Polygenic Risk Scores Expand to Obesity

Torkamani, Ali; Topol, Eric J.

doi:10.1016/j.cell.2019.03.051

Cited by 32 publications

(25 citation statements)

References 8 publications

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“…The second remark that could be extracted from our results is the fact that the overweight status seems to be a midway phenotype (between normal weight and obesity), in which genetics might not play a determinant role (Supplementary Figure S1). Although both remarks had been previously described in the literature [18,22], our approach reinforces these hypotheses and adds novel insights for Iberian populations in Spain, which is quite important considering the well-known genetic interpopulations variability within the European ancestry [43]. All these findings from our cross-sectional study populations 1 and 3 were independently validated also at each pubertal stage of the study population 2 (please see Results Section 4.2.).…”

Section: Discussionsupporting

confidence: 81%

“…The inclusion of adult-BMI SNPs under a pGRS could serve therefore as an excellent predictive, and preventive, tool for facing the obesity-associated morbidity and mortality from the early periods of life. Although some previous studies have already investigated the utility of adult-BMI pGRSs for the management of obesity in children [18][19][20][21][22], no study to date has addressed the question focusing in cardio-metabolic alterations, and never under a longitudinal design comprising the metabolically risky period of puberty. In fact, puberty has been designated as the life stage where the majority of obesity-associated cardio-metabolic derangements arise [23].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Anguita‐Ruiz

González-Gil

Rupérez

et al. 2020

JCM

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Polygenetic risk scores (pGRSs) consisting of adult body mass index (BMI) genetic variants have been widely associated with obesity in children populations. The implication of such obesity pGRSs in the development of cardio-metabolic alterations during childhood as well as their utility for the clinical prediction of pubertal obesity outcomes has been barely investigated otherwise. In the present study, we evaluated the utility of an adult BMI predisposing pGRS for the prediction and pharmacological management of obesity in Spanish children, further investigating its implication in the appearance of cardio-metabolic alterations. For that purpose, we counted on genetics data from three well-characterized children populations (composed of 574, 96 and 124 individuals), following both cross-sectional and longitudinal designs, expanding childhood and puberty. As a result, we demonstrated that the pGRS is strongly associated with childhood BMI Z-Score (B = 1.56, SE = 0.27 and p-value = 1.90 × 10−8), and that could be used as a good predictor of obesity longitudinal trajectories during puberty. On the other hand, we showed that the pGRS is not associated with cardio-metabolic comorbidities in children and that certain environmental factors interact with the genetic predisposition to the disease. Finally, according to the results derived from a weight-reduction metformin intervention in children with obesity, we discarded the utility of the pGRS as a pharmacogenetics marker of metformin response.

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Section: Discussionsupporting

confidence: 81%

Section: Introductionmentioning

confidence: 99%

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Anguita‐Ruiz

González-Gil

Rupérez

et al. 2020

JCM

View full text Add to dashboard Cite

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“…2,11 PGS has a long-standing history both in animal breeding programs and in human genetics. 12 PGS has also been widely applied to a range of genetic applications that include disease risk prediction, [13][14][15][16][17][18][19][20][21][22][23][24][25] genetic prediction of complex traits, 14,15,17,19,[26][27][28][29][30] prioritization of preventive interventions, [31][32][33][34][35][36] understanding missing heritability, [37][38][39][40] modeling polygenic adaptation, 41 genomic selection in animal breeding programs, 42,43 transcriptome-wide association studies (TWASs), [44][45][46] and, more recently, Mendelian randomization analysis. [47][48][49] Accurate construction of PGS can facilitate disease prevention and intervention at an early stage and can aid in the development of personalized medicine.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

Yang

Zhou

2020

The American Journal of Human Genetics

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Accurate construction of polygenic scores (PGS) can enable early diagnosis of diseases and facilitate the development of personalized medicine. Accurate PGS construction requires prediction models that are both adaptive to different genetic architectures and scalable to biobank scale datasets with millions of individuals and tens of millions of genetic variants. Here, we develop such a method called Deterministic Bayesian Sparse Linear Mixed Model (DBSLMM). DBSLMM relies on a flexible modeling assumption on the effect size distribution to achieve robust and accurate prediction performance across a range of genetic architectures. DBSLMM also relies on a simple deterministic search algorithm to yield an approximate analytic estimation solution using summary statistics only. The deterministic search algorithm, when paired with further algebraic innovations, results in substantial computational savings. With simulations, we show that DBSLMM achieves scalable and accurate prediction performance across a range of realistic genetic architectures. We then apply DBSLMM to analyze 25 traits in UK Biobank. For these traits, compared to existing approaches, DBSLMM achieves an average of 2.03%-101.09% accuracy gain in internal cross-validations. In external validations on two separate datasets, including one from Bio-Bank Japan, DBSLMM achieves an average of 14.74%-522.74% accuracy gain. In these real data applications, DBSLMM is 1.03-28.11 times faster and uses only 7.4%-24.8% of physical memory as compared to other multiple regression-based PGS methods. Overall, DBSLMM represents an accurate and scalable method for constructing PGS in biobank scale datasets.

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“…A 13 kg gradient in weight and a 25-fold gradient in risk of severe obesity were observed in adults across GPRS deciles. Although practical considerations on how such a GPRS might be implemented and inform interventions for obesity prevention remain (73) ; and methodological and clinical utility questions have been raised (74) about an equally novel GPRS for coronary artery disease (71) . Nonetheless, these GPRS studies call into question any DTC genetic test and personalised nutrition advice around body weight made on a handful of SNPs.…”

Section: Personalised Nutrition and Consumer Genomicsmentioning

confidence: 99%

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

Moore

2020

Proc. Nutr. Soc.

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Advances in genomics generated the concept that a better understanding of individual characteristics, e.g. genotype, will lead to improved tailoring of pharmaceutical and nutritional therapies. Subsequent developments in proteomics and metabolomics, in addition to wearable technologies for tracking parameters, such as dietary intakes, physical activity, heart rate and blood glucose, have further driven this idea. Alongside these innovations, there has been a rapid rise in companies offering direct-to-consumer genetic and/or microbiome testing, in combination with the marketing of personalised nutrition services. Key scientific questions include how disparate datasets are integrated, how accurate are current predictions and how these may be developed in the future. In this regard, lessons can be learned from systems biology, which aims both to integrate data from different levels of organisation (e.g. genomic, proteomic and metabolomic) and predict the emergent behaviours of biological systems or organisms as a whole. The present paper reviews the origins and recent advancement of ‘big data’ and systems approaches in medicine and nutrition. Conclusions are that systems integration of multiple technologies has generated mechanistic insights and informed the evolution of precision medicine and personalised nutrition. Pertinent ethical issues include who is entitled to access new technologies and how commercial companies are storing, using and/or re-mining consumer data. Questions about efficacy (both long-term behavioural change and health outcomes), cost-benefit and impacts on health inequalities remain to be fully addressed.

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Polygenic Risk Scores Expand to Obesity

Cited by 32 publications

References 8 publications

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Evaluation of the Predictive Ability, Environmental Regulation and Pharmacogenetics Utility of a BMI-Predisposing Genetic Risk Score during Childhood and Puberty

Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets

From personalised nutrition to precision medicine: the rise of consumer genomics and digital health

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