Predictive genomics
[1] To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in genome-wide association (GWA) studies.
[6] The foremost difficulty in achieving this goal is to understand the functionality of these variants with respect to areas of physiological and molecular importance in conjunction with phenotype.
[8] Furthermore, the downstream effect of identifying disease-relevant biomarkers allow for improvements to monitoring disease progression and response-to-treatment, where the implementation of these results into clinical decision support systems (CDSS) facilitate personalised medicine and outcomes.
[7] The significance of translation from research to clinical usage relates to use of the complete knowledge of an individual to develop personalised approaches to disease management.
[10] Therefore, unless individuals have an overwhelming high or low number of risk alleles, there is a limit to the predictive accuracy of their ‘genomic profiles’.
Age-related macular degeneration (AMD) is one of the flagship complex diseases from the genomic revolution with over 19 associated genetic loci replicated in GWA studies.
[15] The genetic predisposition of AMD risk varies from 45% to 71% where highly effectual odds ratios (OR) have been reported (greater than 2.0 per allele in some cases).
[14] Type 2 diabetes (T2D), an extremely common metabolic disorder, has demonstrated interplay between many environmental and genetic risk factors leading to disease onset.
[20] With particular attention to T2D, Evans et al. were able to discern a marginal increase in AUC (+0.04) based on genome-wide information with respect to known susceptible variants.
[20] However, non-genetic based tests such as the Cambridge and Framingham offspring risk scores have been purported to perform better than genetic-risk models with 20 loci.
Currently, although AUC (Area Under the ROC) is the de facto metric in comparing and evaluating the performance of predictive models, there is no consensus as to what kind of score is sufficient for clinical use.
SNPs identified in GWA studies are considered to give better predictive performance if they have high effect sizes of Odds Ratios (OR).
Therefore, although typical GWA studies are able to detect a number of statistically significant loci, they have not been sufficient to fully explain the estimates of theoretical genetic heritability.
[23] Goudey et al. also states that the barrier to expansion of higher order interactions has been limited by the intractability of exhaustive search techniques (see NP-complete).
[25] Furthermore, ethnic specific GWA studies show that each group has varied detectability of variants in terms of: frequency, linkage disequilibrium – the co-inheritance of SNPs through generations – and the actual loci themselves.
[25] Furthermore, Kambouris et al. discusses the use of ‘genomic profiles’ for the performance of elite athletes noting individualised and personalised training regimens for both dietary and physical aspects.
