The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases.
Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogenei...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3795757?pdf=render |
| _version_ | 1811282755479142400 |
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| author | Mirko Manchia Jeffrey Cullis Gustavo Turecki Guy A Rouleau Rudolf Uher Martin Alda |
| author_facet | Mirko Manchia Jeffrey Cullis Gustavo Turecki Guy A Rouleau Rudolf Uher Martin Alda |
| author_sort | Mirko Manchia |
| collection | DOAJ |
| description | Phenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of "non-cases") reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size. |
| first_indexed | 2024-04-13T01:58:15Z |
| format | Article |
| id | doaj.art-684181cb263a42f5af56d17a6a0645e7 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T01:58:15Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-684181cb263a42f5af56d17a6a0645e72022-12-22T03:07:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01810e7629510.1371/journal.pone.0076295The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases.Mirko ManchiaJeffrey CullisGustavo TureckiGuy A RouleauRudolf UherMartin AldaPhenotypic misclassification (between cases) has been shown to reduce the power to detect association in genetic studies. However, it is conceivable that complex traits are heterogeneous with respect to individual genetic susceptibility and disease pathophysiology, and that the effect of heterogeneity has a larger magnitude than the effect of phenotyping errors. Although an intuitively clear concept, the effect of heterogeneity on genetic studies of common diseases has received little attention. Here we investigate the impact of phenotypic and genetic heterogeneity on the statistical power of genome wide association studies (GWAS). We first performed a study of simulated genotypic and phenotypic data. Next, we analyzed the Wellcome Trust Case-Control Consortium (WTCCC) data for diabetes mellitus (DM) type 1 (T1D) and type 2 (T2D), using varying proportions of each type of diabetes in order to examine the impact of heterogeneity on the strength and statistical significance of association previously found in the WTCCC data. In both simulated and real data, heterogeneity (presence of "non-cases") reduced the statistical power to detect genetic association and greatly decreased the estimates of risk attributed to genetic variation. This finding was also supported by the analysis of loci validated in subsequent large-scale meta-analyses. For example, heterogeneity of 50% increases the required sample size by approximately three times. These results suggest that accurate phenotype delineation may be more important for detecting true genetic associations than increase in sample size.http://europepmc.org/articles/PMC3795757?pdf=render |
| spellingShingle | Mirko Manchia Jeffrey Cullis Gustavo Turecki Guy A Rouleau Rudolf Uher Martin Alda The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. PLoS ONE |
| title | The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. |
| title_full | The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. |
| title_fullStr | The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. |
| title_full_unstemmed | The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. |
| title_short | The impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases. |
| title_sort | impact of phenotypic and genetic heterogeneity on results of genome wide association studies of complex diseases |
| url | http://europepmc.org/articles/PMC3795757?pdf=render |
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