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Tsuo K, Shi Z, Ge T, Mandla R, Hou K, Ding Y, Pasaniuc B, Wang Y, Martin AR. All of Us diversity and scale improve polygenic prediction contextually with greatest improvements for under-represented populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.06.606846. [PMID: 39149254 PMCID: PMC11326295 DOI: 10.1101/2024.08.06.606846] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to ~750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count (R 2: 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.
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Affiliation(s)
- Kristin Tsuo
- 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
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zhuozheng Shi
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Tian Ge
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kangcheng Hou
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Yi Ding
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Bogdan Pasaniuc
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Ying Wang
- 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
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - 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
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Crone B, Boyle AP. Enhancing portability of trans-ancestral polygenic risk scores through tissue-specific functional genomic data integration. PLoS Genet 2024; 20:e1011356. [PMID: 39110742 PMCID: PMC11333000 DOI: 10.1371/journal.pgen.1011356] [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: 03/05/2024] [Revised: 08/19/2024] [Accepted: 06/27/2024] [Indexed: 08/21/2024] Open
Abstract
Portability of trans-ancestral polygenic risk scores is often confounded by differences in linkage disequilibrium and genetic architecture between ancestries. Recent literature has shown that prioritizing GWAS SNPs with functional genomic evidence over strong association signals can improve model portability. We leveraged three RegulomeDB-derived functional regulatory annotations-SURF, TURF, and TLand-to construct polygenic risk models across a set of quantitative and binary traits highlighting functional mutations tagged by trait-associated tissue annotations. Tissue-specific prioritization by TURF and TLand provide a significant improvement in model accuracy over standard polygenic risk score (PRS) models across all traits. We developed the Trans-ancestral Iterative Tissue Refinement (TITR) algorithm to construct PRS models that prioritize functional mutations across multiple trait-implicated tissues. TITR-constructed PRS models show increased predictive accuracy over single tissue prioritization. This indicates our TITR approach captures a more comprehensive view of regulatory systems across implicated tissues that contribute to variance in trait expression.
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Affiliation(s)
- Bradley Crone
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alan P. Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Human Genetics, University of Michigan, Ann Arbor, Michigan, United States of America
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Nagpal S, Gibson G. Dual exposure-by-polygenic score interactions highlight disparities across social groups in the proportion needed to benefit. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.29.24311065. [PMID: 39132477 PMCID: PMC11312673 DOI: 10.1101/2024.07.29.24311065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The transferability of polygenic scores across population groups is a major concern with respect to the equitable clinical implementation of genomic medicine. Since genetic associations are identified relative to the population mean, inevitably differences in disease or trait prevalence among social strata influence the relationship between PGS and risk. Here we quantify the magnitude of PGS-by-Exposure (PGSxE) interactions for seven human diseases (coronary artery disease, type 2 diabetes, obesity thresholded to body mass index and to waist-to-hip ratio, inflammatory bowel disease, chronic kidney disease, and asthma) and pairs of 75 exposures in the White-British subset of the UK Biobank study (n=408,801). Across 24,198 PGSxE models, 746 (3.1%) were significant by two criteria, at least three-fold more than expected by chance under each criterion. Predictive accuracy is significantly improved in the high-risk exposures and by including interaction terms with effects as large as those documented for low transferability of PGS across ancestries. The predominant mechanism for PGS×E interactions is shown to be amplification of genetic effects in the presence of adverse exposures such as low polyunsaturated fatty acids, mediators of obesity, and social determinants of ill health. We introduce the notion of the proportion needed to benefit (PNB) which is the cumulative number needed to treat across the range of the PGS and show that typically this is halved in the 70th to 80th percentile. These findings emphasize how individuals experiencing adverse exposures stand to preferentially benefit from interventions that may reduce risk, and highlight the need for more comprehensive sampling across socioeconomic groups in the performance of genome-wide association studies.
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Affiliation(s)
- Sini Nagpal
- Center for Integrative Genomics and School of Biological Sciences, Georgia Institute of Technology Atlanta, GA 30302
| | - Greg Gibson
- Center for Integrative Genomics and School of Biological Sciences, Georgia Institute of Technology Atlanta, GA 30302
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Nibali L, Divaris K, Lu EMC. The promise and challenges of genomics-informed periodontal disease diagnoses. Periodontol 2000 2024. [PMID: 39072804 DOI: 10.1111/prd.12587] [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: 02/18/2024] [Revised: 05/16/2024] [Accepted: 06/07/2024] [Indexed: 07/30/2024]
Abstract
Recent advances in human genomics and the advent of molecular medicine have catapulted our ability to characterize human and health and disease. Scientists and healthcare practitioners can now leverage information on genetic variation and gene expression at the tissue or even individual cell level, and an enormous potential exists to refine diagnostic categories, assess risk in unaffected individuals, and optimize disease management among those affected. This review investigates the progress made in the domains of molecular medicine and genomics as they relate to periodontology. The review summarizes the current evidence of association between genomics and periodontal diseases, including the current state of knowledge that approximately a third of the population variance of periodontitis may be attributable to genetic variation and the management of several monogenic forms of the disease can be augmented by knowledge of the underlying genetic cause. Finally, the paper discusses the potential utility of polygenic risk scores and genetic testing for periodontitis diagnosis now and in the future, in light of applications that currently exist in other areas of medicine and healthcare.
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Affiliation(s)
- Luigi Nibali
- Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - Kimon Divaris
- Department of Pediatric Dentistry and Dental Public Health, Adams School of Dentistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Emily Ming-Chieh Lu
- Periodontology Unit, Centre for Host Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
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5
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Habtewold TD, Wijesiriwardhana P, Biedrzycki RJ, Tekola-Ayele F. Genetic distance and ancestry proportion modify the association between maternal genetic risk score of type 2 diabetes and fetal growth. Hum Genomics 2024; 18:81. [PMID: 39030631 PMCID: PMC11264503 DOI: 10.1186/s40246-024-00645-1] [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: 02/12/2024] [Accepted: 06/27/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Maternal genetic risk of type 2 diabetes (T2D) has been associated with fetal growth, but the influence of genetic ancestry is not yet fully understood. We aimed to investigate the influence of genetic distance (GD) and genetic ancestry proportion (GAP) on the association of maternal genetic risk score of T2D (GRST2D) with fetal weight and birthweight. METHODS Multi-ancestral pregnant women (n = 1,837) from the NICHD Fetal Growth Studies - Singletons cohort were included in the current analyses. Fetal weight (in grams, g) was estimated from ultrasound measurements of fetal biometry, and birthweight (g) was measured at delivery. GRST2D was calculated using T2D-associated variants identified in the latest trans-ancestral genome-wide association study and was categorized into quartiles. GD and GAP were estimated using genotype data of four reference populations. GD was categorized into closest, middle, and farthest tertiles, and GAP was categorized as highest, medium, and lowest. Linear regression analyses were performed to test the association of GRST2D with fetal weight and birthweight, adjusted for covariates, in each GD and GAP category. RESULTS Among women with the closest GD from African and Amerindigenous ancestries, the fourth and third GRST2D quartile was significantly associated with 5.18 to 7.48 g (weeks 17-20) and 6.83 to 25.44 g (weeks 19-27) larger fetal weight compared to the first quartile, respectively. Among women with middle GD from European ancestry, the fourth GRST2D quartile was significantly associated with 5.73 to 21.21 g (weeks 18-26) larger fetal weight. Furthermore, among women with middle GD from European and African ancestries, the fourth and second GRST2D quartiles were significantly associated with 117.04 g (95% CI = 23.88-210.20, p = 0.014) and 95.05 g (95% CI = 4.73-185.36, p = 0.039) larger birthweight compared to the first quartile, respectively. The absence of significant association among women with the closest GD from East Asian ancestry was complemented by a positive significant association among women with the highest East Asian GAP. CONCLUSIONS The association between maternal GRST2D and fetal growth began in early-second trimester and was influenced by GD and GAP. The results suggest the use of genetic GD and GAP could improve the generalizability of GRS.
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Affiliation(s)
- Tesfa Dejenie Habtewold
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Prabhavi Wijesiriwardhana
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Richard J Biedrzycki
- Glotech, Inc., contractor for Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA
| | - Fasil Tekola-Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, 6710B Rockledge Drive, Bethesda, MD, 20892-7004, USA.
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6
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability. HGG ADVANCES 2024; 5:100319. [PMID: 38872309 PMCID: PMC11260573 DOI: 10.1016/j.xhgg.2024.100319] [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] [Received: 01/03/2024] [Revised: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
Since the first genome-wide association studies (GWASs), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in the Joint Analysis of Summary Statistics. We assessed which genetic features of the sets of traits analyzed were associated with an increased detection of variants compared with univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's r between the observed and predicted gain equals 0.43, p < 1.6 × 10-60). Applying an alternative multi-trait approach (Multi-Trait Analysis of GWAS), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outlines practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France.
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, 75015 Paris, France; Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, 75015 Paris, France.
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Tubbs JD, Chen Y, Duan R, Huang H, Ge T. Real-time dynamic polygenic prediction for streaming data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.12.24310357. [PMID: 39040195 PMCID: PMC11261927 DOI: 10.1101/2024.07.12.24310357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Polygenic risk scores (PRSs) are promising tools for advancing precision medicine. However, existing PRS construction methods rely on static summary statistics derived from genome-wide association studies (GWASs), which are often updated at lengthy intervals. As genetic data and health outcomes are continuously being generated at an ever-increasing pace, the current PRS training and deployment paradigm is suboptimal in maximizing the prediction accuracy of PRSs for incoming patients in healthcare settings. Here, we introduce real-time PRS-CS (rtPRS-CS), which enables online, dynamic refinement and calibration of PRS as each new sample is collected, without the need to perform intermediate GWASs. Through extensive simulation studies, we evaluate the performance of rtPRS-CS across various genetic architectures and training sample sizes. Leveraging quantitative traits from the Mass General Brigham Biobank and UK Biobank, we show that rtPRS-CS can integrate massive streaming data to enhance PRS prediction over time. We further apply rtPRS-CS to 22 schizophrenia cohorts in 7 Asian regions, demonstrating the clinical utility of rtPRS-CS in dynamically predicting and stratifying disease risk across diverse genetic ancestries.
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Affiliation(s)
- Justin D. Tubbs
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Yu Chen
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Hailiang Huang
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
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Mabey B, Hughes E, Kucera M, Simmons T, Hullinger B, Pederson HJ, Yehia L, Eng C, Garber J, Gary M, Gordon O, Klemp JR, Mukherjee S, Vijai J, Offit K, Olopade OI, Pruthi S, Kurian A, Robson ME, Whitworth PW, Pal T, Ratzel S, Wagner S, Lanchbury JS, Taber KJ, Slavin TP, Gutin A. Validation of a clinical breast cancer risk assessment tool combining a polygenic score for all ancestries with traditional risk factors. Genet Med 2024; 26:101128. [PMID: 38829299 DOI: 10.1016/j.gim.2024.101128] [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] [Received: 11/02/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 06/05/2024] Open
Abstract
PURPOSE We previously described a combined risk score (CRS) that integrates a multiple-ancestry polygenic risk score (MA-PRS) with the Tyrer-Cuzick (TC) model to assess breast cancer (BC) risk. Here, we present a longitudinal validation of CRS in a real-world cohort. METHODS This study included 130,058 patients referred for hereditary cancer genetic testing and negative for germline pathogenic variants in BC-associated genes. Data were obtained by linking genetic test results to medical claims (median follow-up 12.1 months). CRS calibration was evaluated by the ratio of observed to expected BCs. RESULTS Three hundred forty BCs were observed over 148,349 patient-years. CRS was well-calibrated and demonstrated superior calibration compared with TC in high-risk deciles. MA-PRS alone had greater discriminatory accuracy than TC, and CRS had approximately 2-fold greater discriminatory accuracy than MA-PRS or TC. Among those classified as high risk by TC, 32.6% were low risk by CRS, and of those classified as low risk by TC, 4.3% were high risk by CRS. In cases where CRS and TC classifications disagreed, CRS was more accurate in predicting incident BC. CONCLUSION CRS was well-calibrated and significantly improved BC risk stratification. Short-term follow-up suggests that clinical implementation of CRS should improve outcomes for patients of all ancestries through personalized risk-based screening and prevention.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | | | - Joseph Vijai
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - Kenneth Offit
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | | | | | - Mark E Robson
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Tuya Pal
- Vanderbilt University Medical Center, Nashville, TN
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Lin WD, Liao WL, Chen WC, Liu TY, Chen YC, Tsai FJ. Genome-wide association study identifies novel susceptible loci and evaluation of polygenic risk score for chronic obstructive pulmonary disease in a Taiwanese population. BMC Genomics 2024; 25:607. [PMID: 38886662 PMCID: PMC11184693 DOI: 10.1186/s12864-024-10526-5] [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] [Received: 12/09/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) describes a group of progressive lung diseases causing breathing difficulties. While COPD development typically involves a complex interplay between genetic and environmental factors, genetics play a role in disease susceptibility. This study used genome-wide association studies (GWAS) and polygenic risk score (PRS) to elucidate the genetic basis for COPD in Taiwanese patients. RESULTS GWAS was performed on a Taiwanese COPD case-control cohort with a sample size of 5,442 cases and 17,681 controls. Additionally, the PRS was calculated and assessed in our target groups. GWAS results indicate that although there were no single nucleotide polymorphisms (SNPs) of genome-wide significance, prominent COPD susceptibility loci on or nearby genes such as WWTR1, EXT1, INTU, MAP3K7CL, MAMDC2, BZW1/CLK1, LINC01197, LINC01894, and CFAP95 (C9orf135) were identified, which had not been reported in previous studies. Thirteen susceptibility loci, such as CHRNA4, AFAP1, and DTWD1, previously reported in other populations were replicated and confirmed to be associated with COPD in Taiwanese populations. The PRS was determined in the target groups using the summary statistics from our base group, yielding an effective association with COPD (odds ratio [OR] 1.09, 95% confidence interval [CI] 1.02-1.17, p = 0.011). Furthermore, replication a previous lung function trait PRS model in our target group, showed a significant association of COPD susceptibility with PRS of Forced Expiratory Volume in one second (FEV1)/Forced Vital Capacity (FCV) (OR 0.89, 95% CI 0.83-0.95, p = 0.001). CONCLUSIONS Novel COPD-related genes were identified in the studied Taiwanese population. The PRS model, based on COPD or lung function traits, enables disease risk estimation and enhances prediction before suffering. These results offer new perspectives on the genetics of COPD and serve as a basis for future research.
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Affiliation(s)
- Wei-De Lin
- Department of Medical Research, China Medical University Hospital, Taichung, 404327, Taiwan
- School of Post Baccalaureate Chinese Medicine, China Medical University, Taichung, 404333, Taiwan
| | - Wen-Ling Liao
- Graduate Institute of Integrated Medicine, College of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan
- Center for Personalized Medicine, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Wei-Cheng Chen
- Department of Internal Medicine, Pulmonary and Critical Care Medicine, China Medical University Hospital, Taichung, 404333, Taiwan
- Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 404327, Taiwan
| | - Ting-Yuan Liu
- Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Yu-Chia Chen
- Department of Medical Research, Million-Person Precision Medicine Initiative, China Medical University Hospital, Taichung, 404327, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, 404327, Taiwan.
- School of Chinese Medicine, China Medical University, Taichung, 404333, Taiwan.
- Division of Genetics and Metabolism, China Medical University Children's Hospital, Taichung, 404327, Taiwan.
- Department of Medical Genetics, China Medical University Hospital, Taichung, 404327, Taiwan.
- Department of Medical Laboratory Science and Biotechnology, Asia University, Taichung, 413305, Taiwan.
- Department of Medical Research, China Medical University Hospital, No. 2, Yude Road, North District, Taichung, 404327, Taiwan.
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10
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome wide association study and genomic risk prediction of age related macular degeneration in Israel. Sci Rep 2024; 14:13034. [PMID: 38844476 PMCID: PMC11156861 DOI: 10.1038/s41598-024-63065-0] [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: 12/22/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10-5 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10-9). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Affiliation(s)
- Michelle Grunin
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Daria Triffon
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
| | - Gala Beykin
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Regev Schweiger
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Genetics, University of Cambridge, CB21TN, Cambridge, UK
| | - Liran Tiosano
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Samer Khateb
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Shira Hagbi-Levi
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Batya Rinsky
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Refael Munitz
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Eran Halperin
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Anesthesiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel.
| | - Itay Chowers
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel.
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11
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Laxmi, Golmei P, Srivastava S, Kumar S. Single nucleotide polymorphism-based biomarker in primary hypertension. Eur J Pharmacol 2024; 972:176584. [PMID: 38621507 DOI: 10.1016/j.ejphar.2024.176584] [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] [Received: 01/07/2024] [Revised: 03/19/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024]
Abstract
Primary hypertension is a multiplex and multifactorial disease influenced by various strong components including genetics. Extensive research such as Genome-wide association studies and candidate gene studies have revealed various single nucleotide polymorphisms (SNPs) related to hypertension, providing insights into the genetic basis of the condition. This review summarizes the current status of SNP research in primary hypertension, including examples of hypertension-related SNPs, their location, function, and frequency in different populations. The potential clinical implications of SNP research for primary hypertension management are also discussed, including disease risk prediction, personalized medicine, mechanistic understanding, and lifestyle modifications. Furthermore, this review highlights emerging technologies and methodologies that have the potential to revolutionize the vast understanding of the basis of genetics in primary hypertension. Gene editing holds the potential to target and correct any kind of genetic mutations that contribute to the development of hypertension or modify genes involved in blood pressure regulation to prevent or treat the condition. Advances in computational biology and machine learning enable researchers to analyze large datasets and identify complex genetic interactions contributing to hypertension risk. In conclusion, SNP research in primary hypertension is rapidly evolving with emerging technologies and methodologies that have the potential to transform the knowledge about genetic basis related to the condition. These advances hold promise for personalized prevention and treatment strategies tailored to an individual's genetic profile ultimately improving patient outcomes and reducing healthcare costs.
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Affiliation(s)
- Laxmi
- Department of Pharmacology, Delhi Institute of Pharmaceutical Sciences and Research, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, M B Road, New Delhi, 110017, India
| | - Pougang Golmei
- Department of Pharmacology, Delhi Institute of Pharmaceutical Sciences and Research, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, M B Road, New Delhi, 110017, India
| | - Shriyansh Srivastava
- Department of Pharmacology, Delhi Institute of Pharmaceutical Sciences and Research, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, M B Road, New Delhi, 110017, India
| | - Sachin Kumar
- Department of Pharmacology, Delhi Institute of Pharmaceutical Sciences and Research, Delhi Pharmaceutical Sciences and Research University, Pushp Vihar, M B Road, New Delhi, 110017, India.
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12
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Hrytsenko Y, Shea B, Elgart M, Kurniansyah N, Lyons G, Morrison AC, Carson AP, Haring B, Mitchell BD, Psaty BM, Jaeger BC, Gu CC, Kooperberg C, Levy D, Lloyd-Jones D, Choi E, Brody JA, Smith JA, Rotter JI, Moll M, Fornage M, Simon N, Castaldi P, Casanova R, Chung RH, Kaplan R, Loos RJF, Kardia SLR, Rich SS, Redline S, Kelly T, O'Connor T, Zhao W, Kim W, Guo X, Ida Chen YD, Sofer T. Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores. Sci Rep 2024; 14:12436. [PMID: 38816422 PMCID: PMC11139858 DOI: 10.1038/s41598-024-62945-9] [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: 01/22/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024] Open
Abstract
We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.
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Affiliation(s)
- Yana Hrytsenko
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Benjamin Shea
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael Elgart
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Genevieve Lyons
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alanna C Morrison
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Bernhard Haring
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Department of Medicine III, Saarland University, Homburg, Saarland, Germany
| | - Braxton D Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bruce M Psaty
- Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
- Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Byron C Jaeger
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- The Center for Biostatistics and Data Science, Washington University, St. Louis, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Daniel Levy
- The Population Sciences Branch of the National Heart, Lung and Blood Institute, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Donald Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Eunhee Choi
- Columbia Hypertension Laboratory, Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Jennifer A Brody
- Department of Medicine, University of Washington, Seattle, WA, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Matthew Moll
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- VA Boston Healthcare System, West Roxbury, MA, USA
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Myriam Fornage
- Department of Epidemiology, School of Public Health, Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Noah Simon
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Peter Castaldi
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Ramon Casanova
- Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Taipei City, Taiwan
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty for Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Susan Redline
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
| | - Tanika Kelly
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, USA
| | - Timothy O'Connor
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Program in Health Equity and Population Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Wei Zhao
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tamar Sofer
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- CardioVascular Institute (CVI), Beth Israel Deaconess Medical Center, Boston, MA, USA.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Center for Life Sciences CLS-934, 3 Blackfan St., Boston, MA, 02115, USA.
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13
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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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14
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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15
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Wang Y, He Y, Shi Y, Qian DC, Gray KJ, Winn R, Martin AR. Aspiring toward equitable benefits from genomic advances to individuals of ancestrally diverse backgrounds. Am J Hum Genet 2024; 111:809-824. [PMID: 38642557 PMCID: PMC11080611 DOI: 10.1016/j.ajhg.2024.04.002] [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: 10/05/2023] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/22/2024] Open
Abstract
Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.
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Affiliation(s)
- Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
| | - Yixuan He
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Yue Shi
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Reproductive Medicine Center, the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - David C Qian
- Department of Thoracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kathryn J Gray
- Department of Obstetrics and Gynecology, University of Washington, Seattle, WA, USA
| | - Robert Winn
- Virginia Commonwealth University Massey Cancer Center, Richmond, VA, USA
| | - Alicia R Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA.
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16
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Sun TH, Wang CC, Liu TY, Lo SC, Huang YX, Chien SY, Chu YD, Tsai FJ, Hsu KC. Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling. Nat Commun 2024; 15:3168. [PMID: 38609356 PMCID: PMC11014845 DOI: 10.1038/s41467-024-47472-5] [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: 06/07/2023] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.
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Affiliation(s)
- Ting-Hsuan Sun
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Chia-Chun Wang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Ting-Yuan Liu
- Million-person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shih-Chang Lo
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Yi-Xuan Huang
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Shang-Yu Chien
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Yu-De Chu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan
| | - Fuu-Jen Tsai
- Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- School of Chinese Medicine, China Medical University, Taichung, 40402, Taiwan.
- Division of Pediatric Genetics, Children's Hospital of China Medical University, Taichung, 40447, Taiwan.
- Department of Biotechnology and Bioinformatics, Asia University, Taichung, 41354, Taiwan.
| | - Kai-Cheng Hsu
- Artificial Intelligence Center, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Neurology, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Medicine, China Medical University, Taichung, 40402, Taiwan.
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17
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Zhang T, Zhou G, Klei L, Liu P, Chouldechova A, Zhao H, Roeder K, G'Sell M, Devlin B. Evaluating and improving health equity and fairness of polygenic scores. HGG ADVANCES 2024; 5:100280. [PMID: 38402414 PMCID: PMC10937319 DOI: 10.1016/j.xhgg.2024.100280] [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: 09/28/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024] Open
Abstract
Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.
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Affiliation(s)
- Tianyu Zhang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Geyu Zhou
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peng Liu
- Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexandra Chouldechova
- Microsoft Research NYC, New York, NY 10012, USA; Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Max G'Sell
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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18
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. CELL GENOMICS 2024; 4:100523. [PMID: 38508198 PMCID: PMC11019356 DOI: 10.1016/j.xgen.2024.100523] [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: 08/14/2023] [Revised: 09/15/2023] [Accepted: 02/20/2024] [Indexed: 03/22/2024]
Abstract
Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Leland E Hull
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute, Cambridge, UK
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia R Martin
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA 5000, Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142, USA; Center for Genomic Medicine and Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, Boston, MA 02114, USA; Department of Medicine, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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19
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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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Yiangou K, Mavaddat N, Dennis J, Zanti M, Wang Q, Bolla MK, Abubakar M, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baten A, Behrens S, Bermisheva M, de Gonzalez AB, Białkowska K, Boddicker N, Bodelon C, Bogdanova NV, Bojesen SE, Brantley KD, Brauch H, Brenner H, Camp NJ, Canzian F, Castelao JE, Cessna MH, Chang-Claude J, Chenevix-Trench G, Chung WK, Colonna SV, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dunning AM, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Flyger H, Fritschi L, Gago-Dominguez M, Gentry-Maharaj A, González-Neira A, Guénel P, Hahnen E, Haiman CA, Hamann U, Hartikainen JM, Ho V, Hodge J, Hollestelle A, Honisch E, Hooning MJ, Hoppe R, Hopper JL, Howell S, Howell A, Jakovchevska S, Jakubowska A, Jernström H, Johnson N, Kaaks R, Khusnutdinova EK, Kitahara CM, Koutros S, Kristensen VN, Lacey JV, Lambrechts D, Lejbkowicz F, Lindblom A, Lush M, Mannermaa A, Mavroudis D, Menon U, Murphy RA, Nevanlinna H, Obi N, Offit K, Park-Simon TW, Patel AV, Peng C, Peterlongo P, Pita G, Plaseska-Karanfilska D, Pylkäs K, Radice P, Rashid MU, Rennert G, Roberts E, Rodriguez J, Romero A, Rosenberg EH, Saloustros E, Sandler DP, Sawyer EJ, Schmutzler RK, Scott CG, Shu XO, Southey MC, Stone J, Taylor JA, Teras LR, van de Beek I, Willett W, Winqvist R, Zheng W, Vachon CM, Schmidt MK, Hall P, MacInnis RJ, Milne RL, Pharoah PD, Simard J, Antoniou AC, Easton DF, Michailidou K. Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302043. [PMID: 38410445 PMCID: PMC10896416 DOI: 10.1101/2024.02.12.24302043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.
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Affiliation(s)
- Kristia Yiangou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Maria Zanti
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Mustapha Abubakar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Thomas U. Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Irene L. Andrulis
- Fred A, Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada, M5G 1X5
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, M5S 1A8
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA, 92617
| | - Natalia N. Antonenkova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Kristan J. Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, ON, Canada, K7L 3N6
| | | | - Adinda Baten
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium, 3000
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- St Petersburg State University, St, Petersburg, Russia, 199034
| | | | - Katarzyna Białkowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
| | - Nicholas Boddicker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Clara Bodelon
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Natalia V. Bogdanova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany, 30625
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 2200
| | - Kristen D. Brantley
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Hiltrud Brauch
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany, 72074
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany, 72074
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany, 69120
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Nicola J. Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jose E. Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) Foundation, Complejo Hospitalario Universitario de Santiago, SERGAS, Vigo, Spain, 36312
| | - Melissa H. Cessna
- Department of Pathology, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
- Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Georgia Chenevix-Trench
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 4006
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA, 10032
| | - NBCS Collaborators
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Research, Vestre Viken Hospital, Drammen, Norway, 3019
- Section for Breast- and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Ullevål, Oslo, Norway, 0450
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, 0379
- Department of Pathology, Akershus University Hospital, Lørenskog, Norway, 1478
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway, 1478
- Oslo Breast Cancer Research Consortium, Oslo University Hospital, Oslo, Norway, 0379
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - Sarah V. Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Angela Cox
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Simon S. Cross
- Division of Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA, 19111
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Diana M. Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK, SO17 1BJ
| | - A. Heather Eliassen
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany, 04107
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany, 04103
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - D. Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany, 91054
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia, 6102
| | - Manuela Gago-Dominguez
- Cancer Genetics and Epidemiology Group, Genomic Medicine Group, Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain, 15706
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
- Spanish Network on Rare Diseases (CIBERER)
| | - Pascal Guénel
- Team ‘Exposome and Heredity’, CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France, 94805
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 90033
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jaana M. Hartikainen
- Cancer RC, University of Eastern Finland, Kuopio, Finland, 70210
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
| | - Vikki Ho
- Health Innovation and Evaluation Hub, Université de Montréal Hospital Research Centre (CRCHUM), Montréal, Québec, Canada
| | - James Hodge
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Antoinette Hollestelle
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Ellen Honisch
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 40225
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Reiner Hoppe
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- University of Tübingen, Tübingen, Germany, 72074
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
| | - Sacha Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, UK
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK, M13 9PL
| | - ABCTB Investigators
- Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia, 2145
| | - kConFab Investigators
- Research Department, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, 3000
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia, 3000
| | - Simona Jakovchevska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland, 171-252
| | - Helena Jernström
- Oncology, Clinical Sciences in Lund, Lund University, Lund, Sweden, 221 85
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Elza K. Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- Department of Genetics and Fundamental Medicine, Ufa University of Science and Technology, Ufa, Russia, 450076
| | - Cari M. Kitahara
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA, 20892
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Vessela N. Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - James V. Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA, 91010
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA, 91010
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium, 3000
- VIB Center for Cancer Biology, VIB, Leuven, Belgium, 3001
| | | | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, 171 76
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden, 171 76
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland, 70210
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece, 711 10
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
| | - Rachel A. Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada, V5Z 1L3
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 00290
| | - Nadia Obi
- Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
| | | | - Alpa V. Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Cheng Peng
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy, 20139
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Paolo Radice
- Unit of Predictice Medicine, Molecular Bases of Genetic Risk, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy, 20133
| | - Muhammad U. Rashid
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, Pakistan, 54000
| | - Gad Rennert
- Technion, Faculty of Medicine and Association for Promotion of Research in Precision Medicine, Haifa, Israel
| | - Eleanor Roberts
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 28222
| | - Efraim H. Rosenberg
- Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | | | - Dale P. Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Elinor J. Sawyer
- School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy’s Campus, King’s College London, London, UK
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50931
| | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia, 6000
| | - Jack A. Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Irma van de Beek
- Department of Clinical Genetics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | - Walter Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Celine M. Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands, 1066 CX
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
- Department of Oncology, Södersjukhuset, Stockholm, Sweden, 118 83
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Paul D.P. Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA, 90069
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval Research Center, Québec City, Québec, Canada, G1V 4G2
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
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22
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Ariad D, Madjunkova S, Madjunkov M, Chen S, Abramov R, Librach C, McCoy RC. Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos. Genome Res 2024; 34:70-84. [PMID: 38071472 PMCID: PMC10903951 DOI: 10.1101/gr.278168.123] [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: 06/12/2023] [Accepted: 11/21/2023] [Indexed: 12/19/2023]
Abstract
Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes rely either on population genetic patterns of linkage disequilibrium (LD)-capturing a time-averaged view-or on direct detection of crossovers in gametes or multigeneration pedigrees, which limits data set scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, and the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations show our method's high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ∼150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared with disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.
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Affiliation(s)
- Daniel Ariad
- Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA;
| | - Svetlana Madjunkova
- CReATe Fertility Centre, Toronto, Ontario M5G 1N8, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | | | - Siwei Chen
- CReATe Fertility Centre, Toronto, Ontario M5G 1N8, Canada
| | - Rina Abramov
- CReATe Fertility Centre, Toronto, Ontario M5G 1N8, Canada
| | - Clifford Librach
- CReATe Fertility Centre, Toronto, Ontario M5G 1N8, Canada
- Department of Obstetrics and Gynecology, University of Toronto, Toronto, Ontario M5G 1E2, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada
- Department of Physiology, University of Toronto, Toronto, Ontario M5S 1A8, Canada
| | - Rajiv C McCoy
- Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218, USA;
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23
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Futane A, Jadhav P, Mustafa AH, Srinivasan A, Narayanamurthy V. Aptamer-functionalized MOFs and AI-driven strategies for early cancer diagnosis and therapeutics. Biotechnol Lett 2024; 46:1-17. [PMID: 38155321 DOI: 10.1007/s10529-023-03454-z] [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: 06/30/2023] [Revised: 11/07/2023] [Accepted: 11/20/2023] [Indexed: 12/30/2023]
Abstract
Metal-Organic Frameworks (MOFs) have exceptional inherent properties that make them highly suitable for diverse applications, such as catalysis, storage, optics, chemo sensing, and biomedical science and technology. Over the past decades, researchers have utilized various techniques, including solvothermal, hydrothermal, mechanochemical, electrochemical, and ultrasonic, to synthesize MOFs with tailored properties. Post-synthetic modification of linkers, nodal components, and crystallite domain size and morphology can functionalize MOFs to improve their aptamer applications. Advancements in AI and machine learning led to the development of nonporous MOFs and nanoscale MOFs for medical purposes. MOFs have exhibited promise in cancer therapy, with the successful accumulation of a photosensitizer in cancer cells representing a significant breakthrough. This perspective is focused on MOFs' use as advanced materials and systems for cancer therapy, exploring the challenging aspects and promising features of MOF-based cancer diagnosis and treatment. The paper concludes by emphasizing the potential of MOFs as a transformative technology for cancer treatment and diagnosis.
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Affiliation(s)
- Abhishek Futane
- Department of Engineering Technology, Faculty of Electronics and Computer Technology & Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
| | - Pramod Jadhav
- Faculty of Civil Engineering Technology, Universiti Malaysia Pahang (UMP) Lebuhraya Tun Razak, 26300, Gambang, Kuantan, Pahang, Malaysia
| | - Abu Hasnat Mustafa
- Faculty of Industrial Science and Technology, Universiti Malaysia Pahang, 26300, Gambang, Pahang, Malaysia
| | - Arthi Srinivasan
- Faculty of Chemical and Process Engineering Technology, University Malaysia Pahang (UMP), Lebuhraya Tun Razak, 26300, Gambang, Kunatan, Pahang, Malaysia
| | - Vigneswaran Narayanamurthy
- Department of Engineering Technology, Faculty of Electronics and Computer Technology & Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia.
- Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
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24
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Peyrégne S, Slon V, Kelso J. More than a decade of genetic research on the Denisovans. Nat Rev Genet 2024; 25:83-103. [PMID: 37723347 DOI: 10.1038/s41576-023-00643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 09/20/2023]
Abstract
Denisovans, a group of now extinct humans who lived in Eastern Eurasia in the Middle and Late Pleistocene, were first identified from DNA sequences just over a decade ago. Only ten fragmentary remains from two sites have been attributed to Denisovans based entirely on molecular information. Nevertheless, there has been great interest in using genetic data to understand Denisovans and their place in human history. From the reconstruction of a single high-quality genome, it has been possible to infer their population history, including events of admixture with other human groups. Additionally, the identification of Denisovan DNA in the genomes of present-day individuals has provided insights into the timing and routes of dispersal of ancient modern humans into Asia and Oceania, as well as the contributions of archaic DNA to the physiology of present-day people. In this Review, we synthesize more than a decade of research on Denisovans, reconcile controversies and summarize insights into their population history and phenotype. We also highlight how our growing knowledge about Denisovans has provided insights into our own evolutionary history.
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Affiliation(s)
- Stéphane Peyrégne
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany.
| | - Viviane Slon
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany
- Department of Anatomy and Anthropology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- Department of Human Molecular Genetics and Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
- The Dan David Center for Human Evolution and Biohistory Research, Tel Aviv University, Tel Aviv, Israel
| | - Janet Kelso
- Department of Evolutionary Genetics, Max-Planck-Institute for Evolutionary Anthropology, Leipzig, Germany.
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25
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Aw AJ, McRae J, Rahmani E, Song YS. Highly parameterized polygenic scores tend to overfit to population stratification via random effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577589. [PMID: 38352303 PMCID: PMC10862757 DOI: 10.1101/2024.01.27.577589] [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: 02/22/2024]
Abstract
Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.
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Affiliation(s)
- Alan J. Aw
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Artificial Intelligence Laboratory, Illumina Inc
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina Inc
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles
| | - Yun S. Song
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
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26
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Nakase T, Guerra G, Ostrom QT, Ge T, Melin B, Wrensch M, Wiencke JK, Jenkins RB, Eckel-Passow JE, Bondy ML, Francis SS, Kachuri L. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301112. [PMID: 38260701 PMCID: PMC10802631 DOI: 10.1101/2024.01.10.24301112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.
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Affiliation(s)
- Taishi Nakase
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Geno Guerra
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, 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
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Robert B. Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Melissa L. Bondy
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Francis
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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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: 45] [Impact Index Per Article: 45.0] [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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Bettencourt C, Skene N, Bandres-Ciga S, Anderson E, Winchester LM, Foote IF, Schwartzentruber J, Botia JA, Nalls M, Singleton A, Schilder BM, Humphrey J, Marzi SJ, Toomey CE, Kleifat AA, Harshfield EL, Garfield V, Sandor C, Keat S, Tamburin S, Frigerio CS, Lourida I, Ranson JM, Llewellyn DJ. Artificial intelligence for dementia genetics and omics. Alzheimers Dement 2023; 19:5905-5921. [PMID: 37606627 PMCID: PMC10841325 DOI: 10.1002/alz.13427] [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: 04/05/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/23/2023]
Abstract
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.
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Affiliation(s)
- Conceicao Bettencourt
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Nathan Skene
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Emma Anderson
- Department of Mental Health of Older People, Division of Psychiatry, University College London, London, UK
| | | | - Isabelle F Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Jeremy Schwartzentruber
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
- Illumina Artificial Intelligence Laboratory, Illumina Inc, Foster City, California, USA
| | - Juan A Botia
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Mike Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Andrew Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Christina E Toomey
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, London, UK
| | - Ahmad Al Kleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Eric L Harshfield
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Victoria Garfield
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Cynthia Sandor
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Samuel Keat
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Verona, Italy
| | - Carlo Sala Frigerio
- UK Dementia Research Institute, Queen Square Institute of Neurology, University College London, London, UK
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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Fritsche LG, Nam K, Du J, Kundu R, Salvatore M, Shi X, Lee S, Burgess S, Mukherjee B. Uncovering associations between pre-existing conditions and COVID-19 Severity: A polygenic risk score approach across three large biobanks. PLoS Genet 2023; 19:e1010907. [PMID: 38113267 PMCID: PMC10763941 DOI: 10.1371/journal.pgen.1010907] [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: 08/09/2023] [Revised: 01/03/2024] [Accepted: 12/05/2023] [Indexed: 12/21/2023] Open
Abstract
OBJECTIVE To overcome the limitations associated with the collection and curation of COVID-19 outcome data in biobanks, this study proposes the use of polygenic risk scores (PRS) as reliable proxies of COVID-19 severity across three large biobanks: the Michigan Genomics Initiative (MGI), UK Biobank (UKB), and NIH All of Us. The goal is to identify associations between pre-existing conditions and COVID-19 severity. METHODS Drawing on a sample of more than 500,000 individuals from the three biobanks, we conducted a phenome-wide association study (PheWAS) to identify associations between a PRS for COVID-19 severity, derived from a genome-wide association study on COVID-19 hospitalization, and clinical pre-existing, pre-pandemic phenotypes. We performed cohort-specific PRS PheWAS and a subsequent fixed-effects meta-analysis. RESULTS The current study uncovered 23 pre-existing conditions significantly associated with the COVID-19 severity PRS in cohort-specific analyses, of which 21 were observed in the UKB cohort and two in the MGI cohort. The meta-analysis yielded 27 significant phenotypes predominantly related to obesity, metabolic disorders, and cardiovascular conditions. After adjusting for body mass index, several clinical phenotypes, such as hypercholesterolemia and gastrointestinal disorders, remained associated with an increased risk of hospitalization following COVID-19 infection. CONCLUSION By employing PRS as a proxy for COVID-19 severity, we corroborated known risk factors and identified novel associations between pre-existing clinical phenotypes and COVID-19 severity. Our study highlights the potential value of using PRS when actual outcome data may be limited or inadequate for robust analyses.
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Affiliation(s)
- Lars G. Fritsche
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Kisung Nam
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Jiacong Du
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Ritoban Kundu
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Maxwell Salvatore
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Xu Shi
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
| | - Seunggeun Lee
- Graduate School of Data Science, Seoul National University, Seoul, South Korea
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
- Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Center for Precision Health Data Science, University of Michigan School of Public Health, Ann Arbor, Michigan, United States of America
- Michigan Institute for Data Science, University of Michigan, Ann Arbor, Michigan, United States of America
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30
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Gallagher JH, Vassy JL, Clayman ML. Navigating the uncertainty of precision cancer screening: The role of shared decision-making. PEC INNOVATION 2023; 2:100127. [PMID: 37214512 PMCID: PMC10194244 DOI: 10.1016/j.pecinn.2023.100127] [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: 10/14/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 05/24/2023]
Abstract
Objective Describe how applying a shared decision making (SDM) lens to the implementation of new technologies can improve patient-centeredness. Methods This paper argues that the emergence of polygenic risk scores (PRS) for cancer screening presents an illustrative opportunity to include SDM when novel technologies enter clinical care. Results PRS are novel tools that indicate an individual's genetic risk of a given disease relative to the population. PRS are anticipated to help identify individuals most and least likely to benefit from screening. However, PRS have several types of uncertainty, including validity across populations, disparate computational methods, and inclusion of different genomic data across laboratories. Conclusion Implementing SDM alongside new technologies could prove useful for their ethical and patient-centered utilization. SDM's importance as an approach to decision-making will not diminish, as evidence, uncertainty, and patient values will remain intrinsic to the art and science of clinical care. Innovation SDM can help providers and patients navigate the considerable uncertainty inherent in implementing new technologies, enabling decision-making based on existing evidence and patient values.
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Affiliation(s)
- Joseph H. Gallagher
- Virginia Commonwealth University School of Medicine, Richmond, VA, United States of America
| | - Jason L. Vassy
- Center for Healthcare Organization and Implementation Research (CHOIR), Veterans Health Administration, Bedford MA and Boston MA, United States
- Harvard Medical School, Boston, MA United States
- Brigham and Women’s Hospital, Boston, MA, United States
- Population Precision Health, Ariadne Labs, Boston, MA, United States
| | - Marla L. Clayman
- Center for Healthcare Organization and Implementation Research (CHOIR), Veterans Health Administration, Bedford MA and Boston MA, United States
- UMass Chan School of Medicine, Department of Population and Quantitative Health Sciences, Worcester, MA, United States
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31
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Serpico D. A Wolf in Sheep's Clothing: Idealisations and the aims of polygenic scores. STUDIES IN HISTORY AND PHILOSOPHY OF SCIENCE 2023; 102:72-83. [PMID: 37907020 DOI: 10.1016/j.shpsa.2023.10.006] [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: 01/17/2023] [Revised: 07/13/2023] [Accepted: 10/07/2023] [Indexed: 11/02/2023]
Abstract
Research in pharmacogenomics and precision medicine has recently introduced the concept of Polygenic Scores (PGSs), namely, indexes that aggregate the effects that many genetic variants are predicted to have on individual disease risk. The popularity of PGSs is increasing rapidly, but surprisingly little attention has been paid to the idealisations they make about phenotypic development. Indeed, PGSs rely on quantitative genetics models and methods, which involve considerable theoretical assumptions that have been questioned on various grounds. This comes with epistemological and ethical concerns about the use of PGSs in clinical decision-making. In this paper, I investigate to what extent idealisations in genetics models can impact the data gathering and clinical interpretation of genomics findings, particularly the calculation and predictive accuracy of PGSs. Although idealisations are considered ineliminable components of scientific models, they may be legitimate or not depending on the epistemic aims of a model. I thus analyse how various idealisations have been introduced in classical models and progressively readapted throughout the history of genetic theorising. Notably, this process involved important changes in the epistemic purpose of such idealisations, which raises the question of whether they are legitimate in the context of contemporary genomics.
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Affiliation(s)
- Davide Serpico
- Department of Economics and Management, University of Trento, Via Vigilio Inama 5, 38122, Trento, Italy; Interdisciplinary Centre for Ethics & Institute of Philosophy, Jagiellonian University, Grodzka 52, 31-044 Kraków, Poland.
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32
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Ariad D, Madjunkova S, Madjunkov M, Chen S, Abramov R, Librach C, McCoy RC. Aberrant landscapes of maternal meiotic crossovers contribute to aneuploidies in human embryos. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.543910. [PMID: 37333422 PMCID: PMC10274764 DOI: 10.1101/2023.06.07.543910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Meiotic recombination is crucial for human genetic diversity and chromosome segregation accuracy. Understanding its variation across individuals and the processes by which it goes awry are long-standing goals in human genetics. Current approaches for inferring recombination landscapes either rely on population genetic patterns of linkage disequilibrium (LD)-capturing a time-averaged view-or direct detection of crossovers in gametes or multi-generation pedigrees, which limits dataset scale and availability. Here, we introduce an approach for inferring sex-specific recombination landscapes using data from preimplantation genetic testing for aneuploidy (PGT-A). This method relies on low-coverage (<0.05×) whole-genome sequencing of in vitro fertilized (IVF) embryo biopsies. To overcome the data sparsity, our method exploits its inherent relatedness structure, knowledge of haplotypes from external population reference panels, as well as the frequent occurrence of monosomies in embryos, whereby the remaining chromosome is phased by default. Extensive simulations demonstrate our method's high accuracy, even at coverages as low as 0.02×. Applying this method to PGT-A data from 18,967 embryos, we mapped 70,660 recombination events with ~150 kbp resolution, replicating established sex-specific recombination patterns. We observed a reduced total length of the female genetic map in trisomies compared to disomies, as well as chromosome-specific alterations in crossover distributions. Based on haplotype configurations in pericentromeric regions, our data indicate chromosome-specific propensities for different mechanisms of meiotic error. Our results provide a comprehensive view of the role of aberrant meiotic recombination in the origins of human aneuploidies and offer a versatile tool for mapping crossovers in low-coverage sequencing data from multiple siblings.
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Affiliation(s)
- Daniel Ariad
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Svetlana Madjunkova
- CReATe Fertility Centre, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | | | - Siwei Chen
- CReATe Fertility Centre, Toronto, Canada
| | | | - Clifford Librach
- CReATe Fertility Centre, Toronto, Canada
- Department of Obstetrics and Gynecology, University of Toronto, Toronto, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, Canada
- Department of Physiology, University of Toronto, Toronto, Canada
| | - Rajiv C. McCoy
- Department of Biology, Johns Hopkins University, Baltimore, MD, USA
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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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Fatumo S, Sathan D, Samtal C, Isewon I, Tamuhla T, Soremekun C, Jafali J, Panji S, Tiffin N, Fakim YJ. Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions. Genome Med 2023; 15:87. [PMID: 37904243 PMCID: PMC10614359 DOI: 10.1186/s13073-023-01245-9] [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: 03/04/2023] [Accepted: 10/12/2023] [Indexed: 11/01/2023] Open
Abstract
Early identification of genetic risk factors for complex diseases can enable timely interventions and prevent serious outcomes, including mortality. While the genetics underlying many Mendelian diseases have been elucidated, it is harder to predict risk for complex diseases arising from the combined effects of many genetic variants with smaller individual effects on disease aetiology. Polygenic risk scores (PRS), which combine multiple contributing variants to predict disease risk, have the potential to influence the implementation for precision medicine. However, the majority of existing PRS were developed from European data with limited transferability to African populations. Notably, African populations have diverse genetic backgrounds, and a genomic architecture with smaller haplotype blocks compared to European genomes. Subsequently, growing evidence shows that using large-scale African ancestry cohorts as discovery for PRS development may generate more generalizable findings. Here, we (1) discuss the factors contributing to the poor transferability of PRS in African populations, (2) showcase the novel Africa genomic datasets for PRS development, (3) explore the potential clinical utility of PRS in African populations, and (4) provide insight into the future of PRS in Africa.
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Affiliation(s)
- Segun Fatumo
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda.
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria.
- Department of Non-Communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK.
| | - Dassen Sathan
- H3Africa Bioinformatics Network (H3ABioNet) Node, University of Mauritius, Reduit, Mauritius
| | - Chaimae Samtal
- Laboratory of Biotechnology, Environment, Agri-Food and Health, Faculty of Sciences Dhar El Mahraz-Sidi Mohammed Ben Abdellah University, 30000, Fez, Morocco
| | - Itunuoluwa Isewon
- Department of Computer and Information Sciences, Covenant University, P. M. B. 1023, Ota, Ogun State, Nigeria
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Km 10 Idiroko Road, P.M.B. 1023, Ota, Ogun State, Nigeria
- Covenant Applied Informatics and Communication African Centre of Excellence (CApIC-ACE), Covenant University, P.M.B. 1023, Ota, Ogun State, Nigeria
| | - Tsaone Tamuhla
- Division of Computational Biology, Integrative Biomedical Sciences Department, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research Group, MRC/UVRI and LSHTM, Entebbe, Uganda
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
- Department of Immunology and Molecular Biology, College of Health Science, Makerere University, Kampala, Uganda
| | - James Jafali
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi
- Clinical Infection, Microbiology & Immunology, The University of Liverpool, Liverpool, UK
| | - Sumir Panji
- Computational Biology Group, Department of Integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Nicki Tiffin
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564319. [PMID: 37961722 PMCID: PMC10634875 DOI: 10.1101/2023.10.27.564319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
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Zhang T, Klei L, Liu P, Chouldechova A, Roeder K, G'Sell M, Devlin B. Evaluating and Improving Health Equity and Fairness of Polygenic Scores. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559051. [PMID: 37790341 PMCID: PMC10542523 DOI: 10.1101/2023.09.22.559051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.
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Tynkkynen NP, Törmäkangas T, Palviainen T, Hyvärinen M, Klevjer M, Joensuu L, Kujala U, Kaprio J, Bye A, Sillanpää E. Associations of polygenic inheritance of physical activity with aerobic fitness, cardiometabolic risk factors and diseases: the HUNT study. Eur J Epidemiol 2023; 38:995-1008. [PMID: 37603226 PMCID: PMC10501929 DOI: 10.1007/s10654-023-01029-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: 04/05/2023] [Accepted: 07/10/2023] [Indexed: 08/22/2023]
Abstract
Physical activity (PA), aerobic fitness, and cardiometabolic diseases (CMD) are highly heritable multifactorial phenotypes. Shared genetic factors may underlie the associations between higher levels of PA and better aerobic fitness and a lower risk for CMDs. We aimed to study how PA genotype associates with self-reported PA, aerobic fitness, cardiometabolic risk factors and diseases. PA genotype, which combined variation in over one million of gene variants, was composed using the SBayesR polygenic scoring methodology. First, we constructed a polygenic risk score for PA in the Trøndelag Health Study (N = 47,148) using UK Biobank single nucleotide polymorphism-specific weights (N = 400,124). The associations of the PA PRS and continuous variables were analysed using linear regression models and with CMD incidences using Cox proportional hazard models. The results showed that genotypes predisposing to higher amount of PA were associated with greater self-reported PA (Beta [B] = 0.282 MET-h/wk per SD of PRS for PA, 95% confidence interval [CI] = 0.211, 0.354) but not with aerobic fitness. These genotypes were also associated with healthier cardiometabolic profile (waist circumference [B = -0.003 cm, 95% CI = -0.004, -0.002], body mass index [B = -0.002 kg/m2, 95% CI = -0.004, -0.001], high-density lipoprotein cholesterol [B = 0.004 mmol/L, 95% CI = 0.002, 0.006]) and lower incidence of hypertensive diseases (Hazard Ratio [HR] = 0.97, 95% CI = 0.951, 0.990), stroke (HR = 0.94, 95% CI = 0.903, 0.978) and type 2 diabetes (HR = 0.94, 95 % CI = 0.902, 0.970). Observed associations were independent of self-reported PA. These results support earlier findings suggesting small pleiotropic effects between PA and CMDs and provide new evidence about associations of polygenic inheritance of PA and intermediate cardiometabolic risk factors.
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Affiliation(s)
- Niko Paavo Tynkkynen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Timo Törmäkangas
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Teemu Palviainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, Helsinki, Finland
| | - Matti Hyvärinen
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Marie Klevjer
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Laura Joensuu
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland
| | - Urho Kujala
- Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, Helsinki, Finland
| | - Anja Bye
- Cardiac Exercise Research Group (CERG), Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Elina Sillanpää
- Gerontology Research Center, Faculty of Sport and Health Sciences, University of Jyväskylä, P.O. Box 35 (VIV), Jyväskylä, FIN-40014, Finland.
- The Wellbeing Services County of Central Finland, Jyväskylä, Finland.
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Tan T, Atkinson EG. Strategies for the Genomic Analysis of Admixed Populations. Annu Rev Biomed Data Sci 2023; 6:105-127. [PMID: 37127050 PMCID: PMC10871708 DOI: 10.1146/annurev-biodatasci-020722-014310] [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] [Indexed: 05/03/2023]
Abstract
Admixed populations constitute a large portion of global human genetic diversity, yet they are often left out of genomics analyses. This exclusion is problematic, as it leads to disparities in the understanding of the genetic structure and history of diverse cohorts and the performance of genomic medicine across populations. Admixed populations have particular statistical challenges, as they inherit genomic segments from multiple source populations-the primary reason they have historically been excluded from genetic studies. In recent years, however, an increasing number of statistical methods and software tools have been developed to account for and leverage admixture in the context of genomics analyses. Here, we provide a survey of such computational strategies for the informed consideration of admixture to allow for the well-calibrated inclusion of mixed ancestry populations in large-scale genomics studies, and we detail persisting gaps in existing tools.
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Affiliation(s)
- Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA;
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Nguyen AA, McCarthy AM, Kontos D. Combining Molecular and Radiomic Features for Risk Assessment in Breast Cancer. Annu Rev Biomed Data Sci 2023; 6:299-311. [PMID: 37159874 DOI: 10.1146/annurev-biodatasci-020722-092748] [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] [Indexed: 05/11/2023]
Abstract
Breast cancer risk is highly variable within the population and current research is leading the shift toward personalized medicine. By accurately assessing an individual woman's risk, we can reduce the risk of over/undertreatment by preventing unnecessary procedures or by elevating screening procedures. Breast density measured from conventional mammography has been established as one of the most dominant risk factors for breast cancer; however, it is currently limited by its ability to characterize more complex breast parenchymal patterns that have been shown to provide additional information to strengthen cancer risk models. Molecular factors ranging from high penetrance, or high likelihood that a mutation will show signs and symptoms of the disease, to combinations of gene mutations with low penetrance have shown promise for augmenting risk assessment. Although imaging biomarkers and molecular biomarkers have both individually demonstrated improved performance in risk assessment, few studies have evaluated them together. This review aims to highlight the current state of the art in breast cancer risk assessment using imaging and genetic biomarkers.
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Affiliation(s)
- Alex A Nguyen
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Anne Marie McCarthy
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA;
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Cruz LA, Cooke Bailey JN, Crawford DC. Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. Annu Rev Biomed Data Sci 2023; 6:339-356. [PMID: 37196357 PMCID: PMC10720270 DOI: 10.1146/annurev-biodatasci-122220-113250] [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] [Indexed: 05/19/2023]
Abstract
Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.
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Affiliation(s)
- Lauren A Cruz
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jessica N Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
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Marasa M, Ahram DF, Rehman AU, Mitrotti A, Abhyankar A, Jain NG, Weng PL, Piva SE, Fernandez HE, Uy NS, Chatterjee D, Kil BH, Nestor JG, Felice V, Robinson D, Whyte D, Gharavi AG, Appel GB, Radhakrishnan J, Santoriello D, Bomback A, Lin F, D’Agati VD, Jobanputra V, Sanna-Cherchi S. Implementation and Feasibility of Clinical Genome Sequencing Embedded Into the Outpatient Nephrology Care for Patients With Proteinuric Kidney Disease. Kidney Int Rep 2023; 8:1638-1647. [PMID: 37547535 PMCID: PMC10403677 DOI: 10.1016/j.ekir.2023.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 05/01/2023] [Accepted: 05/22/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction The diagnosis and management of proteinuric kidney diseases such as focal segmental glomerulosclerosis (FSGS) are challenging. Genetics holds the promise to improve clinical decision making for these diseases; however, it is often performed too late to enable timely clinical action and it is not implemented within routine outpatient nephrology visits. Methods We sought to test the implementation and feasibility of clinical rapid genome sequencing (GS) in guiding decision making in patients with proteinuric kidney disease in real-time and embedded in the outpatient nephrology setting. Results We enrolled 10 children or young adults with biopsy-proven FSGS (9 cases) or minimal change disease (1 case). The mean age at enrollment was 16.2 years (range 2-30). The workflow did not require referral to external genetics clinics but was conducted entirely during the nephrology standard-of-care appointments. The total turn-around-time from enrollment to return-of-results and clinical decision averaged 21.8 days (12.4 for GS), which is well within a time frame that allows clinically relevant treatment decisions. A monogenic or APOL1-related form of kidney disease was diagnosed in 5 of 10 patients. The genetic findings resulted in a rectified diagnosis in 6 patients. Both positive and negative GS findings determined a change in pharmacological treatment. In 3 patients, the results were instrumental for transplant evaluation, donor selection, and the immunosuppressive treatment. All patients and families received genetic counseling. Conclusion Clinical GS is feasible and can be implemented in real-time in the outpatient care to help guiding clinical management. Additional studies are needed to confirm the cost-effectiveness and broader utility of clinical GS across the phenotypic and demographic spectrum of kidney diseases.
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Affiliation(s)
- Maddalena Marasa
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Dina F. Ahram
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | | | - Adele Mitrotti
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | | | - Namrata G. Jain
- Division of Pediatric Nephrology, Department of Pediatrics, Columbia University, New York, USA
| | - Patricia L. Weng
- Division of Pediatric Nephrology, Department of Pediatrics, UCLA Medical Center and UCLA Medical Center-Santa Monica, Los Angeles, California, USA
| | - Stacy E. Piva
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Hilda E. Fernandez
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Natalie S. Uy
- Division of Pediatric Nephrology, Department of Pediatrics, Columbia University, New York, USA
| | - Debanjana Chatterjee
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Byum H. Kil
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Jordan G. Nestor
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | | | | | - Dilys Whyte
- Pediatric Specialty Center of Good Samaritan Hospital Medical Center, Babylon, New York, USA
| | - Ali G. Gharavi
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Gerald B. Appel
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Jai Radhakrishnan
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Dominick Santoriello
- Department of Pathology and Cell Biology, Renal Pathology Division, Columbia University Medical Center, New York, USA
| | - Andrew Bomback
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
| | - Fangming Lin
- Division of Pediatric Nephrology, Department of Pediatrics, Columbia University, New York, USA
| | - Vivette D. D’Agati
- Department of Pathology and Cell Biology, Renal Pathology Division, Columbia University Medical Center, New York, USA
| | - Vaidehi Jobanputra
- The New York Genome Center, New York, USA
- Department of Pathology and Cell Biology, Columbia University, New York, USA
| | - Simone Sanna-Cherchi
- Division of Nephrology, Department of Medicine, Columbia University, New York, USA
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Raben TG, Lello L, Widen E, Hsu SDH. Biobank-scale methods and projections for sparse polygenic prediction from machine learning. Sci Rep 2023; 13:11662. [PMID: 37468507 PMCID: PMC10356957 DOI: 10.1038/s41598-023-37580-5] [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] [Received: 03/24/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
In this paper we characterize the performance of linear models trained via widely-used sparse machine learning algorithms. We build polygenic scores and examine performance as a function of training set size, genetic ancestral background, and training method. We show that predictor performance is most strongly dependent on size of training data, with smaller gains from algorithmic improvements. We find that LASSO generally performs as well as the best methods, judged by a variety of metrics. We also investigate performance characteristics of predictors trained on one genetic ancestry group when applied to another. Using LASSO, we develop a novel method for projecting AUC and correlation as a function of data size (i.e., for new biobanks) and characterize the asymptotic limit of performance. Additionally, for LASSO (compressed sensing) we show that performance metrics and predictor sparsity are in agreement with theoretical predictions from the Donoho-Tanner phase transition. Specifically, a future predictor trained in the Taiwan Precision Medicine Initiative for asthma can achieve an AUC of [Formula: see text] and for height a correlation of [Formula: see text] for a Taiwanese population. This is above the measured values of [Formula: see text] and [Formula: see text], respectively, for UK Biobank trained predictors applied to a European population.
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Affiliation(s)
- Timothy G Raben
- Department of Physics and Astronomy, Michigan State University, Michigan, USA.
| | - Louis Lello
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Erik Widen
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
| | - Stephen D H Hsu
- Department of Physics and Astronomy, Michigan State University, Michigan, USA
- Genomic Prediction, Inc., North Brunswick, NJ, USA
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Smith JL, Schaid DJ, Kullo IJ. Implementing Reporting Standards for Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease. Curr Atheroscler Rep 2023; 25:323-330. [PMID: 37223852 PMCID: PMC10495216 DOI: 10.1007/s11883-023-01104-3] [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] [Accepted: 04/13/2023] [Indexed: 05/25/2023]
Abstract
PURPOSE OF REVIEW There is considerable interest in using polygenic risk scores (PRSs) for assessing risk of atherosclerotic cardiovascular disease (ASCVD). A barrier to the clinical use of PRSs is heterogeneity in how PRS studies are reported. In this review, we summarize approaches to establish a uniform reporting framework for PRSs for coronary heart disease (CHD), the most common form of ASCVD. RECENT FINDINGS Reporting standards for PRSs need to be contextualized for disease specific applications. In addition to metrics of predictive performance, reporting standards for PRSs for CHD should include how cases/control were ascertained, degree of adjustment for conventional CHD risk factors, portability to diverse genetic ancestry groups and admixed individuals, and quality control measures for clinical deployment. Such a framework will enable PRSs to be optimized and benchmarked for clinical use.
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Affiliation(s)
- Johanna L Smith
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
- Gonda Vascular Center, Rochester, MN, USA.
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Ding Y, Hou K, Xu Z, Pimplaskar A, Petter E, Boulier K, Privé F, Vilhjálmsson BJ, Olde Loohuis LM, Pasaniuc B. Polygenic scoring accuracy varies across the genetic ancestry continuum. Nature 2023; 618:774-781. [PMID: 37198491 PMCID: PMC10284707 DOI: 10.1038/s41586-023-06079-4] [Citation(s) in RCA: 65] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/12/2023] [Indexed: 05/19/2023]
Abstract
Polygenic scores (PGSs) have limited portability across different groupings of individuals (for example, by genetic ancestries and/or social determinants of health), preventing their equitable use1-3. PGS portability has typically been assessed using a single aggregate population-level statistic (for example, R2)4, ignoring inter-individual variation within the population. Here, using a large and diverse Los Angeles biobank5 (ATLAS, n = 36,778) along with the UK Biobank6 (UKBB, n = 487,409), we show that PGS accuracy decreases individual-to-individual along the continuum of genetic ancestries7 in all considered populations, even within traditionally labelled 'homogeneous' genetic ancestries. The decreasing trend is well captured by a continuous measure of genetic distance (GD) from the PGS training data: Pearson correlation of -0.95 between GD and PGS accuracy averaged across 84 traits. When applying PGS models trained on individuals labelled as white British in the UKBB to individuals with European ancestries in ATLAS, individuals in the furthest GD decile have 14% lower accuracy relative to the closest decile; notably, the closest GD decile of individuals with Hispanic Latino American ancestries show similar PGS performance to the furthest GD decile of individuals with European ancestries. GD is significantly correlated with PGS estimates themselves for 82 of 84 traits, further emphasizing the importance of incorporating the continuum of genetic ancestries in PGS interpretation. Our results highlight the need to move away from discrete genetic ancestry clusters towards the continuum of genetic ancestries when considering PGSs.
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Affiliation(s)
- Yi Ding
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
| | - Kangcheng Hou
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ziqi Xu
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Aditya Pimplaskar
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Ella Petter
- Department of Computer Science, UCLA, Los Angeles, CA, USA
| | - Kristin Boulier
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA
| | - Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
| | - Loes M Olde Loohuis
- Center for Neurobehavioral Genetics, Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, UCLA, Los Angeles, CA, USA.
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Computational Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
- Institute for Precision Health, UCLA, Los Angeles, CA, USA.
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45
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Drouet DE, Liu S, Crawford DC. Assessment of multi-population polygenic risk scores for lipid traits in African Americans. PeerJ 2023; 11:e14910. [PMID: 37214096 PMCID: PMC10198155 DOI: 10.7717/peerj.14910] [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: 08/22/2022] [Accepted: 01/25/2023] [Indexed: 05/24/2023] Open
Abstract
Polygenic risk scores (PRS) based on genome-wide discoveries are promising predictors or classifiers of disease development, severity, and/or progression for common clinical outcomes. A major limitation of most risk scores is the paucity of genome-wide discoveries in diverse populations, prompting an emphasis to generate these needed data for trans-population and population-specific PRS construction. Given diverse genome-wide discoveries are just now being completed, there has been little opportunity for PRS to be evaluated in diverse populations independent from the discovery efforts. To fill this gap, we leverage here summary data from a recent genome-wide discovery study of lipid traits (HDL-C, LDL-C, triglycerides, and total cholesterol) conducted in diverse populations represented by African Americans, Hispanics, Asians, Native Hawaiians, Native Americans, and others by the Population Architecture using Genomics and Epidemiology (PAGE) Study. We constructed lipid trait PRS using PAGE Study published genetic variants and weights in an independent African American adult patient population linked to de-identified electronic health records and genotypes from the Illumina Metabochip (n = 3,254). Using multi-population lipid trait PRS, we assessed levels of association for their respective lipid traits, clinical outcomes (cardiovascular disease and type 2 diabetes), and common clinical labs. While none of the multi-population PRS were strongly associated with the tested trait or outcome, PRSLDL-Cwas nominally associated with cardiovascular disease. These data demonstrate the complexity in applying PRS to real-world clinical data even when data from multiple populations are available.
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Affiliation(s)
- Domenica E. Drouet
- Department of Medicine, Case Western Reserve University, Cleveland, OH, United States of America
| | - Shiying Liu
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Dana C. Crawford
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States of America
- Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States of America
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46
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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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47
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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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48
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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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49
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Truong B, Hull LE, Ruan Y, Huang QQ, Hornsby W, Martin H, van Heel DA, Wang Y, Martin AR, Lee SH, Natarajan P. Integrative polygenic risk score improves the prediction accuracy of complex traits and diseases. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.21.23286110. [PMID: 36865265 PMCID: PMC9980241 DOI: 10.1101/2023.02.21.23286110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/01/2023]
Abstract
Polygenic risk scores (PRS) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. Validation and transferability of existing PRS across independent datasets and diverse ancestries are limited, which hinders the practical utility and exacerbates health disparities. We propose PRSmix, a framework that evaluates and leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture. We applied PRSmix to 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% CI: [1.10; 1.3]; P-value = 9.17 × 10-5) and 1.19-fold (95% CI: [1.11; 1.27]; P-value = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI: [1.40; 2.04]; P-value = 7.58 × 10-6) and 1.42-fold (95% CI: [1.25; 1.59]; P-value = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously established cross-trait-combination method with scores from pre-defined correlated traits, we demonstrated that our method can improve prediction accuracy for coronary artery disease up to 3.27-fold (95% CI: [2.1; 4.44]; P-value after FDR correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Affiliation(s)
- Buu Truong
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Leland E. Hull
- Division of General Internal Medicine, 100 Cambridge Street,
Massachusetts General Hospital, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
| | - Yunfeng Ruan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Qin Qin Huang
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - Whitney Hornsby
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
| | - Hilary Martin
- Department of Human Genetics, Wellcome Sanger Institute,
Cambridge, UK
| | - David A. van Heel
- Blizard Institute, Barts and the London School of Medicine and
Dentistry, Queen Mary University of London, London, UK
| | - Ying Wang
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - Alicia R. Martin
- Stanley Center for Psychiatric Research, Broad Institute of
Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General
Hospital, Boston, MA, USA
| | - S. Hong Lee
- Australian Centre for Precision Health, University of South
Australia Cancer Research Institute, University of South Australia, Adelaide, SA, 5000,
Australia
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and the Cardiovascular
Disease Initiative, Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA
02142
- Center for Genomic Medicine and Cardiovascular Research Center,
Massachusetts General Hospital, 185 Cambridge Street, Boston, MA, 02114
- Department of Medicine, Harvard Medical School, 25 Shattuck
Street, Boston, MA 02115
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50
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Meyer MN, Appelbaum PS, Benjamin DJ, Callier SL, Comfort N, Conley D, Freese J, Garrison NA, Hammonds EM, Harden KP, Lee SSJ, Martin AR, Martschenko DO, Neale BM, Palmer RHC, Tabery J, Turkheimer E, Turley P, Parens E. Wrestling with Social and Behavioral Genomics: Risks, Potential Benefits, and Ethical Responsibility. Hastings Cent Rep 2023; 53 Suppl 1:S2-S49. [PMID: 37078667 PMCID: PMC10433733 DOI: 10.1002/hast.1477] [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] [Indexed: 04/21/2023]
Abstract
In this consensus report by a diverse group of academics who conduct and/or are concerned about social and behavioral genomics (SBG) research, the authors recount the often-ugly history of scientific attempts to understand the genetic contributions to human behaviors and social outcomes. They then describe what the current science-including genomewide association studies and polygenic indexes-can and cannot tell us, as well as its risks and potential benefits. They conclude with a discussion of responsible behavior in the context of SBG research. SBG research that compares individuals within a group according to a "sensitive" phenotype requires extra attention to responsible conduct and to responsible communication about the research and its findings. SBG research (1) on sensitive phenotypes that (2) compares two or more groups defined by (a) race, (b) ethnicity, or (c) genetic ancestry (where genetic ancestry could easily be misunderstood as race or ethnicity) requires a compelling justification to be conducted, funded, or published. All authors agree that this justification at least requires a convincing argument that a study's design could yield scientifically valid results; some authors would additionally require the study to have a socially favorable risk-benefit profile.
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