Efficient permutation-based genome-wide association studies for normal and skewed phenotypic distributions

Abstract

Motivation Genome-wide association studies (GWAS) are an integral tool for studying the architecture of complex genotype and phenotype relationships. Linear mixed models (LMMs) are commonly used to detect associations between genetic markers and a trait of interest, while at the same time allowing to account for population structure and cryptic relatedness. Assumptions of LMMs include a normal distribution of the residuals and that the genetic markers are independent and identically distributed—both assumptions are often violated in real data. Permutation-based methods can help to overcome some of these limitations and provide more realistic thresholds for the discovery of true associations. Still, in practice, they are rarely implemented due to the high computational complexity. Results We propose permGWAS, an efficient LMM reformulation based on 4D tensors that can provide permutation-based significance thresholds. We show that our method outperforms current state-of-the-art LMMs with respect to runtime and that permutation-based thresholds have lower false discovery rates for skewed phenotypes compared to the commonly used Bonferroni threshold. Furthermore, using permGWAS we re-analyzed more than 500 Arabidopsis thaliana phenotypes with 100 permutations each in less than 8 days on a single GPU. Our re-analyses suggest that applying a permutation-based threshold can improve and refine the interpretation of GWAS results. Availability and implementation permGWAS is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

Natural variation of warm temperature‐induced raffinose accumulation identifies TREHALOSE‐6‐PHOSPHATE SYNTHASE 1 as a modulator of thermotolerance

“…An imputed version of the genotypic data for the 250 accessions was used (Arouisse et al, 2020). The association analysis was performed with permGWAS (John et al, 2022) and was based on a linear mixed model that corrected for population structure. Only single‐nucleotide polymorphisms (SNPs) with a minor allele frequency above 0.05 were considered, resulting in 1 420 453 SNPs for the final model.…”

“…An imputed version of the genotypic data for the 250 accessions was used (Arouisse et al, 2020). The association analysis was performed with permGWAS (John et al, 2022) and was based on a linear mixed model that corrected for population structure.…”

permGWAS2: Enhanced and Accelerated Permutation-based Genome-Wide Association Studies

“…Finally, the adjusted threshold is given as the α-th percentile of the minimal pvalues. This permutation-based threshold is able to control the family-wise error rate, as shown in John et al (2022). However, the permutation strategy presented above does not take into account the underlying population structure of the given phenotype.…”

“…Permutation-based significance thresholds provide a robust alternative to Bonferroni thresholds that can limit false-positive associations in statistical hypothesis tests (Che et al, 2014;Nicod et al, 2016;John et al, 2022). One of the main challenges in using permutation-based approaches is the high computational cost, which was recently addressed in John et al (2022). There we introduced permGWAS, a Python framework capable of computing efficient batch-wise LMMs using 3-and 4-dimensional tensors.…”

HeliantHOME, a public and centralized database of phenotypic sunflower data

“…A primary example are genome-wide association studies (GWAS). For this purpose, univariate association tests (associations between single point-mutations with a certain phenotype) that account for population structure, such as FaSTLMM (Factored Spectrally Transformed www.nature.com/scientificdata www.nature.com/scientificdata/ Linear Mixed Models) 34 or permutation based GWAS 35 can be used. The data might also be used for the comparison or development of new phenotype prediction methods.…”