Feeding Drosophila gut microbiomes from young and old flies modifies the microbiome.

It is becoming increasingly evident that the myriad of microbes in the gut, within cells and attached to body parts (or roots of plants), play crucial roles for the host. Although this has been known for decades, recent developments in molecular biology allow for expanded insight into the abundance and function of these microbes. Here we used the vinegar fly, Drosophila melanogaster, to investigate fitness measures across the lifetime of flies fed a suspension of gut microbes harvested from young or old flies, respectively. Our hypothesis was that flies constitutively enriched with a 'Young microbiome' would live longer and be more agile at old age (i.e. have increased healthspan) compared to flies enriched with an 'Old microbiome'. Three major take home messages came out of our study: (1) the gut microbiomes of young and old flies differ markedly; (2) feeding flies with Young and Old microbiomes altered the microbiome of recipient flies and (3) the two different microbial diets did not have any effect on locomotor activity nor lifespan of the recipient flies, contradicting our working hypothesis. Combined, these results provide novel insight into the interplay between hosts and their microbiomes and clearly highlight that the phenotypic effects of gut transplants and probiotics can be complex and unpredictable.

Predicting the presence of coronary plaques featuring high-risk characteristics using polygenic risk scores and targeted proteomics in patients with suspected coronary artery disease.

BACKGROUND: The presence of coronary plaques with high-risk characteristics is strongly associated with adverse cardiac events beyond the identification of coronary stenosis. Testing by coronary computed tomography angiography (CCTA) enables the identification of high-risk plaques (HRP). Referral for CCTA is presently based on pre-test probability estimates including clinical risk factors (CRFs); however, proteomics and/or genetic information could potentially improve patient selection for CCTA and, hence, identification of HRP. We aimed to (1) identify proteomic and genetic features associated with HRP presence and (2) investigate the effect of combining CRFs, proteomics, and genetics to predict HRP presence. METHODS: Consecutive chest pain patients (n = 1462) undergoing CCTA to diagnose obstructive coronary artery disease (CAD) were included. Coronary plaques were assessed using a semi-automatic plaque analysis tool. Measurements of 368 circulating proteins were obtained with targeted Olink panels, and DNA genotyping was performed in all patients. Imputed genetic variants were used to compute a multi-trait multi-ancestry genome-wide polygenic score (GPSMult). HRP presence was defined as plaques with two or more high-risk characteristics (low attenuation, spotty calcification, positive remodeling, and napkin ring sign). Prediction of HRP presence was performed using the glmnet algorithm with repeated fivefold cross-validation, using CRFs, proteomics, and GPSMult as input features. RESULTS: HRPs were detected in 165 (11%) patients, and 15 input features were associated with HRP presence. Prediction of HRP presence based on CRFs yielded a mean area under the receiver operating curve (AUC) ± standard error of 73.2 ± 0.1, versus 69.0 ± 0.1 for proteomics and 60.1 ± 0.1 for GPSMult. Combining CRFs with GPSMult increased prediction accuracy (AUC 74.8 ± 0.1 (P = 0.004)), while the inclusion of proteomics provided no significant improvement to either the CRF (AUC 73.2 ± 0.1, P = 1.00) or the CRF + GPSMult (AUC 74.6 ± 0.1, P = 1.00) models, respectively. CONCLUSIONS: In patients with suspected CAD, incorporating genetic data with either clinical or proteomic data improves the prediction of high-risk plaque presence. TRIAL REGISTRATION: https://clinicaltrials.gov/ct2/show/NCT02264717 (September 2014).

A Genetic Analysis of Current Medication Use in the UK Biobank.

Genomics has been forecasted to revolutionise human health by improving medical treatment through a better understanding of the molecular mechanisms of human diseases. Despite great successes of the last decade's genome-wide association studies (GWAS), the results have been translated to genomic medicine to a limited extent. One route to get closer to improved medical treatment could be by understanding the genetics of medication use. Current medication profiles from 335,744 individuals from the UK Biobank were obtained, and a GWAS was conducted to identify common genetic variants associated with current medication use. In total, 59 independent loci were identified for medication use, and approximately 18% of the total variation was attributable to common genetic variation. The largest fraction of genetic variance for current medication use was captured by variants with low-to-medium minor allele frequency, with coding, conserved genomic regions and transcription start sites being enriched for associated variants. The average correlation (R) between medication use and the polygenic score was 0.14. The results further demonstrated that individuals with higher polygenic burden for medication use were, on average, sicker and had a higher risk for adverse drug reactions. These results provide an insight into the genetic contribution of medication use and pave the way for developments of novel multiple trait polygenic scores, which include the genetically informed medication use.

The soil microbiome of Lolium perenne L. depends on host genotype, is modified by nitrogen level and varies across season.

Genotype by environment interactions (G × E) are frequently observed in herbage production. Understanding the underlying biological mechanisms is important for achieving stable and predictive outputs across production environments. The microbiome is gaining increasing attention as a significant contributing factor to G × E. Here, we focused on the soil microbiome of perennial ryegrass (Lolium perenne L.) grown under field conditions and investigated the soil microbiome variation across different ryegrass varieties to assess whether environmental factors, such as seasonality and nitrogen levels, affect the microbial community. We identified bacteria, archaea, and fungi operational taxonomic units (OTUs) and showed that seasonality and ryegrass variety were the two factors explaining the largest fraction of the soil microbiome diversity. The strong and significant variety-by-treatment-by-seasonal cut interaction for ryegrass dry matter was associated with the number of unique OTUs within each sample. We identified seven OTUs associated with ryegrass dry matter variation. An OTU belonging to the Solirubrobacterales (Thermoleophilales) order was associated with increased plant biomass, supporting the possibility of developing engineered microbiomes for increased plant yield. Our results indicate the importance of incorporating different layers of biological data, such as genomic and soil microbiome data to improve the prediction accuracy of plant phenotypes grown across heterogeneous environments.

Diurnal variation in genetic parameters for locomotor activity in Drosophila melanogaster assessed under natural thermal conditions.

In nature, organisms are exposed to variable and occasionally stressful environmental conditions. Responses to diurnal and seasonal fluctuations, such as temperature and food accessibility, involve adaptive behavioural and physiological changes. While much work has been done on understanding the genetic architecture and evolutionary potential of stress tolerance traits under constant thermal conditions, there has been less focus on the quantitative genetic background in variable environments. In this study, we use the Drosophila Genetic Reference Panel (DGRP) to investigate the locomotor activity, a key behavioural trait, under variable natural thermal conditions during the summer in a temperate environment. Male flies from 100 DGRP lines were exposed to natural thermal and light conditions in Drosophila activity monitors across three experimental days. We found that activity was highly temperature and time dependent and varied between lines both within and between days. Furthermore, we observed variation in genetic and environmental variance components, with low to moderate estimates of the heritability for locomotor activity, consistently peaking in the afternoons. Moreover, we showed that the estimated genetic correlations of locomotor activity between two time points decreased, as the absolute differences in ambient temperature increased. In conclusion, we find that the genetic background for locomotor activity is environment specific, and we conclude that more variable and unpredictable future temperatures will likely have a strong impact on the evolutionary trajectories of behavioural traits in ectotherms.

A genome-wide association study of social trust in 33,882 Danish blood donors.

Social trust is a heritable trait that has been linked with physical health and longevity. In this study, we performed genome-wide association studies of self-reported social trust in n = 33,882 Danish blood donors. We observed genome-wide and local evidence of genetic similarity with other brain-related phenotypes and estimated the single nucleotide polymorphism-based heritability of trust to be 6% (95% confidence interval = (2.1, 9.9)). In our discovery cohort (n = 25,819), we identified one significantly associated locus (lead variant: rs12776883) in an intronic enhancer region of PLPP4, a gene highly expressed in brain, kidneys, and testes. However, we could not replicate the signal in an independent set of donors who were phenotyped a year later (n = 8063). In the subsequent meta-analysis, we found a second significantly associated variant (rs71543507) in an intergenic enhancer region. Overall, our work confirms that social trust is heritable, and provides an initial look into the genetic factors that influence it.

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

SUMMARY: Here, we present an expanded utility of the R package qgg for genetic analyses of complex traits and diseases. One of the major updates of the package is, that it now includes Bayesian linear regression modeling procedures, 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 summary data. 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 and data formats. AVAILABILITY AND IMPLEMENTATION: The methodologies are implemented in the publicly available R software package, qgg, using fast and memory efficient algorithms in C++ and is available on 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 at our GitHub page https://psoerensen.github.io/qgg/.

Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain.

BACKGROUND: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS). METHODS: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning. RESULTS: Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients. CONCLUSIONS: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT02264717.

Pregnancy-Associated Bleeding and Genetics: Five Sequence Variants in the Myometrium and Progesterone Signaling Pathway are associated with postpartum hemorrhage.

Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severity of these complications in maternal-fetal health. Here, we investigated the genetic variation underlying aspects of pregnancy-associated bleeding and identified five loci associated with PPH through a meta-analysis of 21,512 cases and 259,500 controls. Functional annotation analysis indicated candidate genes, HAND2, TBX3, and RAP2C/FRMD7, at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors (PGR). Furthermore, there were strong genetic correlations with birth weight, gestational duration, and uterine fibroids. Early bleeding during pregnancy (28,898 cases and 302,894 controls) yielded no genome-wide association signals, but showed strong genetic correlation with a variety of human traits, indicative of polygenic and pleiotropic effects. Our results suggest that postpartum bleeding is related to myometrium dysregulation, whereas early bleeding is a complex trait related to underlying health and possibly socioeconomic status.

Danish study of Non-Invasive Testing in Coronary Artery Disease 3 (Dan-NICAD 3): study design of a controlled study on optimal diagnostic strategy.

INTRODUCTION: Current guideline recommend functional imaging for myocardial ischaemia if coronary CT angiography (CTA) has shown coronary artery disease (CAD) of uncertain functional significance. However, diagnostic accuracy of selective myocardial perfusion imaging after coronary CTA is currently unclear. The Danish study of Non-Invasive testing in Coronary Artery Disease 3 trial is designed to evaluate head to head the diagnostic accuracy of myocardial perfusion imaging with positron emission tomography (PET) using the tracers 82Rubidium (82Rb-PET) compared with oxygen-15 labelled water PET (15O-water-PET) in patients with symptoms of obstructive CAD and a coronary CT scan with suspected obstructive CAD. METHODS AND ANALYSIS: This prospective, multicentre, cross-sectional study will include approximately 1000 symptomatic patients without previous CAD. Patients are included after referral to coronary CTA. All patients undergo a structured interview and blood is sampled for genetic and proteomic analysis and a coronary CTA. Patients with possible obstructive CAD at coronary CTA are examined with both 82Rb-PET, 15O-water-PET and invasive coronary angiography with three-vessel fractional flow reserve and thermodilution measurements of coronary flow reserve. After enrolment, patients are followed with Seattle Angina Questionnaires and follow-up PET scans in patients with an initially abnormal PET scan and for cardiovascular events in 10 years. ETHICS AND DISSEMINATION: Ethical approval was obtained from Danish regional committee on health research ethics. Written informed consent will be provided by all study participants. Results of this study will be disseminated via articles in international peer-reviewed journal. TRIAL REGISTRATION NUMBER: NCT04707859.

Functionally Validating Evolutionary Conserved Risk Genes for Parkinson's Disease in Drosophila melanogaster.

Parkinson's disease (PD) is a heterogeneous and complex neurodegenerative disorder and large-scale genetic studies have identified >130 genes associated with PD. Although genomic studies have been decisive for our understanding of the genetic contributions underlying PD, these associations remain as statistical associations. Lack of functional validation limits the biological interpretation; however, it is labour extensive, expensive, and time consuming. Therefore, the ideal biological system for functionally validating genetic findings must be simple. The study aim was to assess systematically evolutionary conserved PD-associated genes using Drosophila melanogaster. From a literature review, a total of 136 genes have found to be associated with PD in GWAS studies, of which 11 are strongly evolutionary conserved between Homo sapiens and D. melanogaster. By ubiquitous gene expression knockdown of the PD-genes in D. melanogaster, the flies' escape response was investigated by assessing their negative geotaxis response, a phenotype that has previously been used to investigate PD in D. melanogaster. Gene expression knockdown was successful in 9/11 lines, and phenotypic consequences were observed in 8/9 lines. The results provide evidence that genetically modifying expression levels of PD genes in D. melanogaster caused reduced climbing ability of the flies, potentially supporting their role in dysfunctional locomotion, a hallmark of PD.

Strong Sex-Dependent Effects of Malnutrition on Life- and Healthspan in Drosophila melanogaster.

Insufficient intake of essential nutrients, malnutrition is a major issue for millions of people and has a strong impact on the distribution and abundance of species in nature. In this study, we investigated the effect of malnutrition on several fitness components in the vinegar fly Drosophila melanogaster. Four diets with different nutritional values, including three diluted diets of an optimal nutritional balanced diet, were used as feed sources. The effect of malnutrition on fitness components linked to healthspan, the period of life spent in good health conditions, was evaluated by quantifying the flies' lifespan, locomotor activity, heat stress tolerance, lipid content, and dry weight. The results showed that malnutrition had severe negative impact, such as reduced lifespan, locomotor activity, heat stress tolerance, fat content, and dry weight. The negative phenotypic effects were highly sex-dependent, with males being more negatively impacted by malnutrition compared to females. These findings highlight important detrimental and sex-specific effects of malnutrition not only on lifespan but also on traits related to healthspan.

Migraine-Associated Mutation in the Na,K-ATPase Leads to Disturbances in Cardiac Metabolism and Reduced Cardiac Function.

Background Mutations in ATP1A2 gene encoding the Na,K-ATPase α2 isoform are associated with familial hemiplegic migraine type 2. Migraine with aura is a known risk factor for heart disease. The Na,K-ATPase is important for cardiac function, but its role for heart disease remains unknown. We hypothesized that ATP1A2 is a susceptibility gene for heart disease and aimed to assess the underlying disease mechanism. Methods and Results Mice heterozygous for the familial hemiplegic migraine type 2-associated G301R mutation in the Atp1a2 gene (α2+/G301R mice) and matching wild-type controls were compared. Reduced expression of the Na,K-ATPase α2 isoform and increased expression of the α1 isoform were observed in hearts from α2+/G301R mice (Western blot). Left ventricular dilation and reduced ejection fraction were shown in hearts from 8-month-old α2+/G301R mice (cardiac magnetic resonance imaging), and this was associated with reduced nocturnal blood pressure (radiotelemetry). Cardiac function and blood pressure of 3-month-old α2+/G301R mice were similar to wild-type mice. Amplified Na,K-ATPase-dependent Src kinase/Ras/Erk1/2 (p44/42 mitogen-activated protein kinase) signaling was observed in hearts from 8-month-old α2+/G301R mice, and this was associated with mitochondrial uncoupling (respirometry), increased oxidative stress (malondialdehyde measurements), and a heart failure-associated metabolic shift (hyperpolarized magnetic resonance). Mitochondrial membrane potential (5,5´,6,6´-tetrachloro-1,1´,3,3´-tetraethylbenzimidazolocarbocyanine iodide dye assay) and mitochondrial ultrastructure (transmission electron microscopy) were similar between the groups. Proteomics of heart tissue further suggested amplified Src/Ras/Erk1/2 signaling and increased oxidative stress and provided the molecular basis for systolic dysfunction in 8-month-old α2+/G301R mice. Conclusions Our findings suggest that ATP1A2 mutation leads to disturbed cardiac metabolism and reduced cardiac function mediated via Na,K-ATPase-dependent reactive oxygen species signaling through the Src/Ras/Erk1/2 pathway.

Multi-Trait Genomic Risk Stratification for Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM) is continuously rising with more disease cases every year. T2DM is a chronic disease with many severe comorbidities and therefore remains a burden for the patient and the society. Disease prevention, early diagnosis, and stratified treatment are important elements in slowing down the increase in diabetes prevalence. T2DM has a substantial genetic component with an estimated heritability of 40-70%, and more than 500 genetic loci have been associated with T2DM. Because of the intrinsic genetic basis of T2DM, one tool for risk assessment is genome-wide genetic risk scores (GRS). Current GRS only account for a small proportion of the T2DM risk; thus, better methods are warranted for more accurate risk assessment. T2DM is correlated with several other diseases and complex traits, and incorporating this information by adjusting effect size of the included markers could improve risk prediction. The aim of this study was to develop multi-trait (MT)-GRS leveraging correlated information. We used phenotype and genotype information from the UK Biobank, and summary statistics from two independent T2DM studies. Marker effects for T2DM and seven correlated traits, namely, height, body mass index, pulse rate, diastolic and systolic blood pressure, smoking status, and information on current medication use, were estimated (i.e., by logistic and linear regression) within the UK Biobank. These summary statistics, together with the two independent training summary statistics, were incorporated into the MT-GRS prediction in different combinations. The prediction accuracy of the MT-GRS was improved by 12.5% compared to the single-trait GRS. Testing the MT-GRS strategy in two independent T2DM studies resulted in an elevated accuracy by 50-94%. Finally, combining the seven information traits with the two independent T2DM studies further increased the prediction accuracy by 34%. Across comparisons, body mass index and current medication use were the two traits that displayed the largest weights in construction of the MT-GRS. These results explicitly demonstrate the added benefit of leveraging correlated information when constructing genetic scores. In conclusion, constructing GRS not only based on the disease itself but incorporating genomic information from other correlated traits as well is strongly advisable for obtaining improved individual risk stratification.

Genotype and Trait Specific Responses to Rapamycin Intake in Drosophila melanogaster.

Rapamycin is a powerful inhibitor of the TOR (Target of Rapamycin) pathway, which is an evolutionarily conserved protein kinase, that plays a central role in plants and animals. Rapamycin is used globally as an immunosuppressant and as an anti-aging medicine. Despite widespread use, treatment efficiency varies considerably across patients, and little is known about potential side effects. Here we seek to investigate the effects of rapamycin by using Drosophila melanogaster as model system. Six isogenic D. melanogaster lines were assessed for their fecundity, male longevity and male heat stress tolerance with or without rapamycin treatment. The results showed increased longevity and heat stress tolerance for male flies treated with rapamycin. Conversely, the fecundity of rapamycin-exposed individuals was lower than for flies from the non-treated group, suggesting unwanted side effects of the drug in D. melanogaster. We found strong evidence for genotype-by-treatment interactions suggesting that a 'one size fits all' approach when it comes to treatment with rapamycin is not recommendable. The beneficial responses to rapamycin exposure for stress tolerance and longevity are in agreement with previous findings, however, the unexpected effects on reproduction are worrying and need further investigation and question common believes that rapamycin constitutes a harmless drug.

Prediction of complex phenotypes using the Drosophila melanogaster metabolome.

Understanding the genotype-phenotype map and how variation at different levels of biological organization is 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 melanogaster 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. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. 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, evolutionary biology and animal and plant breeding.

Polygenic Risk Score Prediction for Endometriosis.

Endometriosis is a major health care challenge because many young women with endometriosis go undetected for an extended period, which may lead to pain sensitization. Clinical tools to better identify candidates for laparoscopy-guided diagnosis are urgently needed. Since endometriosis has a strong genetic component, there is a growing interest in using genetics as part of the clinical risk assessment. The aim of this work was to investigate the discriminative ability of a polygenic risk score (PRS) for endometriosis using three different cohorts: surgically confirmed cases from the Western Danish endometriosis referral Center (249 cases, 348 controls), cases identified from the Danish Twin Registry (DTR) based on ICD-10 codes from the National Patient Registry (140 cases, 316 controls), and replication analysis in the UK Biobank (2,967 cases, 256,222 controls). Patients with adenomyosis from the DTR (25 cases) and from the UK Biobank (1,883 cases) were included for comparison. The PRS was derived from 14 genetic variants identified in a published genome-wide association study with more than 17,000 cases. The PRS was associated with endometriosis in surgically confirmed cases [odds ratio (OR) = 1.59, p = 2.57× 10-7] and in cases from the DTR biobank (OR = 1.50, p = 0.0001). Combining the two Danish cohorts, each standard deviation increase in PRS was associated with endometriosis (OR = 1.57, p = 2.5× 10-11), as well as the major subtypes of endometriosis; ovarian (OR = 1.72, p = 6.7× 10-5), infiltrating (OR = 1.66, p = 2.7× 10-9), and peritoneal (OR = 1.51, p = 2.6 × 10-3). These findings were replicated in the UK Biobank with a much larger sample size (OR = 1.28, p < 2.2× 10-16). The PRS was not associated with adenomyosis, suggesting that adenomyosis is not driven by the same genetic risk variants as endometriosis. Our results suggest that a PRS captures an increased risk of all types of endometriosis rather than an increased risk for endometriosis in specific locations. Although the discriminative accuracy is not yet sufficient as a stand-alone clinical utility, our data demonstrate that genetics risk variants in form of a simple PRS may add significant new discriminatory value. We suggest that an endometriosis PRS in combination with classical clinical risk factors and symptoms could be an important step in developing an urgently needed endometriosis risk stratification tool.

qgg: an R package for large-scale quantitative genetic analyses.

SUMMARY: Here, we present the R package qgg, which provides an environment for large-scale genetic analyses of quantitative traits and diseases. The qgg package provides an infrastructure for efficient processing of large-scale genetic data and functions for estimating genetic parameters, and performing single and multiple marker association analyses and genomic-based predictions of phenotypes. AVAILABILITY AND IMPLEMENTATION: The qgg package is freely available. For the latest updates, user guides and example scripts, consult the main page http://psoerensen.github.io/qgg. The current release is available from CRAN (https://CRAN.R-project.org/package=qgg) for all major operating systems. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genetic Signatures of Drug Response Variability in Drosophila melanogaster.

Knowledge of the genetic basis underlying variation in response to environmental exposures or treatments is important in many research areas. For example, knowing the set of causal genetic variants for drug responses could revolutionize personalized medicine. We used Drosophila melanogaster to investigate the genetic signature underlying behavioral variability in response to methylphenidate (MPH), a drug used in the treatment of attention-deficit/hyperactivity disorder. We exposed a wild-type D. melanogaster population to MPH and a control treatment, and observed an increase in locomotor activity in MPH-exposed individuals. Whole-genome transcriptomic analyses revealed that the behavioral response to MPH was associated with abundant gene expression alterations. To confirm these patterns in a different genetic background and to further advance knowledge on the genetic signature of drug response variability, we used a system of inbred lines, the Drosophila Genetic Reference Panel (DGRP). Based on the DGRP, we showed that the behavioral response to MPH was strongly genotype-dependent. Using an integrative genomic approach, we incorporated known gene interactions into the genomic analyses of the DGRP, and identified putative candidate genes for variability in drug response. We successfully validated 71% of the investigated candidate genes by gene expression knockdown. Furthermore, we showed that MPH has cross-generational behavioral and transcriptomic effects. Our findings establish a foundation for understanding the genetic mechanisms driving genotype-specific responses to medical treatment, and highlight the opportunities that integrative genomic approaches have in optimizing medical treatment of complex diseases.