Table 3.

Summary of Challenges Facing Genetic CVD Risk Prediction, Their Implications and Potential Solutions

Challenges for risk prediction	Possible Issues and/or Implications	Potential solutions
General considerations for CVD prediction
	Conventional risk factors explain a large proportion of the risk for CVD	Genetic risk must be incremental to standard factors and family history
	Family history information is predictive, easily obtained and free
	Determining predictive performance of genetic information	Use of a combination of c-statistic and reclassification measures
Biases in genetic effect sizes from GWAS
	Use of extreme case and extreme controls	GWAS for incident CVD in population-based cohorts
	Incidence-Prevalence bias
	Survivor bias
Allelic architecture of CVD
	Small to very small effect sizes	Larger sample sizes
	Hundreds to thousands genes may underlie CVD risk
Missing heritability
	Inaccurate estimates of heritability	Heritability by identity-by-descent methods
	Gene-gene and gene-environment studies	Case-only and family-based studies
	Poorly penetrant SNPs	Larger sample sizes
	Identifying causal variants	Sequencing, studies in population with narrow LD (e.g. African-Americans)
	Structural variants (i.e. CNVs)
	Rare variants	Exome and whole genome sequencing, studies in populations with narrow LD, Family-based studies, Founder populations
	Imprecise phenotypes	Deep phenotyping using -omics methods
Large number of genes explain genetic risk
	Unique genetic signature for each individual	Larger sample sizes
	High genetic risk will be rare
Translation of genetic risk prediction to clinical practice
	External validation	Cohort studies in appropriate populations
	Generalizability across ethnicities	Cohort studies in diverse ethnicities, re-calibration
	Optimize false postive and false negative rates using appropriate cut-offs Assessment of predictive values and likelihood ratios in populations with differing baseline risks	Evaluation of prediction in individuals of varying baseline risk.
	Efficacy and effectiveness (i.e. need for screening RCTs)	Randomized screening trials
	Cost-effectiveness	Cost-effectivenes studies
	Clinical utility over other -omic approaches	Evaluation of genomic predictors vs other -omics predictors