Xilin Jiang, Chris Holmes, Gil McVean
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.
Many studies have demonstrated the potential utility of using genetic risk factors for predicting individual risk of common diseases, ranging from heart disease [1,2] to breast cancer  and auto-immune conditions . Genetic risk coefficients can be estimated from cross-sectional genome-wide association studies, which estimate enrichment of common genetic variants among clinically-ascertained (or sometimes self-reported) cases.
Materials and methods
We use the genotype data, basic demographic data and Hospital Episode Statistics (HES) data from 409,694 individuals of British Isles ancestry in the UK Biobank dataset . 31 ICD-10 codes were identified with a prevalence above 5% and for which at least 20 independent associated variants were previously identified using the TreeWAS model . Of these, we analysed 24 that correspond to specific disease conditions (as opposed to procedures). These are listed in Table 1. For each ICD-10 code, we combined the primary and secondary diagnoses from the HES data.
Estimating age-dependency of genetic risk score in prediction:
The SNPs of interest are obtained through prior TreeWas analysis, where we select variants that have Bayes factors (BF) ≥ 5 (BF is computed for a single variant’s effect over the TreeWas model) and posterior probability (PP) ≥ 0.99 for target diseases . We further filtered the set of SNPs to ensure LD-independence (loci kept with absolute Pearson correlation coefficient smaller than 0.2).
Genetic factors influence lifetime risk for common and complex diseases through modulating a large number of molecular, cellular and tissue phenotypes, many of which are also likely to be affected by acute exposure and persistent environment [26–28]. Despite such complexity, remarkable progress has been made in identifying factors, both genetic and non-genetic, that influence risk, each of which may only have a small effect, but which, in aggregate, have substantial and clinically relevant predictive value [29–31].
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. Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. We thank Brieuc Lehmann and Luke Jostins-Dean for discussion and comments on the manuscript.
Citation: Jiang X, Holmes C, McVean G (2021) The impact of age on genetic risk for common diseases. PLoS Genet 17(8): e1009723.
Editor: Teri Manolio, National Human Genome Research Institute, UNITED STATES
Received: January 5, 2021; Accepted: July 16, 2021; Published: August 26, 2021.
Copyright: © 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.
Data Availability: 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.
Funding: 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 (EPSRChttps://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..
Competing interests: 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.