. 2019 Apr 18;177(3):587-596.e9.

doi: 10.1016/j.cell.2019.03.028.

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Amit V Khera¹, Mark Chaffin², Kaitlin H Wade³, Sohail Zahid⁴, Joseph Brancale⁵, Rui Xia⁶, Marina Distefano⁷, Ozlem Senol-Cosar⁷, Mary E Haas², Alexander Bick⁸, Krishna G Aragam⁹, Eric S Lander¹⁰, George Davey Smith¹¹, Heather Mason-Suares⁷, Myriam Fornage⁶, Matthew Lebo⁷, Nicholas J Timpson³, Lee M Kaplan⁵, Sekar Kathiresan¹²

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: avkhera@mgh.harvard.edu.
² Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³ MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK; Population Health Science, Bristol Medical School, Bristol, Bristol BS8 1TH, UK; Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK.
⁴ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
⁵ Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Obesity, Metabolism, and Nutrition Institute, Massachusetts General Hospital, Boston, MA 02114, USA.
⁶ The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
⁷ Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA 02139, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Harvard Medical School, Cambridge, MA 02115, USA.
⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Program in Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA.
¹¹ MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK; Population Health Science, Bristol Medical School, Bristol, Bristol BS8 1TH, UK.
¹² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: skathiresan1@mgh.harvard.edu.

PMID: 31002795
PMCID: PMC6661115
DOI: 10.1016/j.cell.2019.03.028

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Amit V Khera et al. Cell. 2019.

. 2019 Apr 18;177(3):587-596.e9.

doi: 10.1016/j.cell.2019.03.028.

Authors

Affiliations

¹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: avkhera@mgh.harvard.edu.
² Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
³ MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK; Population Health Science, Bristol Medical School, Bristol, Bristol BS8 1TH, UK; Avon Longitudinal Study of Parents and Children, Bristol BS8 1TH, UK.
⁴ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
⁵ Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Obesity, Metabolism, and Nutrition Institute, Massachusetts General Hospital, Boston, MA 02114, USA.
⁶ The Brown Foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
⁷ Laboratory for Molecular Medicine, Partners HealthCare Personalized Medicine, Cambridge, MA 02139, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Pathology, Harvard Medical School, Cambridge, MA 02115, USA.
⁸ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
⁹ Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
¹⁰ Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Program in Health Sciences and Technology, Harvard Medical School, Boston, MA 02115, USA.
¹¹ MRC Integrative Epidemiology Unit, University of Bristol, Bristol BS8 1TH, UK; Population Health Science, Bristol Medical School, Bristol, Bristol BS8 1TH, UK.
¹² Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Division of Cardiology, Massachusetts General Hospital, Boston, MA 02114, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA. Electronic address: skathiresan1@mgh.harvard.edu.

PMID: 31002795
PMCID: PMC6661115
DOI: 10.1016/j.cell.2019.03.028

Abstract

Severe obesity is a rapidly growing global health threat. Although often attributed to unhealthy lifestyle choices or environmental factors, obesity is known to be heritable and highly polygenic; the majority of inherited susceptibility is related to the cumulative effect of many common DNA variants. Here we derive and validate a new polygenic predictor comprised of 2.1 million common variants to quantify this susceptibility and test this predictor in more than 300,000 individuals ranging from middle age to birth. Among middle-aged adults, we observe a 13-kg gradient in weight and a 25-fold gradient in risk of severe obesity across polygenic score deciles. In a longitudinal birth cohort, we note minimal differences in birthweight across score deciles, but a significant gradient emerged in early childhood and reached 12 kg by 18 years of age. This new approach to quantify inherited susceptibility to obesity affords new opportunities for clinical prevention and mechanistic assessment.

Keywords: UK Biobank; body mass index; genetic risk prediction; genomic medicine; human genetics; melanocortin 4 receptor; polygenic score; severe obesity; weight.

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Figures

**Figure 1.. Derivation, validation, and testing of a genome-wide polygenic score for obesity**
A genome-wide polygenic score (GPS) for obesity was derived by starting with two independent datasets: first, a list of 2,100,302 common genetic variants and estimated impact of each on BMI from a large GWAS study (Locke et al., 2015), and second, genetic information from 503 individuals of European ancestry from the 1000 Genomes Study used to measure ‘linkage disequilibrium,’ the correlation between genetic variants (The 1000 Genomes Project Consortium, 2015). Candidate GPSs were derived using the LDPred computational algorithm, a Bayesian approach to calculate a posterior mean effect for all variants based on a prior (effect size and statistical significance in the previous GWAS) and subsequent shrinkage based on linkage disequilibrium (Vilhjalmsson et al., 2015). The five candidate LDPred scores vary with respect to the tuning parameter ρ (that is, the proportion of variants assumed to be causal), as previously recommended. A sixth polygenic score was derived based on only the 141 independent variants that had achieved genome-wide levels of statistical significance in the previous GWAS. The optimal GPS was chosen based on maximal correlation with BMI in the UK Biobank validation dataset (N = 119,951 Europeans), and subsequently tested in multiple independent testing datasets of 306,135 individuals. *See also* Tables S1, S2, *and* S3.

**Figure 2.. Relationship of genome-wide polygenic score distribution with body mass index, weight, and severe obesity**
288,016 middle-aged UK Biobank participants were binned into 10 deciles according to the polygenic score. Body mass index (Panel A), weight (Panel B), and prevalence of severe obesity (Panel C) each increased across deciles of the polygenic score (p < 0.0001 for each). Significant differences in clinical categories of obesity were noted (Panel D) when participants were stratified into three categories – bottom decile, deciles 2–9, and top decile. Underweight refers to BMI < 18.5 kg/m², normal as 18.5 to 24.9 kg/m², overweight as 25.0 to 29.9 kg/m², obesity as 30.0 to 39.9 kg/m², and severe obesity as ≥ 40 kg/m² (NHLBI Expert Panel, 1998). *See also* Figures S1, S2, *and* S3.

**Figure 3.. Association of high genome-wide polygenic score with extreme obesity and bariatric surgery**
We considered the top 10% of the distribution as a ‘carrier’ of high genome-wide polygenic score (GPS), represented by the shading in panel A, and compared risk of obesity-related outcomes to the remaining 90% of the distribution. The x-axis represents the polygenic score, with values scaled to a mean of 0 and standard deviation 1 to facilitate interpretation. In Panel B, the relationship of high GPS to extreme obesity and treatment with bariatric surgery was quantified using logistic regression. CI – confidence interval

**Figure 4.. Association of high genome-wide polygenic score with cardiometabolic diseases.**
The relationship of high genome-wide polygenic score (GPS), defined as the top decile of the score distribution, with the prevalence of six cardiometabolic diseases was determined in a logistic regression model within the UK Biobank testing dataset of 288,016 participants. CI – confidence interval

**Figure 5.. Association of GPS with incident severe obesity among young adults**
Among 3,722 young adults in the Framingham Offspring and Coronary Artery Risk Development in Young Adults studies, individuals were stratified based on their GPS into three categories – bottom decile, deciles 2–9, and top decile. Incident severe obesity is plotted according to GPS category over a median follow-up of 27 years (p < 0.0001 for each between-group comparison).

**Figure 6.. Association of obesity GPS decile with weight from birth to 18 years.**
Within the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, 7,861 participants were stratified according to decile of the GPS distribution. Average weight and 95% confidence interval within each decile is displayed at 6 representative ages; corresponding sample size for number of participants with follow-up weight available at each time point is provided (Panels A-F). P-value for linear trend across deciles was 0.003 at birth (A) and < 0.0001 at all subsequent ages. See also Figure S4 and Figure S5.

See this image and copyright information in PMC

Comment in

Polygenic Risk Scores Expand to Obesity.
Torkamani A, Topol E. Torkamani A, et al. Cell. 2019 Apr 18;177(3):518-520. doi: 10.1016/j.cell.2019.03.051. Cell. 2019. PMID: 31002792
Keeping score with obesity.
Burgess DJ. Burgess DJ. Nat Rev Genet. 2019 Jun;20(6):320-321. doi: 10.1038/s41576-019-0132-4. Nat Rev Genet. 2019. PMID: 31024085 No abstract available.

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Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Affiliations

Polygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood

Authors

Affiliations

Abstract

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MeSH terms

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Medical