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Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 2020; 21:493-502. [PMID: 32235907 DOI: 10.1038/s41576-020-0224-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2020] [Indexed: 01/03/2023]
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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Wu Q, Xiao X, Xu Y. Performance of FRAX in Predicting Fractures in US Postmenopausal Women with Varied Race and Genetic Profiles. J Clin Med 2020; 9:E285. [PMID: 31968614 PMCID: PMC7019759 DOI: 10.3390/jcm9010285] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 01/06/2020] [Accepted: 01/14/2020] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Whether the Fracture Risk Assessment Tool (FRAX) performed differently in estimating the 10-year fracture probability in women of different genetic profiling and race remained unclear. METHODS The genomic data in the Women's Health Initiative (WHI) study was analyzed (n = 23,981). The genetic risk score (GRS) was calculated from 14 fracture-associated single nucleotide polymorphisms (SNPs) for each participant. FRAX without bone mineral density (BMD) was used to estimate fracture probability. RESULTS FRAX significantly overestimated the risk of major osteoporotic fracture (MOF) in the WHI study. The most significant overestimation was observed in women with low GRS (predicted/observed ratio (POR): 1.61, 95% CI: 1.45-1.79) specifically Asian women (POR: 3.5, 95% CI 2.48-4.81) and in African American women (POR: 2.59, 95% CI: 2.33-2.87). Compared to the low GRS group, the 10-year probability of MOF adjusted for the FRAX score was 21% and 30% higher in the median GRS group and high GRS group, respectively. Asian, African American, and Hispanic women respectively had a 78%, 76%, and 56% lower hazard than Caucasian women after the FRAX score was adjusted. The results were similar for hip fractures. CONCLUSIONS Our study suggested the FRAX performance varies significantly by both genetic profile and race in postmenopausal women.
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Affiliation(s)
- Qing Wu
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USA; (X.X.); (Y.X.)
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada Las Vegas, NV 89154, USA
| | - Xiangxue Xiao
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USA; (X.X.); (Y.X.)
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada Las Vegas, NV 89154, USA
| | - Yingke Xu
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas, NV 89154, USA; (X.X.); (Y.X.)
- Department of Environmental and Occupational Health, School of Public Health, University of Nevada Las Vegas, NV 89154, USA
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53
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Vilhjálmsson BJ, Privé F. Headaches and polygenic scores. NEUROLOGY-GENETICS 2019; 5:e368. [PMID: 31872052 PMCID: PMC6878834 DOI: 10.1212/nxg.0000000000000368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Bjarni J Vilhjálmsson
- The National Centre for Register-Based Research (B.J.V., F.P.), Aarhus University; and The Lundbeck Foundation Initiative for Integrative Psychiatric Research (B.J.V., F.P.), iPSYCH, Aarhus, Denmark
| | - Florian Privé
- The National Centre for Register-Based Research (B.J.V., F.P.), Aarhus University; and The Lundbeck Foundation Initiative for Integrative Psychiatric Research (B.J.V., F.P.), iPSYCH, Aarhus, Denmark
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Gorlov I, Xiao X, Mayes M, Gorlova O, Amos C. SNP eQTL status and eQTL density in the adjacent region of the SNP are associated with its statistical significance in GWA studies. BMC Genet 2019; 20:85. [PMID: 31718536 PMCID: PMC6852916 DOI: 10.1186/s12863-019-0786-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2019] [Accepted: 10/18/2019] [Indexed: 01/05/2023] Open
Abstract
Background Over the relatively short history of Genome Wide Association Studies (GWASs), hundreds of GWASs have been published and thousands of disease risk-associated SNPs have been identified. Summary statistics from the conducted GWASs are often available and can be used to identify SNP features associated with the level of GWAS statistical significance. Those features could be used to select SNPs from gray zones (SNPs that are nominally significant but do not reach the genome-wide level of significance) for targeted analyses. Methods We used summary statistics from recently published breast and lung cancer and scleroderma GWASs to explore the association between the level of the GWAS statistical significance and the expression quantitative trait loci (eQTL) status of the SNP. Data from the Genotype-Tissue Expression Project (GTEx) were used to identify eQTL SNPs. Results We found that SNPs reported as eQTLs were more significant in GWAS (higher -log10p) regardless of the tissue specificity of the eQTL. Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant in the GWAS compared to those reported as eQTL in only one tissue type. eQTL density in the ±5 kb adjacent region of a given SNP was also positively associated with the level of GWAS statistical significance regardless of the eQTL status of the SNP. We found that SNPs located in the regions of high eQTL density were more likely to be located in regulatory elements (transcription factor or miRNA binding sites). When SNPs were stratified by the level of statistical significance, the proportion of eQTLs was positively associated with the mean level of statistical significance in the group. The association curve reaches a plateau around -log10p ≈ 5. The observed associations suggest that quasi-significant SNPs (10− 5 < p < 5 × 10− 8) and SNPs at the genome wide level of statistical significance (p < 5 × 10− 8) may have a similar proportions of risk associated SNPs. Conclusions The results of this study indicate that the SNP’s eQTL status, as well as eQTL density in the adjacent region are positively associated with the level of statistical significance of the SNP in GWAS.
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Affiliation(s)
- Ivan Gorlov
- The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA.
| | - Xiangjun Xiao
- Department of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
| | - Maureen Mayes
- Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School, Houston, TX, USA
| | - Olga Gorlova
- The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936, One Medical Center Dr., Dartmouth-Hitchcock Medical Center, Lebanon, NH, 03756, USA
| | - Christopher Amos
- Department of Medicine, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA
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Lupo PJ, Mitchell LE, Jenkins MM. Genome-wide association studies of structural birth defects: A review and commentary. Birth Defects Res 2019; 111:1329-1342. [PMID: 31654503 DOI: 10.1002/bdr2.1606] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 09/21/2019] [Accepted: 10/02/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND While there is strong evidence that genetic risk factors play an important role in the etiologies of structural birth defects, compared to other diseases, there have been relatively few genome-wide association studies (GWAS) of these conditions. We reviewed the current landscape of GWAS conducted for birth defects, noting novel insights, and future directions. METHODS This article reviews the literature with regard to GWAS of structural birth defects. Key defects included in this review include oral clefts, congenital heart defects (CHDs), biliary atresia, pyloric stenosis, hypospadias, craniosynostosis, and clubfoot. Additionally, other issues related to GWAS are considered, including the assessment of polygenic risk scores and issues related to genetic ancestry, as well as utilizing genome-wide single nucleotide polymorphism array data to evaluate gene-environment interactions and Mendelian randomization. RESULTS For some birth defects, including oral clefts and CHDs, several novel susceptibility loci have been identified and replicated through GWAS, including 8q24 for oral clefts, DGKK for hypospadias, and 4p16 for CHDs. Relatively common birth defects for which there are currently no published GWAS include neural tube defects, anotia/microtia, anophthalmia/microphthalmia, gastroschisis, and omphalocele. CONCLUSIONS Overall, GWAS have been successful in identifying several novel susceptibility genes and genomic regions for structural birth defects. These findings have provided new insights into the etiologies of these phenotypes. However, GWAS have been underutilized for understanding the genetic etiologies of several birth defects.
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Affiliation(s)
- Philip J Lupo
- Department of Pediatrics, Section of Hematology-Oncology, Baylor College of Medicine, Houston, Texas
| | - Laura E Mitchell
- Department of Epidemiology, Human Genetics and Environmental Sciences, UTHealth School of Public Health, Houston, Texas
| | - Mary M Jenkins
- National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia
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Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The Need for Multi-Omics Biomarker Signatures in Precision Medicine. Int J Mol Sci 2019; 20:ijms20194781. [PMID: 31561483 PMCID: PMC6801754 DOI: 10.3390/ijms20194781] [Citation(s) in RCA: 238] [Impact Index Per Article: 47.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/11/2019] [Accepted: 09/25/2019] [Indexed: 12/12/2022] Open
Abstract
Recent advances in omics technologies have led to unprecedented efforts characterizing the molecular changes that underlie the development and progression of a wide array of complex human diseases, including cancer. As a result, multi-omics analyses—which take advantage of these technologies in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and other omics areas—have been proposed and heralded as the key to advancing precision medicine in the clinic. In the field of precision oncology, genomics approaches, and, more recently, other omics analyses have helped reveal several key mechanisms in cancer development, treatment resistance, and recurrence risk, and several of these findings have been implemented in clinical oncology to help guide treatment decisions. However, truly integrated multi-omics analyses have not been applied widely, preventing further advances in precision medicine. Additional efforts are needed to develop the analytical infrastructure necessary to generate, analyze, and annotate multi-omics data effectively to inform precision medicine-based decision-making.
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Affiliation(s)
- Michael Olivier
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Reto Asmis
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Gregory A Hawkins
- Center for Precision Medicine, Department of Biochemistry, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Timothy D Howard
- Center for Precision Medicine, Department of Biochemistry, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
| | - Laura A Cox
- Center for Precision Medicine, Department of Internal Medicine, Wake Forest Baptist Health Comprehensive Cancer Center, Wake Forest University Health Sciences, Winston-Salem, NC 27157, USA.
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Lambert SA, Abraham G, Inouye M. Towards clinical utility of polygenic risk scores. Hum Mol Genet 2019; 28:R133-R142. [DOI: 10.1093/hmg/ddz187] [Citation(s) in RCA: 249] [Impact Index Per Article: 49.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 07/11/2019] [Accepted: 07/24/2019] [Indexed: 02/06/2023] Open
Abstract
Abstract
Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.
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Affiliation(s)
- Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK
| | - Gad Abraham
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC 3010, Australia
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Substantive Site, Health Data Research UK, Wellcome Genome Campus, Hinxton, UK
- Department of Clinical Pathology, University of Melbourne, Parkville, VIC 3010, Australia
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