Abstract
It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual GWAS nor meta-analytic combinations thereof are helpful for disclosing which genetic variants contribute to explain a particular phenotype. Here, we propose that most of the missing heritability is latent in the GWAS data, which conceals intermediate phenotypes. To uncover such latent information, we propose the PGMRA server that introduces phenomics—the full set of phenotype features of an individual—to identify SNP-set structures in a broader sense, i.e. causally cohesive genotype–phenotype relations. These relations are agnostically identified (without considering disease status of the subjects) and organized in an interpretable fashion. Then, by incorporating a posteriori the subject status within each relation, we can establish the risk surface of a disease in an unbiased mode. This approach complements—instead of replaces—current analysis methods. The server is publically available at http://phop.ugr.es/fenogeno.
Highlights
Phenomics, defined as the acquisition of high-dimensional phenotype data on an organism-wide scale, has arisen as a possibility to address the ‘many-to-many’ relationships that are inherent in the phenotype and genotype domains of a disease [1]
It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population
The interaction of phenomics with genomics in human diseases is usually precluded by a reduction of dimensionality of the phenotype features, which implies the elimination of their explanatory power
Summary
Phenotype–genotype relations have been often established using a modest numbers of single nucleotide polymorphisms (SNPs) associated with limited binary or discrete case-control phenotypes in genome-wide association studies (GWAS) These studies suffer from limited reproducibility, difficulties in finding causal SNPs because tagged SNPs are not necessarily causal, as well as in detecting multiple genetic sources (missing heritability), and inability to detect epistatic consequences [4,5,6]. New methods are needed to identify, in an unbiased fashion, interpretable SNP-set structures in a broad sense, based on relations between sets of phenotype features coherently linked to SNP sets To address this problem, we developed the PGMRA web server, which encodes methods that independently identify SNP sets and phenotype sets from GWAS data, and uncover optimal—instead of exhaustive—many-to-many phenotype–genotype relations among them. These methods organize the uncovered coherent relations as networks in an interpretable topological fashion that, in turn, describe the risk surface of a disease
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