Power of genome-wide association studies in the presence of interacting loci.Report as inadecuate

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* Corresponding author 1 Génétique épidémiologique et structures des populations humaines 2 Department of Human Genetics

Abstract : Though multiple interacting loci are likely involved in the etiology of complex diseases, early genome-wide association studies GWAS have depended on the detection of the marginal effects of each locus. Here, we evaluate the power of GWAS in the presence of two linked and potentially associated causal loci for several models of interaction between them and find that interacting loci may give rise to marginal relative risks that are not generally considered in a one-locus model. To derive power under realistic situations, we use empirical data generated by the HapMap ENCODE project for both allele frequencies and linkage disequilibrium LD structure. The power is also evaluated in situations where the causal single nucleotide polymorphisms SNPs may not be genotyped, but rather detected by proxy using a SNP in LD. A common simplification for such power computations assumes that the sample size necessary to detect the effect at the tSNP is the sample size necessary to detect the causal locus directly divided by the LD measure r2 between the two. This assumption, which we call the -proportionality assumption-, is a simplification of the many factors that contribute to the strength of association at a marker, and has recently been criticized as unreasonable Terwilliger and Hiekkalinna 2006 Eur J Hum Genet 144:426-437, in particular in the presence of interacting and associated loci. We find that this assumption does not introduce much error in single locus models of disease, but may do so in so in certain two-locus models.

keyword : linkage disequilibrium genome-wide tagSNPs

Author: Joseph Pickrell - Françoise Clerget-Darpoux - Catherine Bourgain -

Source: https://hal.archives-ouvertes.fr/


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