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      The impact of age on genetic risk for common diseases

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      PLoS Genetics
      Public Library of Science

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          Abstract

          Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.

          Author summary

          The genes we inherit from our parents influence our risk for almost all diseases, from cancer to severe infections. With the explosion of genomic technologies, we are now able to use an individual’s genome to make useful predictions about future disease risk. However, recent work has shown that the predictive value of genetic information varies by context, including age, sex and ethnicity. In this paper we introduce, validate and apply new statistical methods for investigating the relationship between age and the contributions of genetic risk. These methods allow us to ask questions such as whether relative risk is constant over time, precisely how relative risk changes over time and whether all genetic risk factors have similar age profiles. By applying the methods to data from the UK Biobank, a prospective study of 500,000 people, we show that there is a tendency for genetic relative risk to decline with increasing age. We consider a series of possible explanations for the observation and conclude that there must be processes acting that we are currently unaware of, such as distinct phases of life in which genetic risk manifests itself, or interactions between genes and the environment.

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          Most cited references43

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          Second-generation PLINK: rising to the challenge of larger and richer datasets

          PLINK 1 is a widely used open-source C/C++ toolset for genome-wide association studies (GWAS) and research in population genetics. However, the steady accumulation of data from imputation and whole-genome sequencing studies has exposed a strong need for even faster and more scalable implementations of key functions. In addition, GWAS and population-genetic data now frequently contain probabilistic calls, phase information, and/or multiallelic variants, none of which can be represented by PLINK 1's primary data format. To address these issues, we are developing a second-generation codebase for PLINK. The first major release from this codebase, PLINK 1.9, introduces extensive use of bit-level parallelism, O(sqrt(n))-time/constant-space Hardy-Weinberg equilibrium and Fisher's exact tests, and many other algorithmic improvements. In combination, these changes accelerate most operations by 1-4 orders of magnitude, and allow the program to handle datasets too large to fit in RAM. This will be followed by PLINK 2.0, which will introduce (a) a new data format capable of efficiently representing probabilities, phase, and multiallelic variants, and (b) extensions of many functions to account for the new types of information. The second-generation versions of PLINK will offer dramatic improvements in performance and compatibility. For the first time, users without access to high-end computing resources can perform several essential analyses of the feature-rich and very large genetic datasets coming into use.
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            The UK Biobank resource with deep phenotyping and genomic data

            The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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              Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

              A key public health need is to identify individuals at high risk for a given disease to enable enhanced screening or preventive therapies. Because most common diseases have a genetic component, one important approach is to stratify individuals based on inherited DNA variation. 1 Proposed clinical applications have largely focused on finding carriers of rare monogenic mutations at several-fold increased risk. Although most disease risk is polygenic in nature, 2–5 it has not yet been possible to use polygenic predictors to identify individuals at risk comparable to monogenic mutations. Here, we develop and validate genome-wide polygenic scores for five common diseases. The approach identifies 8.0%, 6.1%, 3.5%, 3.2% and 1.5% of the population at greater than three-fold increased risk for coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For CAD, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk. 6 We propose that it is time to contemplate the inclusion of polygenic risk prediction in clinical care and discuss relevant issues.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                26 August 2021
                August 2021
                : 17
                : 8
                : e1009723
                Affiliations
                [1 ] Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom
                [2 ] Department of Statistics, University of Oxford, Oxford, United Kingdom
                [3 ] Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
                [4 ] The Alan Turing Institute, London, United Kingdom
                National Human Genome Research Institute, UNITED STATES
                Author notes

                G.M. is a director of and shareholder in Genomics PLC, and is a partner in Peptide Groove LLP. The other authors declare no competing financial interests.

                Author information
                https://orcid.org/0000-0001-6773-9182
                https://orcid.org/0000-0002-5012-4162
                Article
                PGENETICS-D-21-00007
                10.1371/journal.pgen.1009723
                8389405
                34437535
                09b79a0a-1958-4a2e-b421-d9b8e28b2017
                © 2021 Jiang et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 5 January 2021
                : 16 July 2021
                Page count
                Figures: 6, Tables: 1, Pages: 24
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100010269, Wellcome Trust;
                Award ID: BST00080-H503.01
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 100956/Z/13/Z
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100007421, Li Ka Shing Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100012338, Alan Turing Institute;
                Award ID: EP/R018561/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/R018561/1
                Award Recipient :
                Funded by Wellcome (BST00080-H503.01 to XJ, 100956/Z/13/Z to GM, https://wellcome.org); the Li Ka Shing Foundation (to GM, https://www.lksf.org); The Alan Turing Institute ( https://www.turing.ac.uk), Health Data Research UK ( https://www.hdruk.ac.uk), the Medical Research Council UK ( https://mrc.ukri.org), the Engineering and Physical Sciences Research Council (EPSRC https://epsrc.ukri.org) through the Bayes4Health programme Grant EP/R018561/1, and AI for Science and Government UK Research and Innovation (UKRI, https://www.turing.ac.uk/research/asg) (to CH).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Genetics of Disease
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Medicine and Health Sciences
                Epidemiology
                Medical Risk Factors
                Cancer Risk Factors
                Medicine and Health Sciences
                Oncology
                Cancer Risk Factors
                Medicine and Health Sciences
                Medical Conditions
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Cardiology
                Cardiovascular Medicine
                Cardiovascular Diseases
                Cardiovascular Disease Risk
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Skin Neoplasms
                Malignant Skin Neoplasms
                Medicine and Health Sciences
                Dermatology
                Skin Neoplasms
                Malignant Skin Neoplasms
                Biology and Life Sciences
                Genetics
                Genetics of Disease
                Genetic Predisposition
                Physical Sciences
                Mathematics
                Discrete Mathematics
                Combinatorics
                Permutation
                Physical Sciences
                Mathematics
                Algebra
                Polynomials
                Custom metadata
                This research has been conducted using the UK Biobank Resource; application number 12788. This work uses data provided by patients and collected by the NHS as part of their care and Support. The code generated during this study is available at https://github.com/Xilin-Jiang/longitudinal_genetic_analysis.

                Genetics
                Genetics

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