Alternative Methods for H1 Simulations in Genome-Wide Association Studies

V. Perduca; C. Sinoquet; R. Mourad; G. Nuel

doi:10.1159/000336194

Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes

10.1101/2020.10.09.333146 ◽

2020 ◽

Author(s):

Wenjian Bi ◽

Wei Zhou ◽

Rounak Dey ◽

Bhramar Mukherjee ◽

Joshua N Sampson ◽

...

Keyword(s):

Mixed Model ◽

Type I Error ◽

Association Studies ◽

Error Rates ◽

Genome Wide Association ◽

Alternative Methods ◽

Type I ◽

Genome Wide Association Studies ◽

Type I Error Rates ◽

Genome Wide

AbstractIn genome-wide association studies (GWAS), ordinal categorical phenotypes are widely used to measure human behaviors, satisfaction, and preferences. However, due to the lack of analysis tools, methods designed for binary and quantitative traits have often been used inappropriately to analyze categorical phenotypes, which produces inflated type I error rates or is less powerful. To accurately model the dependence of an ordinal categorical phenotype on covariates, we propose an efficient mixed model association test, Proportional Odds Logistic Mixed Model (POLMM). POLMM is demonstrated to be computationally efficient to analyze large datasets with hundreds of thousands of genetic related samples, can control type I error rates at a stringent significance level regardless of the phenotypic distribution, and is more powerful than other alternative methods. We applied POLMM to 258 ordinal categorical phenotypes on array-genotypes and imputed samples from 408,961 individuals in UK Biobank. In total, we identified 5,885 genome-wide significant variants, of which 424 variants (7.2%) are rare variants with MAF < 0.01.

Download Full-text

Comparing genome-wide association study results from different measurements of an underlying phenotype

10.1101/313445 ◽

2018 ◽

Author(s):

Joseph L. Gage ◽

de Leon Natalia ◽

Clayton Murray

Keyword(s):

Large Scale ◽

Genome Wide Association Study ◽

Association Studies ◽

Roc Curves ◽

Genome Wide Association ◽

Alternative Methods ◽

Genome Wide Association Studies ◽

Genome Wide ◽

Study Results ◽

The Difference

AbstractIncreasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait’s AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS.

Download Full-text