Pitfall and challenge | Perspective |
---|---|
Complex epistatic interactions | - Better algorithms and control for phenotype and subphenotype studies. Data analysis is the next most expensive tool to develop. |
Genetic heterogeneity | - Larger size cohorts. |
Pleiotropy | - Familial studies to control for environmental and stochastic factors. |
History of mutations and difference in allele frequencies. | - Description and study of population genetic structure in light of reported information from other reported and publicly available data. |
Population stratification | - Usage of newly reported algorithms for admixture analysis and pan-meta-analysis approaches. |
Genetics in admixed populations | |
Statistical power and sample size | - Correspondence in the use of specific clinical criteria or diagnostic biomarkers to define phenotypes to enhance prediction and diagnosis. |
Refining the phenotype - subphenotypes | Development and application of bioinformatical approaches to classify disease as quantitative and categorical entities. |
Family based studies versus case–control studies | Application of classical genetic and epidemiological tools to characterize new information available for other ‘omic’ layers in the context of the genome from a familial and population viewpoint. |
Gene-environment interaction | Further research in environmental factors that might influence onset of disease (for example, tobacco, coffee consumption, organic solvents) |
Post-genomic era (‘omics’) | Use of the publicly available ‘omic’ information already reported (for example, ENCODE, GEO, HapMap, 1000 genomes project) to explore, replicate and hypothesize new experimental functional designs. |
Personalized medicine | Genomic medicine-generated information to be applicable from the bench to bedside and also from the bedside to bench. |
Pharmacogenomics | Disentangle markers capable of predicting and diagnosing risk of disease even before onset of symptoms and signs. |