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Rohde, P. D.

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

3 recordsLinked to original sources

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↗

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↗

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↗