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Sun X, Ma S, Guo Y, Chen C, Pan L, Cui Y, Chen Z, Dijkhuizen RM, Zhou Y, Boltze J, Yu Z, Li P. The association between air pollutant exposure and cerebral small vessel disease imaging markers with modifying effects of PRS-defined genetic susceptibility. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 281:116638. [PMID: 38944013 DOI: 10.1016/j.ecoenv.2024.116638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/01/2024]
Abstract
Studies have highlighted a possible link between air pollution and cerebral small vessel disease (CSVD) imaging markers. However, the exact association and effects of polygenic risk score (PRS) defined genetic susceptibility remains unclear. This cross-sectional study used data from the UK Biobank. Participants aged 40-69 years were recruited between the year 2006 and 2010. The annual average concentrations of NOX, NO2, PM2.5, PM2.5-10, PM2.5 absorbance, and PM10, were estimated, and joint exposure to multiple air pollutants was reflected in the air pollution index (APEX). Air pollutant exposure was classified into the low (T1), intermediate (T2), and high (T3) tertiles. Three CSVD markers were used: white matter hyper-intensity (WMH), mean diffusivity (MD), and fractional anisotropy (FA). The first principal components of the MD and FA measures in the 48 white matter tracts were analysed. The sample consisted of 44,470 participants from the UK Biobank. The median (T1-T3) concentrations of pollutants were as follows: NO2, 25.5 (22.4-28.7) μg/m3; NOx, 41.3 (36.2-46.7) μg/m3; PM10, 15.9 (15.4-16.4) μg/m3; PM2.5, 9.9 (9.5-10.3) μg/m3; PM2.5 absorbance, 1.1 (1.0-1.2) per metre; and PM2.5-10, 6.1 (5.9-6.3) μg/m3. Compared with the low group, the high group's APEX, NOX, and PM2.5 levels were associated with increased WMH volumes, and the estimates (95 %CI) were 0.024 (0.003, 0.044), 0.030 (0.010, 0.050), and 0.032 (0.011, 0.053), respectively, after adjusting for potential confounders. APEX, PM10, PM2.5 absorbance, and PM2.5-10 exposure in the high group were associated with increased FA values compared to that in the low group. Sex-specific analyses revealed associations only in females. Regarding the combined associations of air pollutant exposure and PRS-defined genetic susceptibility with CSVD markers, the associations of NO2, NOX, PM2.5, and PM2.5-10 with WMH were more profound in females with low PRS-defined genetic susceptibility, and the associations of PM10, PM2.5, and PM2.5 absorbance with FA were more profound in females with higher PRS-defined genetic susceptibility. Our study demonstrated that air pollutant exposure may be associated with CSVD imaging markers, with females being more susceptible, and that PRS-defined genetic susceptibility may modify the associations of air pollutants.
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Affiliation(s)
- Xiaowei Sun
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Shiyang Ma
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yunlu Guo
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Caiyang Chen
- Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Lijun Pan
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Yidan Cui
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Zengai Chen
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Yan Zhou
- Department of Radiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
| | - Johannes Boltze
- School of Life Sciences, University of Warwick, Coventry, UK.
| | - Zhangsheng Yu
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Peiying Li
- Clinical Research Center, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Department of Anesthesiology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China; Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Outcomes Research Consortium, Cleveland, OH, United States.
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2
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Im C, Neupane A, Baedke JL, Lenny B, Delaney A, Dixon SB, Chow EJ, Mostoufi-Moab S, Yang T, Richard MA, Gramatges MM, Lupo PJ, Sharafeldin N, Bhatia S, Armstrong GT, Hudson MM, Ness KK, Robison LL, Yasui Y, Wilson CL, Sapkota Y. Trans-Ancestral Genetic Risk Factors for Treatment-Related Type 2 Diabetes Mellitus in Survivors of Childhood Cancer. J Clin Oncol 2024; 42:2306-2316. [PMID: 38652878 PMCID: PMC11209771 DOI: 10.1200/jco.23.02281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/01/2024] [Accepted: 02/28/2024] [Indexed: 04/25/2024] Open
Abstract
PURPOSE Type 2 diabetes mellitus (T2D) is a prevalent long-term complication of treatment in survivors of childhood cancer, with marked racial/ethnic differences in burden. In this study, we investigated trans-ancestral genetic risks for treatment-related T2D. PATIENTS AND METHODS Leveraging whole-genome sequencing data from the St Jude Lifetime Cohort (N = 3,676, 304 clinically ascertained cases), we conducted ancestry-specific genome-wide association studies among survivors of African and European genetic ancestry (AFR and EUR, respectively) followed by trans-ancestry meta-analysis. Trans-/within-ancestry replication including data from the Childhood Cancer Survivor Study (N = 5,965) was required for prioritization. Three external general population T2D polygenic risk scores (PRSs) were assessed, including multiancestry PRSs. Treatment risk effect modification was evaluated for prioritized loci. RESULTS Four novel T2D risk loci showing trans-/within-ancestry replication evidence were identified, with three loci achieving genome-wide significance (P < 5 × 10-8). Among these, common variants at 5p15.2 (LINC02112), 2p25.3 (MYT1L), and 19p12 (ZNF492) showed evidence of modifying alkylating agent-related T2D risk in both ancestral groups, but showed disproportionately greater risk in AFR survivors (AFR odds ratios [ORs], 3.95-17.81; EUR ORs, 2.37-3.32). In survivor-specific RNA-sequencing data (N = 207), the 19p12 locus variant was associated with greater ZNF492 expression dysregulation after exposures to alkylators. Elevated T2D risks across ancestry groups were only observed with increasing values for multiancestry T2D PRSs and were especially increased among survivors treated with alkylators (top v bottom quintiles: ORAFR, 20.18; P = .023; OREUR, 13.44; P = 1.3 × 10-9). CONCLUSION Our findings suggest therapy-related genetic risks contribute to the increased T2D burden among non-Hispanic Black childhood cancer survivors. Additional study of how therapy-related genetic susceptibility contributes to this disparity is needed.
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Affiliation(s)
- Cindy Im
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Achal Neupane
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Jessica L. Baedke
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Brian Lenny
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Angela Delaney
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Division of Endocrinology, Department of Pediatric Medicine, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Stephanie B. Dixon
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Eric J. Chow
- Public Health Sciences and Clinical Research Divisions, Fred Hutchinson Research Center, Seattle, WA, 98109, USA
| | - Sogol Mostoufi-Moab
- Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, PA, 19146, USA
| | - Tianzhong Yang
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Melissa A. Richard
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - M. Monica Gramatges
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Philip J. Lupo
- Section of Hematology-Oncology, Department of Pediatrics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Noha Sharafeldin
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Smita Bhatia
- Institute for Cancer Outcomes and Survivorship, University of Alabama at Birmingham, Birmingham, AL, 35223, USA
| | - Gregory T. Armstrong
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Melissa M. Hudson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Kirsten K. Ness
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Leslie L. Robison
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yutaka Yasui
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Carmen L. Wilson
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
| | - Yadav Sapkota
- Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, Memphis, TN, 38105, USA
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Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, Kodama EN, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Matsuda K, Izumi Y, Kinoshita K, Tamiya G, Hozawa A, Yamamoto M. Genetic Risk, Healthy Lifestyle Adherence, and Risk of Developing Diabetes in the Japanese Population. J Atheroscler Thromb 2024:64906. [PMID: 38910120 DOI: 10.5551/jat.64906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/25/2024] Open
Abstract
AIM This study examined the relationship between genetic risk, healthy lifestyle, and risk of developing diabetes. METHODS This prospective cohort study included 11,014 diabetes-free individuals ≥ 20 years old from the Tohoku Medical Megabank Community-based cohort study. Lifestyle scores, including the body mass index, smoking, physical activity, and gamma-glutamyl transferase (marker of alcohol consumption), were assigned, and participants were categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score (PRS) was constructed based on the type 2 diabetes loci from the BioBank Japan study. A multiple logistic regression model was used to estimate the association between genetic risk, healthy lifestyle, and diabetes incidence and to calculate the area under the receiver operating characteristic curve (AUROC). RESULT Of the 11,014 adults included (67.8% women; mean age [standard deviation], 59.1 [11.3] years old), 297 (2.7%) developed diabetes during a mean 4.3 (0.8) years of follow-up. Genetic and lifestyle score is independently associated with the development of diabetes. Compared with the low genetic risk and ideal lifestyle groups, the odds ratio was 3.31 for the low genetic risk and poor lifestyle group. When the PRS was integrated into a model including the lifestyle and family history, the AUROC significantly improved to 0.719 (95% confidence interval [95% CI]: 0.692-0.747) compared to a model including only the lifestyle and family history (0.703 [95% CI, 0.674-0.732]). CONCLUSION Our findings indicate that adherence to a healthy lifestyle is important for preventing diabetes, regardless of genetic risk. In addition, genetic risk might provide information beyond lifestyle and family history to stratify individuals at high risk of developing diabetes.
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Affiliation(s)
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University
- Kyoto Women's University
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University
- Human Care Research Team, Tokyo metropolitan Institute for Geriatrics and Gerontology
| | - Akira Narita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Taku Obara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Eiichi N Kodama
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | | | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
- Suzuki Memorial Hospital
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo
| | - Yoko Izumi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kengo Kinoshita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- RIKEN Center for Advanced Intelligence Project
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
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4
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Chen T, Pham G, Fox L, Adler N, Wang X, Zhang J, Byun J, Han Y, Saunders GRB, Liu D, Bray MJ, Ramsey AT, McKay J, Bierut L, Amos CI, Hung RJ, Lin X, Zhang H, Chen LS. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304556. [PMID: 38562690 PMCID: PMC10984046 DOI: 10.1101/2024.03.19.24304556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations. Methods We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry. Results Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU. Conclusion Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.
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Armstrong ND, Patki A, Srinivasasainagendra V, Ge T, Lange LA, Kottyan L, Namjou B, Shah AS, Rasmussen-Torvik LJ, Jarvik GP, Meigs JB, Karlson EW, Limdi NA, Irvin MR, Tiwari HK. Variant level heritability estimates of type 2 diabetes in African Americans. Sci Rep 2024; 14:14009. [PMID: 38890458 PMCID: PMC11189523 DOI: 10.1038/s41598-024-64711-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 06/12/2024] [Indexed: 06/20/2024] Open
Abstract
Type 2 diabetes (T2D) is caused by both genetic and environmental factors and is associated with an increased risk of cardiorenal complications and mortality. Though disproportionately affected by the condition, African Americans (AA) are largely underrepresented in genetic studies of T2D, and few estimates of heritability have been calculated in this race group. Using genome-wide association study (GWAS) data paired with phenotypic data from ~ 19,300 AA participants of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, Genetics of Hypertension Associated Treatments (GenHAT) study, and the Electronic Medical Records and Genomics (eMERGE) network, we estimated narrow-sense heritability using two methods: Linkage-Disequilibrium Adjusted Kinships (LDAK) and Genome-Wide Complex Trait Analysis (GCTA). Study-level heritability estimates adjusting for age, sex, and genetic ancestry ranged from 18% to 34% across both methods. Overall, the current study narrows the expected range for T2D heritability in this race group compared to prior estimates, while providing new insight into the genetic basis of T2D in AAs for ongoing genetic discovery efforts.
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Affiliation(s)
- Nicole D Armstrong
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Amit Patki
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Tian Ge
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Amy S Shah
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center &, The University of Cincinnati, Cincinnati, OH, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Elizabeth W Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Nita A Limdi
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Marguerite R Irvin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
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6
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Jee YH, Thibord F, Dominguez A, Sept C, Boulier K, Venkateswaran V, Ding Y, Cherlin T, Verma SS, Faro VL, Bartz TM, Boland A, Brody JA, Deleuze JF, Emmerich J, Germain M, Johnson AD, Kooperberg C, Morange PE, Pankratz N, Psaty BM, Reiner AP, Smadja DM, Sitlani CM, Suchon P, Tang W, Trégouët DA, Zöllner S, Pasaniuc B, Damrauer SM, Sanna S, Snieder H, Kabrhel C, Smith NL, Kraft P. Multi-ancestry polygenic risk scores for venous thromboembolism. Hum Mol Genet 2024:ddae097. [PMID: 38879759 DOI: 10.1093/hmg/ddae097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 05/29/2024] [Accepted: 06/03/2024] [Indexed: 06/25/2024] Open
Abstract
Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality, with large disparities in incidence rates between Black and White Americans. Polygenic risk scores (PRSs) limited to variants discovered in genome-wide association studies in European-ancestry samples can identify European-ancestry individuals at high risk of VTE. However, there is limited evidence on whether high-dimensional PRS constructed using more sophisticated methods and more diverse training data can enhance the predictive ability and their utility across diverse populations. We developed PRSs for VTE using summary statistics from the International Network against Venous Thrombosis (INVENT) consortium genome-wide association studies meta-analyses of European- (71 771 cases and 1 059 740 controls) and African-ancestry samples (7482 cases and 129 975 controls). We used LDpred2 and PRS-CSx to construct ancestry-specific and multi-ancestry PRSs and evaluated their performance in an independent European- (6781 cases and 103 016 controls) and African-ancestry sample (1385 cases and 12 569 controls). Multi-ancestry PRSs with weights tuned in European-ancestry samples slightly outperformed ancestry-specific PRSs in European-ancestry test samples (e.g. the area under the receiver operating curve [AUC] was 0.609 for PRS-CSx_combinedEUR and 0.608 for PRS-CSxEUR [P = 0.00029]). Multi-ancestry PRSs with weights tuned in African-ancestry samples also outperformed ancestry-specific PRSs in African-ancestry test samples (PRS-CSxAFR: AUC = 0.58, PRS-CSx_combined AFR: AUC = 0.59), although this difference was not statistically significant (P = 0.34). The highest fifth percentile of the best-performing PRS was associated with 1.9-fold and 1.68-fold increased risk for VTE among European- and African-ancestry subjects, respectively, relative to those in the middle stratum. These findings suggest that the multi-ancestry PRS might be used to improve performance across diverse populations to identify individuals at highest risk for VTE.
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Affiliation(s)
- Yon Ho Jee
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
| | - Florian Thibord
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, 31 Center Drive, Bethesda, MD 20892, United States
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, 73 Mt. Wayte Ave, Suite #2, Framingham, MA 01702, United States
| | - Alicia Dominguez
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, United States
| | - Corriene Sept
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
| | - Kristin Boulier
- Bioinformatics Interdepartmental Program, University of California Los Angeles, 611 Charles E. Young Drive East, Los Angeles, CA 90095-1570, United States
| | - Vidhya Venkateswaran
- Department of Oral Biology, University of California Los Angeles School of Dentistry, 13-089 CHS, Box 951668, Box 951570, Los Angeles, CA 90095-1668, United States
| | - Yi Ding
- Bioinformatics Interdepartmental Program, University of California Los Angeles, 611 Charles E. Young Drive East, Los Angeles, CA 90095-1570, United States
| | - Tess Cherlin
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St. Philadelphia, PA 19104-4238, United States
| | - Shefali Setia Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St. Philadelphia, PA 19104-4238, United States
| | - Valeria Lo Faro
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB Groningen, The Netherlands
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Dag Hammarskjölds väg 20751 85 Uppsala, Sweden
| | - Traci M Bartz
- Cardiovascular Health Research Unit, Departments of Biostatistics and Medicine, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057 Evry, France
- Laboratory of Excellence in Medical Genomics, GENMED, F-91057 Evry, France
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
| | - Jean-Francois Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, 91057 Evry, France
- Laboratory of Excellence in Medical Genomics, GENMED, F-91057 Evry, France
- Centre d'Etude du Polymorphisme Humain, Fondation Jean Dausset, 27 rue Juliette Dodu, 75010 Paris, France
| | - Joseph Emmerich
- Department of Vascular Medicine, Paris Saint-Joseph Hospital Group, University of Paris, 75014 Paris, France
- INSERM CRESS UMR 1153, F-75005, Paris, France
| | - Marine Germain
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, 31 Center Drive, Bethesda, MD 20892, United States
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, 73 Mt. Wayte Ave, Suite #2, Framingham, MA 01702, United States
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinbson Cancer Center, PO Box 19024, Seattle, WA 98109, United States
| | - Pierre-Emmanuel Morange
- Aix-Marseille University, INSERM, INRAE, Centre de Recherche en CardioVasculaire et Nutrition, Laboratory of Haematology, CRB Assistance Publique - Hôpitaux de Marseille, HemoVasc, 27, boulevard Jean Moulin, 13005 Marseille, France
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota, 420 Delaware Street SE, Minneapolis, MN 55455, United States
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
- Department of Epidemiology, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
- Department of Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
| | - Alexander P Reiner
- Division of Public Health Sciences, Fred Hutchinbson Cancer Center, PO Box 19024, Seattle, WA 98109, United States
- Department of Epidemiology, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
| | - David M Smadja
- Innovative Therapies in Hemostasis, Université de Paris, INSERM, F-75006, Paris, France
- Hematology Department and Biosurgical Research Lab (Carpentier Foundation), Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), F-75015, Paris, France
| | - Colleen M Sitlani
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
| | - Pierre Suchon
- Aix-Marseille University, INSERM, INRAE, Centre de Recherche en CardioVasculaire et Nutrition, Laboratory of Haematology, CRB Assistance Publique - Hôpitaux de Marseille, HemoVasc, 27, boulevard Jean Moulin, 13005 Marseille, France
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 S. 2nd St., Minneapolis, MN 55454, United States
| | - David-Alexandre Trégouët
- Bordeaux Population Health Research Center, University of Bordeaux, INSERM, UMR 1219, Bordeaux, France
| | - Sebastian Zöllner
- Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, United States
| | - Bogdan Pasaniuc
- Department of Oral Biology, University of California Los Angeles School of Dentistry, 13-089 CHS, Box 951668, Box 951570, Los Angeles, CA 90095-1668, United States
| | - Scott M Damrauer
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, 415 Curie Blvd, Philadelphia, PA 19104, United States
- Department of Surgery, Department of Genetics, and Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Building 421, Philadelphia, PA 19104, United States
- Department of Surgery, Corporal Michael Crescenz VA Medical Center, 3900 Woodland Ave, Philadelphia, PA 19104, United States
| | - Serena Sanna
- Department of Genetics, University of Groningen, University Medical Center Groningen (UMCG), PO Box 30.001, 9700 RB Groningen, The Netherlands
- Institute for Genetics and Biomedical Research, National Research Council, SS 554 Km 4,500, 09042 Monserrato CA, Italy
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, PO Box 30.001, 9700 RB Groningen, The Netherlands
| | - Christopher Kabrhel
- Center for Vascular Emergencies, Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, United States
| | - Nicholas L Smith
- Department of Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave, Seattle, WA 98195, United States
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, 1730 Minor Ave, Seattle, WA 98101, United States
- Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, 1660 S Columbian Way, S-152-E, Seattle, WA 98108, United States
| | - Peter Kraft
- Transdivisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 9609 Medical Center Dr, Rockville, MD 20850, United States
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7
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 PMCID: PMC11149372 DOI: 10.1186/s13073-024-01345-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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8
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Huang S, Joshi A, Shi Z, Wei J, Tran H, Zheng SL, Duggan D, Ashworth A, Billings L, Helfand BT, Qamar A, Bulwa Z, Tafur A, Xu J. Combined polygenic scores for ischemic stroke risk factors aid risk assessment of ischemic stroke. Int J Cardiol 2024; 404:131990. [PMID: 38521508 DOI: 10.1016/j.ijcard.2024.131990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/01/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Current risk assessment for ischemic stroke (IS) is limited to clinical variables. We hypothesize that polygenic scores (PGS) of IS (PGSIS) and IS-associated diseases such as atrial fibrillation (AF), venous thromboembolism (VTE), coronary artery disease (CAD), hypertension (HTN), and Type 2 diabetes (T2D) may improve the performance of IS risk assessment. METHODS Incident IS was followed for 479,476 participants in the UK Biobank who did not have an IS diagnosis prior to the recruitment. Lifestyle variables (obesity, smoking and alcohol) at the time of study recruitment, clinical diagnoses of IS-associated diseases, PGSIS, and five PGSs for IS-associated diseases were tested using the Cox proportional-hazards model. Predictive performance was assessed using the C-statistic and net reclassification index (NRI). RESULTS During a median average 12.5-year follow-up, 8374 subjects were diagnosed with IS. Known clinical variables (age, gender, clinical diagnoses of IS-associated diseases, obesity, and smoking) and PGSIS were all independently associated with IS (P < 0.001). In addition, PGSIS and each PGS for IS-associated diseases was also independently associated with IS (P < 0.001). Compared to the clinical model, a joint clinical/PGS model improved the C-statistic for predicting IS from 0.71 to 0.73 (P < 0.001) and significantly reclassified IS risk (NRI = 0.017, P < 0.001), and 6.48% of subjects were upgraded from low to high risk. CONCLUSIONS Adding PGSs of IS and IS-associated diseases to known clinical risk factors statistically improved risk assessment for IS, demonstrating the supplementary value of inherited susceptibility measurement . However, its clinical utility is likely limited due to modest improvements in predictive values.
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Affiliation(s)
- Sarah Huang
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Abhishek Joshi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Huy Tran
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - David Duggan
- Affiliate of City of Hope, Translational Genomics Research Institute, Phoenix, AZ, USA
| | - Annabelle Ashworth
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA
| | - Liana Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Brian T Helfand
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA
| | - Arman Qamar
- Cardiovascular Institute, NorthShore University HealthSystem, Evanston, IL, USA
| | - Zachary Bulwa
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL, USA
| | - Alfonso Tafur
- Cardiovascular Institute, NorthShore University HealthSystem, Evanston, IL, USA
| | - Jianfeng Xu
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL, USA; University of Chicago Pritzker School of Medicine, Chicago, IL, USA.
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9
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Smith JL, Tcheandjieu C, Dikilitas O, Iyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao PS, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e004272. [PMID: 38380516 DOI: 10.1161/circgen.123.004272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (CHD; PRSCHD) for 5 genetic ancestry groups. METHODS We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSPT) and ancestry-based continuous shrinkage priors (PRSCSx) applied to summary statistics from the largest multi-ancestry genome-wide association study meta-analysis for CHD to date, including 1.1 million participants from 5 major genetic ancestry groups. Following training and optimization in the Million Veteran Program, we evaluated the best-performing PRSCHD in 176,988 individuals across 9 diverse cohorts. RESULTS Multi-ancestry PRSPT and PRSCSx outperformed ancestry-specific PRSPT and PRSCSx across a range of tuning values. Two best-performing multi-ancestry PRSCHD (ie, PRSPTmult and PRSCSxmult) and 1 ancestry-specific (PRSCSxEUR) were taken forward for validation. PRSPTmult demonstrated the strongest association with CHD in individuals of South Asian ancestry and European ancestry (odds ratio per 1 SD [95% CI, 2.75 [2.41-3.14], 1.65 [1.59-1.72]), followed by East Asian ancestry (1.56 [1.50-1.61]), Hispanic/Latino ancestry (1.38 [1.24-1.54]), and African ancestry (1.16 [1.11-1.21]). PRSCSxmult showed the strongest associations in South Asian ancestry (2.67 [2.38-3.00]) and European ancestry (1.65 [1.59-1.71]), lower in East Asian ancestry (1.59 [1.54-1.64]), Hispanic/Latino ancestry (1.51 [1.35-1.69]), and the lowest in African ancestry (1.20 [1.15-1.26]). CONCLUSIONS The use of summary statistics from a large multi-ancestry genome-wide meta-analysis improved the performance of PRSCHD in most ancestry groups compared with single-ancestry methods. Despite the use of one of the largest and most diverse sets of training and validation cohorts to date, improvement of predictive performance was limited in African ancestry. This highlights the need for larger genome-wide association study datasets of underrepresented populations to enhance the performance of PRSCHD.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco (C.T.)
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institute, San Francisco, CA (C.T.)
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
| | - Kruthika Iyer
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | - Kazuo Miyazawa
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Austin Hilliard
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University School of Medicine, Palo Alto, CA (K. Iyer, A.H.)
| | | | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA (J.I.R., Y.-D.I.C.)
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital (W.H.-H.S.)
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taiwan (W.H.-H.S.)
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Center, Philadelphia, PA (K.-M.C.)
| | - Stavroula Kanoni
- Queen Mary University of London, Cambridge, United Kingdom (S.K.)
| | - Philip S Tsao
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Kaoru Ito
- Riken Center for Integrative Medical Sciences, Yokohama City, Japan (K.M., K. Ito)
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | - Shoa L Clarke
- VA Palo Alto Health Care System (C.T., A.H., P.S.T., S.L.C.)
- Stanford University, Stanford, CA (P.S.T., S.L.C., T.L.A.)
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN (M.K., D.J.S.)
| | | | - Iftikhar J Kullo
- Department of Cardiovascular Medicine (J.L.S., O.D., I.J.K.), Mayo Clinic, Rochester, MN
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Irvin MR, Ge T, Patki A, Srinivasasainagendra V, Armstrong ND, Davis B, Jones AC, Perez E, Stalbow L, Lebo M, Kenny E, Loos RJ, Ng MC, Smoller JW, Meigs JB, Lange LA, Karlson EW, Limdi NA, Tiwari HK. Polygenic Risk for Type 2 Diabetes in African Americans. Diabetes 2024; 73:993-1001. [PMID: 38470993 PMCID: PMC11109789 DOI: 10.2337/db23-0232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 03/06/2024] [Indexed: 03/14/2024]
Abstract
African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Marguerite R. Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | | | - Nicole D. Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Brittney Davis
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Alana C. Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Emma Perez
- Department of Medicine, Brigham and Women's Hospital, Boston, MA
- Mass General Brigham Personalized Medicine, Boston, MA
| | - Lauren Stalbow
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew Lebo
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Mass General Brigham Personalized Medicine, Boston, MA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA
| | - Eimear Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Maggie C.Y. Ng
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - James B. Meigs
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Leslie A. Lange
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO
| | - Elizabeth W. Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA
- Mass General Brigham Personalized Medicine, Boston, MA
| | - Nita A. Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Hemant K. Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
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11
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024:dmae012. [PMID: 38805697 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d'Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l'infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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12
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Huang YJ, Chen CH, Yang HC. AI-enhanced integration of genetic and medical imaging data for risk assessment of Type 2 diabetes. Nat Commun 2024; 15:4230. [PMID: 38762475 PMCID: PMC11102564 DOI: 10.1038/s41467-024-48618-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/08/2024] [Indexed: 05/20/2024] Open
Abstract
Type 2 diabetes (T2D) presents a formidable global health challenge, highlighted by its escalating prevalence, underscoring the critical need for precision health strategies and early detection initiatives. Leveraging artificial intelligence, particularly eXtreme Gradient Boosting (XGBoost), we devise robust risk assessment models for T2D. Drawing upon comprehensive genetic and medical imaging datasets from 68,911 individuals in the Taiwan Biobank, our models integrate Polygenic Risk Scores (PRS), Multi-image Risk Scores (MRS), and demographic variables, such as age, sex, and T2D family history. Here, we show that our model achieves an Area Under the Receiver Operating Curve (AUC) of 0.94, effectively identifying high-risk T2D subgroups. A streamlined model featuring eight key variables also maintains a high AUC of 0.939. This high accuracy for T2D risk assessment promises to catalyze early detection and preventive strategies. Moreover, we introduce an accessible online risk assessment tool for T2D, facilitating broader applicability and dissemination of our findings.
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Affiliation(s)
- Yi-Jia Huang
- Institute of Public Health, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chun-Houh Chen
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Hsin-Chou Yang
- Institute of Public Health, National Yang-Ming Chiao-Tung University, Taipei, Taiwan.
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan.
- Biomedical Translation Research Center, Academia Sinica, Taipei, Taiwan.
- Department of Statistics, National Cheng Kung University, Tainan, Taiwan.
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13
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Ghouse J, Sveinbjörnsson G, Vujkovic M, Seidelin AS, Gellert-Kristensen H, Ahlberg G, Tragante V, Rand SA, Brancale J, Vilarinho S, Lundegaard PR, Sørensen E, Erikstrup C, Bruun MT, Jensen BA, Brunak S, Banasik K, Ullum H, Verweij N, Lotta L, Baras A, Mirshahi T, Carey DJ, Kaplan DE, Lynch J, Morgan T, Schwantes-An TH, Dochtermann DR, Pyarajan S, Tsao PS, Laisk T, Mägi R, Kozlitina J, Tybjærg-Hansen A, Jones D, Knowlton KU, Nadauld L, Ferkingstad E, Björnsson ES, Ulfarsson MO, Sturluson Á, Sulem P, Pedersen OB, Ostrowski SR, Gudbjartsson DF, Stefansson K, Olesen MS, Chang KM, Holm H, Bundgaard H, Stender S. Integrative common and rare variant analyses provide insights into the genetic architecture of liver cirrhosis. Nat Genet 2024; 56:827-837. [PMID: 38632349 PMCID: PMC11096111 DOI: 10.1038/s41588-024-01720-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 03/18/2024] [Indexed: 04/19/2024]
Abstract
We report a multi-ancestry genome-wide association study on liver cirrhosis and its associated endophenotypes, alanine aminotransferase (ALT) and γ-glutamyl transferase. Using data from 12 cohorts, including 18,265 cases with cirrhosis, 1,782,047 controls, up to 1 million individuals with liver function tests and a validation cohort of 21,689 cases and 617,729 controls, we identify and validate 14 risk associations for cirrhosis. Many variants are located near genes involved in hepatic lipid metabolism. One of these, PNPLA3 p.Ile148Met, interacts with alcohol intake, obesity and diabetes on the risk of cirrhosis and hepatocellular carcinoma (HCC). We develop a polygenic risk score that associates with the progression from cirrhosis to HCC. By focusing on prioritized genes from common variant analyses, we find that rare coding variants in GPAM associate with lower ALT, supporting GPAM as a potential target for therapeutic inhibition. In conclusion, this study provides insights into the genetic underpinnings of cirrhosis.
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Affiliation(s)
- Jonas Ghouse
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anne-Sofie Seidelin
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Helene Gellert-Kristensen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Gustav Ahlberg
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Søren A Rand
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Joseph Brancale
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Silvia Vilarinho
- Section of Digestive Diseases, Department of Internal Medicine, and Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Pia Rengtved Lundegaard
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Erik Sørensen
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Christian Erikstrup
- Department of Clinical Immunology, Aarhus University Hospital, Aarhus, Denmark
| | - Mie Topholm Bruun
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Søren Brunak
- Translational Disease Systems Biology, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Karina Banasik
- Department of Obstetrics and Gynaecology, Copenhagen University Hospital Hvidovre, Copenhagen, Denmark
| | | | - Niek Verweij
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Luca Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger Health System, Danville, PA, USA
| | - David E Kaplan
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure (VINCI), VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Timothy Morgan
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medicine, University of California, Irvine, CA, USA
| | - Tae-Hwi Schwantes-An
- Gastroenterology Section, Veterans Affairs Long Beach Healthcare System, Long Beach, CA, USA
- Department of Medical and Molecular Genetics, Indiana University, Indianapolis, IN, USA
| | - Daniel R Dochtermann
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
| | - Saiju Pyarajan
- Center for Data and Computational Sciences, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Philip S Tsao
- Palo Alto Epidemiology Research and Information Center for Genomics, VA Palo Alto, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Triin Laisk
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Julia Kozlitina
- Eugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - David Jones
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
| | - Kirk U Knowlton
- Intermountain Medical Center, Intermountain Heart Institute, Salt Lake City, UT, USA
- University of Utah, School of Medicine, Salt Lake City, UT, USA
| | - Lincoln Nadauld
- Precision Genomics, Intermountain Healthcare, Saint George, UT, USA
- Stanford University, School of Medicine, Stanford, CA, USA
| | | | - Einar S Björnsson
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Internal Medicine and Emergency Services, Landspitali-The National University Hospital of Iceland, Reykjavik, Iceland
| | - Magnus O Ulfarsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik, Iceland
| | | | | | - Ole B Pedersen
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Clinical Immunology, Zealand University Hospital, Køge, Denmark
| | - Sisse R Ostrowski
- Department of Clinical Immunology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Daniel F Gudbjartsson
- deCODE Genetics/Amgen, Reykjavik, Iceland
- School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | | | - Morten Salling Olesen
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Cardiac Genetics Group, Department of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hilma Holm
- deCODE Genetics/Amgen, Reykjavik, Iceland
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark.
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
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14
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Wang SH, Huang YC, Cheng CW, Chang YW, Liao WL. Impact of the trans-ancestry polygenic risk score on type 2 diabetes risk, onset age and progression among population in Taiwan. Am J Physiol Endocrinol Metab 2024; 326:E547-E554. [PMID: 38363735 DOI: 10.1152/ajpendo.00252.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 02/18/2024]
Abstract
Type 2 diabetes (T2D) prevalence in adults at a younger age has increased but the disease status may go unnoticed. This study aimed to determine whether the onset age and subsequent diabetic complications can be attributed to the polygenic architecture of T2D in the Taiwan Han population. A total of 9,627 cases with T2D and 85,606 controls from the Taiwan Biobank were enrolled. Three diabetic polygenic risk scores (PRSs), PRS_EAS and PRS_EUR, and a trans-ancestry PRS (PRS_META), calculated using summary statistic from East Asian and European populations. The onset age was identified by linking to the National Taiwan Insurance Research Database, and the incidence of different diabetic complications during follow-up was recorded. PRS_META (7.4%) explained a higher variation for T2D status. And the higher percentile of PRS is also correlated with higher percentage of T2D family history and prediabetes status. More, the PRS was negatively associated with onset age (β = -0.91 yr), and this was more evident among males (β = -1.11 vs. -0.76 for males and females, respectively). The hazard ratio of diabetic retinopathy (DR) and diabetic foot were significantly associated with PRS_EAS and PRS_META, respectively. However, the PRS was not associated with other diabetic complications, including diabetic nephropathy, cardiovascular disease, and hypertension. Our findings indicated that diabetic PRS which combined susceptibility variants from cross-population could be used as a tool for early screening of T2D, especially for high-risk populations, such as individuals with high genetic risk, and may be associated with the risk of complications in subjects with T2D. NEW & NOTEWORTHY Our findings indicated that diabetic polygenic risk score (PRS) which combined susceptibility variants from Asian and European population affect the onset age of type 2 diabetes (T2D) and could be used as a tool for early screening of T2D, especially for individuals with high genetic risk, and may be associated with the risk of diabetic complications among people in Taiwan.
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Affiliation(s)
- Shi-Heng Wang
- National Center for Geriatrics and Welfare Research, National Health Research Institutes, Zhunan, Taiwan
- Department of Public Health, China Medical University, Taichung, Taiwan
| | - Yu-Chuen Huang
- School of Chinese Medicine, China Medical University, Taichung, Taiwan
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chun-Wen Cheng
- Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
- Clinical Laboratory, Chung Shan Medical University Hospital, Taichung, Taiwan
| | - Ya-Wen Chang
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, Taiwan
- Center for Personalized Medicine, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
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15
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Andrews SJ, Jonson C, Fulton-Howard B, Renton AE, Yokoyama JS, Yaffe K. The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.27.24306488. [PMID: 38903124 PMCID: PMC11188112 DOI: 10.1101/2024.04.27.24306488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
Importance By integrating genetic and clinical risk factors into genomic-informed dementia risk reports, healthcare providers can offer patients detailed risk profiles to facilitate understanding of individual risk and support the implementation of personalized strategies for promoting brain health. Objective To develop a genomic-informed risk assessment composed of family history, genetic, and clinical risk factors and, in turn, evaluate how the risk assessment predicted incident dementia. Design This longitudinal study included data from two clinical case-control cohorts with an average of 6.6 visits. Secondary analyses were conducted from July 2023 - March 2024. Setting Data were previously collected across multiple US locations from 1994 to 2023. Participants Older adults aged 55+ with whole-genome sequencing and dementia-free at baseline. Exposures An additive score comprising the modified Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (mCAIDE), family history of dementia, APOE genotype, and an AD polygenic risk score. Main Outcomes and Measures The risk of progression to all-cause dementia was evaluated using Cox-proportional hazard models (hazard ratios with 95% confidence intervals [OR 9%CI]). Results A total of 3,429 older adults were included (aged 75 ± 7 years; 59% female; 75% non-Latino White, 15% Black, 5.2% Latino, 3.6% other, and 0.4% Asian; 27% MCI), with 751 participants progressing to dementia. The most common high-risk indicator was a family history of dementia (56%), followed by APOE*ε4 genotype (36%), high mCAIDE score (34%), and high AD-PRS (11%). Most participants had at least one high-risk indicator, with 39% having one, 32% two, 9.8% three, and 1% four. The presence of 1, 2, 3, or 4 risk indicators was associated with a doubling (HR = 1.72, CI: 1.34-2.22, p = 2.5e-05), tripling (HR = 3.09, CI: 2.41-3.95, p = 4.4e-19), quadrupling (HR = 4.46, CI: 3.34-5.94, p = 2.2e-24), and a twelvefold increase (HR = 12.15, CI: 7.33-20.14, p = 3.2e-22) in dementia risk. Conclusion & Relevance We found that most participants in memory and aging clinics had at least one high-risk indicator for dementia. Furthermore, we observed a dose-response relationship where a greater number of risk indicators was associated with an increased risk of incident dementia.
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Affiliation(s)
- Shea J. Andrews
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA
| | - Caroline Jonson
- Department of Neurology, University of California San Francisco, San Francisco, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD USA 20892
- DataTecnica LLC, Washington, DC USA 20037
| | - Brian Fulton-Howard
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Alan E Renton
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Jennifer S Yokoyama
- Department of Neurology, University of California San Francisco, San Francisco, USA
| | - Kristine Yaffe
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, USA
- Department of Neurology, University of California San Francisco, San Francisco, USA
- Department of Epidemiology and Biostatistics, University of California
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16
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Elashi AA, Toor SM, Umlai UKI, Al-Sarraj YA, Taheri S, Suhre K, Abou-Samra AB, Albagha OME. Genome-wide association study and trans-ethnic meta-analysis identify novel susceptibility loci for type 2 diabetes mellitus. BMC Med Genomics 2024; 17:115. [PMID: 38685053 PMCID: PMC11059680 DOI: 10.1186/s12920-024-01855-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND The genetic basis of type 2 diabetes (T2D) is under-investigated in the Middle East, despite the rapidly growing disease prevalence. We aimed to define the genetic determinants of T2D in Qatar. METHODS Using whole genome sequencing of 11,436 participants (2765 T2D cases and 8671 controls) from the population-based Qatar Biobank (QBB), we conducted a genome-wide association study (GWAS) of T2D with and without body mass index (BMI) adjustment. RESULTS We replicated 93 known T2D-associated loci in a BMI-unadjusted model, while 96 known loci were replicated in a BMI-adjusted model. The effect sizes and allele frequencies of replicated SNPs in the Qatari population generally concurred with those from European populations. We identified a locus specific to our cohort located between the APOBEC3H and CBX7 genes in the BMI-unadjusted model. Also, we performed a transethnic meta-analysis of our cohort with a previous GWAS on T2D in multi-ancestry individuals (180,834 T2D cases and 1,159,055 controls). One locus in DYNC2H1 gene reached genome-wide significance in the meta-analysis. Assessing polygenic risk scores derived from European- and multi-ancestries in the Qatari population showed higher predictive performance of the multi-ancestry panel compared to the European panel. CONCLUSION Our study provides new insights into the genetic architecture of T2D in a Middle Eastern population and identifies genes that may be explored further for their involvement in T2D pathogenesis.
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Affiliation(s)
- Asma A Elashi
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Salman M Toor
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Umm-Kulthum Ismail Umlai
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar
| | - Yasser A Al-Sarraj
- Qatar Genome Program (QGP), Qatar Foundation Research, Development and Innovation, Qatar Foundation (QF), Doha, P.O. Box 5825, Qatar
| | - Shahrad Taheri
- Qatar Metabolic Institute, Hamad Medical Corporation, P.O. Box 3050, Doha, Qatar
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, P.O. Box 24144, Qatar
- Department of Biophysics and Physiology, Weill Cornell Medicine, 510065, New York, USA
| | | | - Omar M E Albagha
- College of Health and Life Sciences (CHLS), Hamad Bin Khalifa University (HBKU), Qatar Foundation (QF), Education City, Doha, P.O. Box 34110, Qatar.
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, EH4 2XU, Edinburgh, UK.
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Rosenthal EA, Hsu L, Thomas M, Peters U, Kachulis C, Patterson K, Jarvik GP. Comparing ancestry calibration approaches for a trans-ancestry colorectal cancer polygenic risk score. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.10.23.23296753. [PMID: 37961088 PMCID: PMC10635167 DOI: 10.1101/2023.10.23.23296753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Colorectal cancer (CRC) is a complex disease with monogenic, polygenic and environmental risk factors. Polygenic risk scores (PRS) are being developed to identify high polygenic risk individuals. Due to differences in genetic background, PRS distributions vary by ancestry, necessitating calibration. Methods We compared four calibration methods using the All of Us Research Program Whole Genome Sequence data for a CRC PRS previously developed in participants of European and East Asian ancestry. The methods contrasted results from linear models with A) the entire data set or an ancestrally diverse training set AND B) covariates including principal components of ancestry or admixture. Calibration with the training set adjusted the variance in addition to the mean. Results All methods performed similarly within ancestry with OR (95% C.I.) per s.d. change in PRS: African 1.5 (1.02, 2.08), Admixed American 2.2 (1.27, 3.85), European 1.6 (1.43, 1.89), and Middle Eastern 1.1 (0.71, 1.63). Using admixture and an ancestrally diverse training set provided distributions closest to standard Normal with accurate upper tail frequencies. Conclusion Although the PRS is predictive of CRC risk for most ancestries, its performance varies by ancestry. Post-hoc calibration preserves the risk prediction within ancestries. Training a calibration model on ancestrally diverse participants to adjust both the mean and variance of the PRS, using admixture as covariates, created standard Normal z-scores. These z-scores can be used to identify patients at high polygenic risk, and can be incorporated into comprehensive risk scores including other known risk factors, allowing for more precise risk estimates.
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18
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Riess O, Sturm M, Menden B, Liebmann A, Demidov G, Witt D, Casadei N, Admard J, Schütz L, Ossowski S, Taylor S, Schaffer S, Schroeder C, Dufke A, Haack T. Genomes in clinical care. NPJ Genom Med 2024; 9:20. [PMID: 38485733 PMCID: PMC10940576 DOI: 10.1038/s41525-024-00402-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 02/07/2024] [Indexed: 03/18/2024] Open
Abstract
In the era of precision medicine, genome sequencing (GS) has become more affordable and the importance of genomics and multi-omics in clinical care is increasingly being recognized. However, how to scale and effectively implement GS on an institutional level remains a challenge for many. Here, we present Genome First and Ge-Med, two clinical implementation studies focused on identifying the key pillars and processes that are required to make routine GS and predictive genomics a reality in the clinical setting. We describe our experience and lessons learned for a variety of topics including test logistics, patient care processes, data reporting, and infrastructure. Our model of providing clinical care and comprehensive genomic analysis from a single source may be used by other centers with a similar structure to facilitate the implementation of omics-based personalized health concepts in medicine.
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Affiliation(s)
- Olaf Riess
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany.
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany.
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany.
| | - Marc Sturm
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Benita Menden
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Alexandra Liebmann
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - German Demidov
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Dennis Witt
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Nicolas Casadei
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany
| | - Jakob Admard
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Leon Schütz
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
| | - Stephan Ossowski
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- NGS Competence Center Tübingen, University of Tübingen, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | | | | | - Christopher Schroeder
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
| | - Andreas Dufke
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
| | - Tobias Haack
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, Germany
- Center for Rare Diseases Tübingen, University of Tübingen, Tübingen, Germany
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Yu G, Tam HCH, Huang C, Shi M, Lim CKP, Chan JCN, Ma RCW. Lessons and Applications of Omics Research in Diabetes Epidemiology. Curr Diab Rep 2024; 24:27-44. [PMID: 38294727 PMCID: PMC10874344 DOI: 10.1007/s11892-024-01533-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 01/04/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE OF REVIEW Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies. RECENT FINDINGS We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.
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Affiliation(s)
- Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Henry C H Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Mai Shi
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Cadmon K P Lim
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Chinese University of Hong Kong- Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, HKSAR, China.
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20
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Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
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Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
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21
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Qin M, Wu Y, Fang X, Pan C, Zhong S. Polygenic risk score predicts all-cause death in East Asian patients with prior coronary artery disease. Front Cardiovasc Med 2024; 11:1296415. [PMID: 38414927 PMCID: PMC10896892 DOI: 10.3389/fcvm.2024.1296415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 01/31/2024] [Indexed: 02/29/2024] Open
Abstract
Introduction Coronary artery disease (CAD) is a highly heritable and multifactorial disease. Numerous genome-wide association studies (GWAS) facilitated the construction of polygenic risk scores (PRS) for predicting future incidence of CAD, however, exclusively in European populations. Furthermore, identifying CAD patients with elevated risks of all-cause death presents a critical challenge in secondary prevention, which will contribute largely to reducing the burden for public healthcare. Methods We recruited a cohort of 1,776 Chinese CAD patients and performed medical follow-up for up to 11 years. A pruning and thresholding method was used to calculate PRS of CAD and its 14 risk factors. Their correlations with all-cause death were computed via Cox regression. Results and discussion We found that the PRS for CAD and its seven risk factors, namely myocardial infarction, ischemic stroke, angina, heart failure, low-density lipoprotein cholesterol, total cholesterol and C-reaction protein, were significantly associated with death (P ≤ 0.05), whereas the PRS of body mass index displayed moderate association (P < 0.1). Elastic-net Cox regression with 5-fold cross-validation was used to integrate these nine PRS models into a meta score, metaPRS, which performed well in stratifying patients at different risks for death (P < 0.0001). Combining metaPRS with clinical risk factors further increased the discerning power and a 4% increase in sensitivity. The metaPRS generated from the genetic susceptibility to CAD and its risk factors can well stratify CAD patients by their risks of death. Integrating metaPRS and clinical risk factors may contribute to identifying patients at higher risk of poor prognosis.
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Affiliation(s)
- Min Qin
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yonglin Wu
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Guangzhou, China
| | - Xianhong Fang
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
| | - Cuiping Pan
- Center for Intelligent Medicine Research, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences, Fudan University, Guangzhou, China
- Center for Evolutionary Biology, Fudan University, Shanghai, China
| | - Shilong Zhong
- School of Medicine, South China University of Technology, Guangzhou, China
- Department of Pharmacy, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Coronary Heart Disease Prevention, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou, China
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22
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Pankratz N, Cole BR, Beutel KM, Liao KP, Ashe J. Parkinson Disease Genetics Extended to African and Hispanic Ancestries in the VA Million Veteran Program. Neurol Genet 2024; 10:e200110. [PMID: 38130828 PMCID: PMC10732342 DOI: 10.1212/nxg.0000000000200110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 10/06/2023] [Indexed: 12/23/2023]
Abstract
Background and Objectives Nearly all genetic analyses of Parkinson disease (PD) have been in populations of European ancestry. We sought to test the ability of a machine learning method to extract accurate PD diagnoses from an electronic medical record (EMR) system, to see whether genetic variants identified in European populations generalize to individuals of African and Hispanic ancestries, and to compare the rates of PD across ancestries. Methods A machine learning method using natural language processing was applied to EMRs of US veterans participating in the VA Million Veteran Program (MVP) to identify individuals with PD. These putative cases were vetted via blind chart review by a movement disorder specialist. A polygenic risk score (PRS) of 90 established genetic variants whose genotypes were imputed from a customized Axiom Biobank Array was evaluated in different case groups. Results The EMR prediction scores had a distinct trimodal distribution, with 97% of the high group and only 30% of the middle group having a credible diagnosis of PD. Using the 3,542 cases from the high group matched 4:1 to controls, the PRS was highly predictive in individuals of European ancestry (n = 3,137 cases; OR = 1.82; p = 8.01E-48), and nearly identical effect sizes were seen in individuals of African (n = 184; OR = 2.07; p = 3.4E-4) and Hispanic ancestries (n = 221; OR = 2.13; p = 3.9E-6). The PRS was much less predictive for the 2,757 European ancestry cases who had an ICD code for PD but for whom the machine learning method had a lower confidence in their diagnosis. No novel ancestry-specific genetic variants were identified. Individuals with African ancestry had one-quarter the rate of PD compared with European or Hispanic ancestries aged 60-70 years and one half the rate in the 70-80 years age range. African American cases had a higher proportion of their DNA originating in Europe compared with African American controls. Discussion Machine learning can reliably classify PD using data from a large EMR. Larger studies of non-European populations are required to confirm the generalizability of PD risk variants identified in populations of European ancestry and the increased risk coming from a higher proportion of European DNA in African Americans.
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Affiliation(s)
- Nathan Pankratz
- From the Department of Laboratory Medicine and Pathology (N.P., B.R.C., K.M.B.), School of Medicine, University of Minnesota, Minneapolis; Division of Rheumatology (K.P.L.), Immunology, and Allergy, Brigham and Women's Hospital; Department of Biomedical Informatics (K.P.L.), Harvard Medical School; Division of Data Sciences (K.P.L.), VA Boston Healthcare System, MA; Department of Neurology (J.A.), University of Minnesota Medical School; and Department of Neurology (J.A.), Minneapolis Veterans Affairs Health Care System, MN
| | - Benjamin R Cole
- From the Department of Laboratory Medicine and Pathology (N.P., B.R.C., K.M.B.), School of Medicine, University of Minnesota, Minneapolis; Division of Rheumatology (K.P.L.), Immunology, and Allergy, Brigham and Women's Hospital; Department of Biomedical Informatics (K.P.L.), Harvard Medical School; Division of Data Sciences (K.P.L.), VA Boston Healthcare System, MA; Department of Neurology (J.A.), University of Minnesota Medical School; and Department of Neurology (J.A.), Minneapolis Veterans Affairs Health Care System, MN
| | - Kathleen M Beutel
- From the Department of Laboratory Medicine and Pathology (N.P., B.R.C., K.M.B.), School of Medicine, University of Minnesota, Minneapolis; Division of Rheumatology (K.P.L.), Immunology, and Allergy, Brigham and Women's Hospital; Department of Biomedical Informatics (K.P.L.), Harvard Medical School; Division of Data Sciences (K.P.L.), VA Boston Healthcare System, MA; Department of Neurology (J.A.), University of Minnesota Medical School; and Department of Neurology (J.A.), Minneapolis Veterans Affairs Health Care System, MN
| | - Katherine P Liao
- From the Department of Laboratory Medicine and Pathology (N.P., B.R.C., K.M.B.), School of Medicine, University of Minnesota, Minneapolis; Division of Rheumatology (K.P.L.), Immunology, and Allergy, Brigham and Women's Hospital; Department of Biomedical Informatics (K.P.L.), Harvard Medical School; Division of Data Sciences (K.P.L.), VA Boston Healthcare System, MA; Department of Neurology (J.A.), University of Minnesota Medical School; and Department of Neurology (J.A.), Minneapolis Veterans Affairs Health Care System, MN
| | - James Ashe
- From the Department of Laboratory Medicine and Pathology (N.P., B.R.C., K.M.B.), School of Medicine, University of Minnesota, Minneapolis; Division of Rheumatology (K.P.L.), Immunology, and Allergy, Brigham and Women's Hospital; Department of Biomedical Informatics (K.P.L.), Harvard Medical School; Division of Data Sciences (K.P.L.), VA Boston Healthcare System, MA; Department of Neurology (J.A.), University of Minnesota Medical School; and Department of Neurology (J.A.), Minneapolis Veterans Affairs Health Care System, MN
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23
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Lennon NJ, Kottyan LC, Kachulis C, Abul-Husn NS, Arias J, Belbin G, Below JE, Berndt SI, Chung WK, Cimino JJ, Clayton EW, Connolly JJ, Crosslin DR, Dikilitas O, Velez Edwards DR, Feng Q, Fisher M, Freimuth RR, Ge T, Glessner JT, Gordon AS, Patterson C, Hakonarson H, Harden M, Harr M, Hirschhorn JN, Hoggart C, Hsu L, Irvin MR, Jarvik GP, Karlson EW, Khan A, Khera A, Kiryluk K, Kullo I, Larkin K, Limdi N, Linder JE, Loos RJF, Luo Y, Malolepsza E, Manolio TA, Martin LJ, McCarthy L, McNally EM, Meigs JB, Mersha TB, Mosley JD, Musick A, Namjou B, Pai N, Pesce LL, Peters U, Peterson JF, Prows CA, Puckelwartz MJ, Rehm HL, Roden DM, Rosenthal EA, Rowley R, Sawicki KT, Schaid DJ, Smit RAJ, Smith JL, Smoller JW, Thomas M, Tiwari H, Toledo DM, Vaitinadin NS, Veenstra D, Walunas TL, Wang Z, Wei WQ, Weng C, Wiesner GL, Yin X, Kenny EE. Selection, optimization and validation of ten chronic disease polygenic risk scores for clinical implementation in diverse US populations. Nat Med 2024; 30:480-487. [PMID: 38374346 PMCID: PMC10878968 DOI: 10.1038/s41591-024-02796-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Polygenic risk scores (PRSs) have improved in predictive performance, but several challenges remain to be addressed before PRSs can be implemented in the clinic, including reduced predictive performance of PRSs in diverse populations, and the interpretation and communication of genetic results to both providers and patients. To address these challenges, the National Human Genome Research Institute-funded Electronic Medical Records and Genomics (eMERGE) Network has developed a framework and pipeline for return of a PRS-based genome-informed risk assessment to 25,000 diverse adults and children as part of a clinical study. From an initial list of 23 conditions, ten were selected for implementation based on PRS performance, medical actionability and potential clinical utility, including cardiometabolic diseases and cancer. Standardized metrics were considered in the selection process, with additional consideration given to strength of evidence in African and Hispanic populations. We then developed a pipeline for clinical PRS implementation (score transfer to a clinical laboratory, validation and verification of score performance), and used genetic ancestry to calibrate PRS mean and variance, utilizing genetically diverse data from 13,475 participants of the All of Us Research Program cohort to train and test model parameters. Finally, we created a framework for regulatory compliance and developed a PRS clinical report for return to providers and for inclusion in an additional genome-informed risk assessment. The initial experience from eMERGE can inform the approach needed to implement PRS-based testing in diverse clinical settings.
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Affiliation(s)
| | - Leah C Kottyan
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Josh Arias
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Gillian Belbin
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Sonja I Berndt
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - James J Cimino
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | - David R Crosslin
- Tulane University, New Orleans, LA, USA
- University of Washington, Seattle, WA, USA
| | | | | | - QiPing Feng
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | - Tian Ge
- Mass General Brigham, Boston, MA, USA
| | | | | | | | | | - Maegan Harden
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Margaret Harr
- Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Joel N Hirschhorn
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Clive Hoggart
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Li Hsu
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | | | | | | | | | - Amit Khera
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Katie Larkin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nita Limdi
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yuan Luo
- Northwestern University, Evanston, IL, USA
| | | | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Li McCarthy
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | - Tesfaye B Mersha
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | | | - Bahram Namjou
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Nihal Pai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | | | | | - Cynthia A Prows
- Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | | | - Heidi L Rehm
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dan M Roden
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | | | - Hemant Tiwari
- University of Alabama at Birmingham, Birmingham, AL, USA
| | | | | | | | | | - Zhe Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wei-Qi Wei
- Vanderbilt University Medical Center, Nashville, TN, USA
| | | | | | | | - Eimear E Kenny
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Sheerin CM, O’Hara-Payne RK, Lancaster EE, Suarez-Rivas H, Chatzinakos C, Prom-Wormley EC, Peterson RE. Examining interactions between polygenic scores and interpersonal trauma exposure on alcohol consumption and use disorder in an ancestrally diverse college cohort. Front Genet 2024; 14:1274381. [PMID: 38361984 PMCID: PMC10868390 DOI: 10.3389/fgene.2023.1274381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 11/22/2023] [Indexed: 02/17/2024] Open
Abstract
Introduction: Genetic factors impact alcohol consumption and use disorder (AUD), with large-scale genome-wide association studies (GWAS) identifying numerous associated variants. Aggregate genetic methods in combination with important environmental factors (e.g., interpersonal trauma [IPT]) can be applied to expand our understanding of the ways by which genetic and environmental variables work together to influence alcohol consumption and disordered use. The present study aimed to detail the relationships between genome-wide polygenic scores (PGS) for alcohol phenotypes (i.e., alcohol consumption and AUD status) and IPT exposure as well as the interaction between them across ancestry. Methods: Data were drawn from the Spit for Science (S4S) study, a US college student population, where participants reported on IPT exposure prior to college and alcohol consumption and problems during college (N = 9,006; ancestry: 21.3% African [AFR], 12.5% Admixed Americas [AMR], 9.6% East Asian [EAS], 48.1% European [EUR], 8.6% South Asian [SAS]). Two trans-ancestry PGS were constructed, one for alcohol consumption and another for AUD, using large-scale GWAS summary statistics from multiple ancestries weighted using PRS-CSx. Regression models were applied to test for the presence of associations between alcohol-PGS and IPT main and interaction effects. Results: In the meta-analysis across ancestry groups, IPT exposure and PGS were significantly associated with alcohol consumption (βIPT = 0.31, P IPT = 0.0002; βPGS = 0.09, P PGS = 0.004) and AUD (ORIPT = 1.12, P IPT = 3.5 × 10-8; ORPGS = 1.02, P PGS = 0.002). No statistically significant interactions were detected between IPT and sex nor between IPT and PGS. When inspecting ancestry specific results, the alcohol consumption-PGS and AUD-PGS were only statistically significant in the EUR ancestry group (βPGS = 0.09, P PGS = 0.04; ORPGS = 1.02, P PGS = 0.022, respectively). Discussion: IPT exposure prior to college was strongly associated with alcohol outcomes in this college-age sample, which could be used as a preventative measure to identify students at high risk for problematic alcohol use. Additionally, results add to developing evidence of polygenic score association in meta-analyzed samples, highlighting the importance of continued efforts to increase ancestral representation in genetic studies and inclusive analytic approaches to increase the generalizability of results from genetic association studies.
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Affiliation(s)
- Christina M. Sheerin
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Rowan K. O’Hara-Payne
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Center for Biological Data Science, Virginia Commonwealth University, Richmond, VA, United States
| | - Eva E. Lancaster
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
| | - Hailie Suarez-Rivas
- Department of Psychology, Virginia Commonwealth University, Richmond, VA, United States
| | - Chris Chatzinakos
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
| | - Elizabeth C. Prom-Wormley
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Epidemiology, Virginia Commonwealth University, Richmond, VA, United States
| | - Roseann E. Peterson
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry, Virginia Commonwealth University, Richmond, VA, United States
- Department of Psychiatry and Behavioral Sciences, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
- Institute for Genomics in Health, SUNY Downstate Health Sciences University, Brooklyn, NY, United States
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25
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Alamad B, Elliott K, Knight JC. Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity. CAMBRIDGE PRISMS. PRECISION MEDICINE 2024; 2:e3. [PMID: 38549844 PMCID: PMC10953767 DOI: 10.1017/pcm.2023.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 11/15/2023] [Accepted: 12/18/2023] [Indexed: 04/26/2024]
Abstract
The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.
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Affiliation(s)
- Bana Alamad
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Kate Elliott
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Julian C. Knight
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Chinese Academy of Medical Science Oxford Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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26
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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Kachuri L, Chatterjee N, Hirbo J, Schaid DJ, Martin I, Kullo IJ, Kenny EE, Pasaniuc B, Witte JS, Ge T. Principles and methods for transferring polygenic risk scores across global populations. Nat Rev Genet 2024; 25:8-25. [PMID: 37620596 PMCID: PMC10961971 DOI: 10.1038/s41576-023-00637-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/26/2023]
Abstract
Polygenic risk scores (PRSs) summarize the genetic predisposition of a complex human trait or disease and may become a valuable tool for advancing precision medicine. However, PRSs that are developed in populations of predominantly European genetic ancestries can increase health disparities due to poor predictive performance in individuals of diverse and complex genetic ancestries. We describe genetic and modifiable risk factors that limit the transferability of PRSs across populations and review the strengths and weaknesses of existing PRS construction methods for diverse ancestries. Developing PRSs that benefit global populations in research and clinical settings provides an opportunity for innovation and is essential for health equity.
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Affiliation(s)
- Linda Kachuri
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jibril Hirbo
- Department of Medicine Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Iman Martin
- Division of Genomic Medicine, National Human Genome Research Institute, Bethesda, MD, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bogdan Pasaniuc
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - John S Witte
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Department of Genetics, Stanford University, Stanford, CA, USA.
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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28
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Billings LK, Shi Z, Wei J, Rifkin AS, Zheng SL, Helfand BT, Ilbawi N, Dunnenberger HM, Hulick PJ, Qamar A, Xu J. Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes. J Clin Endocrinol Metab 2023; 109:107-113. [PMID: 37560999 DOI: 10.1210/clinem/dgad456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/10/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023]
Abstract
CONTEXT Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.
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Affiliation(s)
- Liana K Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Andrew S Rifkin
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Brian T Helfand
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Surgery, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nadim Ilbawi
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Henry M Dunnenberger
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Peter J Hulick
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Arman Qamar
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jianfeng Xu
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
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Takase M, Nakaya N, Nakamura T, Kogure M, Hatanaka R, Nakaya K, Chiba I, Kanno I, Nochioka K, Tsuchiya N, Hirata T, Narita A, Obara T, Ishikuro M, Uruno A, Kobayashi T, N Kodama E, Hamanaka Y, Orui M, Ogishima S, Nagaie S, Fuse N, Sugawara J, Kuriyama S, Tsuji I, Tamiya G, Hozawa A, Yamamoto M. Influence of Diabetes Family History on the Associations of Combined Genetic and Lifestyle Risks with Diabetes in the Tohoku Medical Megabank Community-Based Cohort Study. J Atheroscler Thromb 2023; 30:1950-1965. [PMID: 37813642 DOI: 10.5551/jat.64425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/11/2023] Open
Abstract
AIM The influence of family history of diabetes, probably reflecting genetic and lifestyle factors, on the association of combined genetic and lifestyle risks with diabetes is unknown. We examined these associations. METHODS This cross-sectional study included 9,681 participants in the Tohoku Medical Megabank Community-based Cohort Study. A lifestyle score, which was categorized into ideal, intermediate, and poor lifestyles, was given. Family history was obtained through a self-reported questionnaire. A polygenic risk score (PRS) was constructed in the target data (n=1,936) using publicly available genome-wide association study summary statistics from BioBank Japan. For test data (n=7,745), we evaluated PRS performance and examined the associations of combined family history and genetic and lifestyle risks with diabetes. Diabetes was defined as non-fasting blood glucose ≥ 200 mmHg, HbA1c ≥ 6.5%, and/or self-reported diabetes treatment. RESULTS In test data, 467 (6.0%) participants had diabetes. Compared with a low genetic risk and an ideal lifestyle without a family history, the odds ratio (OR) was 3.73 (95% confidence interval [CI], 1.92-7.00) for a lower genetic risk and a poor lifestyle without a family history. Family history was significantly associated with diabetes (OR, 3.58 [95% CI, 1.73-6.98]), even in those with a low genetic risk and an ideal lifestyle. Even among participants who had an ideal lifestyle without a family history, a high genetic risk was associated with diabetes (OR, 2.49 [95% CI, 1.65-3.85]). Adding PRS to family history and conventional lifestyle risk factors improved the prediction ability for diabetes. CONCLUSIONS Our findings support the notion that a healthy lifestyle is important to prevent diabetes regardless of genetic risk.
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Affiliation(s)
| | - Naoki Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomohiro Nakamura
- Tohoku Medical Megabank Organization, Tohoku University
- Kyoto Women fs University
| | - Mana Kogure
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Rieko Hatanaka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kumi Nakaya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ippei Chiba
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Ikumi Kanno
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Kotaro Nochioka
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Naho Tsuchiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Takumi Hirata
- Tohoku Medical Megabank Organization, Tohoku University
- Institute for Clinical and Translational Science, Nara Medical University
| | - Akira Narita
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Taku Obara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Mami Ishikuro
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Akira Uruno
- Tohoku Medical Megabank Organization, Tohoku University
| | - Tomoko Kobayashi
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
| | - Eiichi N Kodama
- Graduate School of Medicine, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | | | - Masatsugu Orui
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Soichi Ogishima
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Satoshi Nagaie
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Nobuo Fuse
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Junichi Sugawara
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- Tohoku University Hospital, Tohoku University
- Suzuki Memorial Hospital
| | - Shinichi Kuriyama
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- International Research Institute of Disaster Science, Tohoku University
| | - Ichiro Tsuji
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Gen Tamiya
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
- RIKEN Center for Advanced Intelligence Project
| | - Atsushi Hozawa
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
| | - Masayuki Yamamoto
- Graduate School of Medicine, Tohoku University
- Tohoku Medical Megabank Organization, Tohoku University
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30
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Vassy JL, Brunette CA, Lebo MS, MacIsaac K, Yi T, Danowski ME, Alexander NVJ, Cardellino MP, Christensen KD, Gala M, Green RC, Harris E, Jones NE, Kerman BJ, Kraft P, Kulkarni P, Lewis ACF, Lubitz SA, Natarajan P, Antwi AA. The GenoVA study: Equitable implementation of a pragmatic randomized trial of polygenic-risk scoring in primary care. Am J Hum Genet 2023; 110:1841-1852. [PMID: 37922883 PMCID: PMC10645559 DOI: 10.1016/j.ajhg.2023.10.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 11/07/2023] Open
Abstract
Polygenic risk scores (PRSs) hold promise for disease risk assessment and prevention. The Genomic Medicine at Veterans Affairs (GenoVA) Study is addressing three main challenges to the clinical implementation of PRSs in preventive care: defining and determining their clinical utility, implementing them in time-constrained primary care settings, and countering their potential to exacerbate healthcare disparities. The study processes used to test patients, report their PRS results to them and their primary care providers (PCPs), and promote the use of those results in clinical decision-making are modeled on common practices in primary care. The following diseases were chosen for their prevalence and familiarity to PCPs: coronary artery disease; type 2 diabetes; atrial fibrillation; and breast, colorectal, and prostate cancers. A randomized clinical trial (RCT) design and primary outcome of time-to-new-diagnosis of a target disease bring methodological rigor to the question of the clinical utility of PRS implementation. The study's pragmatic RCT design enhances its relevance to how PRS might reasonably be implemented in primary care. Steps the study has taken to promote health equity include the thoughtful handling of genetic ancestry in PRS construction and reporting and enhanced recruitment strategies to address underrepresentation in research participation. To date, enhanced recruitment efforts have been both necessary and successful: participants of underrepresented race and ethnicity groups have been less likely to enroll in the study than expected but ultimately achieved proportional representation through targeted efforts. The GenoVA Study experience to date offers insights for evaluating the clinical utility of equitable PRS implementation in adult primary care.
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Affiliation(s)
- Jason L Vassy
- VA Boston Healthcare System, Boston, MA, USA; Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA.
| | - Charles A Brunette
- VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Matthew S Lebo
- Harvard Medical School, Boston, MA, USA; Laboratory for Molecular Medicine, Mass General Brigham, Boston, MA, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Thomas Yi
- VA Boston Healthcare System, Boston, MA, USA
| | | | - Nicholas V J Alexander
- VA Boston Healthcare System, Boston, MA, USA; Bucharest University Emergency Hospital, Bucharest, Romania; Bucharest University of Economic Studies, Bucharest, Romania
| | | | - Kurt D Christensen
- Harvard Medical School, Boston, MA, USA; Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Manish Gala
- Harvard Medical School, Boston, MA, USA; Division of Gastroenterology and Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Robert C Green
- Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA; Department of Medicine (Genetics), Mass General Brigham, Boston, MA, USA
| | | | - Natalie E Jones
- VA Boston Healthcare System, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Benjamin J Kerman
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Peter Kraft
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | | | - Anna C F Lewis
- Department of Medicine (Genetics), Mass General Brigham, Boston, MA, USA; Edmond and Lily Safra Center for Ethics, Harvard University, Boston, MA, USA
| | - Steven A Lubitz
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA; Novartis Institutes for BioMedical Research, Novartis, Basel, Basel-Stadt, Switzerland
| | - Pradeep Natarajan
- Harvard Medical School, Boston, MA, USA; Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
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31
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Reeskamp LF, Shim I, Dron JS, Ibrahim S, Tromp TR, Fahed AC, Patel AP, Hutten BA, Stroes ES, Hovingh GK, Khera AV. Polygenic Background Modifies Risk of Coronary Artery Disease Among Individuals With Heterozygous Familial Hypercholesterolemia. JACC. ADVANCES 2023; 2:100662. [PMID: 38938725 PMCID: PMC11198615 DOI: 10.1016/j.jacadv.2023.100662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 05/22/2023] [Accepted: 06/28/2023] [Indexed: 06/29/2024]
Abstract
Background Heterozygous familial hypercholesterolemia (HeFH) is a monogenic disorder characterized by increased circulating low-density lipoprotein cholesterol and accelerated atherosclerosis. Even among this high-risk group, prior studies note considerable variability in risk of coronary artery disease (CAD). Objectives The purpose of this study was to evaluate the cumulative impact of many common DNA variants-as quantified by a polygenic score-on incident CAD among individuals carrying a HeFH variant. Methods We analyzed data from a prospective cohort study of 1,315 individuals who carried a HeFH variant and 1,315 matched family noncarriers derived from a nationwide screening program in the Netherlands, with subsequent replication in 151,009 participants of the UK Biobank. Results Despite identification and lipid management within the Dutch screening program, 84 (6.4%) of HeFH variant carriers developed CAD as compared to 45 (3.4%) of matched family members (median follow-up 10.2 years, HR 1.88, 95% CI: 1.31-2.70). Among HeFH variant carriers, a polygenic score was associated with CAD with an effect size similar to low-density lipoprotein cholesterol - HR of 1.35 (95% CI: 1.07-1.70) and 1.41 (95% CI: 1.17-1.70) per standard deviation increase, respectively. When compared to noncarriers, CAD risk increased from 1.24-fold (95% CI: 0.64-2.34) to 3.37-fold (95% CI: 2.11-5.36) across quintiles of the polygenic score. A similar risk gradient, 1.36-fold (95% CI: 0.65-2.85) to 2.88-fold (95% CI: 1.59-5.20), was observed in 429 carriers in the UK Biobank. Conclusions In 2 cohort studies involving 1,744 individuals with genetically confirmed HeFH - the largest study to date - risk of CAD varied according to polygenic background, in some cases approaching the risk observed in noncarriers.
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Affiliation(s)
- Laurens F. Reeskamp
- Department of Vascular Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Internal Medicine, OLVG, Amsterdam, the Netherlands
| | - Injeong Shim
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Jacqueline S. Dron
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Shirin Ibrahim
- Department of Vascular Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Tycho R. Tromp
- Department of Vascular Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Akl C. Fahed
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Aniruddh P. Patel
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Barbara A. Hutten
- Department of Clinical Epidemiology and Data Science, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Erik S.G. Stroes
- Department of Vascular Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Internal Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
| | - G. Kees Hovingh
- Department of Vascular Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
- Department of Internal Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands
- Novo Nordisk, København, Denmark
| | - Amit V. Khera
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
- Verve Therapeutics, Cambridge, Massachusetts, USA
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32
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Srinivasan S, Wu P, Mercader JM, Udler MS, Porneala BC, Bartz TM, Floyd JS, Sitlani C, Guo X, Haessler J, Kooperberg C, Liu J, Ahmad S, van Duijn C, Liu CT, Goodarzi MO, Florez JC, Meigs JB, Rotter JI, Rich SS, Dupuis J, Leong A. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies. J Endocr Soc 2023; 7:bvad123. [PMID: 37841955 PMCID: PMC10576255 DOI: 10.1210/jendso/bvad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Indexed: 10/17/2023] Open
Abstract
Context Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight. Objective We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D. Results The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes. Conclusion In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Josep M Mercader
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Miriam S Udler
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bianca C Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Colleen Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Xiquing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ching-Ti Liu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22903, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
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Thomas M, Su YR, Rosenthal EA, Sakoda LC, Schmit SL, Timofeeva MN, Chen Z, Fernandez-Rozadilla C, Law PJ, Murphy N, Carreras-Torres R, Diez-Obrero V, van Duijnhoven FJB, Jiang S, Shin A, Wolk A, Phipps AI, Burnett-Hartman A, Gsur A, Chan AT, Zauber AG, Wu AH, Lindblom A, Um CY, Tangen CM, Gignoux C, Newton C, Haiman CA, Qu C, Bishop DT, Buchanan DD, Crosslin DR, Conti DV, Kim DH, Hauser E, White E, Siegel E, Schumacher FR, Rennert G, Giles GG, Hampel H, Brenner H, Oze I, Oh JH, Lee JK, Schneider JL, Chang-Claude J, Kim J, Huyghe JR, Zheng J, Hampe J, Greenson J, Hopper JL, Palmer JR, Visvanathan K, Matsuo K, Matsuda K, Jung KJ, Li L, Le Marchand L, Vodickova L, Bujanda L, Gunter MJ, Matejcic M, Jenkins MA, Slattery ML, D'Amato M, Wang M, Hoffmeister M, Woods MO, Kim M, Song M, Iwasaki M, Du M, Udaltsova N, Sawada N, Vodicka P, Campbell PT, Newcomb PA, Cai Q, Pearlman R, Pai RK, Schoen RE, Steinfelder RS, Haile RW, Vandenputtelaar R, Prentice RL, Küry S, Castellví-Bel S, Tsugane S, Berndt SI, Lee SC, Brezina S, Weinstein SJ, Chanock SJ, Jee SH, Kweon SS, Vadaparampil S, Harrison TA, Yamaji T, Keku TO, Vymetalkova V, Arndt V, Jia WH, Shu XO, Lin Y, Ahn YO, Stadler ZK, Van Guelpen B, Ulrich CM, Platz EA, Potter JD, Li CI, Meester R, Moreno V, Figueiredo JC, Casey G, Lansdorp Vogelaar I, Dunlop MG, Gruber SB, Hayes RB, Pharoah PDP, Houlston RS, Jarvik GP, Tomlinson IP, Zheng W, Corley DA, Peters U, Hsu L. Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations. Nat Commun 2023; 14:6147. [PMID: 37783704 PMCID: PMC10545678 DOI: 10.1038/s41467-023-41819-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 09/19/2023] [Indexed: 10/04/2023] Open
Abstract
Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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Affiliation(s)
- Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, USA
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, 98195, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, USA
| | - Maria N Timofeeva
- Danish Institute for Advanced Study (DIAS), Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, U, Germany
| | - Zhishan Chen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ceres Fernandez-Rozadilla
- Instituto de Investigacion Sanitaria de Santiago (IDIS), Choupana sn, 15706, Santiago de Compostela, Spain
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Philip J Law
- Division of Genetics and Epidemiology, The Institute of Cancer Reseach, London, SW7 3RP, UK
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Robert Carreras-Torres
- Digestive Diseases and Microbiota Group, Girona Biomedical Research Institute (IDIBGI), Salt, 17190, Girona, Spain
| | - Virginia Diez-Obrero
- Unit of Biomarkers and Susceptibility, Oncology Data Analytics Program, Catalan Institute of Oncology, Barcelona, 08908, Spain
- Colorectal Cancer Group, ONCOBELL Program, Bellvitge Biomedical Research Institute, Barcelona, 08908, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, 08908, Spain
| | | | - Shangqing Jiang
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Aesun Shin
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul National University Cancer Research Institute, Seoul, South Korea
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Amanda I Phipps
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | | | - Andrea Gsur
- .Center for Cancer Research, Medical University Vienna, Vienna, Austria
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Ann G Zauber
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA, USA
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
| | - Caroline Y Um
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Chris Gignoux
- Colorado Center for Personalized Medicine, University of Colorado - Anschutz Medical Campus, Aurora, CO, USA
| | - Christina Newton
- Department of Population Science, American Cancer Society, Atlanta, GA, USA
| | - Christopher A Haiman
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Conghui Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - Daniel D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC, 3000, Australia
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3000, Australia
- Genomic Medicine and Family Cancer Clinic, The Royal Melbourne Hospital, Parkville, VIC, 3000, Australia
| | - David R Crosslin
- Department of Bioinformatics and Medical Education, University of Washington Medical Center, Seattle, WA, 98195, USA
| | - David V Conti
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Dong-Hyun Kim
- Department of Social and Preventive Medicine, Hallym University College of Medicine, Okcheon-dong, South Korea
| | - Elizabeth Hauser
- VA Cooperative Studies Program Epidemiology Center, Durham Veterans Affairs Health Care System, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University Medical Center, Durham, NC, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
- Department of Epidemiology, University of Washington School of Public Health, Seattle, WA, USA
| | - Erin Siegel
- Cancer Epidemiology Program, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Fredrick R Schumacher
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gad Rennert
- Department of Community Medicine and Epidemiology, Lady Davis Carmel Medical Center, Haifa, Israel
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Graham G Giles
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
| | - Isao Oze
- .Division of Cancer Epidemiology and Prevention, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Jae Hwan Oh
- .Research Institute and Hospital, National Cancer Center, Goyang, South Korea, South Korea
| | - Jeffrey K Lee
- .Department of Gastroenterology, Kaiser Permanente San Francisco Medical Center, San Francisco, CA, USA
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48104, USA
| | | | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg, Germany
| | - Jeongseon Kim
- Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Gyeonggi-do, South Korea
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jiayin Zheng
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden, Germany
| | - Joel Greenson
- Department of Pathology, University of Michigan, Ann Arbor, MI, 48104, USA
| | - John L Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
- Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea
| | - Julie R Palmer
- Slone Epidemiology Center, School of Medicine, Boston University, Boston, MA, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keitaro Matsuo
- Division of Molecular and Clinical Epidemiology, Aichi Cancer Center Research Institute, Nagoya, Japan
| | - Koichi Matsuda
- Laboratory of Clinical Genome Sequencing, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Keum Ji Jung
- Institute for Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA, USA
| | | | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
- Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - Luis Bujanda
- Department of Gastroenterology, Biodonostia Health Research Institute, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Universidad del País Vasco (UPV/EHU), San Sebastián, Spain
| | - Marc J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Mark A Jenkins
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC, 3000, Australia
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Mauro D'Amato
- Department of Medicine and Surgery, LUM University, Camassima, Italy
- Gastrointestinal Genetics Lab, CIC bioGUNE-BRTA, Derio, Spain
| | - Meilin Wang
- Department of Environmental Genomics, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - Michelle Kim
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Mingyang Song
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Departments of Epidemiology and Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Mulong Du
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Natalia Udaltsova
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Peter T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rachel Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH, USA
| | - Rish K Pai
- Department of Laboratory Medicine and Pathology, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Robert S Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Robert W Haile
- Samuel Oschin Comprehensive Cancer Institute, CEDARS-SINAI, Los Angeles, CA, USA
| | - Rosita Vandenputtelaar
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Ross L Prentice
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Sébastien Küry
- Nantes Université, CHU Nantes, Service de Génétique Médicale, F-44000, Nantes, France
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona, Spain
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Soo Chin Lee
- National University Cancer Institute, Singapore, Singapore
| | - Stefanie Brezina
- .Center for Cancer Research, Medical University Vienna, Vienna, Austria
| | - Stephanie J Weinstein
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Stephen J Chanock
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sun Ha Jee
- Department of Epidemiology and Health Promotion, Graduate School of Public Health, Yonsei University, Seoul, Korea
| | - Sun-Seog Kweon
- Department of Preventive Medicine, Chonnam National University Medical School, Gwangju, Korea
- Jeonnam Regional Cancer Center, Chonnam National University Hwasun Hospital, Hwasun, Korea
| | - Susan Vadaparampil
- Departments of Epidemiology and Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, National Cancer Center, Tokyo, Japan
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC, USA
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic
- Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wei-Hua Jia
- State Key Laboratory of Oncology in South China, Cancer Center, Sun Yat-sen University, Guangzhou, China
| | - Xiao-Ou Shu
- Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yi Lin
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Yoon-Ok Ahn
- Department of Preventive Medicine, Seoul National University College of Medicine, Seoul National University Cancer Research Institute, Seoul, South Korea
| | - Zsofia K Stadler
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT, USA
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Reinier Meester
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
- ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Jane C Figueiredo
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Iris Lansdorp Vogelaar
- Department of Public Health, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Malcolm G Dunlop
- Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, U, Germany
| | - Stephen B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, CA, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, The Institute of Cancer Reseach, London, SW7 3RP, UK
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA, 98195, USA
| | - Ian P Tomlinson
- Edinburgh Cancer Research Centre, Institute of Genomics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Department of Gastroenterology, Kaiser Permanente Medical Center, San Francisco, CA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
- Department of Epidemiology, University of Washington, Seattle, WA, USA.
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA.
- Department of Biostatistics, University of Washington, Seattle, WA, USA.
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Szczerbinski L, Mandla R, Schroeder P, Porneala BC, Li JH, Florez JC, Mercader JM, Manning AK, Udler MS. Algorithms for the identification of prevalent diabetes in the All of Us Research Program validated using polygenic scores - a new resource for diabetes precision medicine. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.05.23295061. [PMID: 37732265 PMCID: PMC10508798 DOI: 10.1101/2023.09.05.23295061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
OBJECTIVE The study aimed to develop and validate algorithms for identifying people with type 1 and type 2 diabetes in the All of Us Research Program (AoU) cohort, using electronic health record (EHR) and survey data. RESEARCH DESIGN AND METHODS Two sets of algorithms were developed, one using only EHR data (EHR), and the other using a combination of EHR and survey data (EHR+). Their performance was evaluated by testing their association with polygenic scores for both type 1 and type 2 diabetes. RESULTS For type 1 diabetes, the EHR-only algorithm showed a stronger association with T1D polygenic score (p=3×10-5) than the EHR+. For type 2 diabetes, the EHR+ algorithm outperformed both the EHR-only and the existing AoU definition, identifying additional cases (25.79% and 22.57% more, respectively) and showing stronger association with T2D polygenic score (DeLong p=0.03 and 1×10-4, respectively). CONCLUSIONS We provide new validated definitions of type 1 and type 2 diabetes in AoU, and make them available for researchers. These algorithms, by ensuring consistent diabetes definitions, pave the way for high-quality diabetes research and future clinical discoveries.
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Affiliation(s)
- Lukasz Szczerbinski
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
- Cardiology Division, Department of Medicine and Cardiovascular Research Institute, University of California, San Francisco, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Bianca C. Porneala
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Josephine H. Li
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jose C. Florez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Josep M. Mercader
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Department of Medicine, Massachusetts General+ Hospital, Boston, USA
| | - Miriam S. Udler
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and MIT, Cambridge, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
- Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Lin BD, Pries LK, van Os J, Luykx JJ, Rutten BPF, Guloksuz S. Adjusting for population stratification in polygenic risk score analyses: a guide for model specifications in the UK Biobank. J Hum Genet 2023; 68:653-656. [PMID: 37188914 DOI: 10.1038/s10038-023-01161-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/06/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023]
Abstract
The current study was conducted to provide a general guidance for model specifications in polygenic risk score (PRS) analyses of the UK Biobank, such as adjusting for covariates (i.e. age, sex, recruitment centers, and genetic batch) and the number of principal components (PCs) that need to be included. To cover behavioral, physical and mental health outcomes, we evaluated three continuous outcomes (BMI, smoking, drinking) and two binary outcomes (Major Depressive Disorder and educational attainment). We applied 3280 (656 per phenotype) different models including different sets of covariates. We evaluated these different model specifications by comparing regression parameters such as R2, coefficients, and P values, as well as ANOVA tests. Findings suggest that only up to three PCs appears to be sufficient for controlling population stratification for most outcomes, whereas including other covariates (particularly age and sex) appears to be more essential for model performance.
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Affiliation(s)
- Bochao Danae Lin
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Preventive Medicine, Institute of Biomedical Informatics, Bioinformatics Center, School of Basic Medical Sciences, Henan University, Kaifeng, China
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Lotta-Katrin Pries
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Jurjen J Luykx
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
- Department of Psychiatry, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
- GGNet Mental Health, Warnsveld, The Netherlands
- Department of Translational Neuroscience, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Bart P F Rutten
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sinan Guloksuz
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University Medical Centre, Maastricht, The Netherlands.
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
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Badré A, Pan C. Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis. PLoS Comput Biol 2023; 19:e1011211. [PMID: 37418352 DOI: 10.1371/journal.pcbi.1011211] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/23/2023] [Indexed: 07/09/2023] Open
Abstract
Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.
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Affiliation(s)
- Adrien Badré
- School of Computer Science, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Chongle Pan
- School of Computer Science, University of Oklahoma, Norman, Oklahoma, United States of America
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, Oklahoma, United States of America
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37
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Smith JL, Tcheandjieu C, Dikilitas O, lyer K, Miyazawa K, Hilliard A, Lynch J, Rotter JI, Chen YDI, Sheu WHH, Chang KM, Kanoni S, Tsao P, Ito K, Kosel M, Clarke SL, Schaid DJ, Assimes TL, Kullo IJ. A Multi-Ancestry Polygenic Risk Score for Coronary Heart Disease Based on an Ancestrally Diverse Genome-Wide Association Study and Population-Specific Optimization. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.02.23290896. [PMID: 37609230 PMCID: PMC10441485 DOI: 10.1101/2023.06.02.23290896] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background Predictive performance of polygenic risk scores (PRS) varies across populations. To facilitate equitable clinical use, we developed PRS for coronary heart disease (PRSCHD) for 5 genetic ancestry groups. Methods We derived ancestry-specific and multi-ancestry PRSCHD based on pruning and thresholding (PRSP+T) and continuous shrinkage priors (PRSCSx) applied on summary statistics from the largest multi-ancestry genome-wide meta-analysis for CHD to date, including 1.1 million participants from 5 continental populations. Following training and optimization of PRSCHD in the Million Veteran Program, we evaluated predictive performance of the best performing PRSCHD in 176,988 individuals across 9 cohorts of diverse genetic ancestry. Results Multi-ancestry PRSP+T outperformed ancestry specific PRSP+T across a range of tuning values. In training stage, for all ancestry groups, PRSCSx performed better than PRSP+T and multi-ancestry PRS outperformed ancestry-specific PRS. In independent validation cohorts, the selected multi-ancestry PRSP+T demonstrated the strongest association with CHD in individuals of South Asian (SAS) and European (EUR) ancestry (OR per 1SD[95% CI]; 2.75[2.41-3.14], 1.65[1.59-1.72]), followed by East Asian (EAS) (1.56[1.50-1.61]), Hispanic/Latino (HIS) (1.38[1.24-1.54]), and weakest in African (AFR) ancestry (1.16[1.11-1.21]). The selected multi-ancestry PRSCSx showed stronger associacion with CHD in comparison within each ancestry group where the association was strongest in SAS (2.67[2.38-3.00]) and EUR (1.65[1.59-1.71]), progressively decreasing in EAS (1.59[1.54-1.64]), HIS (1.51[1.35-1.69]), and lowest in AFR (1.20[1.15-1.26]). Conclusions Utilizing diverse summary statistics from a large multi-ancestry genome-wide meta-analysis led to improved performance of PRSCHD in most ancestry groups compared to single-ancestry methods. Improvement of predictive performance was limited, specifically in AFR and HIS, despite use of one of the largest and most diverse set of training and validation cohorts to date. This highlights the need for larger GWAS datasets of AFR and HIS individuals to enhance performance of PRSCHD.
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Affiliation(s)
- Johanna L. Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Catherine Tcheandjieu
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Gladstone Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Kruthika lyer
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Kazuo Miyazawa
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Austin Hilliard
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University School of Medicine, Palo Alto, CA, USA
| | - Julie Lynch
- Salt Lake City VA Met CTR., Salt Lake City, UT, USA
| | - Jerome I. Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Kyong-Mi Chang
- Corporal Michael J Crescenz VA Medical Ctr. Philadelphia, PA, USA
| | | | - Phil Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Kaoru Ito
- Riken Ctr. for Integrative Medical Sciences, Yokohama City, Japan
| | - Matthew Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Shoa L. Clarke
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford University, Stanford, CA, USA
| | - Daniel J. Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | | | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
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Kamarajan C, Pandey AK, Chorlian DB, Meyers JL, Kinreich S, Pandey G, Subbie-Saenz de Viteri S, Zhang J, Kuang W, Barr PB, Aliev F, Anokhin AP, Plawecki MH, Kuperman S, Almasy L, Merikangas A, Brislin SJ, Bauer L, Hesselbrock V, Chan G, Kramer J, Lai D, Hartz S, Bierut LJ, McCutcheon VV, Bucholz KK, Dick DM, Schuckit MA, Edenberg HJ, Porjesz B. Predicting Alcohol-Related Memory Problems in Older Adults: A Machine Learning Study with Multi-Domain Features. Behav Sci (Basel) 2023; 13:bs13050427. [PMID: 37232664 DOI: 10.3390/bs13050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/08/2023] [Accepted: 05/11/2023] [Indexed: 05/27/2023] Open
Abstract
Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.
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Affiliation(s)
- Chella Kamarajan
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Ashwini K Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - David B Chorlian
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jacquelyn L Meyers
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Sivan Kinreich
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Gayathri Pandey
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Stacey Subbie-Saenz de Viteri
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Jian Zhang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Weipeng Kuang
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Peter B Barr
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
| | - Fazil Aliev
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Andrey P Anokhin
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | | | - Samuel Kuperman
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Laura Almasy
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alison Merikangas
- The Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sarah J Brislin
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Lance Bauer
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Victor Hesselbrock
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - Grace Chan
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
- Department of Psychiatry, University of Connecticut, Farmington, CT 06030, USA
| | - John Kramer
- Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA
| | - Dongbing Lai
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Sarah Hartz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Laura J Bierut
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Vivia V McCutcheon
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Kathleen K Bucholz
- Department of Psychiatry, School of Medicine, Washington University, St. Louis, MO 63110, USA
| | - Danielle M Dick
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ 08854, USA
| | - Marc A Schuckit
- Department of Psychiatry, University of California, San Diego, CA 92103, USA
| | | | - Bernice Porjesz
- Henri Begleiter Neurodynamics Lab, Department of Psychiatry and Behavioral Science, SUNY Downstate Health Sciences University, Brooklyn, NY 11203, USA
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Lee H, Choi J, Kim NY, Kim JI, Moon MK, Lee S, Park KS, Kwak SH. Earlier Age at Type 2 Diabetes Diagnosis Is Associated With Increased Genetic Risk of Cardiovascular Disease. Diabetes Care 2023; 46:1085-1090. [PMID: 36939558 PMCID: PMC10154664 DOI: 10.2337/dc22-2144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/24/2023] [Indexed: 03/21/2023]
Abstract
OBJECTIVE We investigated genetic risk of cardiovascular disease (CVD) by age at type 2 diabetes (T2D) diagnosis. RESEARCH DESIGN AND METHODS We compared incident CVD events by age at T2D diagnosis using UK Biobank (N = 12,321) and the Seoul National University Hospital (SNUH) cohort (N = 1,165). Genetic risk was quantified using polygenic risk score (PRS). RESULTS Individuals with earlier T2D diagnosis had higher CVD risk. In UK Biobank, the effect size of coronary artery disease (CAD) PRS on incident CAD was largest in individuals diagnosed with T2D at ages 30-39 years (hazard ratio 2.25; 95% CI 1.56-3.26) and decreased as age at diagnosis increased: ages 40-49 (1.51; 1.30-1.75), 50-59 (1.36; 1.24-1.50), and 60-69 years (1.30; 1.14-1.48) (Pinteraction = 0.0031). A similar trend was observed in the SNUH cohort. This increased genetic risk associated with earlier T2D diagnosis was largely mitigated by a healthy lifestyle. CONCLUSIONS Individuals with an earlier T2D diagnosis have a higher genetic risk of CAD, and this information could be used to tailor lifestyle interventions.
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Affiliation(s)
- Hyunsuk Lee
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 2Department of Translational Medicine, Seoul National University College of Medicine, Seoul, Korea
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
| | - Jaewon Choi
- 4Division of Data Science Research, Innovative Biomedical Technology Research Institute, Seoul National University Hospital, Seoul, Korea
| | - Na Yeon Kim
- 5Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Jong-Il Kim
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
- 6Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea
| | - Min Kyong Moon
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- 8Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Korea
| | - Seunggeun Lee
- 5Graduate School of Data Science, Seoul National University, Seoul, Korea
| | - Kyong Soo Park
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 3Genomic Medicine Institute, Medical Research Center, Seoul National University College of Medicine, Seoul, Korea
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
- 9Department of Genomic Medicine, Seoul National University Hospital, Seoul, Korea
| | - Soo Heon Kwak
- 1Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
- 7Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea
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40
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Ahern J, Thompson W, Fan CC, Loughnan R. Comparing Pruning and Thresholding with Continuous Shrinkage Polygenic Score Methods in a Large Sample of Ancestrally Diverse Adolescents from the ABCD Study ®. Behav Genet 2023; 53:292-309. [PMID: 37017779 PMCID: PMC10655749 DOI: 10.1007/s10519-023-10139-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/28/2023] [Indexed: 04/06/2023]
Abstract
Using individuals' genetic data researchers can generate Polygenic Scores (PS) that are able to predict risk for diseases, variability in different behaviors as well as anthropomorphic measures. This is achieved by leveraging models learned from previously published large Genome-Wide Association Studies (GWASs) associating locations in the genome with a phenotype of interest. Previous GWASs have predominantly been performed in European ancestry individuals. This is of concern as PS generated in samples with a different ancestry to the original training GWAS have been shown to have lower performance and limited portability, and many efforts are now underway to collect genetic databases on individuals of diverse ancestries. In this study, we compare multiple methods of generating PS, including pruning and thresholding and Bayesian continuous shrinkage models, to determine which of them is best able to overcome these limitations. To do this we use the ABCD Study, a longitudinal cohort with deep phenotyping on individuals of diverse ancestry. We generate PS for anthropometric and psychiatric phenotypes using previously published GWAS summary statistics and examine their performance in three subsamples of ABCD: African ancestry individuals (n = 811), European ancestry Individuals (n = 6703), and admixed ancestry individuals (n = 3664). We find that the single ancestry continuous shrinkage method, PRScs (CS), and the multi ancestry meta method, PRScsx Meta (CSx Meta), show the best performance across ancestries and phenotypes.
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Affiliation(s)
- Jonathan Ahern
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA.
| | - Wesley Thompson
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, 9500 Gilman Drive, La Jolla, San Diego, CA, 92161, USA
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
| | - Chun Chieh Fan
- Center for Population Neuroscience and Genetics, Laureate Institute for Brain Research, Tulsa, OK, 74103, USA
- Department of Radiology, University of California, San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92037, USA
| | - Robert Loughnan
- Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Center for Human Development, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92161, USA
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 06/06/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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Adam Y, Sadeeq S, Kumuthini J, Ajayi O, Wells G, Solomon R, Ogunlana O, Adetiba E, Iweala E, Brors B, Adebiyi E. Polygenic Risk Score in African populations: progress and challenges. F1000Res 2023; 11:175. [PMID: 37273966 PMCID: PMC10233318 DOI: 10.12688/f1000research.76218.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/10/2023] [Indexed: 11/23/2023] Open
Abstract
Polygenic Risk Score (PRS) analysis is a method that predicts the genetic risk of an individual towards targeted traits. Even when there are no significant markers, it gives evidence of a genetic effect beyond the results of Genome-Wide Association Studies (GWAS). Moreover, it selects single nucleotide polymorphisms (SNPs) that contribute to the disease with low effect size making it more precise at individual level risk prediction. PRS analysis addresses the shortfall of GWAS by taking into account the SNPs/alleles with low effect size but play an indispensable role to the observed phenotypic/trait variance. PRS analysis has applications that investigate the genetic basis of several traits, which includes rare diseases. However, the accuracy of PRS analysis depends on the genomic data of the underlying population. For instance, several studies show that obtaining higher prediction power of PRS analysis is challenging for non-Europeans. In this manuscript, we review the conventional PRS methods and their application to sub-Saharan African communities. We conclude that lack of sufficient GWAS data and tools is the limiting factor of applying PRS analysis to sub-Saharan populations. We recommend developing Africa-specific PRS methods and tools for estimating and analyzing African population data for clinical evaluation of PRSs of interest and predicting rare diseases.
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Affiliation(s)
- Yagoub Adam
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Suraju Sadeeq
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Judit Kumuthini
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Olabode Ajayi
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Gordon Wells
- South African National Bioinformatics Institute, Life Sciences Building, University of Western Cape, Cape Town, South Africa
- Centre for Proteomic and Genomic Research, Cape Town, Western Cape, South Africa
| | - Rotimi Solomon
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Olubanke Ogunlana
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Emmanuel Adetiba
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Electrical & Information Engineering (EIE), Covenant University, Ota, Ogun State, 112212, Nigeria
- HRA, Institute for Systems Science, Durban University of Technology, Durban, South Africa
| | - Emeka Iweala
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept of Biochemistry, Covenant University, Ota, Ogun State, 112212, Nigeria
| | - Benedikt Brors
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
- German Cancer Consortium (DKTK), Heidelberg, Germany
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Ogun State, 112212, Nigeria
- Covenant Applied Informatics and Communication Africa Centre of Excellence (CApIC-ACE), Covenant University, Ota, Ogun State, 112212, Nigeria
- Dept Computer & Information Sciences, Covenant University, Ota, Ogun State, 112212, Nigeria
- Applied Bioinformatics Division, German Cancer Research Center (DKFZ), Heidelberg, 69120, Germany
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He Y, Qian DC, Diao JA, Cho MH, Silverman EK, Gusev A, Manrai AK, Martin AR, Patel CJ. Prediction and stratification of longitudinal risk for chronic obstructive pulmonary disease across smoking behaviors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.04.23288086. [PMID: 37066248 PMCID: PMC10104210 DOI: 10.1101/2023.04.04.23288086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/01/2023]
Abstract
Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We hypothesize that considering other socioeconomic and environmental factors can better predict and stratify the risk of COPD in both non-smokers and smokers. We performed longitudinal analysis of COPD in the UK Biobank to develop the Socioeconomic and Environmental Risk Score (SERS) which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. We tested the ability of SERS to predict and stratify the risk of COPD in current, previous, and never smokers of European and non-European ancestries in comparison to a composite genome-wide polygenic risk score (PGS). We tested associations using Cox regression models and assessed the predictive performance of models using Harrell's C index. SERS (C index = 0.770, 95% CI 0.756 to 0.784) was more predictive of COPD than smoking status (C index = 0.738, 95% CI 0.724 to 0.752), pack-years (C index = 0.742, 95% CI 0.727 to 0.756). Compared to the remaining population, individuals in the highest decile of the SERS had hazard ratios (HR) = 7.24 (95% CI 6.51 to 8.05, P < 0.0001) for incident COPD. Never smokers in the highest decile of exposure risk were more likely to develop COPD than previous and current smokers in the lowest decile with HR=4.95 (95% CI 1.56 to 15.69, P=6.65×10-3) and 2.92 (95%CI 1.51 to 5.61, P=1.38×10-3), respectively. In general, the prediction accuracy of SERS was lower in the non-European populations compared to the European evaluation set. In addition to genetic factors, socioeconomic and environmental factors beyond smoking can predict and stratify COPD risk for both non- and smoking individuals. Smoking status is often considered in screening; other non-smoking environmental and non-genetic variables should be evaluated prospectively for their clinical utility.
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Affiliation(s)
- Yixuan He
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - David C. Qian
- Department of Radiation Oncology, Winship Cancer Institute of Emory University, Atlanta, GA, USA
| | - James A. Diao
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
| | - Edwin K. Silverman
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alexander Gusev
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Arjun K. Manrai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chirag J. Patel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts 02215
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Kerman BJ, Brunette CA, Harris EJ, Antwi AA, Lemke AA, Vassy JL. Primary care physician use of patient race and polygenic risk scores in medical decision-making. Genet Med 2023; 25:100800. [PMID: 36748708 PMCID: PMC10085844 DOI: 10.1016/j.gim.2023.100800] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 02/01/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
PURPOSE The use of patient race in medicine is controversial for its potential either to exacerbate or address health disparities. Polygenic risk scores (PRSs) have emerged as a tool for risk stratification models used in preventive medicine. We examined whether PRS results affect primary care physician (PCP) medical decision-making and whether that effect varies by patient race. METHODS Using an online survey with a randomized experimental design among PCPs in a national database, we ascertained decision-making around atherosclerotic cardiovascular disease prevention and prostate cancer screening for case scenario patients who were clinically identical except for randomized reported race. RESULTS Across 369 PCPs (email open rate = 10.8%, partial completion rate = 93.7%), recommendations varied with PRS results in expected directions (low-risk results, no available PRS results, and high-risk results). Still, physicians randomized to scenarios with Black patients were more likely to recommend statin therapy than those randomized to scenarios with White patients (odds ratio = 1.74, 95% CI = 1.16-2.59, P = .007) despite otherwise identical clinical profiles and independent of PRS results. Similarly, physicians were more likely to recommend prostate cancer screening for Black patients than for White patients (odds ratio = 1.58, 95% CI = 1.06-2.35, P = .025) despite otherwise identical clinical and genetic profiles. CONCLUSION Despite advances in precision risk stratification, physicians will likely continue to use patient race implicitly or explicitly in medical decision-making.
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Affiliation(s)
- Benjamin J Kerman
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA
| | | | - Elizabeth J Harris
- Department of Medicine, Harvard Medical School, Boston, MA; Veterans Affairs Boston Healthcare System, Boston, MA
| | | | - Amy A Lemke
- Norton Children's Research Institute, Affiliated with the University of Louisville School of Medicine, Louisville, KY
| | - Jason L Vassy
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA; Department of Medicine, Harvard Medical School, Boston, MA; Veterans Affairs Boston Healthcare System, Boston, MA; Precision Population Health, Ariadne Labs, Boston, MA.
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Park JK, Lu CY. Polygenic Scores in the Direct-to-Consumer Setting: Challenges and Opportunities for a New Era in Consumer Genetic Testing. J Pers Med 2023; 13:jpm13040573. [PMID: 37108959 PMCID: PMC10144199 DOI: 10.3390/jpm13040573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/15/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
Direct-to-consumer (DTC) genetic tests have generated considerable scholarly attention and public intrigue. Although the current consumer genetic testing regime relies on the reporting of individual variants of interest to consumers, there has recently been interest in the possibility of integrating polygenic scores (PGS), which aggregate genetic liability for disease across the entire genome. While PGS have thus far been extensively explored as clinical and public health tools, the use of PGS in consumer genetic testing has not yet received systematic attention, even though they are already in use for some consumer genetic tests. In this narrative review, we highlight the ethical, legal, and social implications of the use of PGS in DTC genetic tests and synthesize existing solutions to these concerns. We organize these concerns into three domains: (1) industry variation; (2) privacy and commercialization; and (3) patient safety and risk. While previously expressed concerns in these domains will remain relevant, the emergence of PGS-based DTC genetic tests raises challenges that will require novel approaches.
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Affiliation(s)
- Jin K Park
- Harvard Medical School, Boston, MA 02115, USA
| | - Christine Y Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02115, USA
- Kolling Institute, Faculty of Medicine and Health, The University of Sydney and the Northern Sydney Local Health District, Sydney, NSW 2077, Australia
- School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia
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Thomas M, Su YR, Rosenthal EA, Sakoda LC, Schmit SL, Timofeeva MN, Chen Z, Fernandez-Rozadilla C, Law PJ, Murphy N, Carreras-Torres R, Diez-Obrero V, van Duijnhoven FJ, Jiang S, Shin A, Wolk A, Phipps AI, Burnett-Hartman A, Gsur A, Chan AT, Zauber AG, Wu AH, Lindblom A, Um CY, Tangen CM, Gignoux C, Newton C, Haiman CA, Qu C, Bishop DT, Buchanan DD, Crosslin DR, Conti DV, Kim DH, Hauser E, White E, Siegel E, Schumacher FR, Rennert G, Giles GG, Hampel H, Brenner H, Oze I, Oh JH, Lee JK, Schneider JL, Chang-Claude J, Kim J, Huyghe JR, Zheng J, Hampe J, Greenson J, Hopper JL, Palmer JR, Visvanathan K, Matsuo K, Matsuda K, Jung KJ, Li L, Marchand LL, Vodickova L, Bujanda L, Gunter MJ, Matejcic M, Jenkins MA, Slattery ML, D'Amato M, Wang M, Hoffmeister M, Woods MO, Kim M, Song M, Iwasaki M, Du M, Udaltsova N, Sawada N, Vodicka P, Campbell PT, Newcomb PA, Cai Q, Pearlman R, Pai RK, Schoen RE, Steinfelder RS, Haile RW, Vandenputtelaar R, Prentice RL, Küry S, Castellví-Bel S, Tsugane S, Berndt SI, Lee SC, Brezina S, Weinstein SJ, Chanock SJ, Jee SH, Kweon SS, Vadaparampil S, Harrison TA, Yamaji T, Keku TO, Vymetalkova V, Arndt V, Jia WH, Shu XO, Lin Y, Ahn YO, Stadler ZK, Van Guelpen B, Ulrich CM, Platz EA, Potter JD, Li CI, Meester R, Moreno V, Figueiredo JC, Casey G, Vogelaar IL, Dunlop MG, Gruber SB, Hayes RB, Pharoah PDP, Houlston RS, Jarvik GP, Tomlinson IP, Zheng W, Corley DA, Peters U, Hsu L. Combining Asian-European Genome-Wide Association Studies of Colorectal Cancer Improves Risk Prediction Across Race and Ethnicity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.19.23284737. [PMID: 36789420 PMCID: PMC9928144 DOI: 10.1101/2023.01.19.23284737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expanded PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS were 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1,681-3,651 cases and 8,696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They were significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values<0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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