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Abstract
Genetics is now known to play a substantial role in the predisposition to obesity and may contribute up to 70% risk for the disease. Over a hundred genes and gene variants related to excess weight have been discovered. Yet, genetic obesity risk does not always translate into actual obesity development, suggesting complex interactions between genetic, behavioral, and environmental influences and resulting epigenetic changes. Rare but serious forms of monogenic obesity typically appear in early childhood. Polygenic obesity is most common and demonstrates strong interplay between genes and the obesogenic environment. This review provides an overview of genetic causes of obesity, potential mechanisms of epigenetic changes, and environmental influences that should diminish obesity bias and offer hope for more effective obesity prevention and intervention strategies.
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352
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Braakhuis A, Monnard CR, Ellis A, Rozga M. Consensus Report of the Academy of Nutrition and Dietetics: Incorporating Genetic Testing into Nutrition Care. J Acad Nutr Diet 2020; 121:545-552. [PMID: 32624395 DOI: 10.1016/j.jand.2020.04.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Indexed: 12/15/2022]
Abstract
Personalization of nutrition advice is a process already familiar to registered dietitian nutritionists, but it is not yet clear whether incorporating genetic results as an added layer of precision improves nutrition-related outcomes. Therefore, an independent workgroup of experts, supported by the Academy's Evidence Analysis Center staff, conducted a systematic review to examine the level of evidence measuring the effect of incorporating genetic testing results into nutrition counseling and care, compared to an alternative intervention or control group, on nutrition-related outcomes. This systematic review revealed that only weak quality evidence is available in the scientific literature and observed that this field is still maturing. Therefore, at present, there is insufficient scientific evidence to determine whether there are effects of incorporating genetic testing into nutrition practice. The workgroup prepared this Consensus Report based on this systematic review to provide considerations for the practical application of incorporating genetic testing into the nutrition care process.
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Affiliation(s)
- Andrea Braakhuis
- Faculty of Medical and Health Science, Discipline of Nutrition, The University of Auckland, Grafton, Auckland, New Zealand
| | | | - Amy Ellis
- University of Alabama, Tuscaloosa, AL
| | - Mary Rozga
- Academy of Nutrition and Dietetics Evidence Analysis Center, Chicago, IL.
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353
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Ramsey AT, Bray M, Acayo Laker P, Bourdon JL, Dorsey A, Zalik M, Pietka A, Salyer P, Waters EA, Chen LS, Bierut LJ. Participatory Design of a Personalized Genetic Risk Tool to Promote Behavioral Health. Cancer Prev Res (Phila) 2020; 13:583-592. [PMID: 32209550 PMCID: PMC7335332 DOI: 10.1158/1940-6207.capr-20-0029] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 02/28/2020] [Accepted: 03/20/2020] [Indexed: 02/07/2023]
Abstract
Despite major advancements in genomic medicine, research to optimize the design and communication of genetically informed interventions in behavioral health has lagged. The goal of this study was to engage potential end users in participatory codesign of a personalized genetically informed risk tool to intervene on high-risk health behaviors. We used structured interviews to examine end-user attitudes and interest in personalized genetics, qualitative interviews to guide iterative design of a genetically informed tool, and questionnaires to assess acceptability and potential utility of the tool. Participants expressed strong demand for using personal genetics to inform smoking and alcohol-related disease risk and guide treatment (78%-95% agreed). Via iterative design feedback, we cocreated a genetically informed risk profile featuring (i) explanation of genetic and phenotypic markers used to construct a risk algorithm, (ii) personalized risks and benefits of healthy behavior change, and (iii) recommended actions with referral to freely available resources. Participants demonstrated sufficient understanding and cited motivating behavior change as the most useful purpose of the tool. In three phases, we confirmed strong desire for personalized genetics on high-risk health behaviors; codesigned a genetically informed profile with potential end users; and found high acceptability, comprehensibility, and perceived usefulness of the profile. As scientific discovery of genomic medicine advances in behavioral health, we must develop the tools to communicate these discoveries to consumers who stand to benefit. The potential of genomic medicine to engage populations and personalize behavioral health treatment depends, in part, on preparatory studies to design for the future implementation of genetically informed interventions.
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Affiliation(s)
- Alex T Ramsey
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.
| | - Michael Bray
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Penina Acayo Laker
- Communication Design, Sam Fox School of Design & Visual Arts, Washington University, St. Louis, Missouri
| | - Jessica L Bourdon
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Amelia Dorsey
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Maia Zalik
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Amanda Pietka
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Patricia Salyer
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Erika A Waters
- Department of Surgery, Washington University School of Medicine, St. Louis, Missouri
| | - Li-Shiun Chen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
| | - Laura J Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri
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354
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Forgetta V, Keller-Baruch J, Forest M, Durand A, Bhatnagar S, Kemp JP, Nethander M, Evans D, Morris JA, Kiel DP, Rivadeneira F, Johansson H, Harvey NC, Mellström D, Karlsson M, Cooper C, Evans DM, Clarke R, Kanis JA, Orwoll E, McCloskey EV, Ohlsson C, Pineau J, Leslie WD, Greenwood CMT, Richards JB. Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study. PLoS Med 2020; 17:e1003152. [PMID: 32614825 PMCID: PMC7331983 DOI: 10.1371/journal.pmed.1003152] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 06/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
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Affiliation(s)
- Vincenzo Forgetta
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | | | - Marie Forest
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Audrey Durand
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - Sahir Bhatnagar
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - John P. Kemp
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Maria Nethander
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - John A. Morris
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Douglas P. Kiel
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard University, Boston, Massachusetts, United States of America
| | | | - Helena Johansson
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Nicholas C. Harvey
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Karlsson
- Department of Orthopaedics and Clinical Sciences, Lund University, Skane University Hospital, Malmö, Sweden
| | - Cyrus Cooper
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - David M. Evans
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom
| | - John A. Kanis
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Eric Orwoll
- Bone and Mineral Unit, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Eugene V. McCloskey
- Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield and Sheffield Teaching Hospitals Foundation Trust, Sheffield, United Kingdom
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joelle Pineau
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - William D. Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Celia M. T. Greenwood
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
| | - J. Brent Richards
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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355
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Chami N, Preuss M, Walker RW, Moscati A, Loos RJF. The role of polygenic susceptibility to obesity among carriers of pathogenic mutations in MC4R in the UK Biobank population. PLoS Med 2020; 17:e1003196. [PMID: 32692746 PMCID: PMC7373259 DOI: 10.1371/journal.pmed.1003196] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 06/16/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Melanocortin 4 receptor (MC4R) deficiency, caused by mutations in MC4R, is the most common cause of monogenic forms of obesity. However, these mutations have often been identified in small-scale, case-focused studies. Here, we assess the penetrance of previously reported MC4R mutations at a population level. Furthermore, we examine why some carriers of pathogenic mutations remain of normal weight, to gain insight into the mechanisms that control body weight. METHODS AND FINDINGS We identified 59 known obesity-increasing mutations in MC4R from the Human Gene Mutation Database (HGMD) and Clinvar. We assessed their penetrance and effect on obesity (body mass index [BMI] ≥ 30 kg/m2) in >450,000 individuals (age 40-69 years) of the UK Biobank, a population-based cohort study. Of these 59 mutations, only 11 had moderate-to-high penetrance and increased the odds of obesity by more than 2-fold. We subsequently focused on these 11 mutations and examined differences between carriers of normal weight and carriers with obesity. Twenty-eight of the 182 carriers of these 11 mutations were of normal weight. Body composition of carriers of normal weight was similar to noncarriers of normal weight, whereas among individuals with obesity, carriers had a somewhat higher BMI than noncarriers (1.44 ± 0.07 standard deviation scores [SDSs] ± standard error [SE] versus 1.29 ± 0.001, P = 0.03), because of greater lean mass (1.44 ± 0.09 versus 1.15 ± 0.002, P = 0.002). Carriers of normal weight more often reported that, already at age 10 years, their body size was below average or average (72%) compared with carriers with obesity (48%) (P = 0.01). To assess the polygenic contribution to body weight in carriers of normal weight and carriers with obesity, we calculated a genome-wide polygenic risk score for BMI (PRSBMI). The PRSBMI of carriers of normal weight (PRSBMI = -0.64 ± 0.18) was significantly lower than of carriers with obesity (0.40 ± 0.11; P = 1.7 × 10-6), and tended to be lower than that of noncarriers of normal weight (-0.29 ± 0.003; P = 0.05). Among carriers, those with a low PRSBMI (bottom quartile) have an approximately 5-kg/m2 lower BMI (approximately 14 kg of body weight for a 1.7-m-tall person) than those with a high PRS (top quartile). Because the UK Biobank population is healthier than the general population in the United Kingdom, penetrance may have been somewhat underestimated. CONCLUSIONS We showed that large-scale data are needed to validate the impact of mutations observed in small-scale and case-focused studies. Furthermore, we observed that despite the key role of MC4R in obesity, the effects of pathogenic MC4R mutations may be countered, at least in part, by a low polygenic risk potentially representing other innate mechanisms implicated in body weight regulation.
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Affiliation(s)
- Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ryan W. Walker
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Ruth J. F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
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356
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Abstract
Risk prediction models have been developed in many contexts to classify individuals according to a single outcome, such as risk of a disease. Emerging “-omic” biomarkers provide panels of features that can simultaneously predict multiple outcomes from a single biological sample, creating issues of multiplicity reminiscent of exploratory hypothesis testing. Here I propose definitions of some basic criteria for evaluating prediction models of multiple outcomes. I define calibration in the multivariate setting and then distinguish between outcome-wise and individual-wise prediction, and within the latter between joint and panel-wise prediction. I give examples such as screening and early detection in which different senses of prediction may be more appropriate. In each case I propose definitions of sensitivity, specificity, concordance, positive and negative predictive value and relative utility. I link the definitions through a multivariate probit model, showing that the accuracy of a multivariate prediction model can be summarised by its covariance with a liability vector. I illustrate the concepts on a biomarker panel for early detection of eight cancers, and on polygenic risk scores for six common diseases.
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Affiliation(s)
- Frank Dudbridge
- Frank Dudbridge, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK.
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357
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Using polygenic scores for identifying individuals at increased risk of substance use disorders in clinical and population samples. Transl Psychiatry 2020; 10:196. [PMID: 32555147 PMCID: PMC7303212 DOI: 10.1038/s41398-020-00865-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 05/22/2020] [Accepted: 05/26/2020] [Indexed: 11/11/2022] Open
Abstract
Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability. There is growing evidence that PRS may prove useful for early identification of those at increased risk for certain diseases. The current potential of PRS for alcohol use disorders (AUD) remains an open question. Using data from both a population-based sample [the FinnTwin12 (FT12) study] and a high-risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of (1) alcohol dependence/alcohol problems, (2) alcohol consumption, and (3) risky behaviors with AUD and other substance use disorder (SUD) criteria. These PRSs explain ~2.5-3.5% of the variance in AUD (across FT12 and COGA) when all PRSs are included in the same model. Calculations of area under the curve (AUC) show PRS provide only a slight improvement over a model with age, sex, and ancestral principal components as covariates. While individuals in the top 20, 10, and 5% of the PRS distribution had greater odds of having an AUD compared to the lower end of the continuum in both COGA and FT12, the point estimates at each threshold were statistically indistinguishable. Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis and opioid) SUD criteria. PRSs are associated with risk for SUD in independent samples. However, usefulness for identifying those at increased risk in their current form is modest, at best. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.
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358
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Efficient polygenic risk scores for biobank scale data by exploiting phenotypes from inferred relatives. Nat Commun 2020; 11:3074. [PMID: 32555176 PMCID: PMC7299943 DOI: 10.1038/s41467-020-16829-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 05/25/2020] [Indexed: 01/06/2023] Open
Abstract
Polygenic risk scores are emerging as a potentially powerful tool to predict future phenotypes of target individuals, typically using unrelated individuals, thereby devaluing information from relatives. Here, for 50 traits from the UK Biobank data, we show that a design of 5,000 individuals with first-degree relatives of target individuals can achieve a prediction accuracy similar to that of around 220,000 unrelated individuals (mean prediction accuracy = 0.26 vs. 0.24, mean fold-change = 1.06 (95% CI: 0.99-1.13), P-value = 0.08), despite a 44-fold difference in sample size. For lifestyle traits, the prediction accuracy with 5,000 individuals including first-degree relatives of target individuals is significantly higher than that with 220,000 unrelated individuals (mean prediction accuracy = 0.22 vs. 0.16, mean fold-change = 1.40 (1.17-1.62), P-value = 0.025). Our findings suggest that polygenic prediction integrating family information may help to accelerate precision health and clinical intervention. Genetic data from large cohorts of unrelated individuals can be used to create polygenic risk scores, which could be used to predict individual risk of developing a specific disease. Here the authors show that smaller cohorts of related individuals can provide similarly powerful predictive ability.
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359
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Exclusive breastfeeding can attenuate body-mass-index increase among genetically susceptible children: A longitudinal study from the ALSPAC cohort. PLoS Genet 2020; 16:e1008790. [PMID: 32525877 PMCID: PMC7289340 DOI: 10.1371/journal.pgen.1008790] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 04/22/2020] [Indexed: 02/02/2023] Open
Abstract
Recent discoveries from large-scale genome-wide association studies (GWASs) explain a larger proportion of the genetic variability to BMI and obesity. The genetic risk associated with BMI and obesity can be assessed by an obesity-specific genetic risk score (GRS) constructed from genome-wide significant genetic variants. The aim of our study is to examine whether the duration and exclusivity of breastfeeding can attenuate BMI increase during childhood and adolescence due to genetic risks. A total sample of 5,266 children (2,690 boys and 2,576 girls) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was used for the analysis. We evaluated the role of breastfeeding (exclusivity and duration) in modulating BMI increase attributed to the GRS from birth to 18 years of age. The GRS was composed of 69 variants associated with adult BMI and 25 non-overlapping SNPs associated with pediatric BMI. In the high genetic susceptible group (upper GRS quartile), exclusive breastfeeding (EBF) to 5 months reduces BMI by 1.14 kg/m2 (95% CI, 0.37 to 1.91, p = 0.0037) in 18-year-old boys, which compensates a 3.9-decile GRS increase. In 18-year-old girls, EBF to 5 months decreases BMI by 1.53 kg/m2 (95% CI, 0.76 to 2.29, p<0.0001), which compensates a 7.0-decile GRS increase. EBF acts early in life by delaying the age at adiposity peak and at adiposity rebound. EBF to 3 months or non-exclusive breastfeeding was associated with a significantly diminished impact on reducing BMI growth during childhood. EBF influences early life growth and development and thus may play a critical role in preventing overweight and obesity among children at high-risk due to genetic factors. Previous studies have shown that EBF is associated with lower BMI during childhood and adolescence. Moreover, a GRS based on 97 genetic variants has been derived from large GWASs and is predictive of BMI in adults and children. However, it remains unclear whether EBF can attenuate the increase in BMI attributed to the GRS in children. Our study was able to characterize the effect of the GRS in children from birth to 18 years of age. Our main results showed that EBF to 5 months has substantial effect in decreasing BMI among children at higher genetic risks. EBF to 3 months or non-exclusive breastfeeding had a significantly diminished effect on reducing BMI growth during childhood. Our study suggests that interventions aimed at reducing the risks of overweight and obesity across the lifespan should start in very early childhood to be impactful, which makes EBF a key candidate intervention. While EBF is beneficial to all children, targeting those carrying multiple BMI/obesity alleles should be a priority to reduce obesity and associated non-communicable diseases.
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360
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Fernandez-Rhodes L, Young KL, Lilly AG, Raffield LM, Highland HM, Wojcik GL, Agler C, M Love SA, Okello S, Petty LE, Graff M, Below JE, Divaris K, North KE. Importance of Genetic Studies of Cardiometabolic Disease in Diverse Populations. Circ Res 2020; 126:1816-1840. [PMID: 32496918 PMCID: PMC7285892 DOI: 10.1161/circresaha.120.315893] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Genome-wide association studies have revolutionized our understanding of the genetic underpinnings of cardiometabolic disease. Yet, the inadequate representation of individuals of diverse ancestral backgrounds in these studies may undercut their ultimate potential for both public health and precision medicine. The goal of this review is to describe the imperativeness of studying the populations who are most affected by cardiometabolic disease, to the aim of better understanding the genetic underpinnings of the disease. We support this premise by describing the current variation in the global burden of cardiometabolic disease and emphasize the importance of building a globally and ancestrally representative genetics evidence base for the identification of population-specific variants, fine-mapping, and polygenic risk score estimation. We discuss the important ethical, legal, and social implications of increasing ancestral diversity in genetic studies of cardiometabolic disease and the challenges that arise from the (1) lack of diversity in current reference populations and available analytic samples and the (2) unequal generation of health-associated genomic data and their prediction accuracies. Despite these challenges, we conclude that additional, unprecedented opportunities lie ahead for public health genomics and the realization of precision medicine, provided that the gap in diversity can be systematically addressed. Achieving this goal will require concerted efforts by social, academic, professional and regulatory stakeholders and communities, and these efforts must be based on principles of equity and social justice.
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Affiliation(s)
- Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, Pennsylvania State University, University Park, PA
| | - Kristin L Young
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Adam G Lilly
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Heather M Highland
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Cary Agler
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Shelly-Ann M Love
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Samson Okello
- Department of Internal Medicine, Mbarara University of Science and Technology, Uganda
- University of Virginia, Charlottesville, VA
- Harvard TH Chan School of Public Health, Boston, MA
| | - Lauren E Petty
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt, TN
- Department of Genetic Medicine, Vanderbilt University, Vanderbilt, TN
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jennifer E Below
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt, TN
- Department of Genetic Medicine, Vanderbilt University, Vanderbilt, TN
| | - Kimon Divaris
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Pediatric and Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Kari E. North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Center for Genome Sciences, Chapel Hill, NC
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361
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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. J Clin Med 2020; 9:jcm9061705. [PMID: 32498346 PMCID: PMC7355743 DOI: 10.3390/jcm9061705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 05/20/2020] [Accepted: 05/29/2020] [Indexed: 11/28/2022] Open
Abstract
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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362
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From personalised nutrition to precision medicine: the rise of consumer genomics and digital health. Proc Nutr Soc 2020; 79:300-310. [PMID: 32468984 DOI: 10.1017/s0029665120006977] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
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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363
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Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments. Genome Med 2020; 12:44. [PMID: 32423490 PMCID: PMC7236300 DOI: 10.1186/s13073-020-00742-5] [Citation(s) in RCA: 573] [Impact Index Per Article: 143.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 05/01/2020] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies have shown unequivocally that common complex disorders have a polygenic genetic architecture and have enabled researchers to identify genetic variants associated with diseases. These variants can be combined into a polygenic risk score that captures part of an individual's susceptibility to diseases. Polygenic risk scores have been widely applied in research studies, confirming the association between the scores and disease status, but their clinical utility has yet to be established. Polygenic risk scores may be used to estimate an individual's lifetime genetic risk of disease, but the current discriminative ability is low in the general population. Clinical implementation of polygenic risk score (PRS) may be useful in cohorts where there is a higher prior probability of disease, for example, in early stages of diseases to assist in diagnosis or to inform treatment choices. Important considerations are the weaker evidence base in application to non-European ancestry and the challenges in translating an individual's PRS from a percentile of a normal distribution to a lifetime disease risk. In this review, we consider how PRS may be informative at different points in the disease trajectory giving examples of progress in the field and discussing obstacles that need to be addressed before clinical implementation.
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Affiliation(s)
- Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, de Crespigny Park, London, SE5 8AF, UK.
- Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London, London, UK.
| | - Evangelos Vassos
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, de Crespigny Park, London, SE5 8AF, UK
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364
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Lowry E, Rautio N, Wasenius N, Bond TA, Lahti J, Tzoulaki I, Dehghan A, Heiskala A, Ala-Mursula L, Miettunen J, Eriksson J, Järvelin MR, Sebert S. Early exposure to social disadvantages and later life body mass index beyond genetic predisposition in three generations of Finnish birth cohorts. BMC Public Health 2020; 20:708. [PMID: 32423423 PMCID: PMC7236362 DOI: 10.1186/s12889-020-08763-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 04/22/2020] [Indexed: 12/14/2022] Open
Abstract
Background The study aimed to explore the association between early life and life-course exposure to social disadvantage and later life body mass index (BMI) accounting for genetic predisposition and maternal BMI. Methods We studied participants of Helsinki Birth Cohort Study born in 1934–1944 (HBCS1934–1944, n = 1277) and Northern Finland Birth Cohorts born in 1966 and 1986 (NFBC1966, n = 5807, NFBC1986, n = 6717). Factor analysis produced scores of social disadvantage based on social and economic elements in early life and adulthood/over the life course, and was categorized as high, intermediate and low. BMI was measured at 62 years in HBCS1934–1944, at 46 years in NFBC1966 and at 16 years in NFBC1986. Multivariable linear regression analysis was used to explore associations between social disadvantages and BMI after adjustments for polygenic risk score for BMI (PRS BMI), maternal BMI and sex. Results The association between exposure to high early social disadvantage and increased later life BMI persisted after adjustments (β = 0.79, 95% CI, 0.33, 1.25, p < 0.001) in NFBC1966. In NFBC1986 this association was attenuated by PRS BMI (p = 0.181), and in HBCS1934–1944 there was no association between high early social disadvantage and increased later life BMI (β 0.22, 95% CI –0.91,1.35, p = 0.700). In HBCS1934–1944 and NFBC1966, participants who had reduced their exposure to social disadvantage during the life-course had lower later life BMI than those who had increased their exposure (β − 1.34, [− 2.37,-0.31], p = 0.011; β − 0.46, [− 0.89,-0.03], p = 0.038, respectively). Conclusions High social disadvantage in early life appears to be associated with higher BMI in later life. Reducing exposure to social disadvantage during the life-course may be a potential pathway for obesity reduction.
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Affiliation(s)
- Estelle Lowry
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland.,Biocenter Oulu, University of Oulu, P.O.Box 8000, Fin-90014, Oulu, Finland.,School of Natural and Built Environment, Queen's University Belfast, Elmwood Avenue Belfast, Belfast, BT7 1NN, UK
| | - Nina Rautio
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland. .,Unit of Primary Health Care, Oulu University Hospital, P.O.Box 10, 90029 OYS, Oulu, Finland.
| | - Niko Wasenius
- Folkhälsan Research Center, Haartmanninkatu 8, 00290, Helsinki, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, P.O. Box 20, 00014, Helsinki, Finland
| | - Tom A Bond
- Department of Epidemiology and Biostatistics, Imperial College, London, SW7 2AZ, UK
| | - Jari Lahti
- Department of Psychology and Logopedics, University of Helsinki, P.O.Box 63, 00014, Helsinki, Finland.,Turku Institute for Advanced Studies, University of Turku, FI-20014, Turku, Finland
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, Imperial College, London, SW7 2AZ, UK.,Department of Hygiene and Epidemiology, University of Ioannina Medical School, 45110, Ioannina, Greece
| | - Abbas Dehghan
- Department of Biostatistics and Epidemiology, MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, W2 1PG, UK
| | - Anni Heiskala
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland
| | - Leena Ala-Mursula
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland
| | - Jouko Miettunen
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland.,Medical Research Center Oulu, Oulu University Hospital and University of Oulu, P.O. Box 8000, FI-90014, Oulu, Finland
| | - Johan Eriksson
- Folkhälsan Research Center, Haartmanninkatu 8, 00290, Helsinki, Finland.,Department of General Practice and Primary Health Care, University of Helsinki, P.O. Box 20, 00014, Helsinki, Finland.,Department of Obstetrics and Gynecology, National University of Singapore, Yong Loo Lin School of Medicine, Singapore, SG, Singapore.,Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore, Singapore
| | - Marjo-Riitta Järvelin
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland.,Department of Epidemiology and Biostatistics, Imperial College, London, SW7 2AZ, UK
| | - Sylvain Sebert
- Center for Life Course Health Research, University of Oulu, P.O.Box 5000, Fin-90014, Oulu, Finland.,Biocenter Oulu, University of Oulu, P.O.Box 8000, Fin-90014, Oulu, Finland.,Department of Genomics, Imperial College London, London, SW7 2AZ, UK
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365
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Cano-Gamez E, Trynka G. From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front Genet 2020; 11:424. [PMID: 32477401 PMCID: PMC7237642 DOI: 10.3389/fgene.2020.00424] [Citation(s) in RCA: 269] [Impact Index Per Article: 67.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Accepted: 04/06/2020] [Indexed: 12/19/2022] Open
Abstract
Genome-wide association studies (GWAS) have successfully mapped thousands of loci associated with complex traits. These associations could reveal the molecular mechanisms altered in common complex diseases and result in the identification of novel drug targets. However, GWAS have also left a number of outstanding questions. In particular, the majority of disease-associated loci lie in non-coding regions of the genome and, even though they are thought to play a role in gene expression regulation, it is unclear which genes they regulate and in which cell types or physiological contexts this regulation occurs. This has hindered the translation of GWAS findings into clinical interventions. In this review we summarize how these challenges have been addressed over the last decade, with a particular focus on the integration of GWAS results with functional genomics datasets. Firstly, we investigate how the tissues and cell types involved in diseases can be identified using methods that test for enrichment of GWAS variants in genomic annotations. Secondly, we explore how to find the genes regulated by GWAS loci using methods that test for colocalization of GWAS signals with molecular phenotypes such as quantitative trait loci (QTLs). Finally, we highlight potential future research avenues such as integrating GWAS results with single-cell sequencing read-outs, designing functionally informed polygenic risk scores (PRS), and validating disease associated genes using genetic engineering. These tools will be crucial to identify new drug targets for common complex diseases.
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Affiliation(s)
- Eddie Cano-Gamez
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Gosia Trynka
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
- Open Targets, Wellcome Genome Campus, Cambridge, United Kingdom
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366
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Czepiel KS, Perez NP, Campoverde Reyes KJ, Sabharwal S, Stanford FC. Pharmacotherapy for the Treatment of Overweight and Obesity in Children, Adolescents, and Young Adults in a Large Health System in the US. Front Endocrinol (Lausanne) 2020; 11:290. [PMID: 32477270 PMCID: PMC7237714 DOI: 10.3389/fendo.2020.00290] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/17/2020] [Indexed: 12/21/2022] Open
Abstract
Lifestyle modifications focused on diet, physical activity, and behavior have a modest impact on weight reduction in children, adolescents, and young adults (YA) with overweight and obesity. Several anti-obesity medications (AOMs) have been approved by the Food and Drug Administration (FDA) for use among adult patients with a body mass index (BMI) ≥27 kg/m2 and at least one obesity-related illness. However, only two FDA-approved AOMs are available for use in children and adolescents, which leads to the frequent off-label use of adult AOMs among this population. We sought to investigate current prescribing patterns of AOMs from school age through to young adulthood in a large unified health system. Using a centralized clinical data registry containing the health data of ~6.5 million patients, individuals aged 5-25 years old with overweight and obesity who were taking one of eight commonly prescribed AOMs from 2009 to 2018 were extracted. A total of 1,720 patients were identified, representing 2,210 medication prescribing instances. The cohort was further stratified as children (5-12 years old), adolescents (13-18 years old), and YA (19-25 years old). The mean BMI at the time of medication initiation was 34.0, 39.1, and 39.6 kg/m2, respectively, which corresponded to a BMI z-score (BMIz) of 2.4 and 2.3 for children and adolescents, respectively. Metformin was the most commonly prescribed medication across all ages, including off-label use for weight-loss among children and adolescents. The most commonly off-label prescribed AOM among YA was topiramate. Multivariable analyses demonstrated phentermine was the most effective AOM, with a 1.54% total body weight among YA (p = 0.05) and a 0.12 decrease in BMIz among adolescents (p = 0.003) greater final weight loss when compared to the respective overall frequency-weighted means. Our study demonstrates a statistically significant weight loss among adolescents and young adults on select pharmacotherapy. The small magnitude of this effect should be interpreted carefully, as it is likely an underestimate in the absence of a true control group. Pharmacotherapy should therefore be considered in conjunction with other multimodal therapies such as lifestyle modification and metabolic and bariatric surgery when treating overweight and obesity.
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Affiliation(s)
- Kathryn S. Czepiel
- Department of Pediatrics, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Numa P. Perez
- Department of General Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Karen J. Campoverde Reyes
- Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Liver Research Center, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Shreya Sabharwal
- Department of Bioethics, Harvard Medical School, Boston, MA, United States
| | - Fatima Cody Stanford
- Neuroendocrine Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Pediatric Endocrinology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- MGH Weight Center, Massachusetts General Hospital, Boston, MA, United States
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367
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Yang S, Zhou X. Accurate and Scalable Construction of Polygenic Scores in Large Biobank Data Sets. Am J Hum Genet 2020; 106:679-693. [PMID: 32330416 PMCID: PMC7212266 DOI: 10.1016/j.ajhg.2020.03.013] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 03/30/2020] [Indexed: 01/24/2023] Open
Abstract
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 BioBank 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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Affiliation(s)
- Sheng Yang
- Department of Biostatistics, Nanjing Medical University, Nanjing, Jiangsu 211166, China; Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA; Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.
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368
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Krawczyk M, Liebe R, Lammert F. Toward Genetic Prediction of Nonalcoholic Fatty Liver Disease Trajectories: PNPLA3 and Beyond. Gastroenterology 2020; 158:1865-1880.e1. [PMID: 32068025 DOI: 10.1053/j.gastro.2020.01.053] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 01/29/2020] [Accepted: 01/29/2020] [Indexed: 12/14/2022]
Abstract
Nonalcoholic fatty liver disease (NAFLD) is on the verge of becoming the leading cause of liver disease. NAFLD develops at the interface between environmental factors and inherited predisposition. Genome-wide association studies, followed by exome-wide analyses, led to identification of genetic risk variants (eg, PNPLA3, TM6SF2, and SERPINA1) and key pathways involved in fatty liver disease pathobiology. Functional studies improved our understanding of these genetic factors and the molecular mechanisms underlying the trajectories from fat accumulation to fibrosis, cirrhosis, and cancer over time. Here, we summarize key NAFLD risk genes and illustrate their interactions in a 3-dimensional "risk space." Although NAFLD genomics sometimes appears to be "lost in translation," we envision clinical utility in trial design, outcome prediction, and NAFLD surveillance.
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Affiliation(s)
- Marcin Krawczyk
- Department of Medicine II (Gastroenterology and Endocrinology), Saarland University Medical Center, Saarland University, Homburg; Laboratory of Metabolic Liver Diseases, Center for Preclinical Research, Department of General, Transplant and Liver Surgery, Medical University of Warsaw, Warsaw, Poland
| | - Roman Liebe
- Department of Medicine II (Gastroenterology and Endocrinology), Saarland University Medical Center, Saarland University, Homburg; Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Frank Lammert
- Department of Medicine II (Gastroenterology and Endocrinology), Saarland University Medical Center, Saarland University, Homburg.
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369
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Ronald A. Editorial: Polygenic scores in child and adolescent psychiatry - strengths, weaknesses, opportunities and threats. J Child Psychol Psychiatry 2020; 61:519-521. [PMID: 32304105 DOI: 10.1111/jcpp.13246] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Polygenic scores estimate an individual's genetic liability for a particular disorder or trait. They are based on current knowledge of the trait's genetic architecture and focus on common genetic variants. In this editorial, I will discuss some of the strengths, weaknesses, opportunities and threats (SWOT) to polygenic scores within the context of child and adolescent psychiatry. I consider how the potential application of polygenic scores in health settings has some parallels with existing practices, but that polygenic scores also undoubtedly raise unique challenges. This SWOT analysis is accompanied by discussion of some new findings using polygenic scores in this issue of Journal of Child Psychology and Psychiatry.
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Affiliation(s)
- Angelica Ronald
- Department of Psychological Sciences, Birkbeck, University of London, London, UK
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370
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Manousaki D, Kämpe A, Forgetta V, Makitie RE, Bardai G, Belisle A, Li R, Andersson S, Makitie O, Rauch F, Richards JB. Increased Burden of Common Risk Alleles in Children With a Significant Fracture History. J Bone Miner Res 2020; 35:875-882. [PMID: 31914204 DOI: 10.1002/jbmr.3956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/11/2019] [Accepted: 12/14/2019] [Indexed: 12/22/2022]
Abstract
Extreme presentations of common disease in children are often presumed to be of Mendelian etiology, but their polygenic basis has not been fully explored. We tested whether children with significant fracture history and no osteogenesis imperfecta (OI) are at increased polygenic risk for fracture. A childhood significant fracture history was defined as the presence of low-trauma vertebral fractures or multiple long bone fractures. We generated a polygenic score of heel ultrasound-derived speed of sound, termed "gSOS," which predicts risk of osteoporotic fracture. We tested if individuals from three cohorts with significant childhood fracture history had lower gSOS. A Canadian cohort included 94 children with suspected Mendelian osteoporosis, of which 68 had negative OI gene panel. Two Finnish cohorts included 59 children with significant fracture history and 22 with suspected Mendelian osteoporosis, among which 18 had no OI. After excluding individuals with OI and ancestral outliers, we generated gSOS estimates and compared their mean to that of a UK Biobank subset, representing the general population. The average gSOS across all three cohorts (n = 131) was -0.47 SD lower than that in UK Biobank (n = 80,027, p = 1.1 × 10-5 ). The gSOS of 78 individuals with suspected Mendelian osteoporosis was even lower (-0.76 SD, p = 5.3 × 10-10 ). Among the 131 individuals with a significant fracture history, we observed 8 individuals with gSOS below minus 2 SD from the mean; their mean lumbar spine DXA-derived bone mineral density Z-score was -1.7 (SD 0.8). In summary, children with significant fracture history but no OI have an increased burden of common risk alleles. This suggests that a polygenic contribution to disease should be considered in children with extreme presentations of fracture. © 2020 American Society for Bone and Mineral Research.
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Affiliation(s)
- Despoina Manousaki
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada
| | - Anders Kämpe
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada
| | - Riikka E Makitie
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Helsinki, Finland.,Molecular Endocrinology Laboratory, Department of Medicine, Hammersmith Campus, Imperial College London, London, UK
| | - Ghalib Bardai
- McGill University, Ingram School of Nursing, and Shriners Hospitals for Children, Montreal, Canada
| | | | - Rui Li
- McGill Genome Center, McGill University, Montreal, Canada
| | - Sture Andersson
- Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Outi Makitie
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Helsinki, Finland.,Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Frank Rauch
- McGill University, Ingram School of Nursing, and Shriners Hospitals for Children, Montreal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada.,Department of Medicine, Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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371
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Polygenic Score for Body Mass Index Is Associated with Disordered Eating in a General Population Cohort. J Clin Med 2020; 9:jcm9041187. [PMID: 32326247 PMCID: PMC7231239 DOI: 10.3390/jcm9041187] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/17/2020] [Accepted: 04/18/2020] [Indexed: 12/17/2022] Open
Abstract
Background: Disordered eating (DE) is common and is associated with body mass index (BMI). We investigated whether genetic variants for BMI were associated with DE. Methods: BMI polygenic scores (PGS) were calculated for participants of the Avon Longitudinal Study of Parents and Children (ALSPAC; N = 8654) and their association with DE tested. Data on DE behaviors (e.g., binge eating and compensatory behaviors) were collected at ages 14, 16, 18 years, and DE cognitions (e.g., body dissatisfaction) at 14 years. Mediation analyses determined whether BMI mediated the association between the BMI-PGS and DE. Results: The BMI-PGS was positively associated with fasting (OR = 1.42, 95% CI = 1.25, 1.61), binge eating (OR = 1.28, 95% CI = 1.12, 1.46), purging (OR = 1.20, 95% CI = 1.02, 1.42), body dissatisfaction (Beta = 0.99, 95% CI = 0.77, 1.22), restrained eating (Beta = 0.14, 95% CI = 0.10, 1.17), emotional eating (Beta = 0.21, 95% CI = 0.052, 0.38), and negatively associated with thin ideal internalization (Beta = −0.15, 95% CI = −0.23, −0.07) and external eating (Beta = −0.19, 95% CI = −0.30, −0.09). These associations were mainly mediated by BMI. Conclusions: Genetic variants associated with BMI are also associated with DE. This association was mediated through BMI suggesting that weight potentially sits on the pathway from genetic liability to DE.
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372
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Williamson K, Nimegeer A, Lean M. Rising prevalence of BMI ≥40 kg/m 2 : A high-demand epidemic needing better documentation. Obes Rev 2020; 21:e12986. [PMID: 32017386 PMCID: PMC7078951 DOI: 10.1111/obr.12986] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 10/31/2019] [Accepted: 11/18/2019] [Indexed: 12/13/2022]
Abstract
Whilst previously rare, some surveys indicate substantial increases in the population with body mass index (BMI) ≥40 kg/m2 since the 1980s. Clinicians report emerging care challenges for this population, often with high resource demands. Accurate prevalence data, gathered using reliable methods, are needed to inform health care practice, planning, and research. We searched digitally for English language sources with measured prevalence data on adult BMI ≥40 collected since 2010. The search strategy included sources identified from recent work by NCD-RisC (2017), grey sources, a literature search to find current sources, and digital snowball searching. Eighteen countries, across five continents, reported BMI ≥40 prevalence data in surveys since 2010: 12% of eligible national surveys examined. Prevalence of BMI ≥40 ranged from 1.3% (Spain) to 7.7% (USA) for all adults, 0.7% (Serbia) to 5.6% (USA) for men, and 1.8% (Poland) to 9.7% (USA) for women. Limited trend data covering recent decades support significant growth of BMI ≥40 population. Methodological limitations include small samples and data collection methods likely to exclude people with very high BMIs. BMI ≥40 data are not routinely reported in international surveys. Lack of data impairs surveillance of population trends, understanding of causation, and societal provision for individuals living with higher weights.
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Affiliation(s)
- Kath Williamson
- School of Medicine, Dentistry and NursingUniversity of GlasgowGlasgowUK
- NHS LothianEdinburghUK
| | - Amy Nimegeer
- MRC/CSO Social and Public Health Sciences UnitUniversity of GlasgowGlasgowUK
| | - Michael Lean
- School of Medicine, Dentistry and NursingUniversity of GlasgowGlasgowUK
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373
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Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 2020; 21:493-502. [PMID: 32235907 DOI: 10.1038/s41576-020-0224-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2020] [Indexed: 01/03/2023]
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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374
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Reed ZE, Suderman MJ, Relton CL, Davis OSP, Hemani G. The association of DNA methylation with body mass index: distinguishing between predictors and biomarkers. Clin Epigenetics 2020; 12:50. [PMID: 32228717 PMCID: PMC7106582 DOI: 10.1186/s13148-020-00841-5] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 03/17/2020] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND DNA methylation is associated with body mass index (BMI), but it is not clear if methylation scores are biomarkers for extant BMI or predictive of future BMI. Here, we explore the causal nature and predictive utility of DNA methylation measured in peripheral blood with BMI and cardiometabolic traits. METHODS Analyses were conducted across the life course using the ARIES cohort of mothers (n = 792) and children (n = 906), for whom DNA methylation and genetic profiles and BMI at multiple time points (3 in children at birth, in childhood and in adolescence; 2 in mothers during pregnancy and in middle age) were available. Genetic and DNA methylation scores for BMI were derived using published associations between BMI and DNA methylation and genotype. Causal relationships between methylation and BMI were assessed using Mendelian randomisation and cross-lagged models. RESULTS The DNA methylation scores in adult women explained 10% of extant BMI variance. However, less extant variance was explained by scores generated in the same women during pregnancy (2% BMI variance) and in older children (15-17 years; 3% BMI variance). Similarly, little extant variance was explained in younger children (at birth and at 7 years; 1% and 2%, respectively). These associations remained following adjustment for smoking exposure and education levels. The DNA methylation score was found to be a poor predictor of future BMI using linear and cross-lagged models, suggesting that DNA methylation variation does not cause later variation in BMI. However, there was some evidence to suggest that BMI is predictive of later DNA methylation. Mendelian randomisation analyses also support this direction of effect, although evidence is weak. Finally, we find that DNA methylation scores for BMI are associated with extant cardiometabolic traits independently of BMI and genetic score. CONCLUSION The age-specific nature of DNA methylation associations with BMI, lack of causal relationship and limited predictive ability of future BMI indicate that DNA methylation is likely influenced by BMI and might more accurately be considered a biomarker of BMI and related outcomes rather than a predictor. Future epigenome-wide association studies may benefit from further examining associations between early DNA methylation and later health outcomes.
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Affiliation(s)
- Zoe E Reed
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.
| | - Matthew J Suderman
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Caroline L Relton
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Oliver S P Davis
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- The Alan Turing Institute, British Library, 96 Euston Road, London, UK
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit (MRC IEU), Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
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375
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Zeisel SH. Precision (Personalized) Nutrition: Understanding Metabolic Heterogeneity. Annu Rev Food Sci Technol 2020; 11:71-92. [DOI: 10.1146/annurev-food-032519-051736] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
People differ in their requirements for and responses to nutrients and bioactive molecules in the diet. Many inputs contribute to metabolic heterogeneity (including variations in genetics, epigenetics, microbiome, lifestyle, diet intake, and environmental exposure). Precision nutrition is not about developing unique prescriptions for individual people but rather about stratifying people into different subgroups of the population on the basis of biomarkers of the above-listed sources of metabolic variation and then using this stratification to better estimate the different subgroups’ dietary requirements, thereby enabling better dietary recommendations and interventions. The hope is that we will be able to subcategorize people into ever-smaller groups that can be targeted in terms of recommendations, but we will never achieve this at the individual level, thus, the choice of precision nutrition rather than personalized nutrition to designate this new field. This review focuses mainly on genetically related sources of metabolic heterogeneity and identifies challenges that need to be overcome to achieve a full understanding of the complex interactions between the many sources of metabolic heterogeneity that make people differ from one another in their requirements for and responses to foods. It also discusses the commercial applications of precision nutrition.
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Affiliation(s)
- Steven H. Zeisel
- Nutrition Research Institute, Department of Nutrition, University of North Carolina, Kannapolis, North Carolina 28081, USA
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376
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Caprio S, Santoro N, Weiss R. Childhood obesity and the associated rise in cardiometabolic complications. Nat Metab 2020; 2:223-232. [PMID: 32694781 PMCID: PMC9425367 DOI: 10.1038/s42255-020-0183-z] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Accepted: 02/17/2020] [Indexed: 02/06/2023]
Abstract
Childhood obesity is one of the most serious global public-health challenges of the twenty-first century. Over the past four decades, the number of children and adolescents with obesity has risen more than tenfold. Worldwide, an increasing number of youth are facing greater exposure to obesity throughout their lives, and this increase will contribute to the early development of type 2 diabetes, fatty liver and cardiovascular complications. Herein, we provide a brief overview of trends in the global shifts in, and environmental and genetic determinants of, childhood obesity. We then discuss recent progress in the elucidation of the central role of insulin resistance, the key element linking obesity and cardiovascular-risk-factor clustering, and the potential mechanisms through which ectopic lipid accumulation leads to insulin resistance and its associated cardiometabolic complications in obese adolescents. In the absence of effective prevention and intervention programs, childhood obesity will have severe public-health consequences for decades to come.
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Affiliation(s)
- Sonia Caprio
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA.
| | - Nicola Santoro
- Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA.
| | - Ram Weiss
- Department of Pediatrics, Ruth Rappaport Children's Hospital, Rambam Medical Center, Technion School of Medicine, Haifa, Israel.
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377
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Graham CAM, Pedlar CR, Hearne G, Lorente-Cebrián S, González-Muniesa P, Mavrommatis Y. The Association of Parental Genetic, Lifestyle, and Social Determinants of Health with Offspring Overweight. Lifestyle Genom 2020; 13:99-106. [PMID: 32069471 DOI: 10.1159/000505749] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Accepted: 01/03/2020] [Indexed: 11/19/2022] Open
Abstract
INTRODUCTION In the UK, the number of comorbidities seen in children has increased along with the worsening obesity rate. These comorbidities worsen into adulthood. Genome-wide association studies have highlighted single nucleotide polymorphisms associated with the weight status of adults and offspring individually. To date, in the UK, parental genetic, lifestyle, and social determinants of health have not been investigated alongside one another as influencers of offspring weight status. A comprehensive obesity prevention scheme would commence prior to conception and involve parental intervention including all known risk factors. This current study aims to identify the proportion of overweight that can be explained by known parental risk factors, including genetic, lifestyle, and social determinants of health with offspring weight status in the UK. METHODS A cross-sectional study was carried out on 123 parents. Parental and offspring anthropometric data and parental lifestyle and social determinants of health data were self-reported. Parental genetic data were collected by use of GeneFiX saliva collection vials and genotype were assessed for brain-derived neurotrophic factor (BDNF) gene rs6265, melanocortin 4 receptor (MC4R) gene rs17782313, transmembrane protein 18 (TMEM18) gene rs2867125, and serine/threonine-protein kinase (TNN13K) gene rs1514175. Associations were assessed between parental data and the weight status of offspring. RESULTS Maternal body mass index modestly predicted child weight status (p < 0.015; R2 = 0.15). More mothers of overweight children carried the MC4R rs17782313 risk allele (77.8%; p = 0.007) compared to mothers of normal-weight children. Additionally, fathers who were not Caucasian and parents who slept for <7 h/night had a larger percentage of overweight children when compared to their counterparts (p = 0.039; p = 0.014, respectively). CONCLUSION Associations exist between the weight status of offspring based solely on parental genetic, lifestyle, and social determinants of health data. Further research is required to appropriately address future interventions based on genetic and lifestyle risk groups on a pre-parent cohort.
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Affiliation(s)
- Catherine A M Graham
- Faculty of Health and Life Sciences, Department of Sport, Health and Social Work, Oxford Brookes University, Oxford, United Kingdom,
| | - Charles R Pedlar
- Faculty of Sport Health and Applied Science, Department of Nutrition, St. Mary's University, Twickenham, London, United Kingdom
| | - Gary Hearne
- Faculty of Science and Technology, Department of Design Engineering and Mathematics, Middlesex University, London, United Kingdom
| | - Silvia Lorente-Cebrián
- Centre for Nutrition Research, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA - Navarra Health Research Institute, Pamplona, Spain.,CIBERobn Physiopathology of Obesity and Nutrition, Centre of Biomedical Research Network, ISCIII, Madrid, Spain
| | - Pedro González-Muniesa
- Department of Nutrition, Food Science and Physiology, School of Pharmacy and Nutrition, Pamplona, University of Navarra, Pamplona, Spain.,Centre for Nutrition Research, School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.,IdiSNA - Navarra Health Research Institute, Pamplona, Spain.,CIBERobn Physiopathology of Obesity and Nutrition, Centre of Biomedical Research Network, ISCIII, Madrid, Spain
| | - Yiannis Mavrommatis
- Faculty of Sport Health and Applied Science, Department of Nutrition, St. Mary's University, Twickenham, London, United Kingdom
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378
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Mosley JD, Gupta DK, Tan J, Yao J, Wells QS, Shaffer CM, Kundu S, Robinson-Cohen C, Psaty BM, Rich SS, Post WS, Guo X, Rotter JI, Roden DM, Gerszten RE, Wang TJ. Predictive Accuracy of a Polygenic Risk Score Compared With a Clinical Risk Score for Incident Coronary Heart Disease. JAMA 2020; 323:627-635. [PMID: 32068817 PMCID: PMC7042849 DOI: 10.1001/jama.2019.21782] [Citation(s) in RCA: 193] [Impact Index Per Article: 48.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
IMPORTANCE Polygenic risk scores comprising millions of single-nucleotide polymorphisms (SNPs) could be useful for population-wide coronary heart disease (CHD) screening. OBJECTIVE To determine whether a polygenic risk score improves prediction of CHD compared with a guideline-recommended clinical risk equation. DESIGN, SETTING, AND PARTICIPANTS A retrospective cohort study of the predictive accuracy of a previously validated polygenic risk score was assessed among 4847 adults of white European ancestry, aged 45 through 79 years, participating in the Atherosclerosis Risk in Communities (ARIC) study and 2390 participating in the Multi-Ethnic Study of Atherosclerosis (MESA) from 1996 through December 31, 2015, the final day of follow-up. The performance of the polygenic risk score was compared with that of the 2013 American College of Cardiology and American Heart Association pooled cohort equations. EXPOSURES Genetic risk was computed for each participant by summing the product of the weights and allele dosage across 6 630 149 SNPs. Weights were based on an international genome-wide association study. MAIN OUTCOMES AND MEASURES Prediction of 10-year first CHD events (including myocardial infarctions, fatal coronary events, silent infarctions, revascularization procedures, or resuscitated cardiac arrest) assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI). RESULTS The study population included 4847 adults from the ARIC study (mean [SD] age, 62.9 [5.6] years; 56.4% women) and 2390 adults from the MESA cohort (mean [SD] age, 61.8 [9.6] years; 52.2% women). Incident CHD events occurred in 696 participants (14.4%) and 227 participants (9.5%), respectively, over median follow-up of 15.5 years (interquartile range [IQR], 6.3 years) and 14.2 (IQR, 2.5 years) years. The polygenic risk score was significantly associated with 10-year CHD incidence in ARIC with hazard ratios per SD increment of 1.24 (95% CI, 1.15 to 1.34) and in MESA, 1.38 (95% CI, 1.21 to 1.58). Addition of the polygenic risk score to the pooled cohort equations did not significantly increase the C statistic in either cohort (ARIC, change in C statistic, -0.001; 95% CI, -0.009 to 0.006; MESA, 0.021; 95% CI, -0.0004 to 0.043). At the 10-year risk threshold of 7.5%, the addition of the polygenic risk score to the pooled cohort equations did not provide significant improvement in reclassification in either ARIC (NRI, 0.018, 95% CI, -0.012 to 0.036) or MESA (NRI, 0.001, 95% CI, -0.038 to 0.076). The polygenic risk score did not significantly improve calibration in either cohort. CONCLUSIONS AND RELEVANCE In this analysis of 2 cohorts of US adults, the polygenic risk score was associated with incident coronary heart disease events but did not significantly improve discrimination, calibration, or risk reclassification compared with conventional predictors. These findings suggest that a polygenic risk score may not enhance risk prediction in a general, white middle-aged population.
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Affiliation(s)
- Jonathan D. Mosley
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
| | - Deepak K. Gupta
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Jingyi Tan
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Department of Pediatrics, Torrance, California
| | - Jie Yao
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Department of Pediatrics, Torrance, California
| | - Quinn S. Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee
| | - Christian M. Shaffer
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Suman Kundu
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Cassianne Robinson-Cohen
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Bruce M. Psaty
- Departments of Medicine, Epidemiology and Health Services, University of Washington School of Public Health; and Kaiser Permanente Washington Health Research Institute, Seattle, Washington
| | - Stephen S. Rich
- Department of Public Health Sciences, Center for Public Health Genomics, Charlottesville, Virginia
| | - Wendy S. Post
- Department of Medicine, Johns Hopkins University, Baltimore, Maryland
- Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland
| | - Xiuqing Guo
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Department of Pediatrics, Torrance, California
| | - Jerome I Rotter
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Department of Pediatrics, Torrance, California
- Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Department of Medicine, Torrance, California12
| | - Dan M. Roden
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee
| | - Robert E. Gerszten
- Beth Israel Deaconess Medical Center, Division of Cardiovascular Medicine, Boston, Massachusetts
| | - Thomas J. Wang
- Department of Internal Medicine, University of Texas Southwestern Medical Center
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379
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Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 2020; 9:48376. [PMID: 31999256 DOI: 10.1101/629949] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 01/28/2020] [Indexed: 05/25/2023] Open
Abstract
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
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Affiliation(s)
| | - Arbel Harpak
- Department of Biological Sciences, Columbia University, New York, United States
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia University, New York, United States
| | - Dalton Conley
- Department of Sociology, Princeton University, Princeton, United States
- Office of Population Research, Princeton University, Princeton, United States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford University, Stanford, United States
- Department of Biology, Stanford University, Stanford, United States
- Howard Hughes Medical Institute, Stanford University, Stanford, United States
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University, New York, United States
- Department of Systems Biology, Columbia University, New York, United States
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380
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Mostafavi H, Harpak A, Agarwal I, Conley D, Pritchard JK, Przeworski M. Variable prediction accuracy of polygenic scores within an ancestry group. eLife 2020; 9:e48376. [PMID: 31999256 PMCID: PMC7067566 DOI: 10.7554/elife.48376] [Citation(s) in RCA: 214] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 01/28/2020] [Indexed: 12/13/2022] Open
Abstract
Fields as diverse as human genetics and sociology are increasingly using polygenic scores based on genome-wide association studies (GWAS) for phenotypic prediction. However, recent work has shown that polygenic scores have limited portability across groups of different genetic ancestries, restricting the contexts in which they can be used reliably and potentially creating serious inequities in future clinical applications. Using the UK Biobank data, we demonstrate that even within a single ancestry group (i.e., when there are negligible differences in linkage disequilibrium or in causal alleles frequencies), the prediction accuracy of polygenic scores can depend on characteristics such as the socio-economic status, age or sex of the individuals in which the GWAS and the prediction were conducted, as well as on the GWAS design. Our findings highlight both the complexities of interpreting polygenic scores and underappreciated obstacles to their broad use.
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Affiliation(s)
| | - Arbel Harpak
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | - Ipsita Agarwal
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
| | - Dalton Conley
- Department of Sociology, Princeton UniversityPrincetonUnited States
- Office of Population Research, Princeton UniversityPrincetonUnited States
| | - Jonathan K Pritchard
- Department of Genetics, Stanford UniversityStanfordUnited States
- Department of Biology, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Molly Przeworski
- Department of Biological Sciences, Columbia UniversityNew YorkUnited States
- Department of Systems Biology, Columbia UniversityNew YorkUnited States
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381
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Kiiskinen T, Mars NJ, Palviainen T, Koskela J, Rämö JT, Ripatti P, Ruotsalainen S, Palotie A, Madden PAF, Rose RJ, Kaprio J, Salomaa V, Mäkelä P, Havulinna AS, Ripatti S. Genomic prediction of alcohol-related morbidity and mortality. Transl Psychiatry 2020; 10:23. [PMID: 32066667 PMCID: PMC7026428 DOI: 10.1038/s41398-019-0676-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 10/23/2019] [Accepted: 11/14/2019] [Indexed: 12/12/2022] Open
Abstract
While polygenic risk scores (PRS) have been shown to predict many diseases and risk factors, the potential of genomic prediction in harm caused by alcohol use has not yet been extensively studied. Here, we built a novel polygenic risk score of 1.1 million variants for alcohol consumption and studied its predictive capacity in 96,499 participants from the FinnGen study and 39,695 participants from prospective cohorts with detailed baseline data and up to 25 years of follow-up time. A 1 SD increase in the PRS was associated with 11.2 g (=0.93 drinks) higher weekly alcohol consumption (CI = 9.85-12.58 g, p = 2.3 × 10-58). The PRS was associated with alcohol-related morbidity (4785 incident events) and the risk estimate between the highest and lowest quintiles of the PRS was 1.83 (95% CI = 1.66-2.01, p = 1.6 × 10-36). When adjusted for self-reported alcohol consumption, education, marital status, and gamma-glutamyl transferase blood levels in 28,639 participants with comprehensive baseline data from prospective cohorts, the risk estimate between the highest and lowest quintiles of the PRS was 1.58 (CI = 1.26-1.99, p = 8.2 × 10-5). The PRS was also associated with all-cause mortality with a risk estimate of 1.33 between the highest and lowest quintiles (CI = 1.20-1.47, p = 4.5 × 10-8) in the adjusted model. In conclusion, the PRS for alcohol consumption independently associates for both alcohol-related morbidity and all-cause mortality. Together, these findings underline the importance of heritable factors in alcohol-related health burden while highlighting how measured genetic risk for an important behavioral risk factor can be used to predict related health outcomes.
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Affiliation(s)
- Tuomo Kiiskinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Nina J Mars
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jukka Koskela
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Joel T Rämö
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Pietari Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, Boston, MA, USA
| | - Pamela A F Madden
- Department of Psychiatry, Washington University School of Medicine in St.Louis, St.Louis, MO, USA
| | - Richard J Rose
- Department of Psychological and Brain Sciences, Indiana University Bloomington, Bloomington, IN, USA
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Pia Mäkelä
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Department of Public Health, Clinicum, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
- The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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382
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Alligier M, Barrès R, Blaak EE, Boirie Y, Bouwman J, Brunault P, Campbell K, Clément K, Farooqi IS, Farpour-Lambert NJ, Frühbeck G, Goossens GH, Hager J, Halford JCG, Hauner H, Jacobi D, Julia C, Langin D, Natali A, Neovius M, Oppert JM, Pagotto U, Palmeira AL, Roche H, Rydén M, Scheen AJ, Simon C, Sorensen TIA, Tappy L, Yki-Järvinen H, Ziegler O, Laville M. OBEDIS Core Variables Project: European Expert Guidelines on a Minimal Core Set of Variables to Include in Randomized, Controlled Clinical Trials of Obesity Interventions. Obes Facts 2020; 13:1-28. [PMID: 31945762 PMCID: PMC7098277 DOI: 10.1159/000505342] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Accepted: 12/08/2019] [Indexed: 12/17/2022] Open
Abstract
Heterogeneity of interindividual and intraindividual responses to interventions is often observed in randomized, controlled trials for obesity. To address the global epidemic of obesity and move toward more personalized treatment regimens, the global research community must come together to identify factors that may drive these heterogeneous responses to interventions. This project, called OBEDIS (OBEsity Diverse Interventions Sharing - focusing on dietary and other interventions), provides a set of European guidelines for a minimal set of variables to include in future clinical trials on obesity, regardless of the specific endpoints. Broad adoption of these guidelines will enable researchers to harmonize and merge data from multiple intervention studies, allowing stratification of patients according to precise phenotyping criteria which are measured using standardized methods. In this way, studies across Europe may be pooled for better prediction of individuals' responses to an intervention for obesity - ultimately leading to better patient care and improved obesity outcomes.
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Affiliation(s)
- Maud Alligier
- FCRIN/FORCE Network, Centre de Recherche en Nutrition Humaine Rhône-Alpes, Lyon, France
| | - Romain Barrès
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ellen E Blaak
- Department of Human Biology, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Yves Boirie
- University Clermont Auvergne, CHU Clermont-Ferrand, Clinical Nutrition Department, Unité de Nutrition Humaine, CRNH Auvergne, Clermont-Ferrand, France
| | - Jildau Bouwman
- TNO (Netherlands Organization for Applied Scientific Research), Microbiology and Systems Biology, Zeist, The Netherlands
| | - Paul Brunault
- Equipe de Liaison et de Soins en Addictologie, CHRU de Tours, and UMR 1253, iBrain, Université de Tours, Inserm, and Qualipsy EE 1901, Université de Tours, Tours, France
| | - Kristina Campbell
- KC Microbiome Communications Group, Victoria, British Columbia, Canada
| | - Karine Clément
- Sorbonne University/INSERM, Nutrition and Obesities, Systemic Approaches Research Unit, and Assistance Publique Hôpitaux de Paris, Nutrition Department, Pitié-Salpêtrière Hospital, Paris, France
| | - I Sadaf Farooqi
- Wellcome-MRC Institute of Metabolic Science and NIHR Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Nathalie J Farpour-Lambert
- Obesity Prevention and Care Program Contrepoids, Service of Therapeutic Education for Chronic Diseases, Department of Community Health, Primary Care and Emergency, Geneva University Hospitals and University of Geneva, Geneva, Switzerland
| | - Gema Frühbeck
- Department of Endocrinology and Nutrition, Clínica Universidad de Navarra, University of Navarra & CIBEROBN, IdiSNA, Pamplona, Spain
| | - Gijs H Goossens
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Jorg Hager
- Metabolic Phenotyping, Nestlé Institute of Health Sciences, Lausanne, Switzerland
| | - Jason C G Halford
- Psychological Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Hans Hauner
- Institute of Nutritional Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - David Jacobi
- L'Institut du Thorax, INSERM, CNRS, UNIV Nantes, CHU Nantes, Nantes, France
| | - Chantal Julia
- Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center (CRESS), Inra, Cnam, Paris 13 University and Public Health Department, Avicenne Hospital, AP-HP, Bobigny, France
| | - Dominique Langin
- Institute of Metabolic and Cardiovascular Diseases, I2MC, Inserm, Paul Sabatier University, and Department of Medical Biochemistry, Toulouse University Hospitals, Toulouse, France
| | - Andrea Natali
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Martin Neovius
- Department of Medicine (Solna), Karolinska Institute, Stockholm, Sweden
| | - Jean Michel Oppert
- Department of Nutrition, Pitié-Salpêtrière Hospital (AP-HP), Sorbonne University, Institute of Cardiometabolism and Nutrition (ICAN), Paris, France
| | - Uberto Pagotto
- Department of Medical and Surgical Sciences, Alma Mater Studiorum University of Bologna, Policlinic S. Orsola, Endocrinology Unit, Bologna, Italy
| | - Antonio L Palmeira
- CIPER, PANO-SR, Faculty of Human Kinetics, University of Lisbon, Lisbon, Portugal
| | - Helen Roche
- Nutrigenomics Research Group, UCD Institute of Food & Health, University College Dublin, Dublin, Ireland
| | - Mikael Rydén
- Department of Medicine (H7), Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - André J Scheen
- Liège University, Division of Diabetes, Nutrition and Metabolic Disorders, CHU Liège, Liège, Belgium
| | - Chantal Simon
- CarMen Laboratory, INSERM 1060, INRA 1397, University of Lyon, Oullins, France
| | - Thorkild I A Sorensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, and Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Luc Tappy
- Physiology Department, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Hannele Yki-Järvinen
- University of Helsinki, Helsinki University Hospital, and Minerva Foundation Institute for Medical Research, Helsinki, Finland
| | - Olivier Ziegler
- Department of Endocrinology, Diabetes and Nutrition, Hôpital Brabois Adultes, CHRU de Nancy, Vandoeuvre Lès Nancy, France
| | - Martine Laville
- FCRIN/FORCE Network, Centre de Recherche en Nutrition Humaine Rhône-Alpes, Université de Lyon, Hospices Civils de Lyon, Lyon, France,
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383
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Abstract
Obesity is one of the most serious health challenges of our time. In this issue of Cell, Khera and co-authors demonstrate the striking ability of genetics, in the form of a polygenic risk score, to identify those individuals at high risk for obesity. This genetic risk expresses itself early as childhood obesity, reinforcing the notion that early prevention is essential to combatting the obesity epidemic.
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Affiliation(s)
- Ali Torkamani
- Scripps Research Translational Institute, La Jolla, CA 92037, USA; Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA 92037, USA
| | - Eric Topol
- Scripps Research Translational Institute, La Jolla, CA 92037, USA; Department of Molecular Medicine, Scripps Research, La Jolla, CA 92037, USA.
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384
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Hüls A, Czamara D. Methodological challenges in constructing DNA methylation risk scores. Epigenetics 2020; 15:1-11. [PMID: 31318318 PMCID: PMC6961658 DOI: 10.1080/15592294.2019.1644879] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Revised: 06/28/2019] [Accepted: 07/09/2019] [Indexed: 12/23/2022] Open
Abstract
Polygenic approaches often access more variance of complex traits than is possible by single variant approaches. For genotype data, genetic risk scores (GRS) are widely used for risk prediction as well as in association and interaction studies. Recently, interest has been growing in transferring GRS approaches to DNA methylation data (methylation risk scores, MRS), which can be used 1) as biomarkers for environmental exposures, 2) in association analyses in which single CpG sites do not achieve significance, 3) as dimension reduction approach in interaction and mediation analyses, and 4) to predict individual risks of disease or treatment success. Most GRS approaches can directly be transferred to methylation data. However, since methylation data is more sensitive to confounding, e.g. by age and tissue, it is more complex to find appropriate external weights. In this review, we will outline the adaption of current GRS approaches to methylation data and highlight occurring challenges.
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Affiliation(s)
- Anke Hüls
- Department of Human Genetics, Emory University, Atlanta, GA, USA
- Centre for Molecular Medicine and Therapeutics, BC Children’s Hospital Research Institute, and Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
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385
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Bahorski JS, Schneider-Worthington CR, Chandler-Laney PC. Modified eating in the absence of hunger test is associated with appetitive traits in infants. Eat Behav 2020; 36:101342. [PMID: 31759281 PMCID: PMC9327243 DOI: 10.1016/j.eatbeh.2019.101342] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 10/29/2019] [Accepted: 11/06/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Appetite traits and feeding practices have been linked to children's weight status. For example, eating in response to food cues (high food responsiveness (FR)), and poor regulation of intake (low satiety responsiveness (SR)), increase risk for obesity. Appetitive traits of infants, and feeding practices, are typically measured by parent-report. The purpose of this study was to use a modified eating in the absence of hunger (EAH) paradigm, measuring infants' intake 30 min after a typical meal, to test whether infant acceptance of a second meal is associated with parent-reported appetitive traits or feeding practices. METHODS Healthy infants aged 3-5 months (N = 54) were fed a typical meal and then offered a second meal by bottle 30 min later. Appetitive traits and feeding practices were assessed with surveys. Analyses of covariance were used to assess whether appetitive traits differed by acceptance of the second meal after adjusting for covariates. RESULTS Fifty-nine percent of infants accepted the second meal and these infants had greater parent-reported FR (M = 3.06 ± 0.58 vs M = 2.43 ± 0.80, p < 0.01) and lower SR (M = 1.87 ± 0.62 vs M = 2.67 ± 0.87, p = 0.01), compared to infants who rejected it. Group differences remained after adjusting for infant age, feeding mode, weight-for-length, and maternal body mass index. No other appetitive traits or feeding practices differed by group. CONCLUSIONS Results expand research in older children by showing that infant response to a modified EAH protocol is associated with parent-reported FR and SR. Future research with this protocol in infants should investigate the consistency of this behavior across time and examine whether response to this protocol predicts subsequent growth.
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Affiliation(s)
- Jessica S. Bahorski
- University of Alabama at Birmingham, School of Nursing, 1701 University Blvd, Birmingham, AL 35294, USA,Corresponding author: Florida State University, College of Nursing, 98 Varsity Way, Tallahassee, FL 32306, USA. (J.S. Bahorski)
| | | | - Paula C. Chandler-Laney
- University of Alabama at Birmingham, Department of Nutrition Sciences, 1675 University Blvd, Birmingham, AL 35233, USA
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386
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Yu H, Shi Z, Lin X, Bao Q, Jia H, Wei J, Helfand BT, Zheng SL, Duggan D, Lu D, Mo Z, Xu J. Broad- and narrow-sense validity performance of three polygenic risk score methods for prostate cancer risk assessment. Prostate 2020; 80:83-87. [PMID: 31634418 DOI: 10.1002/pros.23920] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Accepted: 10/02/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. METHODS We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad-sense validity) and score values (narrow-sense validity). RESULTS A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk-associated SNPs. For broad-sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10-11 , 7.64 × 10-13 , and 7.51 × 10-10 for GRS, P + T, and LDpred, respectively. For narrow-sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. CONCLUSIONS The performance of GRS was better than P + T and LDpred. Fewer and well-established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.
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Affiliation(s)
- Hongjie Yu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Xiaoling Lin
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Quanwa Bao
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Haifei Jia
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - Siqun L Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
| | - David Duggan
- Translational Genomics Research Institute, An Affiliate of City of Hope, Phoenix, Arizona
| | - Daru Lu
- State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai, China
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, Illinois
- Fudan Institute of Urology, Huashan Hospital, Fudan University, Shanghai, China
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387
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Jehan S, Zizi F, Pandi-Perumal SR, McFarlane SI, Jean-Louis G, Myers AK. Energy imbalance: obesity, associated comorbidities, prevention, management and public health implications. ADVANCES IN OBESITY, WEIGHT MANAGEMENT & CONTROL 2020; 10:146-161. [PMID: 33305001 PMCID: PMC7725222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The prevalence of obesity has been continually increasing, as have its associated comorbidities and health care costs. Effective management of obesity and early intervention measures are necessary to overcome this global issue. The responsibility for preventing and managing this global epidemic does not lie solely on an individual, but also on the entire health care system. Policy makers-nationally and globally-must play their roles to solve the issue. In this review article, we examine methods of controlling and managing obesity through interventions, such as a low caloric diet, physical exercise, pharmacological guidance, and bariatric surgical procedures. While health care professionals should educate patients about all available treatment options for severe obesity, bariatric surgical procedures have increased in popularity and are considered very beneficial with outcomes fruitful in managing severe obesity.
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Affiliation(s)
- Shazia Jehan
- Department of Population Health, Center for Healthful Behavior Change, USA
| | - Ferdinand Zizi
- Department of Population Health, Center for Healthful Behavior Change, USA
| | | | | | | | - Alyson K Myers
- Department of Internal Medicine, Division of Endocrinology, USA
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388
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Wood TR, Owens N. Using synthetic datasets to bridge the gap between the promise and reality of basing health-related decisions on common single nucleotide polymorphisms. F1000Res 2019. [DOI: 10.12688/f1000research.21797.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background: While the academic genetic literature has clearly shown that common genetic single nucleotide polymorphisms (SNPs), and even large polygenic SNP risk scores, cannot reliably be used to determine risk of disease or to personalize interventions, a significant industry of companies providing SNP-based recommendations still exists. Healthcare practitioners must therefore be able to navigate between the promise and reality of these tools, including being able to interpret the literature that is associated with a given risk or suggested intervention. One significant hurdle to this process is the fact that most population studies of common SNPs only provide average (+/- error) phenotypic or risk descriptions for a given genotype, which hides the true heterogeneity of the population and reduces the ability of an individual to determine how they themselves or their patients might truly be affected. Methods: We generated synthetic datasets generated from descriptive phenotypic data published on common SNPs associated with obesity, elevated fasting blood glucose, and methylation status. Using simple statistical theory and full graphical representation of the generated data, we developed a method by which anybody can better understand phenotypic heterogeneity in a population, as well as the degree to which common SNPs truly drive disease risk. Results: Individual risk SNPs had a <10% likelihood of effecting the associated phenotype (bodyweight, fasting glucose, or homocysteine levels). Example polygenic risk scores including the SNPs most associated with obesity and type 2 diabetes only explained 2% and 5% of the final phenotype, respectively. Conclusions: The data suggest that most disease risk is dominated by the effect of the modern environment, providing further evidence to support the pursuit of lifestyle-based interventions that are likely to be beneficial regardless of genetics.
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389
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Liao LN, Li TC, Li CI, Liu CS, Lin WY, Lin CH, Yang CW, Chen CC, Chang CT, Yang YF, Liu YL, Kuo HL, Tsai FJ, Lin CC. Genetic risk score for risk prediction of diabetic nephropathy in Han Chinese type 2 diabetes patients. Sci Rep 2019; 9:19897. [PMID: 31882689 PMCID: PMC6934611 DOI: 10.1038/s41598-019-56400-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 12/10/2019] [Indexed: 11/09/2022] Open
Abstract
We evaluated whether genetic information could offer improvement on risk prediction of diabetic nephropathy (DN) while adding susceptibility variants into a risk prediction model with conventional risk factors in Han Chinese type 2 diabetes patients. A total of 995 (including 246 DN cases) and 519 (including 179 DN cases) type 2 diabetes patients were included in derivation and validation sets, respectively. A genetic risk score (GRS) was constructed with DN susceptibility variants based on findings of our previous genome-wide association study. In derivation set, areas under the receiver operating characteristics (AUROC) curve (95% CI) for model with clinical risk factors only, model with GRS only, and model with clinical risk factors and GRS were 0.75 (0.72-0.78), 0.64 (0.60-0.68), and 0.78 (0.75-0.81), respectively. In external validation sample, AUROC for model combining conventional risk factors and GRS was 0.70 (0.65-0.74). Additionally, the net reclassification improvement was 9.98% (P = 0.001) when the GRS was added to the prediction model of a set of clinical risk factors. This prediction model enabled us to confirm the importance of GRS combined with clinical factors in predicting the risk of DN and enhanced identification of high-risk individuals for appropriate management of DN for intervention.
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Affiliation(s)
- Li-Na Liao
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan
| | - Tsai-Chung Li
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan.,Department of Healthcare Administration, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
| | - Chia-Ing Li
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chiu-Shong Liu
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.,Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Wen-Yuan Lin
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Chih-Hsueh Lin
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Chuan-Wei Yang
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Ching-Chu Chen
- Division of Endocrinology and Metabolism, Department of Medicine, China Medical University Hospital, Taichung, Taiwan.,School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
| | - Chiz-Tzung Chang
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Ya-Fei Yang
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Yao-Lung Liu
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
| | - Huey-Liang Kuo
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan.,Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan.,Graduate Institute of Clinical Medical Science, College of Medicine, China Medical University, Taichung, Taiwan
| | - Fuu-Jen Tsai
- School of Chinese Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan. .,Human Genetic Laboratory, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
| | - Cheng-Chieh Lin
- School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan. .,Department of Medical Research, China Medical University Hospital, Taichung, Taiwan. .,Department of Family Medicine, China Medical University Hospital, Taichung, Taiwan.
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390
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Chiesa ST, Charakida M, Deanfield JE. Adolescent health and future cardiovascular disability: it’s never too early to think about prevention. Eur Heart J 2019; 41:1511-1513. [DOI: 10.1093/eurheartj/ehz869] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Abstract
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Affiliation(s)
- Scott T Chiesa
- Institute of Cardiovascular Science, University College London, London, UK
| | - Marietta Charakida
- Department of Imaging Science and Biomedical Engineering, King’s College London, London, UK
| | - John E Deanfield
- Institute of Cardiovascular Science, University College London, London, UK
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391
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Ross LM, Slentz CA, Kraus WE. Evaluating Individual Level Responses to Exercise for Health Outcomes in Overweight or Obese Adults. Front Physiol 2019; 10:1401. [PMID: 31798463 PMCID: PMC6867965 DOI: 10.3389/fphys.2019.01401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 10/30/2019] [Indexed: 11/13/2022] Open
Abstract
Background Understanding group responses to a given exercise exposure is becoming better developed; however, understanding of individual responses to specific exercise exposures is significantly underdeveloped and must advance before personalized exercise medicine can become a functional reality. Herein, utilizing data from the STRRIDE studies, we address some of the key issues surrounding our efforts to develop better understanding of individual exercise responsiveness. Methods We assessed individual cardiometabolic and cardiorespiratory fitness responses in subjects successfully completing STRRIDE I (n = 227) and STRRIDE II (n = 155). Subjects were previously sedentary, overweight or obese men and women with mild-to-moderate dyslipidemia. Subjects were randomized to either an inactive control group or to an exercise training program. Training groups varied to test the differential effects of exercise amount, intensity, and mode on cardiometabolic health outcomes. Measures included fasting plasma glucose, insulin, and lipids; blood pressure, minimal waist circumference, visceral adipose tissue, and peak VO2. Absolute change scores were calculated for each subject as post-intervention minus pre-intervention values in order to evaluate the heterogeneity of health factor responsiveness to exercise training. Results For subjects completing one of the aerobic training programs, change in peak VO2 ranged from a loss of 37% to a gain of 77%. When ranked by magnitude of change, we observed discordant responses among changes in peak VO2 with changes in visceral adipose tissue, HDL-C, triglycerides, and fasting plasma insulin. There was also not a clear, direct relationship observed between magnitudes of individual response in the aforementioned variables with aerobic training adherence levels. This same pattern of highly variable and discordant responses was displayed even when considering subjects with adherence levels greater than 70%. Conclusion Our findings illustrate the unclear relationship between magnitude of individual response for a given outcome with training adherence and specific exercise exposure. These discordant and heterogeneous responses highlight the difficult nature of developing understanding for how individuals will respond to any given exposure. Further investigation into the biological, physiological, and genetics factors affecting individual responsiveness is vital to making personalized exercise medicine a reality.
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Affiliation(s)
- Leanna M Ross
- Duke University Medical Center, Duke Molecular Physiology Institute, Durham, NC, United States
| | - Cris A Slentz
- Duke University Medical Center, Duke Molecular Physiology Institute, Durham, NC, United States.,Division of Cardiology, School of Medicine, Duke University, Durham, NC, United States
| | - William E Kraus
- Duke University Medical Center, Duke Molecular Physiology Institute, Durham, NC, United States.,Division of Cardiology, School of Medicine, Duke University, Durham, NC, United States.,Urbaniak Sports Sciences Institute, School of Medicine, Duke University, Durham, NC, United States
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392
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Lin CH, Shao L, Zhang YM, Tu YJ, Zhang Y, Tomlinson B, Chan P, Liu Z. An evaluation of liraglutide including its efficacy and safety for the treatment of obesity. Expert Opin Pharmacother 2019; 21:275-285. [PMID: 31790314 DOI: 10.1080/14656566.2019.1695779] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: The prevalence of obesity is increasing worldwide and associated conditions, particularly type 2 diabetes mellitus (T2DM), also show increasing prevalence. Lifestyle intervention should be the first line of management for obesity but additional pharmacotherapy is often required and bariatric surgery is appropriate in more severe cases. Drugs acting as glucagon-like peptide-1 receptor agonists (GLP-1RAs) developed for the management of T2DM reduce body weight and liraglutide is the first GLP-1RA to be approved for the treatment of obesity in patients with and without T2DM.Areas covered: In this review of relevant published material, the authors summarize the pharmacokinetics, pharmacodynamics, clinical efficacy and safety of liraglutide for the treatment of obesity.Expert opinion: Liraglutide effectively reduces body weight and body fat through mechanisms involving reduced appetite and lowered energy intake, independent of its glucose-lowering effects. Like most of the other medications currently available for obesity, liraglutide has some common adverse effects, although generally not serious ones. Liraglutide has additional benefits in reducing cardiovascular events in patients with T2DM but the cost and the need for daily injections may limit its use in obesity. Newer GLP-1RAs, such as semaglutide, or other drugs in development for obesity may have advantages over liraglutide.
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Affiliation(s)
- Chen-Hsiu Lin
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University, Taipei, Taiwan
| | - Li Shao
- The VIP Department, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yu-Mei Zhang
- The VIP Department, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yu-Ju Tu
- The VIP Department, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yuzhen Zhang
- Research Center for Translational Medicine, Shanghai East Hospital Affiliated to Tongji University School of Medicine, Shanghai, China
| | - Brian Tomlinson
- Research Center for Translational Medicine, Shanghai East Hospital Affiliated to Tongji University School of Medicine, Shanghai, China.,Faculty of Medicine, Macau University of Science and Technology, Taipa, Macau
| | - Paul Chan
- Division of Cardiology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan
| | - Zhongmin Liu
- Department of Cardiac Surgery, Shanghai East Hospital, Tongji University, Shanghai, China
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393
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Elemans LM, Cervera IP, Riley SE, Wafer R, Fong R, Tandon P, Minchin JE. Quantitative analyses of adiposity dynamics in zebrafish. Adipocyte 2019; 8:330-338. [PMID: 31411107 PMCID: PMC6768273 DOI: 10.1080/21623945.2019.1648175] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
Adipose tissues often exhibit subtle, quantitative differences between individuals, leading to a graded series of adiposity phenotypes at the population level. Robust, quantitative analyses are vital for studying these differences. In this Commentary we highlight two articles from our lab that employ sensitive new methods in zebrafish capable of delineating complex and quantitative adiposity phenotypes. In the first article, we utilized in vivo imaging to systematically quantify zebrafish adipose tissues. We identified 34 regionally distinct zebrafish adipose tissues and developed statistical models to predict the size and variance of each adipose tissue over the course of zebrafish growth. We then employed these models to identify effects of strain and diet on adipose tissue growth. In the second article, we employed deep phenotyping to study complex disease-related adiposity traits. Using this methodology, we identified that adipose tissues have unique capacities to re-deposit lipid following food restriction and re-feeding. These distinct re-deposition potentials led to widespread fat distribution changes following re-feeding. We discuss how these novel findings may provide relevance to health conditions such as anorexia nervosa. Together, the strategies described in these two articles can be used as unbiased and quantitative methods to uncover new relationships between genotype, diet and adiposity.
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Affiliation(s)
- Loes M.H. Elemans
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | | | - Susanna E. Riley
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Rebecca Wafer
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Rosalyn Fong
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - Panna Tandon
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
| | - James E.N. Minchin
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh EH16 4TJ, UK
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394
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Janssens ACJW. Validity of polygenic risk scores: are we measuring what we think we are? Hum Mol Genet 2019; 28:R143-R150. [PMID: 31504522 PMCID: PMC7013150 DOI: 10.1093/hmg/ddz205] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 08/14/2019] [Accepted: 08/14/2019] [Indexed: 12/16/2022] Open
Abstract
Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
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Affiliation(s)
- A Cecile J W Janssens
- Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, USA
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395
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Lumish HS, O'Reilly M, Reilly MP. Sex Differences in Genomic Drivers of Adipose Distribution and Related Cardiometabolic Disorders: Opportunities for Precision Medicine. Arterioscler Thromb Vasc Biol 2019; 40:45-60. [PMID: 31747800 DOI: 10.1161/atvbaha.119.313154] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
This review focuses on the human genetics, epidemiology, and molecular pathophysiology of sex differences in central obesity, adipose distribution, and related cardiometabolic disorders. Distribution of fat is important for cardiometabolic health, with peripheral fat depots having a protective effect and central visceral fat depots conferring a detrimental effect on health. There are important sex differences in fat distribution that are masked when studying body mass index as a measure of obesity. From epidemiological, murine, and in vitro studies, several mechanisms have been proposed to explain the sex differences in adipose distribution, including sex hormonal effects, cell-intrinsic properties, and the microenvironment in fat depots. More recently, human genetics have revealed hundreds of loci for central obesity providing disruptive opportunities for mechanistic discoveries and clinical translation. A striking feature is that over one-third of these loci have reproducible but poorly understood sexual dimorphic associations with central obesity, most having stronger effects in women. Understanding the genetic and molecular mechanisms of adipose distribution and its sexual dimorphism in humans provides a unique opportunity to promote the use of precision medicine for early identification of at-risk individuals, and the development of novel therapeutic strategies for central obesity and related cardiometabolic disorders.
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Affiliation(s)
- Heidi S Lumish
- From the Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY (H.S.L., M.O., M.P.R.)
| | - Marcella O'Reilly
- From the Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY (H.S.L., M.O., M.P.R.)
| | - Muredach P Reilly
- From the Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY (H.S.L., M.O., M.P.R.).,Irving Institute for Clinical and Translational Research, Columbia University, New York, NY (M.P.R.)
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396
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Damask A, Steg PG, Schwartz GG, Szarek M, Hagström E, Badimon L, Chapman MJ, Boileau C, Tsimikas S, Ginsberg HN, Banerjee P, Manvelian G, Pordy R, Hess S, Overton JD, Lotta LA, Yancopoulos GD, Abecasis GR, Baras A, Paulding C. Patients With High Genome-Wide Polygenic Risk Scores for Coronary Artery Disease May Receive Greater Clinical Benefit From Alirocumab Treatment in the ODYSSEY OUTCOMES Trial. Circulation 2019; 141:624-636. [PMID: 31707832 DOI: 10.1161/circulationaha.119.044434] [Citation(s) in RCA: 129] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Alirocumab, an antibody that blocks PCSK9 (proprotein convertase subtilisin/kexin type 9), was associated with reduced major adverse cardiovascular events (MACE) and death in the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab). In this study, higher baseline levels of low-density lipoprotein cholesterol (LDL-C) predicted greater benefit from alirocumab treatment. Recent studies indicate high polygenic risk scores (PRS) for coronary artery disease (CAD) identify individuals at higher risk who derive increased benefit from statins. We performed post hoc analyses to determine whether high PRS for CAD identifies higher-risk individuals, independent of baseline LDL-C and other known risk factors, who might derive greater benefit from alirocumab treatment. METHODS ODYSSEY OUTCOMES was a randomized, double-blind, placebo-controlled trial comparing alirocumab or placebo in 18 924 patients with acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin treatment. The primary endpoint (MACE) comprised death of CAD, nonfatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. A genome-wide PRS for CAD comprising 6 579 025 genetic variants was evaluated in 11 953 patients with available DNA samples. Analysis of MACE risk was performed in placebo-treated patients, whereas treatment benefit analysis was performed in all patients. RESULTS The incidence of MACE in the placebo group was related to PRS for CAD: 17.0% for high PRS patients (>90th percentile) and 11.4% for lower PRS patients (≤90th percentile; P<0.001); this PRS relationship was not explained by baseline LDL-C or other established risk factors. Both the absolute and relative reduction of MACE by alirocumab compared with placebo was greater in high versus low PRS patients. There was an absolute reduction by alirocumab in high versus low PRS groups of 6.0% and 1.5%, respectively, and a relative risk reduction by alirocumab of 37% in the high PRS group (hazard ratio, 0.63 [95% CI, 0.46-0.86]; P=0.004) versus a 13% reduction in the low PRS group (hazard ratio, 0.87 [95% CI, 0.78-0.98]; P=0.022; interaction P=0.04). CONCLUSIONS A high PRS for CAD is associated with elevated risk for recurrent MACE after acute coronary syndrome and a larger absolute and relative risk reduction with alirocumab treatment, providing an independent tool for risk stratification and precision medicine.
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Affiliation(s)
- Amy Damask
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - P Gabriel Steg
- Université de Paris, Hôpital Bichat, Assistance Publique-Hôpitaux de Paris, INSERM U1148, France (P.G.S.)
| | | | - Michael Szarek
- Department of Biostatistics and Epidemiology, SUNY Downstate School of Public Health, Brooklyn, NY (M.S.)
| | - Emil Hagström
- Department of Medical Sciences, Cardiology, Uppsala University, Sweden (E.H.)
| | - Lina Badimon
- Cardiovascular Program-ICCC, CiberCV, IR-Hospital de la Santa Creu i Sant Pau, Barcelona, Spain (L.B.)
| | - M John Chapman
- Endocrinology Metabolism Division, Pitie-Salpetriere University Hospital, Sorbonne University and National Institute for Health and Medical Research (INSERM), Paris, France (M.J.C.)
| | - Catherine Boileau
- Université de Paris, INSERM U1148 and Genetics Department, APHP-Hospital Bichat-Claude Bernard, France (C.B.)
| | - Sotirios Tsimikas
- Sulpizio Cardiovascular Center, Division of Cardiovascular Medicine, University of California San Diego, La Jolla (S.T.)
| | - Henry N Ginsberg
- Irving Institute for Clinical and Translational Research, Columbia University, New York (H.N.G.)
| | - Poulabi Banerjee
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Garen Manvelian
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Robert Pordy
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Sibylle Hess
- Sanofi Aventis Deutschland GmbH, Translational Medicine and Early Development, Biomarkers and Clinical Bioanalyses, Frankfurt, Germany (S.H.)
| | - John D Overton
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Luca A Lotta
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - George D Yancopoulos
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Goncalo R Abecasis
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Aris Baras
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
| | - Charles Paulding
- Regeneron Pharmaceuticals Inc, Tarrytown, NY (A.D., P.B., G.M., R.P., J.D.O., L.A.L., G.D.Y., G.R.A., A.B., C.P.)
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397
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Choi SH, Jurgens SJ, Weng LC, Pirruccello JP, Roselli C, Chaffin M, Lee CJY, Hall AW, Khera AV, Lunetta KL, Lubitz SA, Ellinor PT. Monogenic and Polygenic Contributions to Atrial Fibrillation Risk: Results From a National Biobank. Circ Res 2019; 126:200-209. [PMID: 31691645 DOI: 10.1161/circresaha.119.315686] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
RATIONALE Genome-wide association studies have identified over 100 genetic loci for atrial fibrillation (AF); recent work described an association between loss-of-function (LOF) variants in TTN and early-onset AF. OBJECTIVE We sought to determine the contribution of rare and common genetic variation to AF risk in the general population. METHODS The UK Biobank is a population-based study of 500 000 individuals including a subset with genome-wide genotyping and exome sequencing. In this case-control study, we included AF cases and controls of genetically determined white-European ancestry; analyses were performed using a logistic mixed-effects model adjusting for age, sex, the first 4 principal components of ancestry, empirical relationships, and case-control imbalance. An exome-wide, gene-based burden analysis was performed to examine the relationship between AF and rare, high-confidence LOF variants in genes with ≥10 LOF carriers. A polygenic risk score for AF was estimated using the LDpred algorithm. We then compared the contribution of AF polygenic risk score and LOF variants to AF risk. RESULTS The study included 1546 AF cases and 41 593 controls. In an analysis of 9099 genes with sufficient LOF variant carriers, a significant association between AF and rare LOF variants was observed in a single gene, TTN (odds ratio, 2.71, P=2.50×10-8). The association with AF was more significant (odds ratio, 6.15, P=3.26×10-14) when restricting to LOF variants located in exons highly expressed in cardiac tissue (TTNLOF). Overall, 0.44% of individuals carried TTNLOF variants, of whom 14% had AF. Among individuals in the highest 0.44% of the AF polygenic risk score only 9.3% had AF. In contrast, the AF polygenic risk score explained 4.7% of the variance in AF susceptibility, while TTNLOF variants only accounted for 0.2%. CONCLUSIONS Both monogenic and polygenic factors contribute to AF risk in the general population. While rare TTNLOF variants confer a substantial AF penetrance, the additive effect of many common variants explains a larger proportion of genetic susceptibility to AF.
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Affiliation(s)
- Seung Hoan Choi
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Sean J Jurgens
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Lu-Chen Weng
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.).,Cardiovascular Research Center, Massachusetts General Hospital, Boston (L.-C.W., A.W.H., S.A.L., P.T.E.)
| | - James P Pirruccello
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Carolina Roselli
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Mark Chaffin
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Christina J-Y Lee
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Amelia W Hall
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.).,Cardiovascular Research Center, Massachusetts General Hospital, Boston (L.-C.W., A.W.H., S.A.L., P.T.E.)
| | - Amit V Khera
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.)
| | - Kathryn L Lunetta
- NHLBI and Boston University's Framingham Heart Study, Framingham, MA (K.L.L.).,Department of Biostatistics, Boston University School of Public Health, MA (K.L.L.)
| | - Steven A Lubitz
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.).,Cardiovascular Research Center, Massachusetts General Hospital, Boston (L.-C.W., A.W.H., S.A.L., P.T.E.)
| | - Patrick T Ellinor
- From the Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., L.-C.W., J.P.P., C.R., M.C., C.J.-Y.L., A.W.H., A.V.K., S.A.L., P.T.E.).,Cardiovascular Research Center, Massachusetts General Hospital, Boston (L.-C.W., A.W.H., S.A.L., P.T.E.)
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398
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de Toro-Martín J, Guénard F, Bouchard C, Tremblay A, Pérusse L, Vohl MC. The Challenge of Stratifying Obesity: Attempts in the Quebec Family Study. Front Genet 2019; 10:994. [PMID: 31649740 PMCID: PMC6796792 DOI: 10.3389/fgene.2019.00994] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 09/18/2019] [Indexed: 01/23/2023] Open
Abstract
Background and aims: Obesity is a major health problem worldwide. Given the heterogeneous obesity phenotype, an optimal obesity stratification would improve clinical management. Since obesity has a strong genetic component, we aimed to develop a polygenic risk score (PRS) to stratify obesity according to the genetic background of the individuals. Methods: A total of 231 single nucleotide polymorphisms (SNP) significantly associated to body mass index (BMI) from 21 genome-wide association studies were genotyped or imputed in 881 subjects from the Quebec Family Study (QFS). The population was randomly split into discovery (80%; n = 704) and validation (20%; n = 177) samples with similar obesity (BMI ≥ 30) prevalence (27.8% and 28.2%, respectively). Family-based associations with obesity were tested for every SNP in the discovery sample and a weighed and continuous PRS231 was constructed. Generalized linear mixed effects models were used to test the association of PRS231 with obesity in the QFS discovery sample and validated in the QFS replication sample. Furthermore, the Fatty Acid Sensor (FAS) Study (n = 141; 27.7% obesity prevalence) was used as an independent sample to replicate the results. Results: The linear trend test demonstrated a significant association of PRS231 with obesity in the QFS discovery sample (ORtrend = 1.19 [95% CI, 1.14-1.24]; P = 2.0x10-16). We also found that the obesity prevalence was significantly greater in the higher PRS231 quintiles compared to the lowest quintile. Significant and consistent results were obtained in the QFS validation sample for both the linear trend test (ORtrend = 1.16 [95% CI, 1.07-1.26]; P = 6.7x10-4), and obesity prevalence across quintiles. These results were partially replicated in the FAS sample (ORtrend = 1.12 [95% CI, 1.02-1.24]; P = 2.2x10-2). PRS231 explained 7.5%, 3.2%, and 1.2% of BMI variance in QFS discovery, QFS validation, and FAS samples, respectively. Conclusions: These results revealed that genetic background in the form of a 231 BMI-associated PRS has a significant impact on obesity, but a limited potential to accurately stratify it. Further studies are encouraged on larger populations.
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Affiliation(s)
- Juan de Toro-Martín
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, QC, Canada.,School of Nutrition, Université Laval, Quebec, QC, Canada
| | - Frédéric Guénard
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, QC, Canada.,School of Nutrition, Université Laval, Quebec, QC, Canada
| | - Claude Bouchard
- Human Genomics Laboratory, Pennington Biomedical Research Center, Baton Rouge, LA, United States
| | - Angelo Tremblay
- Department of Kinesiology, Université Laval, Quebec, QC, Canada.,Quebec Heart and Lung Institute Research Center, Quebec, QC, Canada
| | - Louis Pérusse
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, QC, Canada.,Department of Kinesiology, Université Laval, Quebec, QC, Canada
| | - Marie-Claude Vohl
- Institute of Nutrition and Functional Foods (INAF), Université Laval, Quebec, QC, Canada.,School of Nutrition, Université Laval, Quebec, QC, Canada
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399
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Bierut LJ. Using Genomic Profiling for Understanding and Improving Response to Smoking Cessation Treatment. CURR EPIDEMIOL REP 2019; 6:486-490. [DOI: 10.1007/s40471-019-00220-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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400
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Smeland OB, Frei O, Fan CC, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet 2019; 29:152-159. [PMID: 31464996 PMCID: PMC10752571 DOI: 10.1097/ypg.0000000000000234] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genome-wide association studies have transformed psychiatric genetics and provided novel insights into the genetic etiology of psychiatric disorders. Two major discoveries have emerged; the disorders are polygenic, with a large number of common variants each with a small effect and many genetic variants influence more than one phenotype, suggesting shared genetic etiology. These concepts have the potential to revolutionize the current classification system with diagnostic categories and facilitate development of better treatments. However, to reach clinical impact, we need larger samples and better analytical tools, as most polygenic factors remain undetected. We here present statistical approaches designed to improve the yield of existing genome-wide association studies for polygenic phenotypes. We review how these tools have informed the current knowledge on the genetic architecture of psychiatric disorders, focusing on schizophrenia, bipolar disorder and major depression, and overlap with psychological and cognitive traits. We discuss application of statistical tools for stratification and prediction.
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Affiliation(s)
- Olav B. Smeland
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Chun-Chieh Fan
- Center for Human Development, University of California, San Diego, USA
| | - Alexey Shadrin
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Anders M. Dale
- Department of Radiology, University of California, USA
- Department of Neuroscience, University of California, San Diego, USA
- Center for Multimodal Imaging and Genetics, University of California, San Diego, La Jolla, California, USA
| | - Ole A. Andreassen
- NORMENT Centre, Institute of Clinical Medicine, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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