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In cats, mutations in myosin binding protein C (encoded by the MYBPC3 gene) have been associated with hypertrophic cardiomyopathy (HCM). However, the molecular mechanisms linking these mutations to HCM remain unknown. Here, we establish Drosophila melanogaster as a model to understand this connection by generating flies harboring MYBPC3 missense mutations (A31P and R820W) associated with feline HCM. The A31P and R820W flies displayed cardiovascular defects in their heart rates and exercise endurance. We used RNA-seq to determine which processes are misregulated in the presence of mutant MYBPC3 alleles. Transcriptome analysis revealed significant downregulation of genes encoding small nucleolar RNA (snoRNAs) in exercised female flies harboring the mutant alleles compared to flies that harbor the wild-type allele. Other processes that were affected included the unfolded protein response and immune/defense responses. These data show that mutant MYBPC3 proteins have widespread effects on the transcriptome of co-regulated genes. Transcriptionally differentially expressed genes are also candidate genes for future evaluation as genetic modifiers of HCM as well as candidate genes for genotype by exercise environment interaction effects on the manifestation of HCM; in cats as well as humans.

Prenatal exposure to ethanol causes a wide range of adverse physiological, behavioral and cognitive consequences. However, identifying allelic variants and genetic networks associated with variation in susceptibility to prenatal alcohol exposure is challenging in human populations, since time and frequency of exposure and effective dose cannot be determined quantitatively and phenotypic manifestations are diverse. Here, we harnessed the power of natural variation in the Drosophila melanogaster Genetic Reference Panel (DGRP) to identify genes and genetic networks associated with variation in sensitivity to developmental alcohol exposure. We measured development time from egg to adult and viability of 201 DGRP lines reared on regular or ethanol- supplemented medium and identified polymorphisms associated with variation in susceptibility to developmental ethanol exposure. We also documented genotype-dependent variation in sensorimotor behavior after developmental exposure to ethanol using the startle response assay in a subset of 39 DGRP lines. Genes associated with development, including development of the nervous system, featured prominently among genes that harbored variants associated with differential sensitivity to developmental ethanol exposure. Many of them have human orthologs and mutational analyses and RNAi targeting functionally validated a high percentage of candidate genes. Analysis of genetic interaction networks identified Cyclin E (CycE) as a central, highly interconnected hub gene. Cyclin E encodes a protein kinase associated with cell cycle regulation and is prominently expressed in ovaries. Thus, exposure to ethanol during development of Drosophila melanogaster might serve as a genetic model for translational studies on fetal alcohol spectrum disorder.

Epistasis—nonlinear genetic interactions between polymorphic loci—is the genetic basis of canalization and speciation, and epistatic interactions can be used to infer genetic networks affecting quantitative traits. However, the role that epistasis plays in the genetic architecture of quantitative traits is controversial. Here, we compared the genetic architecture of three Drosophila life history traits in the sequenced inbred lines of the Drosophila melanogaster Genetic Reference Panel (DGRP) and a large outbred, advanced intercross population derived from 40 DGRP lines (Flyland). We assessed allele frequency changes between pools of individuals at the extremes of the distribution for each trait in the Flyland population by deep DNA sequencing. The genetic architecture of all traits was highly polygenic in both analyses. Surprisingly, none of the SNPs associated with the traits in Flyland replicated in the DGRP and vice versa. However, the majority of these SNPs participated in at least one epistatic interaction in the DGRP. Despite apparent additive effects at largely distinct loci in the two populations, the epistatic interactions perturbed common, biologically plausible, and highly connected genetic networks. Our analysis underscores the importance of epistasis as a principal factor that determines variation for quantitative traits and provides a means to uncover genetic networks affecting these traits. Knowledge of epistatic networks will contribute to our understanding of the genetic basis of evolutionarily and clinically important traits and enhance predictive ability at an individualized level in medicine and agriculture.

Identification of risk alleles for human behavioral disorders through genomewide association studies (GWAS) has been hampered by a daunting multiple testing problem. This problem can be circumvented for some phenotypes by combining genomewide studies in model organisms with subsequent candidate gene association analyses in human populations. Here, we characterized genetic networks that underlie the response to ethanol exposure in Drosophila melanogaster by measuring ethanol knockdown time in 40 wild-derived inbred Drosophila lines. We associated phenotypic variation in ethanol responses with genomewide variation in gene expression and identified modules of correlated transcripts associated with a first and second exposure to ethanol vapors as well as the induction of tolerance. We validated the computational networks and assessed their robustness by transposon-mediated disruption of focal genes within modules in a laboratory inbred strain, followed by measurements of transcript abundance of connected genes within the module. Many genes within the modules have human orthologs, which provides a stepping stone for the identification of candidate genes associated with alcohol drinking behavior in human populations. We demonstrated the potential of this translational approach by identifying seven intronic single nucleotide polymorphisms of the Malic Enzyme 1 (ME1) gene that are associated with cocktail drinking in 1687 individuals of the Framingham Offspring cohort, implicating that variation in levels of cytoplasmic malic enzyme may contribute to variation in alcohol consumption.

A major challenge of biology is understanding the relationship between molecular genetic variation and variation in quantitative traits, including fitness. This relationship determines our ability to predict phenotypes from genotypes and to understand how evolutionary forces shape variation within and between species. Previous efforts to dissect the genotype-phenotype map were based on incomplete genotypic information. Here, we describe the Drosophila melanogaster Genetic Reference Panel (DGRP), a community resource for analysis of population genomics and quantitative traits. The DGRP consists of fully sequenced inbred lines derived from a natural population. Population genomic analyses reveal reduced polymorphism in centromeric autosomal regions and the X chromosome, evidence for positive and negative selection, and rapid evolution of the X chromosome. Many variants in novel genes, most at low frequency, are associated with quantitative traits and explain a large fraction of the phenotypic variance. The DGRP facilitates genotype-phenotype mapping using the power of Drosophila genetics.

Determining the genetic architecture of complex traits is challenging because phenotypic variation arises from interactions between multiple, environmentally sensitive alleles. We quantified genome-wide transcript abundance and phenotypes for six ecologically relevant traits in D. melanogaster wild-derived inbred lines. We observed 10,096 genetically variable transcripts and high heritabilities for all organismal phenotypes. The transcriptome is highly genetically intercorrelated, forming 241 transcriptional modules. Modules are enriched for transcripts in common pathways, gene ontology categories, tissue-specific expression and transcription factor binding sites. The high degree of transcriptional connectivity allows us to infer genetic networks and the function of predicted genes from annotations of other genes in the network. Regressions of organismal phenotypes on transcript abundance implicate several hundred candidate genes that form modules of biologically meaningful correlated transcripts affecting each phenotype. Overlapping transcripts in modules associated with different traits provide insight into the molecular basis of pleiotropy between complex traits. Natural populations harbor a wide range of phenotypic variation for all aspects of morphology, physiology, behaviors and disease susceptibility. Knowledge of the genetic basis of this variation is important for understanding adaptive evolution, deriving elite domestic crop and animal strains and improving human health. However, determining the genetic architecture of natural phenotypic variation is challenging because most phenotypic variation is attributable to segregating alleles at many interacting genes with environmentally sensitive effects 1,2 .