The Drosophila melanogaster Genetic Reference Panel (DGRP) is a community resource of 205 sequenced inbred lines, derived to improve our understanding of the effects of naturally occurring genetic variation on molecular and organismal phenotypes. We used an integrated genotyping strategy to identify 4,853,802 single nucleotide polymorphisms (SNPs) and 1,296,080 non-SNP variants. Our molecular population genomic analyses show higher deletion than insertion mutation rates and stronger purifying selection on deletions. Weaker selection on insertions than deletions is consistent with our observed distribution of genome size determined by flow cytometry, which is skewed toward larger genomes. Insertion/deletion and single nucleotide polymorphisms are positively correlated with each other and with local recombination, suggesting that their nonrandom distributions are due to hitchhiking and background selection. Our cytogenetic analysis identified 16 polymorphic inversions in the DGRP. Common inverted and standard karyotypes are genetically divergent and account for most of the variation in relatedness among the DGRP lines. Intriguingly, variation in genome size and many quantitative traits are significantly associated with inversions. Approximately 50% of the DGRP lines are infected with Wolbachia, and four lines have germline insertions of Wolbachia sequences, but effects of Wolbachia infection on quantitative traits are rarely significant. The DGRP complements ongoing efforts to functionally annotate the Drosophila genome. Indeed, 15% of all D. melanogaster genes segregate for potentially damaged proteins in the DGRP, and genome-wide analyses of quantitative traits identify novel candidate genes. The DGRP lines, sequence data, genotypes, quality scores, phenotypes, and analysis and visualization tools are publicly available.

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.

Classical quantitative genetic analyses estimate additive and non-additive genetic and environmental components of variance from phenotypes of related individuals without knowing the identities of quantitative trait loci (QTLs). Many studies have found a large proportion of quantitative trait variation can be attributed to the additive genetic variance (V_A), providing the basis for claims that non-additive gene actions are unimportant. In this study, we show that arbitrarily defined parameterizations of genetic effects seemingly consistent with non-additive gene actions can also capture the majority of genetic variation. This reveals a logical flaw in using the relative magnitudes of variance components to indicate the relative importance of additive and non-additive gene actions. We discuss the implications and propose that variance component analyses should not be used to infer the genetic architecture of quantitative traits. There has been a great amount of debate over the relative importance of additivity and non-additivity in quantitative trait variation. The main argument supporting the importance of additivity is the observation that the additive component of genetic variance is much greater than non-additive variance components, while the main argument supporting the importance of non-additivity is the identification of many non-additive effects in genetic mapping studies. By recapitulating many classical results and introducing new alternative parameterizations of genetic effects, we point out some of the common mistakes and misleading arguments in using variance component analyses to infer genetic architecture, specifically the gene actions of QTLs. Because of the wide applications of variance component analyses, our study has profound implications and clarifies some of the most confusing concepts in quantitative genetics in the genomics era.

Insect cuticular hydrocarbons (CHCs) prevent desiccation and serve as chemical signals that mediate social interactions. Drosophila melanogaster CHCs have been studied extensively, but the genetic basis for individual variation in CHC composition is largely unknown. We quantified variation in CHC profiles in the D. melanogaster Genetic Reference Panel (DGRP) and identified novel CHCs. We used principal component (PC) analysis to extract PCs that explain the majority of CHC variation and identified polymorphisms in or near 305 and 173 genes in females and males, respectively, associated with variation in these PCs. In addition, 17 DGRP lines contain the functional Desat2 allele characteristic of African and Caribbean D. melanogaster females (more 5,9-C27:2 and less 7,11-C27:2, female sex pheromone isomers). Disruption of expression of 24 candidate genes affected CHC composition in at least one sex. These genes are associated with fatty acid metabolism and represent mechanistic targets for individual variation in CHC composition.

Significance: RNA provides a link between variation at the DNA and phenotypic levels. We measured the abundances of RNA products of protein-coding genes and novel transcribed regions in a population of wild-derived inbred strains of Drosophila melanogaster whose genome sequences are also available. We exploited this unique resource to characterize the genetic basis of transcriptome diversity. We found high complexity of the genetic control of gene expression, including widespread sexual dimorphism, highly modularized expression patterns with involvement of novel RNA transcripts, and frequent epistatic interactions among expression quantitative trait loci (QTLs) which often give rise to variance expression QTLs. This study highlights the importance and general applicability of integrating expression phenotypes to understand the genetic architecture of complex quantitative phenotypes.