Functional Validation of Candidate Genes Detected by Genomic Feature Models

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.

Drosophila as a Model for Human Disease: Insights into Rare and Ultra-Rare Diseases

Rare and ultra-rare diseases constitute a significant medical challenge due to their low prevalence and the limited understanding of their origin and underlying mechanisms. These disorders often exhibit phenotypic diversity and molecular complexity that represent a challenge to biomedical research. There are more than 6000 different rare diseases that affect nearly 300 million people worldwide. However, the prevalence of each rare disease is low, and in consequence, the biomedical resources dedicated to each rare disease are limited and insufficient to effectively achieve progress in the research. The use of animal models to investigate the mechanisms underlying pathogenesis has become an invaluable tool. Among the animal models commonly used in research, Drosophila melanogaster has emerged as an efficient and reliable experimental model for investigating a wide range of genetic disorders, and to develop therapeutic strategies for rare and ultra-rare diseases. It offers several advantages as a research model including short life cycle, ease of laboratory maintenance, rapid life cycle, and fully sequenced genome that make it highly suitable for studying genetic disorders. Additionally, there is a high degree of genetic conservation from Drosophila melanogaster to humans, which allows the extrapolation of findings at the molecular and cellular levels. Here, I examine the role of Drosophila melanogaster as a model for studying rare and ultra-rare diseases and highlight its significant contributions and potential to biomedical research. High-throughput next-generation sequencing (NGS) technologies, such as whole-exome sequencing and whole-genome sequencing (WGS), are providing massive amounts of information on the genomic modifications present in rare diseases and common complex traits. The sequencing of exomes or genomes of individuals affected by rare diseases has enabled human geneticists to identify rare variants and identify potential loci associated with novel gene-disease relationships. Despite these advances, the average rare disease patient still experiences significant delay until receiving a diagnosis. Furthermore, the vast majority (95%) of patients with rare conditions lack effective treatment or a cure. This scenario is enhanced by frequent misdiagnoses leading to inadequate support. In consequence, there is an urgent need to develop model organisms to explore the molecular mechanisms underlying these diseases and to establish the genetic origin of these maladies. The aim of this review is to discuss the advantages and limitations of Drosophila melanogaster, hereafter referred as Drosophila, as an experimental model for biomedical research, and the applications to study human disease. The main question to address is whether Drosophila is a valid research model to study human disease, and in particular, rare and ultra-rare diseases.

Experimental Evolution in a Warming World: The Omics Era

A comprehensive understanding of the genetic mechanisms that shape species responses to thermal variation is essential for more accurate predictions of the impacts of climate change on biodiversity. Experimental evolution with high-throughput resequencing approaches (evolve and resequence) is a highly effective tool that has been increasingly employed to elucidate the genetic basis of adaptation. The number of thermal evolve and resequence studies is rising, yet there is a dearth of efforts to integrate this new wealth of knowledge. Here, we review this literature showing how these studies have contributed to increase our understanding on the genetic basis of thermal adaptation. We identify two major trends: highly polygenic basis of thermal adaptation and general lack of consistency in candidate targets of selection between studies. These findings indicate that the adaptive responses to specific environments are rather independent. A review of the literature reveals several gaps in the existing research. Firstly, there is a paucity of studies done with organisms of diverse taxa. Secondly, there is a need to apply more dynamic and ecologically relevant thermal environments. Thirdly, there is a lack of studies that integrate genomic changes with changes in life history and behavioral traits. Addressing these issues would allow a more in-depth understanding of the relationship between genotype and phenotype. We highlight key methodological aspects that can address some of the limitations and omissions identified. These include the need for greater standardization of methodologies and the utilization of new technologies focusing on the integration of genomic and phenotypic variation in the context of thermal adaptation.

Focus on your locus with a massively parallel reporter assay

A growing number of variants associated with risk for neurodevelopmental disorders have been identified by genome-wide association and whole genome sequencing studies. As common risk variants often fall within large haplotype blocks covering long stretches of the noncoding genome, the causal variants within an associated locus are often unknown. Similarly, the effect of rare noncoding risk variants identified by whole genome sequencing on molecular traits is seldom known without functional assays. A massively parallel reporter assay (MPRA) is an assay that can functionally validate thousands of regulatory elements simultaneously using high-throughput sequencing and barcode technology. MPRA has been adapted to various experimental designs that measure gene regulatory effects of genetic variants within cis- and trans-regulatory elements as well as posttranscriptional processes. This review discusses different MPRA designs that have been or could be used in the future to experimentally validate genetic variants associated with neurodevelopmental disorders. Though MPRA has limitations such as it does not model genomic context, this assay can help narrow down the underlying genetic causes of neurodevelopmental disorders by screening thousands of sequences in one experiment. We conclude by describing future directions of this technique such as applications of MPRA for gene-by-environment interactions and pharmacogenetics.

In vivo identification and validation of novel potential predictors for human cardiovascular diseases

Genetics crucially contributes to cardiovascular diseases (CVDs), the global leading cause of death. Since the majority of CVDs can be prevented by early intervention there is a high demand for the identification of predictive causative genes. While genome wide association studies (GWAS) correlate genes and CVDs after diagnosis and provide a valuable resource for such causative candidate genes, often preferentially those with previously known or suspected function are addressed further. To tackle the unaddressed blind spot of understudied genes, we particularly focused on the validation of human heart phenotype-associated GWAS candidates with little or no apparent connection to cardiac function. Building on the conservation of basic heart function and underlying genetics from fish to human we combined CRISPR/Cas9 genome editing of the orthologs of human GWAS candidates in isogenic medaka with automated high-throughput heart rate analysis. Our functional analyses of understudied human candidates uncovered a prominent fraction of heart rate associated genes from adult human patients impacting on the heart rate in embryonic medaka already in the injected generation. Following this pipeline, we identified 16 GWAS candidates with potential diagnostic and predictive power for human CVDs.

Accounting for genetic architecture for body weight improves accuracy of predicting breeding values in a commercial line of broilers

BLUP (best linear unbiased prediction) is the standard for predicting breeding values, where different assumptions can be made on variance-covariance structure, which may influence predictive ability. Herein, we compare accuracy of prediction of four derived-BLUP models: (a) a pedigree relationship matrix (PBLUP), (b) a genomic relationship matrix (GBLUP), (c) a weighted genomic relationship matrix (WGBLUP) and (d) a relationship matrix based on genomic features that consisted of only a subset of SNP selected on a priori information (GFBLUP). We phenotyped a commercial population of broilers for body weight (BW) in five successive weeks and genotyped them using a 50k SNP array. We compared predictive ability of univariate models using conservative cross-validation method, where each full-sib group was divided into two folds. Results from cross-validation showed, with WGBLUP model, a gain in accuracy from 2% to 7% compared with GBLUP model. Splitting the additive genetic matrix into two matrices, based on significance level of SNP (Gf : estimated with only set of SNP selected on significance level, Gr : estimated with the remaining SNP), led to a gain in accuracy from 1% to 70%, depending on the proportion of SNP used to define Gf . Thus, information from GWAS in models improves predictive ability of breeding values for BW in broilers. Increasing the power of detection of SNP effects, by acquiring more data or improving methods for GWAS, will help improve predictive ability.

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

The ability to accurately predict complex trait phenotypes from genetic and genomic data are critical for the implementation of personalized medicine and precision agriculture; however, prediction accuracy for most complex traits is currently low. Here, we used data on whole genome sequences, deep RNA sequencing, and high quality phenotypes for three quantitative traits in the ∼200 inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) to compare the prediction accuracies of gene expression and genotypes for three complex traits. We found that expression levels (r = 0.28 and 0.38, for females and males, respectively) provided higher prediction accuracy than genotypes (r = 0.07 and 0.15, for females and males, respectively) for starvation resistance, similar prediction accuracy for chill coma recovery (null for both models and sexes), and lower prediction accuracy for startle response (r = 0.15 and 0.14 for female and male genotypes, respectively; and r = 0.12 and 0.11, for females and male transcripts, respectively). Models including both genotype and expression levels did not outperform the best single component model. However, accuracy increased considerably for all the three traits when we included gene ontology (GO) category as an additional layer of information for both genomic variants and transcripts. We found strongly predictive GO terms for each of the three traits, some of which had a clear plausible biological interpretation. For example, for starvation resistance in females, GO:0033500 (r = 0.39 for transcripts) and GO:0032870 (r = 0.40 for transcripts), have been implicated in carbohydrate homeostasis and cellular response to hormone stimulus (including the insulin receptor signaling pathway), respectively. In summary, this study shows that integrating different sources of information improved prediction accuracy and helped elucidate the genetic architecture of three Drosophila complex phenotypes.

Genomic Prediction Informed by Biological Processes Expands Our Understanding of the Genetic Architecture Underlying Free Amino Acid Traits in Dry Arabidopsis Seeds

Plant growth, development, and nutritional quality depends upon amino acid homeostasis, especially in seeds. However, our understanding of the underlying genetics influencing amino acid content and composition remains limited, with only a few candidate genes and quantitative trait loci identified to date. Improved knowledge of the genetics and biological processes that determine amino acid levels will enable researchers to use this information for plant breeding and biological discovery. Toward this goal, we used genomic prediction to identify biological processes that are associated with, and therefore potentially influence, free amino acid (FAA) composition in seeds of the model plant Arabidopsis thaliana. Markers were split into categories based on metabolic pathway annotations and fit using a genomic partitioning model to evaluate the influence of each pathway on heritability explained, model fit, and predictive ability. Selected pathways included processes known to influence FAA composition, albeit to an unknown degree, and spanned four categories: amino acid, core, specialized, and protein metabolism. Using this approach, we identified associations for pathways containing known variants for FAA traits, in addition to finding new trait-pathway associations. Markers related to amino acid metabolism, which are directly involved in FAA regulation, improved predictive ability for branched chain amino acids and histidine. The use of genomic partitioning also revealed patterns across biochemical families, in which serine-derived FAAs were associated with protein related annotations and aromatic FAAs were associated with specialized metabolic pathways. Taken together, these findings provide evidence that genomic partitioning is a viable strategy to uncover the relative contributions of biological processes to FAA traits in seeds, offering a promising framework to guide hypothesis testing and narrow the search space for candidate genes.

Genomic Prediction of (Mal)Adaptation Across Current and Future Climatic Landscapes

Signals of local adaptation have been found in many plants and animals, highlighting the heterogeneity in the distribution of adaptive genetic variation throughout species ranges. In the coming decades, global climate change is expected to induce shifts in the selective pressures that shape this adaptive variation. These changes in selective pressures will likely result in varying degrees of local climate maladaptation and spatial reshuffling of the underlying distributions of adaptive alleles. There is a growing interest in using population genomic data to help predict future disruptions to locally adaptive gene-environment associations. One motivation behind such work is to better understand how the effects of changing climate on populations’ short-term fitness could vary spatially across species ranges. Here we review the current use of genomic data to predict the disruption of local adaptation across current and future climates. After assessing goals and motivationsunderlying the approach, we review the main steps and associated statistical methods currently in use and explore our current understanding of the limits and future potential of using genomics to predict climate change (mal)adaptation.

Genetic Networks Underlying Natural Variation in Basal and Induced Activity Levels in Drosophila melanogaster

Exercise is recommended by health professionals across the globe as part of a healthy lifestyle to prevent and/or treat the consequences of obesity. While overall, the health benefits of exercise and an active lifestyle are well understood, very little is known about how genetics impacts an individual's inclination for and response to exercise. To address this knowledge gap, we investigated the genetic architecture underlying natural variation in activity levels in the model system Drosophila melanogaster Activity levels were assayed in the Drosophila Genetics Reference Panel fly strains at baseline and in response to a gentle exercise treatment using the Rotational Exercise Quantification System. We found significant, sex-dependent variation in both activity measures and identified over 100 genes that contribute to basal and induced exercise activity levels. This gene set was enriched for genes with functions in the central nervous system and in neuromuscular junctions and included several candidate genes with known activity phenotypes such as flightlessness or uncoordinated movement. Interestingly, there were also several chromatin proteins among the candidate genes, two of which were validated and shown to impact activity levels. Thus, the study described here reveals the complex genetic architecture controlling basal and exercise-induced activity levels in D. melanogaster and provides a resource for exercise biologists.

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.