Biology subjects

Freudenthal, J. A.

Publications and source records attributed to Freudenthal, J. A..

3 recordsLinked to original sources

bioRxiv2022

Efficient Permutation-based Genome-wide Association Studies for Normal and Skewed Phenotypic Distributions

MotivationGenome-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 the 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 its high computational complexity. ResultsWe propose permGWAS, an efficient linear mixed model 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 a permutation-based threshold has a lower false discovery rate for skewed phenotypes compared to the commonly used Bonferroni threshold. Furthermore, using permGWAS we re-analysed more than 500 Arabidopsis thaliana phenotypes with 100 permutations each in less than eight days on a single GPU. Our re-analyses suggest that applying a permutation-based threshold can improve and refine the interpretation of GWAS results. AvailabilitypermGWAS is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.

bioinformatics↗

bioRxiv2019

GWAS-Flow: A GPU accelerated framework for efficient permutation based genome-wide association studies

MotivationGenome-wide association studies (GWAS) are one of the most commonly used methods to detect associations between complex traits and genomic polymorphisms. As both genotyping and phenotyping of large populations has become easier, typical modern GWAS have to cope with massive amounts of data. Thus, the computational demand for these analyses grew remarkably during the last decades. This is especially true, if one wants to implement permutation-based significance thresholds, instead of using the naive Bonferroni threshold. Permutation-based methods have the advantage to provide an adjusted multiple hypothesis correction threshold that takes the underlying phenotypic distribution into account and will thus remove the need to find the correct transformation for non Gaussian phenotypes. To enable efficient analyses of large datasets and the possibility to compute permutation-based significance thresholds, we used the machine learning framework TensorFlow to develop a linear mixed model (GWAS-Flow) that can make use of the available CPU or GPU infrastructure to decrease the time of the analyses especially for large datasets.\n\nResultsWe were able to show that our application GWAS-Flow outperforms custom GWAS scripts in terms of speed without loosing accuracy. Apart from p-values, GWAS-Flow also computes summary statistics, such as the effect size and its standard error for each individual marker. The CPU-based version is the default choice for small data, while the GPU-based version of GWAS-Flow is especially suited for the analyses of big data.\n\nAvailabilityGWAS-Flow is freely available on GitHub (https://github.com/Joyvalley/GWAS_Flow) and is released under the terms of the MIT-License.

bioinformatics↗

bioRxiv2019

The landscape of chloroplast genome assembly tools

BackgroundChloroplasts are intracellular organelles that enable plants to conduct photosynthesis. They arose through the symbiotic integration of a prokaryotic cell into an eukaryotic host cell and still contain their own genomes with distinct genomic information. Plastid genomes accommodate essential genes and are regularly utilized in biotechnology or phylogenetics. Different assemblers that are able to assess the plastid genome have been developed. These assemblers often use data of whole genome sequencing experiments, which usually contain reads from the complete chloroplast genome. ResultsThe performance of different assembly tools has never been systematically compared. Here we present a benchmark of seven chloroplast assembly tools, capable of succeeding in more than 60% of known real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. The examination of 105 data sets from species with unknown plastid genomes leads to the assembly of 20 novel chloroplast genomes. ConclusionsWe create docker images for each tested tool that are freely available for the scientific community and ensure reproducibility of the analyses. These containers allow the analysis and screening of data sets for chloroplast genomes using standard computational infrastructure. Thus, large scale screening for chloroplasts within genomic sequencing data is feasible.

bioinformatics↗