Abstract

BackgroundThe clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear.MethodsWe used data from Biobank Japan (n = 70,657–128,305) and developed novel East Asian-specific genome-wide polygenic risk scores (PRSs) for four lipid traits. We validated (n = 4271) and subsequently tested associations of these scores with 3-year lipid changes in adolescents (n = 620), carotid intima-media thickness (cIMT) in adult women (n = 781), dyslipidemia (n = 7723), and coronary heart disease (CHD) (n = 2374 cases and 6246 controls) in type 2 diabetes (T2D) patients.ResultsOur PRSs aggregating 84–549 genetic variants (0.251 < correlation coefficients (r) < 0.272) had comparably stronger association with lipid variations than the typical PRSs derived based on the genome-wide significant variants (0.089 < r < 0.240). Our PRSs were robustly associated with their corresponding lipid levels (7.5 × 10− 103 < P < 1.3 × 10− 75) and 3-year lipid changes (1.4 × 10− 6 < P < 0.0130) which started to emerge in childhood and adolescence. With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in TC (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in HDL-C (β ± SE = 0.057 ± 0.003), and 8.4% in LDL-C (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS. However, their predictive power was attenuated in T2D patients (0.183 < r < 0.231). When we included each PRS (for TC, TG, and LDL-C) in addition to the clinical factors and PCs, the AUC for dyslipidemia was significantly increased by 0.032–0.057 in the general population (7.5 × 10− 3 < P < 0.0400) and 0.029–0.069 in T2D patients (2.1 × 10− 10 < P < 0.0428). Moreover, the quintile of TC-related PRS was moderately associated with cIMT in adult women (β ± SE = 0.011 ± 0.005, Ptrend = 0.0182). Independent of conventional risk factors, the quintile of PRSs for TC [OR (95% CI) = 1.07 (1.03–1.11)], TG [OR (95% CI) = 1.05 (1.01–1.09)], and LDL-C [OR (95% CI) = 1.05 (1.01–1.09)] were significantly associated with increased risk of CHD in T2D patients (4.8 × 10− 4 < P < 0.0197). Further adjustment for baseline lipid drug use notably attenuated the CHD association.ConclusionsThe PRSs derived and validated here highlight the potential for early genomic screening and personalized risk assessment for cardiovascular disease.

Highlights

  • The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear

  • Our Polygenic risk score (PRS) were robustly associated with their corresponding lipid levels (7.5 × 10− 103 < P < 1.3 × 10− 75) and 3-year lipid changes (1.4 × 10− 6 < P < 0.0130) which started to emerge in childhood and adolescence

  • With the adjustments for principal components (PCs), sex, age, and body mass index, there was an elevation of 5.3% in total cholesterol (TC) (β ± SE = 0.052 ± 0.002), 11.7% in TG (β ± SE = 0.111 ± 0.006), 5.8% in High-density lipoprotein cholesterol (HDL-C) (β ± SE = 0.057 ± 0.003), and 8.4% in Low-density lipoprotein cholesterol (LDL-C) (β ± SE = 0.081 ± 0.004) per one standard deviation increase in the corresponding PRS

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Summary

Introduction

The clinical utility of personal genomic information in identifying individuals at increased risks for dyslipidemia and cardiovascular diseases remains unclear. With the development of novel computational algorithms and the availability of large datasets, increasing number of PRSs for common diseases, which fully captured genome-wide variation, have been derived and validated [6, 7] These approaches utilized full results from previous genome-wide association studies and an external reference panel to construct the PRSs mainly based on two strategies: (1) liberalization of the significance thresholds for variant inclusion while accounting for linkage disequilibrium (LD) patterns in a population; and (2) assignment of new weightings to variants using the Bayesian method that infers the posterior mean effect for each variant by assuming a prior effect from GWAS summary statistics, the information of genomic correlation, and a prespecified proportion of causal variants. Khera et al recently constructed six genome-wide PRSs, incorporating information from 5218 to 6,917,436 common genetic variants, to predict the risks of developing CHD, atrial fibrillation, type 2 diabetes (T2D), inflammatory bowel disease, breast cancer, and severe obesity in participants of mostly European ancestry [7, 8]

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