22 research outputs found

    From Interaction to Co-Association —A Fisher <i>r</i>-To-<i>z</i> Transformation-Based Simple Statistic for Real World Genome-Wide Association Study

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    <div><p>Currently, the genetic variants identified by genome wide association study (GWAS) generally only account for a small proportion of the total heritability for complex disease. One crucial reason is the underutilization of gene-gene joint effects commonly encountered in GWAS, which includes their main effects and co-association. However, gene-gene co-association is often customarily put into the framework of gene-gene interaction vaguely. From the causal graph perspective, we elucidate in detail the concept and rationality of gene-gene co-association as well as its relationship with traditional gene-gene interaction, and propose two Fisher <i>r-to-z</i> transformation-based simple statistics to detect it. Three series of simulations further highlight that gene-gene co-association refers to the extent to which the joint effects of two genes differs from the main effects, not only due to the traditional interaction under the nearly independent condition but the correlation between two genes. The proposed statistics are more powerful than logistic regression under various situations, cannot be affected by linkage disequilibrium and can have acceptable false positive rate as long as strictly following the reasonable GWAS data analysis roadmap. Furthermore, an application to gene pathway analysis associated with leprosy confirms in practice that our proposed gene-gene co-association concepts as well as the correspondingly proposed statistics are strongly in line with reality.</p></div

    Simulations for clarifying the relationship between co-association and interaction.

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    <p>a for Type I co-association; b for Type II co-association; c for Type III co-association given fixed interaction effect 0.2 and different correlation; d for Type III co-association given fixed correlation 0.4 and different interaction effects. The case with no main effects (), one main effects (, ) and two main effects (, ) are shown by blue, red and black lines respectively.</p

    A causal graph framework for two SNPs affected the disease.

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    <p> and represents main effects, denotes the traditional interaction, the nondirectional arc between SNP1 and SNP2 (correlation <i>r</i>) indicated that the two variables are associated for reasons other than affecting one another.</p