun, Seok-Soo
    INVESTIGATIVE AND CLINICAL UROLOGY, 2021, 62 (03) : 256 - 266
  • [7] An obesity genetic risk score predicts risk of insulin resistance among Chinese children
    Xi, Bo
    Zhao, Xiaoyuan
    Shen, Yue
    Wu, Lijun
    Hou, Dongqing
    Cheng, Hong
    Mi, Jie
    ENDOCRINE, 2014, 47 (03) : 825 - 832
  • [8] Risk prediction of future cardiac arrest by evaluation of a genetic risk score alone and in combination with traditional risk factors
    Ohlsson, Marcus Andreas
    Kennedy, Linn Maria Anna
    Juhlin, Tord
    Melander, Olle
    RESUSCITATION, 2020, 146 : 74 - 79
  • [9] Predicting Stroke Through Genetic Risk Functions The CHARGE Risk Score Project
    Ibrahim-Verbaas, Carla A.
    Fornage, Myriam
    Bis, Joshua C.
    Choi, Seung Hoan
    Psaty, Bruce M.
    Meigs, James B.
    Rao, Madhu
    Nalls, Mike
    Fontes, Joao D.
    O'Donnell, Christopher J.
    Kathiresan, Sekar
    Ehret, Georg B.
    Fox, Caroline S.
    Malik, Rainer
    Dichgans, Martin
    Schmidt, Helena
    Lahti, Jari
    Heckbert, Susan R.
    Lumley, Thomas
    Rice, Kenneth
    Rotter, Jerome I.
    Taylor, Kent D.
    Folsom, Aaron R.
    Boerwinkle, Eric
    Rosamond, Wayne D.
    Shahar, Eyal
    Gottesman, Rebecca F.
    Koudstaal, Peter J.
    Amin, Najaf
    Wieberdink, Renske G.
    Dehghan, Abbas
    Hofman, Albert
    Uitterlinden, Andre G.
    DeStefano, Anita L.
    Debette, Stephanie
    Xue, Luting
    Beiser, Alexa
    Wolf, Philip A.
    DeCarli, Charles
    Ikram, M. Arfan
    Seshadri, Sudha
    Mosley, Thomas H., Jr.
    Longstreth, W. T., Jr.
    van Duijn, Cornelia M.
    Launer, Lenore J.
    STROKE, 2014, 45 (02) : 403 - 412
  • [10] Contemporary Considerations for Constructing a Genetic Risk Score: An Empirical Approach
    Goldstein, Benjamin A.
    Yang, Lingyao
    Salfati, Elias
    Assimes, Themistoclies L.
    GENETIC EPIDEMIOLOGY, 2015, 39 (06) : 439 - 445