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Dikilitas O, Schaid DJ, Kosel ML, Carroll RJ, Chute CG, Denny JA, Fedotov A, Feng Q, Hakonarson H, Jarvik GP, Lee MTM, Pacheco JA, Rowley R, Sleiman PM, Stein CM, Sturm AC, Wei WQ, Wiesner GL, Williams MS, Zhang Y, Manolio TA, Kullo IJ. Predictive Utility of Polygenic Risk Scores for Coronary Heart Disease in Three Major Racial and Ethnic Groups. Am J Hum Genet 2020; 106:707-716. [PMID: 32386537 DOI: 10.1016/j.ajhg.2020.04.002] [Citation(s) in RCA: 82] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 03/31/2020] [Indexed: 12/28/2022] Open
Abstract
Because polygenic risk scores (PRSs) for coronary heart disease (CHD) are derived from mainly European ancestry (EA) cohorts, their validity in African ancestry (AA) and Hispanic ethnicity (HE) individuals is unclear. We investigated associations of "restricted" and genome-wide PRSs with CHD in three major racial and ethnic groups in the U.S. The eMERGE cohort (mean age 48 ± 14 years, 58% female) included 45,645 EA, 7,597 AA, and 2,493 HE individuals. We assessed two restricted PRSs (PRSTikkanen and PRSTada; 28 and 50 variants, respectively) and two genome-wide PRSs (PRSmetaGRS and PRSLDPred; 1.7 M and 6.6 M variants, respectively) derived from EA cohorts. Over a median follow-up of 11.1 years, 2,652 incident CHD events occurred. Hazard and odds ratios for the association of PRSs with CHD were similar in EA and HE cohorts but lower in AA cohorts. Genome-wide PRSs were more strongly associated with CHD than restricted PRSs were. PRSmetaGRS, the best performing PRS, was associated with CHD in all three cohorts; hazard ratios (95% CI) per 1 SD increase were 1.53 (1.46-1.60), 1.53 (1.23-1.90), and 1.27 (1.13-1.43) for incident CHD in EA, HE, and AA individuals, respectively. The hazard ratios were comparable in the EA and HE cohorts (pinteraction = 0.77) but were significantly attenuated in AA individuals (pinteraction= 2.9 × 10-3). These results highlight the potential clinical utility of PRSs for CHD as well as the need to assemble diverse cohorts to generate ancestry- and ethnicity PRSs.
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Affiliation(s)
- Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Daniel J Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Kosel
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Christopher G Chute
- Schools of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Joshua A Denny
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University Medical Center, New York, NY 10032, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Gail P Jarvik
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | | | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Robb Rowley
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Patrick M Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - C Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | | | - Wei-Qi Wei
- Department of Biomedical Informatics, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Georgia L Wiesner
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | | | | | - Teri A Manolio
- National Human Genome Research Institute, Bethesda, MD 20892, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA.
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152
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Andrews SJ, Fulton-Howard B, Goate A. Interpretation of risk loci from genome-wide association studies of Alzheimer's disease. Lancet Neurol 2020; 19:326-335. [PMID: 31986256 PMCID: PMC8176461 DOI: 10.1016/s1474-4422(19)30435-1] [Citation(s) in RCA: 170] [Impact Index Per Article: 42.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 10/18/2019] [Accepted: 10/23/2019] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alzheimer's disease is a debilitating and highly heritable neurological condition. As such, genetic studies have sought to understand the genetic architecture of Alzheimer's disease since the 1990s, with successively larger genome-wide association studies (GWAS) and meta-analyses. These studies started with a small sample size of 1086 individuals in 2007, which was able to identify only the APOE locus. In 2013, the International Genomics of Alzheimer's Project (IGAP) did a meta-analysis of all existing GWAS using data from 74 046 individuals, which stood as the largest Alzheimer's disease GWAS until 2018. This meta-analysis discovered 19 susceptibility loci for Alzheimer's disease in populations of European ancestry. RECENT DEVELOPMENTS Three new Alzheimer's disease GWAS published in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have substantially increased the number of known susceptibility loci in Alzheimer's disease to 40. The first, an updated GWAS from IGAP, included 94 437 individuals and discovered 24 susceptibility loci. Although IGAP sought to increase sample size by recruiting additional clinical cases and controls, the two other studies used parental family history of Alzheimer's disease to define proxy cases and controls in the UK Biobank for a genome-wide association by proxy, which was meta-analysed with data from GWAS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals. These two studies identified 27 and 29 susceptibility loci, respectively. However, the three studies were not independent because of the large overlap in their participants, and interpretation can be challenging because different variants and genes were highlighted by each study, even in the same locus. Furthermore, neither the variant with the strongest Alzheimer's disease association nor the nearest gene are necessarily causal. This situation presents difficulties for experimental studies, drug development, and other future research. WHERE NEXT?: The ultimate goal of understanding the genetic architecture of Alzheimer's disease is to characterise novel biological pathways that underly Alzheimer's disease pathogenesis and to identify novel drug targets. GWAS have successfully contributed to the characterisation of the genetic architecture of Alzheimer's disease, with the identification of 40 susceptibility loci; however, this does not equate to the discovery of 40 Alzheimer's disease genes. To identify Alzheimer's disease genes, these loci need to be mapped to variants and genes through functional genomics studies that combine annotation of variants, gene expression, and gene-based or pathway-based analyses. Such studies are ongoing and have validated several genes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to map all GWAS loci.
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Affiliation(s)
- Shea J Andrews
- Ronald M Loeb Center for Alzheimer's disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Brian Fulton-Howard
- Ronald M Loeb Center for Alzheimer's disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alison Goate
- Ronald M Loeb Center for Alzheimer's disease, Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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153
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Zhong H, Magee MJ, Huang Y, Hui Q, Gwinn M, Gandhi NR, Sun YV. Evaluation of the Host Genetic Effects of Tuberculosis-Associated Variants Among Patients With Type 1 and Type 2 Diabetes Mellitus. Open Forum Infect Dis 2020; 7:ofaa106. [PMID: 32328508 PMCID: PMC7166116 DOI: 10.1093/ofid/ofaa106] [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: 12/16/2019] [Accepted: 03/24/2020] [Indexed: 11/17/2022] Open
Abstract
Background Understanding the link between tuberculosis (TB) and diabetes is increasingly important as public health responds to the growing global burden of noncommunicable diseases. Genetic association studies have identified numerous host genetic variants linked to TB; however, potential host genetic mechanisms linking TB and diabetes remain unexplored. Methods We used genetic and phenotypic data from the UK Biobank to evaluate the association of 6 previously reported TB-related host genetic variants (genome-wide significant associations from published studies) with diabetes. The study included 409 692 adults of European ancestry including 2177 with type 1 diabetes mellitus (T1DM) and 13 976 with type 2 diabetes mellitus (T2DM), defined by ICD-10 diagnosis codes. Results Of the 6 TB-associated single nucleotide polymorphisms (SNPs), 2 were associated with T1DM and 3 with T2DM, after adjusting for age, sex, body mass index, smoking, alcohol use, and population structure. After correction for multiple testing, SNPs rs2894257 and rs3135359 (HLA-DRA-DQA1) were associated with T1DM (rs2894257: odds ratio [OR], 1.32; 95% confidence interval [CI], 1.21–1.45; rs3135359: OR, 1.72; 95% CI, 1.57–1.88) and T2DM (rs2894257: OR, 1.11; 95% CI, 1.08–1.15; rs3135359: OR, 1.06; 95% CI, 1.025–1.096). The associations with T2DM weakened for rs2894257 and rs3135359 after further exclusion of probable T1DM cases defined by International Statistical Classification of Diseases and Related Health Problems (ICD-10) codes. SNP rs4733781 on chromosome 8 (ASAP1 gene) was associated with T2DM after exclusion of T1DM cases. Conclusions Our findings suggest that common host genetic effects may play a role in the molecular mechanism linking TB and diabetes. Future large genetic studies of TB and diabetes should focus on developing countries with high burdens of infectious and chronic diseases.
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Affiliation(s)
- Huimin Zhong
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Matthew J Magee
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Yunfeng Huang
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Qin Hui
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Marta Gwinn
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA
| | - Neel R Gandhi
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.,Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.,Division of Infectious Diseases, School of Medicine, Emory University, Atlanta, Georgia, USA
| | - Yan V Sun
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA.,Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, Georgia, USA
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154
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Bentley AR, Callier SL, Rotimi CN. Evaluating the promise of inclusion of African ancestry populations in genomics. NPJ Genom Med 2020; 5:5. [PMID: 32140257 PMCID: PMC7042246 DOI: 10.1038/s41525-019-0111-x] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/16/2019] [Indexed: 12/24/2022] Open
Abstract
The lack of representation of diverse ancestral backgrounds in genomic research is well-known, and the resultant scientific and ethical limitations are becoming increasingly appreciated. The paucity of data on individuals with African ancestry is especially noteworthy as Africa is the birthplace of modern humans and harbors the greatest genetic diversity. It is expected that greater representation of those with African ancestry in genomic research will bring novel insights into human biology, and lead to improvements in clinical care and improved understanding of health disparities. Now that major efforts have been undertaken to address this failing, is there evidence of these anticipated advances? Here, we evaluate the promise of including diverse individuals in genomic research in the context of recent literature on individuals of African ancestry. In addition, we discuss progress and achievements on related technological challenges and diversity among scientists conducting genomic research.
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Affiliation(s)
- Amy R Bentley
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
| | - Shawneequa L Callier
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA.,2Department of Clinical Research and Leadership, The George Washington University School of Medicine and Health Sciences, Washington, DC USA
| | - Charles N Rotimi
- 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA
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155
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Neumeyer S, Hemani G, Zeggini E. Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci. Trends Mol Med 2020; 26:232-241. [PMID: 31718940 DOI: 10.1016/j.molmed.2019.10.004] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2019] [Revised: 10/10/2019] [Accepted: 10/10/2019] [Indexed: 02/07/2023]
Abstract
Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.
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Affiliation(s)
- Sonja Neumeyer
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Gibran Hemani
- Medical Research Council (MRC) Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
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156
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157
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Non-genetic factors and polymorphisms in genes CYP2C9 and VKORC1: predictive algorithms for TTR in Brazilian patients on warfarin. Eur J Clin Pharmacol 2019; 76:199-209. [DOI: 10.1007/s00228-019-02772-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Accepted: 09/24/2019] [Indexed: 01/06/2023]
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158
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Gokcumen O. Archaic hominin introgression into modern human genomes. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2019; 171 Suppl 70:60-73. [PMID: 31702050 DOI: 10.1002/ajpa.23951] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 10/04/2019] [Accepted: 10/08/2019] [Indexed: 01/01/2023]
Abstract
Ancient genomes from multiple Neanderthal and the Denisovan individuals, along with DNA sequence data from diverse contemporary human populations strongly support the prevalence of gene flow among different hominins. Recent studies now provide evidence for multiple gene flow events that leave genetic signatures in extant and ancient human populations. These events include older gene flow from an unknown hominin in Africa predating out-of-Africa migrations, and in the last 50,000-100,000 years, multiple gene flow events from Neanderthals into ancestral Eurasian human populations, and at least three distinct introgression events from a lineage close to Denisovans into ancestors of extant Southeast Asian and Oceanic populations. Some of these introgression events may have happened as late as 20,000 years before present and reshaped the way in which we think about human evolution. In this review, I aim to answer anthropologically relevant questions with regard to recent research on ancient hominin introgression in the human lineage. How have genomic data from archaic hominins changed our view of human evolution? Is there any doubt about whether introgression from ancient hominins to the ancestors of present-day humans occurred? What is the current view of human evolutionary history from the genomics perspective? What is the impact of introgression on human phenotypes?
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Affiliation(s)
- Omer Gokcumen
- Department of Biological Sciences, North Campus, University at Buffalo, Buffalo, New York
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159
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Insights from Ugandan genomes. Nat Rev Genet 2019; 21:4. [PMID: 31695142 DOI: 10.1038/s41576-019-0194-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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160
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Dai M, Liu J, Yang C. LEP: A Statistical Method Integrating Individual-Level and Summary-Level Data of the Same Trait From Different Populations. BIOMEDICAL INFORMATICS INSIGHTS 2019; 11:1178222619881624. [PMID: 31666794 PMCID: PMC6798161 DOI: 10.1177/1178222619881624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 09/19/2019] [Indexed: 11/24/2022]
Abstract
Statistical approaches for integrating multiple data sets in genome-wide
association studies (GWASs) are increasingly important. Proper
utilization of more relevant information is expected to improve
statistical efficiency in the analysis. Among these approaches, LEP
was proposed for joint analysis of individual-level data and
summary-level data in the same population by leveraging pleiotropy.
The key idea of LEP is to explore correlation of the association
status among different data sets while accounting for the
heterogeneity. In this commentary, we show that LEP is applicable to
integrate individual-level data and summary-level data of the same
trait from different populations, providing new insights into the
genetic architecture of different populations.
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Affiliation(s)
- Mingwei Dai
- Center of Statistical Research and School of Statistics, Southwestern University of Finance and Economics, Chengdu, China
| | - Jin Liu
- Centre for Quantitative Medicine, Duke-NUS Medical School, Singapore
| | - Can Yang
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, China
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161
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Houldcroft CJ. Human Herpesvirus Sequencing in the Genomic Era: The Growing Ranks of the Herpetic Legion. Pathogens 2019; 8:E186. [PMID: 31614759 PMCID: PMC6963362 DOI: 10.3390/pathogens8040186] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 10/10/2019] [Accepted: 10/11/2019] [Indexed: 12/19/2022] Open
Abstract
The nine human herpesviruses are some of the most ubiquitous pathogens worldwide, causing life-long latent infection in a variety of different tissues. Human herpesviruses range from mild childhood infections to known tumour viruses and 'trolls of transplantation'. Epstein-Barr virus was the first human herpesvirus to have its whole genome sequenced; GenBank now includes thousands of herpesvirus genomes. This review will cover some of the recent advances in our understanding of herpesvirus diversity and disease that have come about as a result of new sequencing technologies, such as target enrichment and long-read sequencing. It will also look at the problem of resolving mixed-genotype infections, whether with short or long-read sequencing methods; and conclude with some thoughts on the future of the field as herpesvirus population genomics becomes a reality.
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Affiliation(s)
- Charlotte J Houldcroft
- Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambs CB2 0QQ UK.
- Parasites and Microbes, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambs CB10 1SA, UK.
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162
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An Evolutionary Perspective on the Impact of Genomic Copy Number Variation on Human Health. J Mol Evol 2019; 88:104-119. [PMID: 31522275 DOI: 10.1007/s00239-019-09911-6] [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: 07/13/2019] [Accepted: 08/27/2019] [Indexed: 02/06/2023]
Abstract
Copy number variants (CNVs), deletions and duplications of segments of DNA, account for at least five times more variable base pairs in humans than single-nucleotide variants. Several common CNVs were shown to change coding and regulatory sequences and thus dramatically affect adaptive phenotypes involving immunity, perception, metabolism, skin structure, among others. Some of these CNVs were also associated with susceptibility to cancer, infection, and metabolic disorders. These observations raise the possibility that CNVs are a primary contributor to human phenotypic variation and consequently evolve under selective pressures. Indeed, locus-specific haplotype-level analyses revealed signatures of natural selection on several CNVs. However, more traditional tests of selection which are often applied to single-nucleotide variation often have diminished statistical power when applied to CNVs because they often do not show strong linkage disequilibrium with nearby variants. Recombination-based formation mechanisms of CNVs lead to frequent recurrence and gene conversion events, breaking the linkage disequilibrium involving CNVs. Similar methodological challenges also prevent routine genome-wide association studies to adequately investigate the impact of CNVs on heritable human disease. Thus, we argue that the full relevance of CNVs to human health and evolution is yet to be elucidated. We further argue that a holistic investigation of formation mechanisms within an evolutionary framework would provide a powerful framework to understand the functional and biomedical impact of CNVs. In this paper, we review several cases where studies reveal diverse evolutionary histories and unexpected functional consequences of CNVs. We hope that this review will encourage further work on CNVs by both evolutionary and medical geneticists.
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163
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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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