Genetic risk models: Influence of model size on risk estimates and precision

Development and validation of a cardiovascular diseases risk prediction model for Chinese males (CVDMCM)

Background

Death due to cardiovascular diseases (CVD) increased significantly in China. One possible way to reduce CVD is to identify people at risk and provide targeted intervention. We aim to develop and validate a CVD risk prediction model for Chinese males (CVDMCM) to help clinicians identify those males at risk of CVD and provide targeted intervention.

Methods

We conducted a retrospective cohort study of 2,331 Chinese males without CVD at baseline to develop and internally validate the CVDMCM. These participants had a baseline physical examination record (2008–2016) and at least one revisit record by September 2019. With the full cohort, we conducted three models: A model with Framingham CVD risk model predictors; a model with predictors selected by univariate cox proportional hazard model adjusted for age; and a model with predictors selected by LASSO algorithm. Among them, the optimal model, CVDMCM, was obtained based on the Akaike information criterion, the Brier's score, and Harrell's C statistic. Then, CVDMCM, the Framingham CVD risk model, and the Wu's simplified model were all validated and compared. All the validation was carried out by bootstrap resampling strategy (TRIPOD statement type 1b) with the full cohort with 1,000 repetitions.

Results

CVDMCM's Harrell's C statistic was 0.769 (95% CI: 0.738–0.799), and D statistic was 4.738 (95% CI: 3.270–6.864). The results of Harrell's C statistic, D statistic and calibration plot demonstrated that CVDMCM outperformed the Framingham CVD model and Wu's simplified model for 4-year CVD risk prediction.

Conclusions

We developed and internally validated CVDMCM, which predicted 4-year CVD risk for Chinese males with a better performance than Framingham CVD model and Wu's simplified model. In addition, we developed a web calculator–calCVDrisk for physicians to conveniently generate CVD risk scores and identify those males with a higher risk of CVD.

Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.

Application of the genetic risk model for the analysis of predisposition to nonlacunar ischemic stroke

Aim: The purpose of our study was to analyze the predictive ability of the multiplicative model of genetic risk of nonlacunar ischemic stroke (IS) for independent samples from Russia. Patients & methods: A total of 181 patients and 360 healthy controls were included in this study. The discriminative accuracy of model was evaluated by the area under the receiver operating characteristic curve (AUC). Results: Classification model based on 15 single-nucleotide polymorphisms (SNPs), which are associated with a cardioembolic subtype of IS, had an AUC of 0.62 in patients with corresponding subtypes and an AUC of 0.58 for all patients. Conclusion: Risk calculation approach based on IS-associated SNPs had satisfactory performance in predicting the predisposition to the disease.