Using endophenotypes for pathway clusters to map complex disease genes

Abstract

Nature determines the complexity of disease etiology and the likelihood of revealing disease genes. While culprit genes for many monogenic diseases have been successfully unraveled, efforts to map major complex disease genes have not been as productive as hoped. The conceptual framework currently adopted to deal with the heterogeneous nature of complex diseases focuses on using homogeneous internal features of the disease phenotype for mapping. However, phenotypic homogeneity does not equal genotypic homogeneity. In this report, we advocate working with well-measured phenotypes portrayed by amounts of transcripts and activities of gene products or their metabolites, which are pertinent to relatively small pathway clusters. Reliable and controlled measures for oligogenic traits resulting from proper dissection efforts may enhance statistical power. The large amounts of information obtained on gene and protein expression from technological advances can add to the power of gene finding, particularly for diseases with unclear etiology. Data-mining tools for dimension reduction can assist biologists to reveal novel molecular endophenotypes. However, there are still hurdles to overcome, including high cost, relatively poor reproducibility and comparability among platforms, the cross-sectional nature of the information, and the accessibility of human tissues. Concerted efforts are required to carry out large-scale prospective studies that are integrated at the levels of phenotype characterization, high throughput experimental techniques, data analyses, and beyond.

How pleiotropic genetics of the musculoskeletal system can inform genomics and phenomics of aging

“…By identifying which measurements contribute most to defining the underlying hidden variables, phenotyping efforts can focus on such "integrating" measures that conceptually resemble phenotypic moduli, which can then be utilized as quantitative traits for gene mapping (Pan et al 2006;Schulze and McMahon 2004), therefore decreasing the problem of multiple testing. This will generate the "integrator" phenotypes that are probably more proximal to the "endophenotype" of disease.…”

A genome-wide association study identifies new loci for ACE activity: potential implications for response to ACE inhibitor

“…Therefore, an efficient approach would be to dissect complex diseases into more homogeneous endo-or intermediate phenotypes that tend to have greater heritability than the original phenotype. 5,6 It has been proposed that this approach, by focusing on a narrower disease facet, could help uncover the genes related to the subsets of the original complex disease and contribute to the understanding the disease etiology.…”

“…12 Therefore, the ACE activity represents an upstream and internal facet of hypertension, which has a reasonably high heritability (estimated range: 0.2-0.7) 12,13 and small intraindividual variations. 14 The ACE activity consequently falls into socalled 'good intermediate phenotype' category described by Pan et al 6 and others for discovering variants with potential involvement in blood pressure regulation, cardiovascular function and other biological processes. We performed a GWAS by using 560 168 single nucleotide polymorphism (SNP) genotypes derived from the Illumina HumanHap550 SNP chip in 400 subjects with young-onset hypertension (YOH) to identify the quantitative trait loci associated with the ACE activity and to evaluate the efficacy of the GWAS for mapping up the genes on a highly heritable trait.…”

Incorporating endophenotypes into allele‐sharing based linkage tests

“…We note that de Geus [2002] and Gottesman and Gould [2003] provided sets of criteria for endophenotypes, which require that endophenotypes must be reliable and stable traits, must show evidence of genetic influences (with reasonably high heritability), and must be associated with the disease of interest (by considering phenotypic correlation), among others, and believe that by reducing phenotypic complexity and moving physiologically ''closer'' to the gene products, one may boost the statistical power to identify these genes. Successful examples, analysis strategies, and outstanding issues of using endophenotypes in genetic studies are discussed, for example, in Keating et al [1991], Comuzzie et al [2001], Keating and Sanguinetti [2001], de Geus [2002], Gottesman and Gould [2003], Pan et al [2006], and references therein.…”