Disease model distortion in association studies
Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a singel nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
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Wiley
2011
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| _version_ | 1797083270610944000 |
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| author | Vukcevic, D Hechter, E Spencer, C Donnelly, P |
| author_facet | Vukcevic, D Hechter, E Spencer, C Donnelly, P |
| author_sort | Vukcevic, D |
| collection | OXFORD |
| description | Most findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a singel nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r<sup>4</sup>, where r² is the usual correlation between the causal and marker loci, in contrast to the well-known result that power to detect a multiplicative effect decays as a function of r². We perform a simulation study with empirical patterns of LD to assess how this disease model distortion is likely to impact GWAS results. Among loci where associated is detected, we observe that there is reasonable power to detect substantial deviations from the multiplicative model, such as for dominant and recessive models. Thus, it is worth explicitly testing for such deviations routinely. |
| first_indexed | 2024-03-07T01:39:21Z |
| format | Journal article |
| id | oxford-uuid:9653401a-bbef-4310-8752-7860f2746494 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:39:21Z |
| publishDate | 2011 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:9653401a-bbef-4310-8752-7860f27464942022-03-26T23:52:07ZDisease model distortion in association studiesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:9653401a-bbef-4310-8752-7860f2746494Statistics (see also social sciences)Genetics (medical sciences)EnglishOxford University Research Archive - ValetWiley2011Vukcevic, DHechter, ESpencer, CDonnelly, PMost findings from genome-wide association studies (GWAS) are consistent with a simple disease model at a singel nucleotide polymorphism, in which each additional copy of the risk allele increases risk by the same multiplicative factor, in contrast to dominance or interaction effects. As others have noted, departures from this multiplicative model are difficult to detect. Here, we seek to quantify this both analytically and empirically. We show that imperfect linkage disequilibrium (LD) between causal and marker loci distorts disease models, with the power to detect such departures dropping off very quickly: decaying as a function of r<sup>4</sup>, where r² is the usual correlation between the causal and marker loci, in contrast to the well-known result that power to detect a multiplicative effect decays as a function of r². We perform a simulation study with empirical patterns of LD to assess how this disease model distortion is likely to impact GWAS results. Among loci where associated is detected, we observe that there is reasonable power to detect substantial deviations from the multiplicative model, such as for dominant and recessive models. Thus, it is worth explicitly testing for such deviations routinely. |
| spellingShingle | Statistics (see also social sciences) Genetics (medical sciences) Vukcevic, D Hechter, E Spencer, C Donnelly, P Disease model distortion in association studies |
| title | Disease model distortion in association studies |
| title_full | Disease model distortion in association studies |
| title_fullStr | Disease model distortion in association studies |
| title_full_unstemmed | Disease model distortion in association studies |
| title_short | Disease model distortion in association studies |
| title_sort | disease model distortion in association studies |
| topic | Statistics (see also social sciences) Genetics (medical sciences) |
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