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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 2024:101128. [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] [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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2
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Fries N, Haworth S, Shaffer J, Esberg A, Divaris K, Marazita M, Johansson I. A Polygenic Score Predicts Caries Experience in Elderly Swedish Adults. J Dent Res 2024; 103:502-508. [PMID: 38584306 PMCID: PMC11047011 DOI: 10.1177/00220345241232330] [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: 04/09/2024] Open
Abstract
Caries is a partially heritable disease, raising the possibility that a polygenic score (PS, a summary of an individual's genetic propensity for disease) might be a useful tool for risk assessment. To date, PS for some diseases have shown clinical utility, although no PS for caries has been evaluated. The objective of the study was to test whether a PS for caries is associated with disease experience or increment in a cohort of Swedish adults. A genome-wide PS for caries was trained using the results of a published genome-wide association meta-analysis and constructed in an independent cohort of 15,460 Swedish adults. Electronic dental records from the Swedish Quality Registry for Caries and Periodontitis (SKaPa) were used to compute the decayed, missing, and filled tooth surfaces (DMFS) index and the number of remaining teeth. The performance of the PS was evaluated by testing the association between the PS and DMFS at a single dental examination, as well as between the PS and the rate of change in DMFS. Participants in the highest and lowest deciles of PS had a mean DMFS of 63.5 and 46.3, respectively. A regression analysis confirmed this association where a 1 standard deviation increase in PS was associated with approximately 4-unit higher DMFS (P < 2 × 10-16). Participants with the highest decile of PS also had greater change in DMFS during follow-up. Results were robust to sensitivity analysis, which adjusted for age, age squared, sex, and the first 20 genetic principal components. Mediation analysis suggested that tooth loss was a strong mediating factor in the association between PS and DMFS but also supported a direct genetic effect on caries. In this cohort, there are clinically meaningful differences in DMFS between participants with high and low PS for caries. The results highlight the potential role of genomic data in improving caries risk assessment.
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Affiliation(s)
| | | | | | | | - K. Divaris
- University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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3
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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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4
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Fahed AC, Natarajan P. Clinical applications of polygenic risk score for coronary artery disease through the life course. Atherosclerosis 2023; 386:117356. [PMID: 37931336 PMCID: PMC10842813 DOI: 10.1016/j.atherosclerosis.2023.117356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 10/02/2023] [Accepted: 10/17/2023] [Indexed: 11/08/2023]
Abstract
Coronary artery disease (CAD) remains a leading cause of morbidity and mortality worldwide, highlighting the limitations of current primary and secondary prevention frameworks. In this review, we detail how the polygenic risk score for CAD can improve our current preventive and treatment frameworks across three clinical applications that span the life course: (i) identification and treatment of people at increased risk early in the life course prior to the onset of clinical risk factors, (ii) improving the precision around risk estimation in middle age, and (ii) guiding treatment decisions and enabling more efficient clinical trials even after the onset of CAD. We end by summarizing the efforts needed as we head towards more widespread use of polygenic risk score for CAD in clinical practice.
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Affiliation(s)
- Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Pradeep Natarajan
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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5
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Guevara E, Gopalan S, Massey DJ, Adegboyega M, Zhou W, Solis A, Anaya AD, Churchill SE, Feldblum J, Lawler RR. Getting it right: Teaching undergraduate biology to undermine racial essentialism. Biol Methods Protoc 2023; 8:bpad032. [PMID: 38023347 PMCID: PMC10674104 DOI: 10.1093/biomethods/bpad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Revised: 11/03/2023] [Accepted: 11/10/2023] [Indexed: 12/01/2023] Open
Abstract
How we teach human genetics matters for social equity. The biology curriculum appears to be a crucial locus of intervention for either reinforcing or undermining students' racial essentialist views. The Mendelian genetic models dominating textbooks, particularly in combination with racially inflected language sometimes used when teaching about monogenic disorders, can increase middle and high school students' racial essentialism and opposition to policies to increase equity. These findings are of particular concern given the increasing spread of racist misinformation online and the misappropriation of human genomics research by white supremacists, who take advantage of low levels of genetics literacy in the general public. Encouragingly, however, teaching updated information about the geographical distribution of human genetic variation and the complex, multifactorial basis of most human traits, reduces students' endorsement of racial essentialism. The genetics curriculum is therefore a key tool in combating misinformation and scientific racism. Here, we describe a framework and example teaching materials for teaching students key concepts in genetics, human evolutionary history, and human phenotypic variation at the undergraduate level. This framework can be flexibly applied in biology and anthropology classes and adjusted based on time availability. Our goal is to provide undergraduate-level instructors with varying levels of expertise with a set of evidence-informed tools for teaching human genetics to combat scientific racism, including an evolving set of instructional resources, as well as learning goals and pedagogical approaches. Resources can be found at https://noto.li/YIlhZ5. Additionally, we hope to generate conversation about integrating modern genetics into the undergraduate curriculum, in light of recent findings about the risks and opportunities associated with teaching genetics.
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Affiliation(s)
- Elaine Guevara
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Shyamalika Gopalan
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Dashiell J Massey
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Mayowa Adegboyega
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Wen Zhou
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
- Department of Evolutionary Anthropology, Duke Kunshan University, Kunshan, Jiangsu 215316, China
| | - Alma Solis
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Alisha D Anaya
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Steven E Churchill
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Joseph Feldblum
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27713, United States
| | - Richard R Lawler
- Department of Sociology and Anthropology, James Madison University, Harrisonburg, Virginia 22807, United States
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6
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Patel AP, Fahed AC. Pragmatic Approach to Applying Polygenic Risk Scores to Diverse Populations. Curr Protoc 2023; 3:e911. [PMID: 37921506 DOI: 10.1002/cpz1.911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Polygenic risk scores (PRS) estimate genetic susceptibility of an individual to disease and have the potential of providing utility in multiple clinical contexts. However, their performance, computation, and reporting in diverse populations remain challenging. Here, we present a pragmatic approach to optimize a PRS for a population of interest that leverages publicly available data and methods and consists of seven steps that are easily implemented without the requirement of expertise in complex genetics: step 1, selecting source genome-wide association studies (GWAS) and imputation; step 2, selecting methods to compute polygenic score; step 3, adjusting scores using principal components of genetic ancestry; step 4, selecting the best performing score; step 5, defining percentiles of a population distribution; step 6, validating performance of the optimized polygenic score; and step 7, implementing the optimized polygenic score in clinical practice. © 2023 Wiley Periodicals LLC.
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Affiliation(s)
- Aniruddh P Patel
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
| | - Akl C Fahed
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
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7
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Shim I, Kuwahara H, Chen N, Hashem MO, AlAbdi L, Abouelhoda M, Won HH, Natarajan P, Ellinor PT, Khera AV, Gao X, Alkuraya FS, Fahed AC. Clinical utility of polygenic scores for cardiometabolic disease in Arabs. Nat Commun 2023; 14:6535. [PMID: 37852978 PMCID: PMC10584889 DOI: 10.1038/s41467-023-41985-1] [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: 01/13/2023] [Accepted: 09/25/2023] [Indexed: 10/20/2023] Open
Abstract
Arabs account for 5% of the world population and have a high burden of cardiometabolic disease, yet clinical utility of polygenic risk prediction in Arabs remains understudied. Among 5399 Arab patients, we optimize polygenic scores for 10 cardiometabolic traits, achieving a performance that is better than published scores and on par with performance in European-ancestry individuals. Odds ratio per standard deviation (OR per SD) for a type 2 diabetes score was 1.83 (95% CI 1.74-1.92), and each SD of body mass index (BMI) score was associated with 1.18 kg/m2 difference in BMI. Polygenic scores associated with disease independent of conventional risk factors, and also associated with disease severity-OR per SD for coronary artery disease (CAD) was 1.78 (95% CI 1.66-1.90) for three-vessel CAD and 1.41 (95% CI 1.29-1.53) for one-vessel CAD. We propose a pragmatic framework leveraging public data as one way to advance equitable clinical implementation of polygenic scores in non-European populations.
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Affiliation(s)
- Injeong Shim
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Hiroyuki Kuwahara
- Computational Biosciences Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - NingNing Chen
- Computational Biosciences Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Mais O Hashem
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Lama AlAbdi
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
- Department of Zoology, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Abouelhoda
- Department of Computation Sciences, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia
| | - Hong-Hee Won
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
| | - Pradeep Natarajan
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Xin Gao
- Computational Biosciences Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
| | - Fowzan S Alkuraya
- Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
| | - Akl C Fahed
- Cardiovascular Research Center, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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8
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Osterman MD, Song YE, Lynn A, Miskimen K, Adams LD, Laux RA, Caywood LJ, Prough MB, Clouse JE, Herington SD, Slifer SH, Fuzzell SL, Hochstetler SD, Main LR, Dorfsman DA, Zaman AF, Ogrocki P, Lerner AJ, Vance JM, Cuccaro ML, Scott WK, Pericak-Vance MA, Haines JL. Founder population-specific weights yield improvements in performance of polygenic risk scores for Alzheimer disease in the Midwestern Amish. HGG ADVANCES 2023; 4:100241. [PMID: 37742071 PMCID: PMC10565871 DOI: 10.1016/j.xhgg.2023.100241] [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: 04/21/2023] [Revised: 09/16/2023] [Accepted: 09/16/2023] [Indexed: 09/25/2023] Open
Abstract
Alzheimer disease (AD) is the most common type of dementia and is estimated to affect 6 million Americans. Risk for AD is multifactorial, including both genetic and environmental risk factors. AD genomic research has generally focused on identification of risk variants. Using this information, polygenic risk scores (PRSs) can be calculated to quantify an individual's relative disease risk due to genetic factors. The Amish are a founder population descended from German and Swiss Anabaptist immigrants. They experienced a genetic bottleneck after arrival in the United States, making their genetic architecture different from the broader European ancestry population. Prior work has demonstrated the lack of transferability of PRSs across populations. Here, we compared the performance of PRSs derived from genome-wide association studies (GWASs) of Amish individuals to those derived from a large European ancestry GWAS. Participants were screened for cognitive impairment with further evaluation for AD. Genotype data were imputed after collection via Illumina genotyping arrays. The Amish individuals were split into two groups based on the primary site of recruitment. For each group, GWAS was conducted with account for relatedness and adjustment for covariates. PRSs were then calculated using weights from the other Amish group. PRS models were evaluated with and without covariates. The Amish-derived PRSs distinguished between dementia status better than the European-derived PRS in our Amish populations and demonstrated performance improvements despite a smaller training sample size. This work highlighted considerations for AD PRS usage in populations that cannot be adequately described by basic race/ethnicity or ancestry classifications.
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Affiliation(s)
- Michael D Osterman
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
| | - Yeunjoo E Song
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Audrey Lynn
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Kristy Miskimen
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Larry D Adams
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Renee A Laux
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Laura J Caywood
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael B Prough
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jason E Clouse
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sharlene D Herington
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Susan H Slifer
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sarada L Fuzzell
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sherri D Hochstetler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Leighanne R Main
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Daniel A Dorfsman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Andrew F Zaman
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paula Ogrocki
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Alan J Lerner
- Department of Neurology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA; Department of Neurology, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Jeffery M Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Michael L Cuccaro
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - William K Scott
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret A Pericak-Vance
- John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA; The Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Jonathan L Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, USA; Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
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Blackwell TS, Gudmundsson G. PRS-ing Forward to Identify Genetic Risk in Idiopathic Pulmonary Fibrosis. Am J Respir Crit Care Med 2023; 208:750-752. [PMID: 37607347 PMCID: PMC10563187 DOI: 10.1164/rccm.202308-1373ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 08/22/2023] [Indexed: 08/24/2023] Open
Affiliation(s)
- Timothy S Blackwell
- Department of Medicine Vanderbilt University Medical Center Nashville, Tennessee
- Department of Cell and Developmental Biology Vanderbilt University Nashville, Tennessee
- Department of Veterans Affairs Medical Center Nashville, Tennessee
| | - Gunnar Gudmundsson
- Faculty of Medicine Landspitali University Hospital and University of Iceland Reykjavik, Iceland
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10
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Chen C, Han L. Multi-omic genetic scores advance disease research. Trends Genet 2023; 39:600-601. [PMID: 37295977 PMCID: PMC10526975 DOI: 10.1016/j.tig.2023.05.002] [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: 05/17/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023]
Abstract
Multi-omic analysis is an effective approach for dissecting the mechanisms of diseases; however, collecting multi-omic data in large populations is time-consuming and costly. Recently, Xu et al. developed genetic scores for multi-omic traits and demonstrated their utilization to gain novel insights, advancing the application of multi-omic data in disease research.
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Affiliation(s)
- Chengxuan Chen
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA; Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA.
| | - Leng Han
- Center for Epigenetics and Disease Prevention, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA; Department of Biostatistics and Health Data Science, School of Medicine, Indiana University, Indianapolis, IN, USA; Department of Translational Medical Sciences, College of Medicine, Texas A&M University, Houston, TX, USA; Brown Center for Immunotherapy, School of Medicine, Indiana University, Indianapolis, IN, USA.
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11
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De Oliveira TC, Secolin R, Lopes-Cendes I. A review of ancestrality and admixture in Latin America and the caribbean focusing on native American and African descendant populations. Front Genet 2023; 14:1091269. [PMID: 36741309 PMCID: PMC9893294 DOI: 10.3389/fgene.2023.1091269] [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: 11/06/2022] [Accepted: 01/09/2023] [Indexed: 01/21/2023] Open
Abstract
Genomics can reveal essential features about the demographic evolution of a population that may not be apparent from historical elements. In recent years, there has been a significant increase in the number of studies applying genomic epidemiological approaches to understand the genetic structure and diversity of human populations in the context of demographic history and for implementing precision medicine. These efforts have traditionally been applied predominantly to populations of European origin. More recently, initiatives in the United States and Africa are including more diverse populations, establishing new horizons for research in human populations with African and/or Native ancestries. Still, even in the most recent projects, the under-representation of genomic data from Latin America and the Caribbean (LAC) is remarkable. In addition, because the region presents the most recent global miscegenation, genomics data from LAC may add relevant information to understand population admixture better. Admixture in LAC started during the colonial period, in the 15th century, with intense miscegenation between European settlers, mainly from Portugal and Spain, with local indigenous and sub-Saharan Africans brought through the slave trade. Since, there are descendants of formerly enslaved and Native American populations in the LAC territory; they are considered vulnerable populations because of their history and current living conditions. In this context, studying LAC Native American and African descendant populations is important for several reasons. First, studying human populations from different origins makes it possible to understand the diversity of the human genome better. Second, it also has an immediate application to these populations, such as empowering communities with the knowledge of their ancestral origins. Furthermore, because knowledge of the population genomic structure is an essential requirement for implementing genomic medicine and precision health practices, population genomics studies may ensure that these communities have access to genomic information for risk assessment, prevention, and the delivery of optimized treatment; thus, helping to reduce inequalities in the Western Hemisphere. Hoping to set the stage for future studies, we review different aspects related to genetic and genomic research in vulnerable populations from LAC countries.
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Affiliation(s)
- Thais C. De Oliveira
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil,The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Rodrigo Secolin
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil,The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Iscia Lopes-Cendes
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil,The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil,*Correspondence: Iscia Lopes-Cendes,
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