Analysing biological pathways in genome-wide association studies. Nat Rev Genet

Center for Applied Genomics, The Childrens Hospital of Philadelphia, Pennsylvania 19104, USA.
Nature Reviews Genetics (Impact Factor: 36.98). 12/2010; 11(12):843-54. DOI: 10.1038/nrg2884
Source: PubMed


Genome-wide association (GWA) studies have typically focused on the analysis of single markers, which often lacks the power to uncover the relatively small effect sizes conferred by most genetic variants. Recently, pathway-based approaches have been developed, which use prior biological knowledge on gene function to facilitate more powerful analysis of GWA study data sets. These approaches typically examine whether a group of related genes in the same functional pathway are jointly associated with a trait of interest. Here we review the development of pathway-based approaches for GWA studies, discuss their practical use and caveats, and suggest that pathway-based approaches may also be useful for future GWA studies with sequencing data.

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    • "Recently in genetic analysis of complex traits the focus has been shifted from single genes identified via genome-wide association studies (GWAS) to genes identified via a functional analysis (Evangelou et al. 2014; Visscher et al. 2012). While genes selected by GWAS represent a selection of variants with (very) high effects on disease risk or on trait genetic variation, sets of genes selected by the functional approach are likely to also contain variants with moderate to small effects manifested through participation in important functional processes (Eleftherohorinou et al. 2009; Wang et al. 2010). "
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    ABSTRACT: Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore - trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.
    Journal of applied genetics 08/2015; DOI:10.1007/s13353-015-0306-5 · 1.48 Impact Factor
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    • "First, it is a mathematical model unlike most set based SNP association analyses, which are thought to primarily consider linkage disequilibrium between SNPs (Liu et al., 2010). In addition, gene pathway based associations are usually not amenable to the inclusion of covariates and are subject to permutation biases (Wang et al., 2010). Other binary array models such as Boolean factor analyses are limited in that they do not consider the set-theoretical structure of the data array (De Boeck and Rosenberg, 1988). "
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    ABSTRACT: The power of SNP association studies to detect valid relationships with clinical phenotypes in schizophrenia is largely limited by the number of SNPs selected and non-specificity of phenotypes. To address this, we first assessed performance on two visual perceptual organization tasks designed to avoid many generalized deficit confounds, Kanizsa shape perception and contour integration, in a schizophrenia patient sample. Then, to reduce the total number of candidate SNPs analyzed in association with perceptual organization phenotypes, we employed a two-stage strategy: first a priori SNPs from three candidate genes were selected (GAD1, NRG1 and DTNBP1); then a Hierarchical Classes Analysis (HICLAS) was performed to reduce the total number of SNPs, based on statistically related SNP clusters. HICLAS reduced the total number of candidate SNPs for subsequent phenotype association analyses from 6 to 3. MANCOVAs indicated that rs10503929 and rs1978340 were associated with the Kanizsa shape perception filling in metric but not the global shape detection metric. rs10503929 was also associated with altered contour integration performance. SNPs not selected by the HICLAS model were unrelated to perceptual phenotype indices. While the contribution of candidate SNPs to perceptual impairments requires further clarification, this study reports the first application of HICLAS as a hypothesis-independent mathematical method for SNP data reduction. HICLAS may be useful for future larger scale genotype-phenotype association studies.
    Schizophrenia Research: Cognition 04/2015; 8(2). DOI:10.1016/j.scog.2015.03.003
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    • "This would be particularly relevant for diseases for which the genetic overlap is not well-understood. " [41] "
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    ABSTRACT: We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.
    Genomics Data 12/2014; 2:202–211. DOI:10.1016/j.gdata.2014.04.004
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