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Genetic and modifiable risk factors combine multiplicatively in common disease.

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posted on 2022-09-20, 10:05 authored by Shichao Pang, Loic Yengo, Christopher P Nelson, Felix Bourier, Lingyao Zeng, Ling Li, Thorsten Kessler, Jeanette Erdmann, Reedik Mägi, Kristi Läll, Andres Metspalu, Bertram Mueller-Myhsok, Nilesh J Samani, Peter M Visscher, Heribert Schunkert


The joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized.


We modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors.


We analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions.


In UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p < 0.01). Irrespective of the affected gene, a single risk allele multiplied the effects of all others carried by a person, resulting in a 2.9-fold stronger effect size in the top versus the bottom decile (p < 0.01) and an exponential increase in risk (R > 0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer.


Alleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed.


Open Access funding enabled and organized by Projekt DEAL. The author’s work was funded by the German Federal Ministry of Education and Research (BMBF) within the framework of ERA-NET on Cardiovascular Disease (Druggable-MI-genes: 01KL1802), within the scheme of target validation (BlockCAD: 16GW0198K), and within the framework of the e:Med research and funding concept (AbCD-Net: 01ZX1706C)

British Heart Foundation (BHF)/German Centre of Cardiovascular Research (DZHK)-collaboration (DZHK-BHF: 81X2600522) and the Leducq Foundation for Cardiovascular Research (PlaqOmics: 18CVD02

German Research Foundation (DFG) as part of the Sonderforschungsbereich SFB 1123 (B02) and the Sonderforschungsbereich SFB TRR 267 (B05)

Bavarian State Ministry of Health and Care who funded this work with DigiMed Bayern (Grant No: DMB-1805-0001) within its Masterplan “Bayern Digital II” and of the German Federal Ministry of Economics and Energy in its scheme of ModulMax (Grant No: ZF4590201BA8)

British Heart Foundation and NJS is an emeritus NIHR Senior Investigator

Australian National Health and Medical Research Council (1113400) and the Australian Research Council (FL180100072, DE200100425)


Author affiliation

Department of Cardiovascular Sciences, University of Leicester


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Clinical research in cardiology


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