Biology subjects

Morgante, F.

Publications and source records attributed to Morgante, F..

12 recordsLinked to original sources

bioRxiv2026

Outbred Drosophila populations reveal diet-dependent genetic effects on development time

Metabolic effects of genetic variation often depend on diet, yet the loci underlying diet-dependent developmental responses remain incompletely defined. Here we combine multi-trait phenotyping of Drosophila Genetic Reference Panel lines with genome-wide mapping in newly developed Drosophila Recombinant Populations. High sugar causes genotype- and life-stage-dependent changes in metabolic and life-history traits, with development time emerging as a highly heritable, sugar-sensitive phenotype. Mapping in 16 outbred advanced intercross populations reveals distinct association landscapes under low- and high-sugar diets, with a concentrated low-sugar signal, a more distributed high-sugar pattern, and identified genotype-by-diet loci including tap, Eip75B and Cerk. Functional perturbation supports diet-dependent effects for several prioritized candidates. Allele-frequency analyses identify operationally defined thrifty-like variants associated with delayed development under high sugar and relatively earlier development under low sugar; these variants are enriched for cell-adhesion, neurodevelopmental, and morphogenetic processes. Together, these results establish an outbred Drosophila framework for dissecting how dietary sugar remodels the genetic architecture of development time.

genetics↗

bioRxiv2025

When does accounting for gene-environment interactions improve complex trait prediction? A case study with Drosophila lifespan

Gene-environment interactions (G x E) have been shown to explain a non-negligible proportion of variance for a plethora of complex traits in different species, including livestock, plants, and humans. While several studies have shown that including G x E can improve prediction accuracy in agricultural species, no increase in accuracy has been observed in human studies. In this work, we sought to investigate the reasons for the contradictory results between agricultural species and humans. Model organisms are useful for studying G x E, since environments can be controlled and genotypes can be replicated across environments. Thus, we used data from a previous study in Drosophila melanogaster, where the authors measured lifespan in different environments and found evidence of G x E. We devised three different cross-validation (CV) scenarios that mimic the relationships between reference and test populations observed in agriculture and human studies, and fitted a few statistical models with and without including G x E. The results showed that G x E explained 8% of lifespan variance. Despite that, models accounting for G x E improved prediction accuracy only in CV scenarios where the same genotypes are observed in both the reference and test populations. While these scenarios are common in agriculture, where individuals of the same family or variety appear in both populations, they are not encountered in human studies, where individuals are unrelated. Thus, our work clarifies in which prediction scenarios we can expect improvements by incorporating G x E into statistical models, and provides an explanation for previous results found in studies involving human populations.

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↗

bioRxiv2025

The role of STN beta oscillations on lower extremity muscle activity in Parkinsonian stepping

Freezing of gait (FOG) is a devastating symptom of Parkinsons disease (PD) often resulting in disabling falls and loss of independence. It affects half of patients, yet current therapeutic strategies are insufficient, and the underlying neural mechanisms remain poorly understood. This study investigated beta oscillation dynamics in the STN during different locomotor states, while examining the effects of levodopa. In particular, it aimed to identify pathological activity by analysing the relationship between the STN and lower limb muscles during stepping. Local field potentials (LFP) in the STN and muscle activity (EMG) of the gastrocnemius and peroneus longus were recorded in 14 PD patients during standing and stepping, ON and OFF levodopa. Levodopa reduced stepping variability, implying improved stepping abilities. Distinct STN beta patterns were observed between stepping and standing, with lower high-beta and higher low-beta during stepping compared to standing, suggesting a distinct role of these frequency bands in motor control during postural and movement states. Levodopa reduced low-beta but increased high-beta activity, highlighting a potential physiological function of high-beta in the STN during standing and stepping. In addition, step-phase specific effects of levodopa included reduced broad-beta band activity in the STN and lower limb muscles during the late-stance and pushing-off phase of the contralateral leg when ON medication. Further analyses suggest that pathological STN activity amplifies muscle activation around movement initiation, potentially reducing the ability of the patient to move freely. These findings offer insight for developing phase-specific stimulation strategies targeting STN beta oscillations during gait.

neuroscience↗

bioRxiv2024

Evoked resonant neural activity outperforms spectral markers in decoding sleep from the subthalamic nucleus

BackgroundDeep brain stimulation is a treatment for advanced Parkinsons disease and currently tuned to target motor symptoms during daytime. Parkinsons disease is associated with multiple nocturnal symptoms such as akinesia, insomnia and sleep fragmentation which may require adjustments of stimulation during sleep for best treatment outcome. ObjectivesThere is a need for a robust biomarker to guide stimulation titration across sleep stages. This study aimed to investigate whether evoked resonant neural activity (ERNA) is modulated during sleep. MethodsWe recorded local field potentials from the subthalamic nucleus of four Parkinsons patients with externalised electrodes while applying single stimulation pulses to investigate the effect of sleep on ERNA. ResultsWe found that ERNA features change with wakefulness and sleep stages, and are correlated with canonical frequency bands and heart rate. We further evaluated the performance of machine learning models in classifying non-REM sleep versus wakefulness and found that ERNA amplitude outperforms all spectral markers. ConclusionsGiven the heterogeneity of spectral features during sleep, their susceptibility to movement artefacts and superior classification accuracy of models using ERNA features, this study paves the way for ERNA as a marker for automatic stimulation titration during sleep and improved patient care.

neuroscience↗

bioRxiv2024

Using lifestyle information in polygenic modeling of blood pressure traits: a simple method to reduce bias

Complex traits are determined by the effects of multiple genetic variants, multiple environmental factors, and potentially their interaction. Predicting complex trait phenotypes from genotypes is a fundamental task in quantitative genetics that was pioneered in agricultural breeding for selection purposes. However, it has recently become important in human genetics. While prediction accuracy for some human complex traits is appreciable, this remains low for most traits. A promising way to improve prediction accuracy is by including not only genetic information but also environmental information in prediction models. However, environmental factors can, in turn, be genetically determined. This phenomenon gives rise to a correlation between the genetic and environmental components of the phenotype, which violates the assumption of independence between the genetic and environmental components of most statistical methods for polygenic modeling. In this work, we investigated the impact of including 27 lifestyle variables as well as genotype information (and their interaction) for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals in UK Biobank. The 27 lifestyle variables were included as either raw variables or adjusted by genetic and other non-genetic factors. The results show that including both lifestyle and genetic data improved prediction accuracy compared to using either piece of information alone. Both prediction accuracy and bias can improve substantially for some traits when the models account for the lifestyle variables after their proper adjustment. Our work confirms the utility of including environmental information in polygenic models of complex traits and highlights the importance of proper handling of the environmental variables. Author summaryMany traits of medical relevance are "complex" in that they are affected by both genetic and environmental factors. Thus, using genetic and environmental information in statistical methods has the potential to increase the accuracy of phenotypic prediction, the ultimate goal of precision medicine. However, the correlation between the genetic and environmental components (that arises when environmental variables are themselves genetically determined) and the correlations between environmental measures can be problematic for most statistical methods used for modeling complex traits. In this work, we investigated these issues using 27 lifestyle measures in addition to genetic information for predicting diastolic blood pressure, systolic blood pressure, and pulse pressure in older individuals. We show that including lifestyle and genetic data resulted in more accurate predictions than either data type alone. Moreover, adjusting the lifestyle measures for the genetic and other non-genetic effects can help improve the predictions further.

genetics↗

bioRxiv2024

Comparing statistical learning methods for complex trait prediction from gene expression

Accurate prediction of complex traits is an important task in quantitative genetics that has become increasingly relevant for personalized medicine. Genotypes have traditionally been used for trait prediction using a variety of methods such as mixed models, Bayesian methods, penalized regressions, dimension reductions, and machine learning methods. Recent studies have shown that gene expression levels can produce higher prediction accuracy than genotypes. However, only a few prediction methods were used in these studies. Thus, a comprehensive assessment of methods is needed to fully evaluate the potential of gene expression as a predictor of complex trait phenotypes. Here, we used data from the Drosophila Genetic Reference Panel (DGRP) to compare the ability of several existing statistical learning methods to predict starvation resistance from gene expression in the two sexes separately. The methods considered differ in assumptions about the distribution of gene effect sizes - ranging from models that assume that every gene affects the trait to more sparse models - and their ability to capture gene-gene interactions. We also used functional annotation (i.e., Gene Ontology (GO)) as an external source of biological information to inform prediction models. The results show that differences in prediction accuracy between methods exist, although they are generally not large. Methods performing variable selection gave higher accuracy in females while methods assuming a more polygenic architecture performed better in males. Incorporating GO annotations further improved prediction accuracy for a few GO terms of biological significance. Biological significance extended to the genes underlying highly predictive GO terms with different genes emerging between sexes. Notably, the Insulin-like Receptor (InR) was prevalent across methods and sexes. Our results confirmed the potential of transcriptomic prediction and highlighted the importance of selecting appropriate methods and strategies in order to achieve accurate predictions.

genetics↗

bioRxiv2024

Improving polygenic prediction from summary data by learning patterns of effect sharing across multiple phenotypes.

Polygenic prediction of complex trait phenotypes has become important in human genetics, especially in the context of precision medicine. Recently, Morgante et al. introduced mr.mash, a flexible and computationally efficient method that models multiple phenotypes jointly and leverages sharing of effects across such phenotypes to improve prediction accuracy. However, a drawback of mr.mash is that it requires individual-level data, which are often not publicly available. In this work, we introduce mr.mash-rss, an extension of the mr.mash model that requires only summary statistics from Genome-Wide Association Studies (GWAS) and linkage disequilibrium (LD) estimates from a reference panel. By using summary data, we achieve the twin goal of increasing the applicability of the mr.mash model to data sets that are not publicly available and making it scalable to biobank-size data. Through simulations, we show that mr.mash-rss is competitive with, and often outperforms, current state-of-the-art methods for single- and multi-phenotype polygenic prediction in a variety of scenarios that differ in the pattern of effect sharing across phenotypes, the number of phenotypes, the number of causal variants, and the genomic heritability. We also present a real data analysis of 16 blood cell phenotypes in UK Biobank, showing that mr.mash-rss achieves higher prediction accuracy than competing methods for the majority of traits, especially when the data has smaller sample size. Author summaryPolygenic prediction refers to the use of an individuals genetic information (i.e., genotypes) to predict traits (i.e., phenotypes), which are often of medical relevance. It is known that some phenotypes are related and are affected by the same genotypes. When this is the case, it is possible to improve the accuracy of predictions by using methods that model multiple phenotypes jointly and account for shared effects. mr.mash is a recently developed multi-phenotype method that can learn which effects are shared and has been shown to improve prediction. However, mr.mash requires large data sets of genetic and phenotypic information collected at the individual level. Such data are often unavailable due to privacy concerns, or are difficult to work with due to the computational resources needed to analyze data of this size. Our work extends mr.mash to require only summary statistics from Genome-Wide Association Studies instead of individual-level data, which are usually publicly available. In addition, the computations using summary statistics do not depend on sample size, making the newly developed mr.mash-rss scalable to extremely large data sets. Using simulations and real data analysis, we show that our method is competitive with other methods for polygenic prediction.

genetics↗

bioRxiv2022

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms. Author summaryPredicting phenotypes from genotypes is a fundamental problem in quantitative genetics. Thanks to recent advances, it is increasingly feasible to collect data on many phenotypes and genome-wide genotypes in large samples. Here, we tackle the problem of predicting multiple phenotypes from genotypes using a new method based on a multivariate, multiple linear regression model. Although the use of a multivariate, multiple linear regression model is not new, in this paper we introduce a flexible and computationally efficient empirical Bayes approach based on this model. This approach uses a prior that captures how the effects of genotypes on phenotypes are shared across the different phenotypes, and then the prior is adapted to the data in order to capture the most prominent sharing patterns present in the data. We assess the benefits of this flexible Bayesian approach in simulated genetic data sets, and we illustrate its application in predicting gene expression measured in multiple human tissues. We show that our methods can outperform competing methods in terms of prediction accuracy, and the computations involved in fitting the model and making the predictions scale well to large data sets.

genetics↗

bioRxiv2022

The aperiodic exponent of subthalamic field potentials reflects excitation/inhibition balance in Parkinsonism: a cross-species study in vivo

AO_SCPLOWBSTRACTC_SCPLOWPeriodic features of neural time series data, such as local field potentials (LFP), are often quantified using power spectra. While the aperiodic exponent of spectra is typically disregarded, it is nevertheless modulated in a physiologically-relevant manner and was recently hypothesised to reflect excitation/inhibition (E/I) balance in neuronal populations. Here, we used a cross-species in vivo electrophysiological approach to test the E/I hypothesis in the context of experimental and idiopathic Parkinsonism. We demonstrate in dopamine-depleted rats that aperiodic exponents and power at 30-100 Hz in subthalamic nucleus (STN) LFPs reflect defined changes in basal ganglia network activity; higher aperiodic exponents tally with lower levels of STN neuron firing and a balance tipped towards inhibition. Using STN-LFPs recorded from awake Parkinsons patients, we show that higher exponents accompany dopaminergic medication and deep brain stimulation (DBS) of STN, consistent with untreated Parkinsons manifesting as reduced inhibition and hyperactivity of STN. These results suggest that the aperiodic exponent of STN-LFPs in Parkinsonism reflects E/I balance, and might be a candidate biomarker for adaptive DBS.

neuroscience↗

bioRxiv2019

Leveraging multiple layers of data to predict Drosophila complex traits

An important challenge in genetics is to be able to predict complex traits accurately. Despite recent advances, prediction accuracy for most complex traits remains low. Here, we used the Drosophila Genetic Reference Panel (DGRP), a collection of 200 lines with whole-genome sequences and deep RNA sequencing data, to evaluate the usefulness of using high-quality gene expression levels compared to relying on genotypes for predicting three complex traits. We found that expression levels provided higher accuracy than genotypes for starvation resistance, similar accuracy for chill coma recovery, and lower accuracy for startle response. Models including both genotype and expressions levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included another layer of information, i.e., gene ontology (GO). We found that a limited number of GO terms, some of which had a clear biological interpretation, were strongly predictive of the traits. In summary, this study shows that integrating different sources of information can improve prediction accuracy, especially when large samples are not available.

genetics↗

bioRxiv2019

Gene Expression Networks in the Drosophila Genetic Reference Panel

A major challenge in modern biology is to understand how naturally occurring variation in DNA sequences affects complex organismal traits through networks of intermediate molecular phenotypes. Here, we performed deep RNA sequencing of 200 Drosophila Genetic Reference Panel inbred lines with complete genome sequences, and mapped expression quantitative trait loci for annotated genes, novel transcribed regions (most of which are long noncoding RNAs), transposable elements and microbial species. We identified host variants that affect expression of transposable elements, independent of their copy number, as well as microbiome composition. We constructed sex-specific expression quantitative trait locus regulatory networks. These networks are enriched for novel transcribed regions and target genes in heterochromatin and euchromatic regions of reduced recombination, and genes regulating transposable element expression. This study provides new insights regarding the role of natural genetic variation in regulating gene expression and generates testable hypotheses for future functional analyses.

genomics↗