Controlling type 1 error rates in genome-wide association studies in plants

Many genetic variants identified in genome-wide association studies (GWAS) are associated with multiple, sometimes seemingly unrelated, traits. This motivates multi-trait association analyses, which have successfully identified novel associated loci for many complex diseases. While appealing, most existing methods focus on analyzing a relatively small number of traits, and may yield inflated Type 1 error rates when a large number of traits need to be analyzed jointly. As deep phenotyping data are becoming rapidly available, we develop a novel method, referred to as aMAT (adaptive multi-trait association test), for multi-trait analysis of any number of traits. We applied aMAT to GWAS summary statistics for a set of 58 volumetric imaging derived phenotypes from the UK Biobank. aMAT had a genomic inflation factor of 1.04, indicating the Type 1 error rate was well controlled. More important, aMAT identified 24 distinct risk loci, 13 of which were ignored by standard GWAS. In comparison, the competing methods either had a suspicious genomic inflation factor or identified much fewer risk loci. Finally, four additional sets of traits have been analyzed and provided similar conclusions.

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Robustness of the linear mixed effects model to error distribution assumptions and the consequences for genome-wide association studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2013-0066 ◽

2014 ◽

Vol 13 (5) ◽

Cited By ~ 6

Author(s):

Nicole M. Warrington ◽

Kate Tilling ◽

Laura D. Howe ◽

Lavinia Paternoster ◽

Craig E. Pennell ◽

...

Keyword(s):

Random Effects ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Linear Mixed Effects ◽

Type 1 Error ◽

Robust Standard Error ◽

Genome Wide ◽

Fixed And Random Effects

AbstractGenome-wide association studies have been successful in uncovering novel genetic variants that are associated with disease status or cross-sectional phenotypic traits. Researchers are beginning to investigate how genes play a role in the development of a trait over time. Linear mixed effects models (LMM) are commonly used to model longitudinal data; however, it is unclear if the failure to meet the models distributional assumptions will affect the conclusions when conducting a genome-wide association study. In an extensive simulation study, we compare coverage probabilities, bias, type 1 error rates and statistical power when the error of the LMM is either heteroscedastic or has a non-Gaussian distribution. We conclude that the model is robust to misspecification if the same function of age is included in the fixed and random effects. However, type 1 error of the genetic effect over time is inflated, regardless of the model misspecification, if the polynomial function for age in the fixed and random effects differs. In situations where the model will not converge with a high order polynomial function in the random effects, a reduced function can be used but a robust standard error needs to be calculated to avoid inflation of the type 1 error. As an illustration, a LMM was applied to longitudinal body mass index (BMI) data over childhood in the ALSPAC cohort; the results emphasised the need for the robust standard error to ensure correct inference of associations of longitudinal BMI with chromosome 16 single nucleotide polymorphisms.

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