| Title: | Transformation of Summary Statistics from Linear Mixed Model Association on All-or-None Traits to Odds Ratio |
| Journal: | Genetics |
| Published: | 1 Apr 2018 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/29429966/ |
| DOI: | https://doi.org/10.1534/genetics.117.300360 |
Publication 3042
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Abstract
In this study, we derived a novel transformation from the genetic effects estimated under the linear (mixed) model to the odds ratio that only rely on genome-wide association study summary statistics. To test the proposed transformations, we used real genotypes from two large, publicly available data sets to simulate all-or-none phenotypes for a set of scenarios that differ in underlying model, disease prevalence, and heritability. Furthermore, we applied these transformations to GWAS summary statistics for type 2 diabetes generated from 108,042 individuals in the UK Biobank. In both simulation and real-data application, we observed very high concordance between the transformed OR from the LMM and either the simulated truth or estimates from logistic regression. The transformations derived and validated in this study improve the comparability of results from prospective and already performed LMM GWAS on complex diseases by providing a reliable transformation to a common comparative scale for the genetic effects.1 Author
- Lloyd-Jones LR et al.
1 Application
| Application ID | Title |
|---|---|
| 12514 | The limits of predicting complex traits and diseases from genetic data |
Enabling scientific discoveries that improve human health