Biology subjects

Grimm, D. G.

Publications and source records attributed to Grimm, D. G..

4 recordsLinked to original sources

bioRxiv2023

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

MotivationPermutation-based significance thresholds have been shown to be a robust alternative to Bonferroni-based significance thresholds in genome-wide association studies (GWAS). However, the implementation of permutation-based thresholds is computationally demanding. The recently published method permGWAS introduced a batch-wise approach using 4D tensors to efficiently compute permutation-based GWAS. However, running multiple univariate tests in parallel leads to many repetitive computations and increased computational resources. More importantly, the previous version of permGWAS does not take into account the population structure when permuting the phenotype. ResultsWe propose permGWAS2, an improved and accelerated version that uses a block matrix decomposition to optimize computations, thereby reducing redundant computations. It also introduces an alternative permutation strategy that takes into account the population structure during permutation. We show that this improved framework provides a more streamlined approach to performing permutation-based GWAS with a lower false discovery rate compared to the previous version and the traditional Bonferroni correction. AvailabilitypermGWAS2 is open-source and publicly available on GitHub for download: https://github.com/grimmlab/permGWAS.

bioinformatics↗

bioRxiv2023

Spatial tumor immune heterogeneity facilitates subtype co-existence and therapy response via AP1 dichotomy in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) displays a high degree of spatial subtype heterogeneity. This intratumoral co-existence of classical and basal-like programs is evident in multi-scale transcriptomic and spatial analyses of resected, advanced-stage and chemotherapy-treated specimens and reciprocally linked to a diverse stromal immune microenvironment as well as worse clinical outcome. However, the underlying mechanisms of intratumoral subtype heterogeneity remain largely unclear. Here, by combining preclinical models, multi-center clinical, bulk and compartment-specific transcriptomic, proteomic, and bioimaging data from human specimens, we identified an interplay between neoplastic intrinsic AP1 transcription factor dichotomy and extrinsic CD68+ macrophages as a driver of intratumoral subtype co-existence along with an immunosuppressive tumor microenvironment with T cell exclusion. Our ATAC-, ChIP-, and RNA-seq analyses revealed that JUNB/AP1- and HDAC-mediated epigenetic programs repress pro-inflammatory immune signatures in tumor cells, antagonizing cJUN/AP1 signaling to favor a therapy-responsive classical neoplastic identity. Through the tumor microenvironment, this dichotomous regulation was further amplified via regional macrophage populations. Moreover, CD68+/TNF-+ cells associated with a reactive phenotype and reduced CD8+ T cell infiltration in human PDAC tumors. Consequently, combined anti-TNF- immunotherapy and chemotherapy resulted in reduced macrophage counts and promoted CD3+/CD8+ T cell infiltration in basal-like PDAC, leading to improved survival in preclinical murine models. We conclude that tumor cell intrinsic epigenetic programs, together with extrinsic microenvironmental cues, facilitate intratumoral subtype heterogeneity and disease progression.

cancer biology↗

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↗