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.

Here we use recently developed computational algorithms and large datasets to derive, validate, and test a robust polygenic predictor of BMI and obesity. This genome-wide polygenic score (GPS) integrates all available common variants into a single quantitative measure of inherited susceptibility. It identifies a subset of the adult population that is at substantial risk of severe obesity—in some cases equivalent to rare monogenic mutations—and others that enjoy considerable protection. The GPS is associated with only minimal differences in birthweight, but it predicts clear differences in weight during early childhood and profound differences in weight trajectory and risk of developing severe obesity in subsequent years.

A recently published genome-wide association study (GWAS) quantified the relationship between each of 2.1 million common genetic variants and BMI in over 300,000 individuals (). None of the individual variants account for a large proportion of the phenotype. The strongest association was noted for a common variant at the FTO locus; the risk allele was associated with a statistically robust but clinically modest increase in weight of approximately 1 kg per inherited risk allele. Obtaining meaningful predictive power thus requires aggregating information from many common variants into a polygenic score (). However, previous efforts to create an effective polygenic score for obesity have had only modest success ().

Inherited susceptibility to obesity can, in rare cases, be attributed to a large-effect mutation that perturbs energy homeostasis or fat deposition (). For example, genetic inactivation of the melanocortin 4 receptor (MC4R) gene is associated with obesity in both mouse models and humans (). However, for the vast majority of severely obese individuals, no such monogenic mutation can be identified (). Their genetic susceptibility may instead result from the cumulative effects of numerous variants with individually modest effects—a “polygenic” model. This paradigm is similar to other complex diseases in which polygenic inheritance, involving many common genetic variants, accounts for the majority of inherited susceptibility ().

Severe obesity, defined as a BMI of 40 kg/mor more, is a rapidly growing public health issue already afflicting 8% of American adults (). Although present in less than 1% of the population in middle-income countries such as India and China, the prevalence of severe obesity in these countries has increased more than 100-fold over the last three decades and shows no signs of slowing (). Individuals with severe obesity are often stigmatized because of the commonly held belief that their condition results primarily from unhealthy lifestyle choices (). However, obesity is known to be heritable, suggesting that inborn DNA variation confers increased susceptibility in some individuals and protection in others ().

NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.

National Institutes of Health Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report.

We modeled the trajectories of weight from birth to 18 years, stratifying individuals according to the top decile of the GPS distribution, deciles 2–9, and the bottom decile. This longitudinal analysis confirmed a separation in weight that starts in early childhood and continues to diverge into adulthood ( Figure S5 ).

Among 7,861 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, individual were stratified based on their GPS into three categories – bottom decile, deciles 2-9, and top decile. Longitudinal weight trajectories from birth to 18 years were modeled using linear spline multi-level models with knot points.

We observed similar results after converting participants’ weights to Z scores—the number of SDs a child’s weight differs from a population and age-specific normative value ( Figure S4 ). The difference in Z score between the top and bottom deciles was 0.11 for birthweight (p = 0.03), but this gradient had increased to 0.75 by 8 years and 0.90 by 18 years (p < 0.0001).

Within the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, 7,861 participants were stratified according to decile of the GPS distribution. Average z-score 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.

The GPS was associated with only small differences in birthweight; the mean was 3.47 kg for those in the top decile versus 3.41 kg for those in the bottom decile, a difference of 0.06 kg (p = 0.02) ( Figures 6 A–6F). By 8 years of age, the difference increased to 3.5 kg (p < 0.0001), with a mean weight of 27.9 versus 24.3 kg. By 18 years of age, the difference reached 12.3 kg (p < 0.0001). Strikingly, this weight difference between top and bottom GPS deciles at 18 years of age (12.3 kg) was comparable with that seen in participants in the UK Biobank at a mean age of 57 years (13.0 kg).

(A–F) Within the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort, 7,861 participants were stratified according to decile of the GPS distribution. The average weight and 95% CI within each decile are displayed at 6 representative ages; the corresponding sample size for the number of participants with follow-up weight available at each time point is provided: (A) birth; (B) 8 months; (C) 18 months; (D) 3.5 years; (E) 8 years; and (F) 18 years. The p value for the linear trend across deciles was 0.003 at birth (A) and less than 0.0001 at all subsequent ages.

Given the gradients in weight and severe obesity observed in adulthood, we next posed the following question: at what age does this gradient first start to emerge? We explored this question in a birth cohort from the United Kingdom, the Avon Longitudinal Study of Parents and Children (ALSPAC) (). The ALSPAC study recruited pregnant mothers in the United Kingdom between 1991 and 1992 and followed offspring with serial weight assessments from time of birth to 18 years of age. We identified 7,861 participants with both weight and genotyping array data available for analysis.

Cohort Profile: the ‘children of the 90s’--the index offspring of the Avon Longitudinal Study of Parents and Children.

Among individuals in the top decile of the GPS, 58 of 371 (15.6%) went on to develop severe obesity compared with 5.6% of those in deciles 2–9 ( Figure 5 ). By contrast, among those in the lowest decile, only 5 of 372 (1.3%) individuals went on to develop severe obesity.

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).

Although only a small minority of individuals are severely obese in early adulthood, the prevalence increases rapidly over subsequent decades (). We hypothesized that the GPS might predict who would go on to develop severe obesity during the transition from young adulthood to middle age. We analyzed data from the Framingham Offspring and Coronary Artery Risk Development in Young Adults (CARDIA) studies, in which participants were weighed at an initial baseline assessment and at additional study visits over the subsequent decades (). We identified 3,722 young adult participants, none of whom were severely obese at time of baseline assessment, in whom GPS calculation was possible. The mean age at baseline assessment was 28.0 years, 48% were female, and the mean BMI was 24.2 kg/m. These individuals were weighed at up to 8 subsequent visits over a median follow-up of 27 years to determine the incidence of severe obesity.

NCD Risk Factor Collaboration (NCD-RisC) Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults.

We hypothesized that individuals in the extreme of the GPS distribution might have an increase in BMI that approaches or exceeds the 4.1 kg/m 2 increase noted for carriers of pathogenic MC4R mutations and tested progressively more extreme tails of the distribution. The top 1.6% of the GPS distribution had a mean BMI 4.1 kg/m 2 higher than the remaining 98.4%—31.4 versus 27.3 kg/m 2 , and 9.1% of these individuals were severely obese.

A total of 9 of the 6,547 sequenced individuals harbored one of the 4 pathogenic MC4R variants, corresponding to a prevalence of 0.14% (95% CI, 0.06% to 0.26%). Subsequent unblinding of phenotype information revealed that the average BMI of these 9 carriers was 32.5 kg/mcompared with 28.4 kg/min the remainder of the population, a difference of 4.1 kg/m(95% CI, 0.8 to 7.3; p = 0.02). However, consistent with recent observations of incomplete penetrance in an adult population (), only one of the 9 carriers was severely obese. An additional 3 were obese, and the remaining 5 were overweight but not obese.

Given that the majority of rare missense mutations have little or no functional effect on protein function (), a clinical laboratory geneticist on our team who was blinded to participant phenotypes classified each of the 24 observed MC4R variants according to current clinical guidelines (), integrating information from population allele frequency data, computational prediction and conservation scores, functional assay data, and prior reports of the variant segregating with obesity. 4 of these 24 variants met these clinical criteria as pathogenic or likely pathogenic for monogenic obesity, including the p.Tyr35Ter premature stop codon noted above, an inactivating frameshift mutation (p.Phe280AlafsX12), and two missense mutations (p.Arg165Gln and p.Glu61Lys) previously shown to segregate with obesity in family studies and impair receptor activity in functional assays. A summary of the evidence used to classify each of the 24 variants is provided in Table S4

ACMG Laboratory Quality Assurance Committee Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology.

We performed whole-exome sequencing of 6,547 UK Biobank participants, identifying 24 rare (allele frequency, <1%) protein-altering variants in the MC4R gene. A total of 54 of the 6,547 individuals (0.8%) harbored one of these variants. The average BMI of these 54 individuals was 30.8 kg/m 2 versus 28.4 kg/m 2 in the remainder of the population, a difference of 2.4 kg/m 2 (95% confidence interval [CI], 1.0 to 3.7; p = 0.001).

Rare inactivating mutations in the MC4R gene are among the most common monogenic mutations for obesity (), but few prior studies have analyzed gene sequencing data and performed clinical-grade variant classification in a large population of unascertained adults.

We next determined the relationship between a high polygenic score and all-cause mortality. Death following enrollment occurred in 8,102 (2.8%) participants over a median follow-up of 7.1 years, including 940 (3.3%) of those in the top decile of the polygenic score distribution and 7,162 (2.8%) in the remainder of the distribution (p < 0.0001). In a survival analysis that additionally included time to death in the statistical model, a high polygenic score was associated with a 19% increased risk of incident mortality (p < 0.0001).

Beyond severe obesity, individuals in the UK Biobank who carried a high GPS were at increased risk for six common cardiometabolic diseases, including a 28% increased risk of coronary artery disease, a 72% increased risk for diabetes mellitus, a 38% increased risk for hypertension, a 34% increased risk for congestive heart failure, a 23% increased risk for ischemic stroke, and a 41% increased risk for venous thromboembolism (p < 0.05 for each; Figure 4 ).

The relationship of high 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.

Another indicator of extreme obesity involves individuals who undergo treatment with bariatric surgery, acknowledging that factors in addition to severity of obesity contribute to the decision to move forward with an invasive procedure to assist with weight loss. We identified 208 such participants in the UK Biobank testing dataset, of whom 81 (38.9%) carried a high GPS. This finding was replicated among 714 severely obese patients treated with bariatric surgery within the Partners HealthCare System (). 238 of these 714 (33%) patients carried a high GPS. A combined analysis of the 922 bariatric surgery participants noted a high GPS in 319 (34.6%). Compared with the remainder of the distribution, a high GPS was associated with a 5.0-fold increased risk of severe obesity treated with bariatric surgery ( Figure 3 B).

Furthermore, the magnitude of risk conferred by a high GPS increased at more extreme levels of observed obesity. The proportion of high-GPS carriers was 9.7% among individuals with a BMI of less than 40 kg/m, 31% among the 5,232 individuals with a BMI of 40 kg/mor more, 42.3% among the 331 individuals with a BMI of 50 kg/mor more, and 61.5% among the 26 individuals with a BMI of 60 kg/mor more. Compared with the remainder of the GPS distribution, a high GPS was associated with a 4.2-, 6.6-, and 14.4-fold increased risk of a BMI of 40, 50, and 60 kg/mor more, respectively ( Figure 3 B).

We sought to mimic this approach using the GPS by labeling the top decile of the GPS distribution as “carriers” and those in the remainder of the distribution as non-carriers ( Figure 3 A). The 10% of the population who carried a “high GPS” demonstrated an average BMI that was 2.9 kg/mhigher and a weight 8.0 kg higher than noncarriers (p < 0.0001 for both comparisons). The results were similar when high-GPS carriers were compared with individuals within the middle quintile of the score distribution instead of the bottom 90% of the distribution, with differences in BMI and weight of 2.6 kg/mand 7.4 kg, respectively.

(B) The relationship of high GPS to extreme obesity and treatment with bariatric surgery was quantified using logistic regression. CI, confidence interval.

(A) We considered the top 10% of the distribution as high-GPS carriers, represented by the shading, and compared the risk of obesity-related outcomes with the remaining 90% of the distribution. The x axis represents the polygenic score, with values scaled to a mean of 0 and SD 1 to facilitate interpretation.

Traditional analyses of rare genetic mutations are performed by comparing heterozygous mutation carriers with noncarriers. An important example is the p.Tyr35Ter premature stop codon in MC4R present in 0.02% of the population and typically inherited as a shared haplotype with the p.Asp37Val missense mutation, which has been shown previously to completely inactivate MC4R activity in in vitro functional assays (). A recent analysis linked this variant to an average weight increase of 7 kg ().

Despite the strength of these associations, polygenic susceptibility to obesity is not deterministic. Among those in the top decile of the GPS, 83% were overweight or obese, but 17% had a BMI within the normal range, and 0.2% were underweight ( Figure 2 D). These results were nearly identical after adjustment of the GPS for genetic background, as assessed by principal components of ancestry ( Figure S3 ).

A sensitivity analysis was performed after adjustment of the genome-wide polygenic score for genetic background, as assessed by the first ten principal components of ancestry. 288,016 middle-aged UK Biobank participants were binned into 10 deciles according to this ancestry-adjusted genome-wide 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/m 2 , normal as 18.5 to 24.9 kg/m 2 , overweight as 25.0 to 29.9 kg/m 2 , obesity as 30.0 to 39.9 kg/m 2 , and severe obesity as ≥ 40 kg/m 2 .

Relationship of Genome-wide Polygenic Score Distribution with BMI, Weight, and Severe Obesity, Including Adjustment for Principal Components of Ancestry, Related to Figure 2

We next stratified the population according to GPS decile and found a striking gradient with respect to BMI, weight, and prevalence of obesity ( Figures 2 A–2C). For example, the average BMI was 30.0 kg/mfor those in the top decile of the GPS and 25.2 kg/mfor those in the bottom decile, a difference of 4.8 kg/m(p < 0.0001). Similarly, the average weight was 85.3 kg for those in the top decile versus 72.2 kg for those in the bottom decile, a difference of 13.0 kg (p < 0.0001). 43.2% of those in the top decile were obese versus 9.5% of those in the bottom decile ( Figure S2 ). Severe obesity was present in 1,621 of 28,784 (5.6%) in the top decile of the GPS versus 69 of 28,834 (0.2%) in the bottom decile, corresponding to a 25-fold gradient in risk of severe obesity (p < 0.0001).

288,016 middle-aged UK Biobank participants were binned into 10 deciles according to the polygenic score, with significant differences in the prevalence of obesity (body mass index ≥ 30 kg/m 2 ) noted across deciles of the polygenic score (p < 0.0001).

(A–D) 288,016 middle-aged UK Biobank participants were binned into 10 deciles according to the polygenic score. BMI (A), weight (B), and prevalence of severe obesity (C) each increased across deciles of the polygenic score (p < 0.0001 for each). Significant differences in clinical categories of obesity were noted (D) when participants were stratified into three categories: bottom decile, deciles 2–9, and top decile. Underweight refers to a BMI of less than 18.5 kg/m, normal as 18.5 to 24.9 kg/m, overweight as 25.0 to 29.9 kg/m, obesity as 30.0 to 39.9 kg/m, and severe obesity as 40 kg/mor more ().

National Institutes of Health Clinical Guidelines on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults--The Evidence Report.

The GPS approximated a normal distribution in the population ( Figure S1 ). The correlation of the GPS and observed BMI was 0.29, identical to the UK Biobank validation dataset. Correlations were similar when participants were stratified into 5-year age bins, ranging from 0.28 to 0.31 ( Table S3 ).

The distribution of the genome-wide polygenic score (GPS) in 288,016 participants of the UK Biobank testing dataset is displayed. The x axis represents the polygenic score, with values scaled to a mean of 0 and standard deviation 1 to facilitate interpretation.

We determined the extent to which the GPS predicted weight and severe obesity in a testing dataset of 288,016 middle-aged participants of the UK Biobank (independent of the 119,951 validation dataset participants studied above). The participant mean age was 57 years, and 55% were female. The mean weight was 78.1 kg, and the mean BMI was 27.4 kg/m 2 . 23.9% of the participants were obese (BMI ≥ 30 kg/m 2 ), and 1.8% met criteria for severe obesity.

Having derived and validated a new polygenic predictor that considerably outperformed earlier scores, we explored the predictive power of the GPS on BMI, weight, and severe obesity in 306,135 individuals of four independent testing datasets, spanning an age spectrum from middle age to time of birth ( Table 1 ).

Our GPS of 2,100,302 variants had substantially greater predictive power than a sixth polygenic score comprised of only the 141 independent variants that had reached genome-wide levels of statistical significance in the prior GWAS. Within the 119,951 participants in the validation dataset, correlation with BMI for this 141-variant score was only 0.133. This lower strength of association using fewer variants is consistent with earlier studies, where predictors of up to 97 variants had a relatively low correlation with measured BMI, ranging from 0.01 to 0.12 ().

A genetic risk score combining 32 SNPs is associated with body mass index and improves obesity prediction in people with major depressive disorder.

Each of the five candidate GPSs was strongly associated with observed BMI (p < 0.0001), with similar correlation coefficients ranging from 0.283 to 0.292 ( Table S1 ). Nearly identical results were obtained after adjustment of each of the candidate GPSs for genetic background, as assessed by principal components of ancestry ( Table S2 ). We selected the best score, with a correlation of 0.292, to take forward into four testing datasets below. Additional details of GPS derivation and validation are provided in Figure 1 and the STAR Methods

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 effect of each on BMI from a large GWAS study () 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 (). Candidate GPSs were derived using the LDPred computational algorithm, a Bayesian approach to calculate a posterior mean effect for all variants based on prior (effect size and statistical significance in the previous GWAS) and subsequent shrinkage based on linkage disequilibrium (). The five candidate LDPred scores vary with respect to the tuning parameter ρ (that is, the proportion of variants assumed to be causal), as recommended previously. 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.

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.

We set out to validate these 5 scores and to choose the best score for further analysis by testing their ability to predict measured BMI in a validation dataset of 119,951 middle-aged adult participants of the UK Biobank. The UK Biobank enrolled participants aged 40 to 69 years from across the United Kingdom and allows linkage of measurements such as BMI to extensive genetic data (). Within this dataset, we estimated the heritability of BMI explained by common variants to be 23.4% using a recently developed approach (), consistent with prior estimates ranging from 17% to 27% ().

UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.

To create a GPS, we obtained the average effects for each of 2,100,302 genetic variants on BMI from the largest published GWAS study of obesity to date (). We used a recently developed computational algorithm to reweight each variant according to the effect size and strength of statistical significance observed in the prior GWAS, the degree of correlation between a variant and others nearby, and a tuning parameter that denotes the proportion of variants with non-zero effect size (). Because the best choice of this tuning parameter is difficult to know a priori, a range of 5 values was tested, as recommended previously ().

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.

Schizophrenia Working Group of the Psychiatric Genomics Consortium, Discovery, Biology, and Risk of Inherited Variants in Breast Cancer (DRIVE) study Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores.

Discussion

We describe a systematic approach to derive and validate a GPS, incorporating information from 2.1 million common genetic variants, to predict polygenic susceptibility to obesity and tested the polygenic score in 306,135 participants from four cohorts. The GPS accurately predicted striking differences in weight, severe obesity, cardiometabolic disease, and overall mortality in middle-aged adults, with the extreme of the GPS distribution inheriting susceptibility to obesity equivalent to rare monogenic mutations in MC4R. The score had minimal association with birthweight, but it was strongly associated with a gradient in weight that started to emerge in early childhood and even larger differences in weight and severe obesity in subsequent decades.

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We note that both a pathogenic MC4R mutation and the extreme of the GPS distribution predisposed individuals to a BMI 4.1 kg/m2 higher than that of the remainder of the population. However, despite an identical effect size, we estimate that extreme GPSs have a prevalence an order of magnitude higher than pathogenic MC4R mutations—1.6% versus 0.14%, respectively.

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Freathy R.M.

Davey Smith G.

et al. Gene-obesogenic environment interactions in the UK Biobank study. Although the average BMI has increased substantially across populations, so too has the variability within any given population, suggesting that an increasingly obesogenic environment may have led to preferential “unmasking” of inherited susceptibility among those with the highest genetic risk (). For example, prior studies suggest that the effect of unhealthy diet, physical activity, and sedentary behavior on BMI are most pronounced in those with a genetic predisposition (). The ability to identify high-risk individuals from the time of birth may facilitate targeted strategies for obesity prevention with increased effect or cost-effectiveness. Given that the weight trajectories of individuals in different GPS deciles start to diverge in early childhood, such interventions may have maximal effect when employed early in life.

Khera et al., 2019 Khera A.V.

Chaffin M.

Zekavat S.M.

Collins R.L.

Roselli C.

Natarajan P.

Lichtman J.H.

D’Onofrio G.

Mattera J.A.

Dreyer R.P.

et al. Whole-Genome Sequencing to Characterize Monogenic and Polygenic Contributions in Patients Hospitalized with Early-Onset Myocardial Infarction. The GPS may also accelerate research insights into the molecular and physiological basis of severe obesity. Traditional research approaches have compared the physiology of severely obese individuals with lean controls. However, it can be difficult to draw inferences from such studies because the observed differences might be either a cause or a consequence of severe obesity. The GPS permits identification of individuals, from the time of birth, who inherit high susceptibility and before clinical disease is manifest. Careful study of individuals at the extremes of a GPS distribution might uncover new causal risk factors or pathways underlying disease. For example, healthy individuals with a high polygenic score for heart attack were enriched for higher blood pressure, increased cholesterol levels, and increased rates of type 2 diabetes; each of these is a well-known and modifiable clinical risk factor (). Similarly, clinical and multi-omic profiling of those at the extremes of a GPS distribution for obesity may uncover the contributions and molecular correlates of pathways related to appetite regulation, fat storage, and microbiome perturbation and might enable identification of clinically relevant subtypes of severe obesity that most benefit from a given pharmacologic or behavioral intervention.

Individuals who maintain normal weight despite an unfavorable GPS or develop severe obesity despite a favorable GPS may be of particular interest. The discordance between polygenic susceptibility and clinical phenotype in these individuals could result from a disproportionate influence of environment, the effect of a rare large-effect mutation not captured by the polygenic score, or other undetermined factors.

Finally, a clear understanding of the genetic predisposition to obesity may help to destigmatize obesity among patients, their health care providers, and the general public.