Biology subjects

Rohde, P. D.

Publications and source records attributed to Rohde, P. D..

9 recordsLinked to original sources

bioRxiv2025

Host genetics and diet jointly shape the microbiome of Drosophila melanogaster but do not predict lifespan or age-related traits

The microbiome is a key determinant of organismal health, yet inter-individual variability and heterogeneous responses to environmental conditions complicates the understanding of its effects on hosts. Here, we present a comprehensive analysis using the Drosophila Genetic Reference Panel (DGRP) to investigate how the interplay between host genetic variation and diet influences microbiome composition, and to assess whether microbiome features in young flies can be used to predict lifespan and age-related traits. Our findings show that adult flies reared on a nutritionally rich control diet exhibited higher microbial richness but lower evenness compared to those on a nutritionally poor restricted diet. Principal component analysis (PCA) highlighted substantial diversity among lines reared on the same diet, and this variation was heritable supported by high heritability estimates for all measured -diversity metrics, including Unique OTU counts, Shannon and Simpson indices, as well as the relative abundances of genera and species with relative abundances exceeding 1%. These results underscore the critical roles of both environmental factors and genetic variation in shaping microbiome composition under different dietary conditions. Moreover, we identified widespread genotype-by-diet interactions, suggesting that the genetic regulation of the microbiome is highly complex. Finally, we found that the microbiome features of young flies including diversity indices, taxonomic abundances, or ordination scores cannot predict age-associated phenotypes (lifespan, locomotor activity, dry weight, and heat knockdown time). Our findings offer valuable insights into the genetic architecture that governs microbiome composition, dietary responses, and aging in Drosophila melanogaster. Author SummaryThe community of microbes living on or inside animals - the microbiome - plays an important role in health and aging, but its composition varies greatly between individuals and across environments, making it challenging to fully understand its importance. In this study, we examined how host genetics and diet shape the microbiome of the fruit fly, Drosophila melanogaster, using a large panel of genetically diverse lines. We found that the microbiome of flies on a restricted nutritionally poor diet were relatively evenly distributed, while the microbiome of flies on a nutritionally rich diet had a higher species diversity. Many features of the microbiome were strongly influenced by the flies genes, and these genetic effects often depended on diet. Finally, we tested whether microbiome features obtained from young flies could predict age-related traits such as lifespan and activity at similar aged flies or later in life, but found no evidence for such links. These results highlight how genetic variation and diet interact to shape the microbiome and its role in aging.

genetics↗

bioRxiv2025

The Role of Genetic Variation in Shaping Phenotypic Responses to Diet in Aging Drosophila melanogaster

Nutrition plays a central role in healthy living, however, extensive variability in individual responses to dietary interventions complicates our understanding of its effects. Here we present a comprehensive study utilizing the Drosophila Genetic Reference Panel (DGRP), investigating how genetic variation influences responses to diet and aging. Quantitative genetic analyses of the impact of dietary restriction on lifespan, locomotor activity, dry weight, and heat knockdown time were performed. Locomotor activity, dry weight and heat knockdown time were measured on the same individual flies. We found significant genotype-by-diet interaction (GDI) and genotype-by-age interaction (GAI) for all traits. Therefore, environmental factors play a crucial role in shaping trait variation at different ages and diets, and/or distinct genetic variation influences these traits at different ages and diets. Our genome wide association study also identified a quantitative trait locus for age-dependent dietary response. The observed GDI and GAI indicates that susceptibility to environmental influences changes as organisms age, which could have significant implications for dietary recommendations and interventions aimed at promoting healthy aging in humans. The identification of associations between DNA sequence variation and age-dependent dietary responses opens new avenues for research into the genetic mechanisms underlying these interactions.

genetics↗

bioRxiv2024

Evaluation of Bayesian Linear Regression Models for Gene Set Prioritization in Complex Diseases

Genome-wide association studies (GWAS) provide valuable insights into the genetic architecture of complex traits, yet interpreting their results remains challenging due to the polygenic nature of most traits. Gene set analysis offers a solution by aggregating genetic variants into biologically relevant pathways, enhancing the detection of coordinated effects across multiple genes. In this study, we present and evaluate a gene set prioritization approach utilizing Bayesian Linear Regression (BLR) models to uncover shared genetic components among different phenotypes and facilitate biological interpretation. Through extensive simulations and analyses of real traits, we demonstrate the efficacy of the BLR model in prioritizing pathways for complex traits. Simulation studies reveal insights into the models performance under various scenarios, highlighting the impact of factors such as the number of causal genes, proportions of causal variants, heritability, and disease prevalence. Application of both single-trait and multi-trait BLR models to real data, specifically GWAS summary data for type 2 diabetes (T2D) and related phenotypes, identifies significant associations with T2D-related pathways. Furthermore, comparison between single- and multi-trait BLR analyses highlights the superior performance of the multi-trait approach in identifying associated pathways, showcasing increased statistical power when analyzing multiple traits jointly. Additionally, enrichment analysis with integrated data from various public resources supports our results, confirming significant enrichment of diabetes-related genes within the top T2D pathways resulting from the multi-trait analysis. The BLR models ability to handle diverse genomic features, perform regularization, conduct variable selection, and integrate information from multiple traits, genders, and ancestries demonstrates its utility in understanding the genetic architecture of complex traits. Our study provides insights into the potential of the BLR model to prioritize gene sets, offering a flexible framework applicable to various datasets. This model presents opportunities for advancing personalized medicine by exploring the genetic underpinnings of multifactorial traits, potentially leading to tailored therapeutic interventions.

genomics↗

bioRxiv2024

Evaluation of Bayesian Linear Regression Derived Gene Set Test Methods

Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations. Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. This suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.

genomics↗

bioRxiv2023

Evaluation of Bayesian Linear Regression Models as a Fine Mapping tool

Our aim was to evaluate Bayesian Linear Regression (BLR) models with BayesC and BayesR priors as a fine mapping tool and compare them to the state-of-the-art external models: FINEMAP, SuSIE-RSS, SuSIE-Inf and FINEMAP-Inf. Based on extensive simulations, we evaluated the different models based on F1 classification score. The different models were applied on quantitative and binary UK Biobank (UKB) phenotypes and evaluated based upon predictive accuracy and features of credible sets (CSs). We used over 533K genotyped and 6.6 million imputed single nucleotide polymorphisms (SNPs) for simulations and UKB phenotypes respectively, from over 335K UKB White British Unrelated samples. We simulated phenotypes from low (GA1) to moderate (GA2) polygenicity, heritability (h2) of 10% and 30%, causal SNPs ({pi}) of 0.1% and 1% sampled genome-wide, and disease prevalence (PV) of 5% and 15%. Single marker summary statistics and in-sample linkage disequilibrium were used to fit models in regions defined by lead SNPs. BayesR improved the F1 score, averaged across all simulations, between 27.26% and 13.32% relative to the external models. Predictive accuracy quantified as variance explained (R2), averaged across all the UKB quantitative phenotypes, with BayesR was decreased by 5.32% (SuSIE-Inf) and 3.71% (FINEMAP-Inf), and was increased by 7.93% (SuSIE-RSS) and 8.3% (BayesC). Area under the receiver operating characteristic curve averaged across all the UKB binary phenotypes, with BayesR was increased between 0.40% and 0.05% relative to the external models. SuSIE-RSS and BayesR, demonstrated the highest number of CSs, with BayesC and BayesR exhibiting the smallest average median size CSs in the UKB phenotypes. The BLR models performed similar to the external models. Specifically, BayesRs performance closely aligned with SuSIE-Inf and FINEMAP-Inf models. Collectively, our findings from both simulations and application of the models in the UKB phenotypes support that the BLR models are efficient fine mapping tools.

genomics↗

bioRxiv2022

Expanded utility of the R package qgg with applications within genomic medicine

SummaryHere, we present an expanded utility of the R package qgg for quantitative genetic and genomic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes five different Bayesian Linear Regression (BLR) models, which provide a unified framework for mapping of genetic variants, estimation of heritability and genomic prediction from either individual level data or from genome-wide association study (GWAS) summary statistics. To showcase some of the novel implementations, we analysed two quantitative trait phenotypes, body mass index and standing height from United Kingdom Biobank (UKB). We compared genomic prediction accuracies from single and multiple trait models, showed accurate estimation of genomic parameters, illustrate how a BLR model can be used to fine map potential causal loci, and finally, provide an extension of gene set enrichment analyses based on the BLR framework. With this release, the qgg package now provides a wealth of the commonly used methods in analysis of complex traits and diseases, without the need to switch between software tools and data formats. AvailabilityOur methodology is implemented in the publicly available R software package qgg using fast and memory efficient algorithms in C++ and is available from CRAN or as a developer version at our GitHub page (https://github.com/psoerensen/qgg). Notes on the implemented statistical genetic models, tutorials and example scripts are available from our accompanied homepage https://qganalytics.com/. Contactpalledr@hst.aau.dk and pso@qgg.au.dk Supplementary informationSupplementary data are available online.

genetics↗

bioRxiv2021

Strong experimental support for the hologenome hypothesis revealed from Drosophila melanogaster selection lines

Recently it has been proposed, that the holobiont, i.e., the host and its associated microbiome, constitute a distinct biological entity, on which selection operates. This is a fascinating idea that so far has limited empirical justification. Here Drosophila melanogaster lines from a large-scale artificial selection experiment, where we selected for stress resistance traits and for longevity, were used to test the hologenome hypothesis. We raised flies from all selection regimes, including a regime where flies were kept at benign standard laboratory condition (control regime) throughout the duration of the experiment, under common garden conditions and sequenced the microbiome of the flies. We found abundant differences in microbial communities between control and selection regimes, but not between replicate lines within the regimes, and microbial diversity was higher in selected relative to control lines. Several major core Drosophila bacterial species were differentially abundant in the different selection regimes despite flies being exposed to similar nutritional and general environmental conditions. Our results support the idea that the host and microbiome genomes have evolved in concert and provide experimental support for the hologenome theory of evolution.

evolutionary biology↗

bioRxiv2020

Prediction of complex phenotypes using the Drosophila metabolome

Understanding the genotype - phenotype map and how variation at different levels of biological organization are associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes; such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Secondly, we identify genes associated with metabolome variation. Thirdly, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analysed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance e.g. in human medicine and animal and plant breeding.

genetics↗

bioRxiv2018

qgg: an R package for large-scale quantitativegenetic analyses

SummaryStudies of complex traits and diseases are strongly dependent on the availability of user-friendly software designed to handle large-scale genetic and phenotypic data. Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and disease phenotypes. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, performing single and multiple marker association analyses, and genomic-based predictions of phenotypes. In particular, we have developed novel predictive models that use information on functional features of the genome that enables more accurate predictions of complex trait phenotypes. We illustrates core facilities of the qgg package by analysing human standing height from the UK Biobank.\n\nAvailability and implementationThe R package qgg is freely available. For latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg/.

genetics↗