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Table 1 Pitfalls and challenges of complex trait analysis

From: What is next after the genes for autoimmunity?

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.