Use of pleiotropy to model genetic interactions in a population

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Differences in lipid metabolism in acquired versus preexisting glucose intolerance during gestation: role of free fatty acids and sphingosine-1-phosphate

BACKGROUND

The prevalence of gestational diabetes mellitus (GDM) is increasing worldwide. There is increasing evidence that GDM is a heterogeneous disease with different subtypes. An important question in this context is whether impaired glucose tolerance (IGT), which is a typical feature of the disease, may already be present before pregnancy and manifestation of the disease. The latter type resembles in its clinical manifestation prediabetes that has not yet manifested as type 2 diabetes (T2DM). Altered lipid metabolism plays a crucial role in the disorder's pathophysiology. The aim was to investigate the role of lipids which are relevant in diabetes-like phenotypes in these both models with different time of initial onset of IGT.

METHODS

Two rodent models reflecting different characteristics of human GDM were used to characterize changes in lipid metabolism occurring during gestation. Since the New Zealand obese (NZO)-mice already exhibit IGT before and during gestation, they served as a subtype model for GDM with preexisting IGT (preIGT) and were compared with C57BL/6 N mice with transient IGT acquired during gestation (aqIGT). While the latter model does not develop manifest diabetes even under metabolic stress conditions, the NZO mouse is prone to severe disease progression later in life. Metabolically healthy Naval Medical Research Institute (NMRI) mice served as controls.

RESULTS

In contrast to the aqIGT model, preIGT mice showed hyperlipidemia during gestation with elevated free fatty acids (FFA), triglycerides (TG), and increased atherogenic index. Interestingly, sphingomyelin (SM) concentrations in the liver decreased during gestation concomitantly with an increase in the sphingosine-1-phosphate (S1P) concentration in plasma. Further, preIGT mice showed impaired hepatic weight adjustment and alterations in hepatic FFA metabolism during gestation. This was accompanied by decreased expression of peroxisome proliferator-activated receptor alpha (PPARα) and lack of translocation of fatty acid translocase (FAT/CD36) to the hepatocellular plasma membrane.

CONCLUSION

The preIGT model showed impaired lipid metabolism both in plasma and liver, as well as features of insulin resistance consistent with increased S1P concentrations, and in these characteristics, the preIGT model differs from the common GDM subtype with aqIGT. Thus, concomitantly elevated plasma FFA and S1P concentrations, in addition to general shifts in sphingolipid fractions, could be an interesting signal that the metabolic disorder existed before gestation and that future pregnancies require more intensive monitoring to avoid complications. This graphical abstract was created with BioRender.com .

A Multilayer Interactome Network Constructed in a Forest Poplar Population Mediates the Pleiotropic Control of Complex Traits

The effects of genes on physiological and biochemical processes are interrelated and interdependent; it is common for genes to express pleiotropic control of complex traits. However, the study of gene expression and participating pathways in vivo at the whole-genome level is challenging. Here, we develop a coupled regulatory interaction differential equation to assess overall and independent genetic effects on trait growth. Based on evolutionary game theory and developmental modularity theory, we constructed multilayer, omnigenic networks of bidirectional, weighted, and positive or negative epistatic interactions using a forest poplar tree mapping population, which were organized into metagalactic, intergalactic, and local interstellar networks that describe layers of structure between modules, submodules, and individual single nucleotide polymorphisms, respectively. These multilayer interactomes enable the exploration of complex interactions between genes, and the analysis of not only differential expression of quantitative trait loci but also previously uncharacterized determinant SNPs, which are negatively regulated by other SNPs, based on the deconstruction of genetic effects to their component parts. Our research framework provides a tool to comprehend the pleiotropic control of complex traits and explores the inherent directional connections between genes in the structure of omnigenic networks.

Effects of kinship correction on inflation of genetic interaction statistics in commonly used mouse populations

It is well understood that variation in relatedness among individuals, or kinship, can lead to false genetic associations. Multiple methods have been developed to adjust for kinship while maintaining power to detect true associations. However, relatively unstudied are the effects of kinship on genetic interaction test statistics. Here, we performed a survey of kinship effects on studies of six commonly used mouse populations. We measured inflation of main effect test statistics, genetic interaction test statistics, and interaction test statistics reparametrized by the Combined Analysis of Pleiotropy and Epistasis (CAPE). We also performed linear mixed model (LMM) kinship corrections using two types of kinship matrix: an overall kinship matrix calculated from the full set of genotyped markers, and a reduced kinship matrix, which left out markers on the chromosome(s) being tested. We found that test statistic inflation varied across populations and was driven largely by linkage disequilibrium. In contrast, there was no observable inflation in the genetic interaction test statistics. CAPE statistics were inflated at a level in between that of the main effects and the interaction effects. The overall kinship matrix overcorrected the inflation of main effect statistics relative to the reduced kinship matrix. The two types of kinship matrices had similar effects on the interaction statistics and CAPE statistics, although the overall kinship matrix trended toward a more severe correction. In conclusion, we recommend using an LMM kinship correction for both main effects and genetic interactions and further recommend that the kinship matrix be calculated from a reduced set of markers in which the chromosomes being tested are omitted from the calculation. This is particularly important in populations with substantial population structure, such as recombinant inbred lines in which genomic replicates are used.

The Combined Analysis of Pleiotropy and Epistasis (CAPE)

Epistasis, or gene-gene interaction, contributes substantially to trait variation in organisms ranging from yeast to humans, and modeling epistasis directly is critical to understanding the genotype-phenotype map. However, inference of genetic interactions is challenging compared to inference of individual allele effects due to low statistical power. Furthermore, genetic interactions can appear inconsistent across different quantitative traits, presenting a challenge for the interpretation of detected interactions. Here we present a method called the Combined Analysis of Pleiotropy and Epistasis (CAPE) that combines information across multiple quantitative traits to infer directed epistatic interactions. By combining information across multiple traits, CAPE not only increases power to detect genetic interactions but also interprets these interactions across traits to identify a single interaction that is consistent across all observed data. This method generates informative, interpretable interaction networks that explain how variants interact with each other to influence groups of related traits. This method could potentially be used to link genetic variants to gene expression, physiological endophenotypes, and higher-level disease traits.

Genetic Interactions Affect Lung Function in Patients with Systemic Sclerosis

Scleroderma, or systemic sclerosis (SSc), is an autoimmune disease characterized by progressive fibrosis of the skin and internal organs. The most common cause of death in people with SSc is lung disease, but the pathogenesis of lung disease in SSc is insufficiently understood to devise specific treatment strategies. Developing targeted treatments requires not only the identification of molecular processes involved in SSc-associated lung disease, but also understanding of how these processes interact to drive pathology. One potentially powerful approach is to identify alleles that interact genetically to influence lung outcomes in patients with SSc. Analysis of interactions, rather than individual allele effects, has the potential to delineate molecular interactions that are important in SSc-related lung pathology. However, detecting genetic interactions, or epistasis, in human cohorts is challenging. Large numbers of variants with low minor allele frequencies, paired with heterogeneous disease presentation, reduce power to detect epistasis. Here we present an analysis that increases power to detect epistasis in human genome-wide association studies (GWAS). We tested for genetic interactions influencing lung function and autoantibody status in a cohort of 416 SSc patients. Using Matrix Epistasis to filter SNPs followed by the Combined Analysis of Pleiotropy and Epistasis (CAPE), we identified a network of interacting alleles influencing lung function in patients with SSc. In particular, we identified a three-gene network comprising WNT5A, RBMS3, and MSI2, which in combination influenced multiple pulmonary pathology measures. The associations of these genes with lung outcomes in SSc are novel and high-confidence. Furthermore, gene coexpression analysis suggested that the interactions we identified are tissue-specific, thus differentiating SSc-related pathogenic processes in lung from those in skin.

Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs

Stable quantitative trait loci (QTL) are important for deployment in marker assisted selection in wheat (Triticum aestivum L.) and other crops. We reported QTL discovery in wheat using a population of 217 recombinant inbred lines and multiple statistical approach including multi-environment, multi-trait and epistatic interactions analysis. We detected nine consistent QTL linked to different traits on chromosomes 1A, 2A, 2B, 5A, 5B, 6A, 6B and 7A. Grain yield QTL were detected on chromosomes 2B.1 and 5B across three or four models of GenStat, MapQTL, and QTLNetwork while the QTL on chromosomes 5A.1, 6A.2, and 7A.1 were only significant with yield from one or two models. The phenotypic variation explained (PVE) by the QTL on 2B.1 ranged from 3.3–25.1% based on single and multi-environment models in GenStat and was pleiotropic or co-located with maturity (days to heading) and yield related traits (test weight, thousand kernel weight, harvest index). The QTL on 5B at 211 cM had PVE range of 1.8–9.3% and had no significant pleiotropic effects. Other consistent QTL detected in this study were linked to yield related traits and agronomic traits. The QTL on 1A was consistent for the number of spikes m^-2 across environments and all the four analysis models with a PVE range of 5.8–8.6%. QTL for kernels spike^-1 were found in chromosomes 1A, 2A.1, 2B.1, 6A.2, and 7A.1 with PVE ranged from 5.6–12.8% while QTL for thousand kernel weight were located on chromosomes 1A, 2B.1, 5A.1, 6A.2, 6B.1 and 7A.1 with PVEranged from 2.7–19.5%. Among the consistent QTL, five QTL had significant epistatic interactions (additive × additive) at least for one trait and none revealed significant additive × additive × environment interactions. Comparative analysis revealed that the region within the confidence interval of the QTL on 5B from 211.4–244.2 cM is also linked to genes for aspartate-semialdehyde dehydrogenase, splicing regulatory glutamine/lysine-rich protein 1 isoform X1, and UDP-glucose 6-dehydrogenase 1-like isoform X1. The stable QTL could be important for further validation, high throughput SNP development, and marker-assisted selection (MAS) in wheat.

Epistatic Networks Jointly Influence Phenotypes Related to Metabolic Disease and Gene Expression in Diversity Outbred Mice

In this study, Tyler et al. analyzed the complex genetic architecture of metabolic disease-related traits using the Diversity Outbred mouse population

Genetic studies of multidimensional phenotypes can potentially link genetic variation, gene expression, and physiological data to create multi-scale models of complex traits. The challenge of reducing these data to specific hypotheses has become increasingly acute with the advent of genome-scale data resources. Multi-parent populations derived from model organisms provide a resource for developing methods to understand this complexity. In this study, we simultaneously modeled body composition, serum biomarkers, and liver transcript abundances from 474 Diversity Outbred mice. This population contained both sexes and two dietary cohorts. Transcript data were reduced to functional gene modules with weighted gene coexpression network analysis (WGCNA), which were used as summary phenotypes representing enriched biological processes. These module phenotypes were jointly analyzed with body composition and serum biomarkers in a combined analysis of pleiotropy and epistasis (CAPE), which inferred networks of epistatic interactions between quantitative trait loci that affect one or more traits. This network frequently mapped interactions between alleles of different ancestries, providing evidence of both genetic synergy and redundancy between haplotypes. Furthermore, a number of loci interacted with sex and diet to yield sex-specific genetic effects and alleles that potentially protect individuals from the effects of a high-fat diet. Although the epistatic interactions explained small amounts of trait variance, the combination of directional interactions, allelic specificity, and high genomic resolution provided context to generate hypotheses for the roles of specific genes in complex traits. Our approach moves beyond the cataloging of single loci to infer genetic networks that map genetic etiology by simultaneously modeling all phenotypes.

Past Roadblocks and New Opportunities in Transcription Factor Network Mapping

One of the principal mechanisms by which cells differentiate and respond to changes in external signals or conditions is by changing the activity levels of transcription factors (TFs). This changes the transcription rates of target genes via the cell's TF network, which ultimately contributes to reconfiguring cellular state. Since microarrays provided our first window into global cellular state, computational biologists have eagerly attacked the problem of mapping TF networks, a key part of the cell's control circuitry. In retrospect, however, steady-state mRNA abundance levels were a poor substitute for TF activity levels and gene transcription rates. Likewise, mapping TF binding through chromatin immunoprecipitation proved less predictive of functional regulation and less amenable to systematic elucidation of complete networks than originally hoped. This review explains these roadblocks and the current, unprecedented blossoming of new experimental techniques built on second-generation sequencing, which hold out the promise of rapid progress in TF network mapping.

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

The widely used statistical methods test interaction for single phenotype. However, we often observe pleotropic genetic interaction effects. The simultaneous gene-gene (GxG) interaction analysis of multiple complementary traits will increase statistical power to detect GxG interactions. Although GxG interactions play an important role in uncovering the genetic structure of complex traits, the statistical methods for detecting GxG interactions in multiple phenotypes remains less developed owing to its potential complexity. Therefore, we extend functional regression model from single variate to multivariate for simultaneous GxG interaction analysis of multiple correlated phenotypes. Large-scale simulations are conducted to evaluate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare power with traditional multivariate pair-wise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for interaction analysis is applied to five phenotypes of exome sequence data from the NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic GxG interactions. 267 pairs of genes that formed a genetic interaction network showed significant evidence of interactions influencing five traits.

Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross

The extent and strength of epistasis is commonly unresolved in genetic studies, and observed epistasis is often difficult to interpret in terms of biological consequences or overall genetic architecture. We investigated the prevalence and consequences of epistasis by analyzing four body composition phenotypes—body weight, body fat percentage, femoral density, and femoral circumference—in a large F2 intercross of B6-lit/lit and C3.B6-lit/lit mice. We used Combined Analysis of Pleiotropy and Epistasis (CAPE) to examine interactions for the four phenotypes simultaneously, which revealed an extensive directed network of genetic loci interacting with each other, circulating IGF1, and sex to influence these phenotypes. The majority of epistatic interactions had small effects relative to additive effects of individual loci, and tended to stabilize phenotypes towards the mean of the population rather than extremes. Interactive effects of two alleles inherited from one parental strain commonly resulted in phenotypes closer to the population mean than the additive effects from the two loci, and often much closer to the mean than either single-locus model. Alternatively, combinations of alleles inherited from different parent strains contribute to more extreme phenotypes not observed in either parental strain. This class of phenotype-stabilizing interactions has effects that are close to additive and are thus difficult to detect except in very large intercrosses. Nevertheless, we found these interactions to be useful in generating hypotheses for functional relationships between genetic loci. Our findings suggest that while epistasis is often weak and unlikely to account for a large proportion of heritable variance, even small-effect genetic interactions can facilitate hypotheses of underlying biology in well-powered studies.

The role of statistical epistasis in the genetic architecture of complex traits has been of great interest to the genetics community since Fisher introduced the concept in 1918. However, assessing epistasis in human and model organism populations has been impeded by limited statistical power. To mitigate this limitation, we analyzed bone and body composition traits in an unusually large mouse intercross population of over 2000 mice, paired with a recently-developed computational approach that leverages information to detect interactions across multiple phenotypes. We discovered a large network of highly significant genetic interactions between variants that influence complex body composition traits. Although epistasis was abundant, the interaction network was dominated by epistasis that stabilizes phenotypes by reducing phenotypic deviation from the parent strains. Nevertheless, the observed network provides an overview of genetic architecture and specific hypotheses of how QTL combine to affect phenotypes. These findings suggest that epistatic effects are generally of lesser magnitude than main QTL effects, and therefore are unlikely to account for major components of variance, but also reinforce genetic interaction analysis as a potent tool for dissecting the biology of complex traits.

The detection and characterization of pleiotropy: discovery, progress, and promise

The impact of a single genetic locus on multiple phenotypes, or pleiotropy, is an important area of research. Biological systems are dynamic complex networks, and these networks exist within and between cells. In humans, the consideration of multiple phenotypes such as physiological traits, clinical outcomes and drug response, in the context of genetic variation, can provide ways of developing a more complete understanding of the complex relationships between genetic architecture and how biological systems function in health and disease. In this article, we describe recent studies exploring the relationships between genetic loci and more than one phenotype. We also cover methodological developments incorporating pleiotropy applied to model organisms as well as humans, and discuss how stepping beyond the analysis of a single phenotype leads to a deeper understanding of complex genetic architecture.

Two-stage genome-wide search for epistasis with implementation to Recombinant Inbred Lines (RIL) populations

OBJECTIVE AND METHODS

This paper proposes an inegrative two-stage genome-wide search for pairwise epistasis on expression quantitative trait loci (eQTL). The traits are clustered into multi-trait complexes that account for correlations between them that may result from common epistasis effects. The search is done by first screening for epistatic regions and then using dense markers within the identified regions, resulting in substantial reduction in the number of tests for epistasis. The FDR is controlled using a hierarchical procedure that accounts for the search structure. Each combination of trait and marker-pair is tested using a model that accounts for both statistical and functional interpretations of epistasis and considers orthogonal effects, such that their contributions to heritability can be estimated individually. We examine the impact of using multi-trait complexes rather than single traits, and of using a hierarchical search for epistasis rather than skipping the initial screen for epistatic regions. We apply the proposed algorithm on Arabidopsis transcription data.

PRINCIPAL FINDINGS

Both epistasis detection power and heritability contributed by epistasis increased when using multi-trait complexes rather than single traits. Epistatic effects common to the eQTLs included in the complexes have higher chance of being identified by analysis of multi-trait complexes, particularly when epistatic effects on individual traits are small. Compared to direct testing for all potential epistatic effects, the hierarchical search was substantially more powerful in detecting epistasis, while controlling the FDR at the desired level. Association in functional roles within genomic regions was observed, supporting an initial screen for epistatic QTLs.

A genetic interaction network model of a complex neurological disease

Absence epilepsy (AE) is a complex, heritable disease characterized by a brief disruption of normal behavior and accompanying spike wave discharges (SWD) on the electroencephalogram. Only a handful of genes has been definitively associated with AE in humans and rodent models. Most studies suggest that genetic interactions play a large role in the etiology and severity of AE, but mapping and understanding their architecture remains a challenge, requiring new computational approaches. Here we use Combined Analysis of Pleiotropy and Epistasis (CAPE) to detect and interpret genetic interactions in a meta-population derived from three C3H x B6 strain crosses, each of which is fixed for a different SWD-causing mutation. Although each mutation causes SWD through a different molecular mechanism, the phenotypes caused by each mutation are exacerbated on the C3H genetic background compared with B6, suggesting common modifiers. By combining information across two phenotypic measures – SWD duration and frequency – CAPE revealed a large, directed genetic network consisting of suppressive and enhancing interactions between loci on 10 chromosomes. These results illustrate the power of CAPE in identifying novel modifier loci and interactions in a complex neurological disease, towards a more comprehensive view of its underlying genetic architecture.

Detecting epistasis in human complex traits. Nat Rev Genet. 2014;15:722-733. [PMID: 25200660 DOI: 10.1038/nrg3747]

Genome-wide association studies (GWASs) have become the focus of the statistical analysis of complex traits in humans, successfully shedding light on several aspects of genetic architecture and biological aetiology. Single-nucleotide polymorphisms (SNPs) are usually modelled as having additive, cumulative and independent effects on the phenotype. Although evidently a useful approach, it is often argued that this is not a realistic biological model and that epistasis (that is, the statistical interaction between SNPs) should be included. The purpose of this Review is to summarize recent directions in methodology for detecting epistasis and to discuss evidence of the role of epistasis in human complex trait variation. We also discuss the relevance of epistasis in the context of GWASs and potential hazards in the interpretation of statistical interaction terms.

Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis

Global transcript expression experiments are commonly used to investigate the biological processes that underlie complex traits. These studies can exhibit complex patterns of pleiotropy when trans-acting genetic factors influence overlapping sets of multiple transcripts. Dissecting these patterns into biological modules with distinct genetic etiology can provide models of how genetic variants affect specific processes that contribute to a trait. Here we identify transcript modules associated with pleiotropic genetic factors and apply genetic interaction analysis to disentangle the regulatory architecture in a mouse intercross study of kidney function. The method, called the combined analysis of pleiotropy and epistasis (CAPE), has been previously used to model genetic networks for multiple physiological traits. It simultaneously models multiple phenotypes to identify direct genetic influences as well as influences mediated through genetic interactions. We first identify candidate trans expression quantitative trait loci (eQTL) and the transcripts potentially affected. We then clustered the transcripts into modules of co-expressed genes, from which we compute summary module phenotypes. Finally, we applied CAPE to map the network of interacting module QTL (modQTL) affecting the gene modules. The resulting network mapped how multiple modQTL both directly and indirectly affect modules associated with metabolic functions and biosynthetic processes. This work demonstrates how the integration of pleiotropic signals in gene expression data can be used to infer a complex hypothesis of how multiple loci interact to co-regulate transcription programs, thereby providing additional constraints to prioritize validation experiments.

CAPE: an R package for combined analysis of pleiotropy and epistasis

Contemporary genetic studies are revealing the genetic complexity of many traits in humans and model organisms. Two hallmarks of this complexity are epistasis, meaning gene-gene interaction, and pleiotropy, in which one gene affects multiple phenotypes. Understanding the genetic architecture of complex traits requires addressing these phenomena, but interpreting the biological significance of epistasis and pleiotropy is often difficult. While epistasis reveals dependencies between genetic variants, it is often unclear how the activity of one variant is specifically modifying the other. Epistasis found in one phenotypic context may disappear in another context, rendering the genetic interaction ambiguous. Pleiotropy can suggest either redundant phenotype measures or gene variants that affect multiple biological processes. Here we present an R package, R/cape, which addresses these interpretation ambiguities by implementing a novel method to generate predictive and interpretable genetic networks that influence quantitative phenotypes. R/cape integrates information from multiple related phenotypes to constrain models of epistasis, thereby enhancing the detection of interactions that simultaneously describe all phenotypes. The networks inferred by R/cape are readily interpretable in terms of directed influences that indicate suppressive and enhancing effects of individual genetic variants on other variants, which in turn account for the variance in quantitative traits. We demonstrate the utility of R/cape by analyzing a mouse backcross, thereby discovering novel epistatic interactions influencing phenotypes related to obesity and diabetes. R/cape is an easy-to-use, platform-independent R package and can be applied to data from both genetic screens and a variety of segregating populations including backcrosses, intercrosses, and natural populations. The package is freely available under the GPL-3 license at http://cran.r-project.org/web/packages/cape.

Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis

High-throughput genetic interaction screens have enabled functional genomics on a network scale. Groups of cofunctional genes commonly exhibit similar interaction patterns across a large network, leading to novel functional inferences for a minority of previously uncharacterized genes within a group. However, such analyses are often unsuited to cases with a few relevant gene variants or sparse annotation. Here we describe an alternative analysis of cell growth signaling using a computational strategy that integrates patterns of pleiotropy and epistasis to infer how gene knockdowns enhance or suppress the effects of other knockdowns. We analyzed the interaction network for RNAi knockdowns of a set of 93 incompletely annotated genes in a Drosophila melanogaster model of cellular signaling. We inferred novel functional relationships between genes by modeling genetic interactions in terms of knockdown-to-knockdown influences. The method simultaneously analyzes the effects of partially pleiotropic genes on multiple quantitative phenotypes to infer a consistent model of each genetic interaction. From these models we proposed novel candidate Ras inhibitors and their Ras signaling interaction partners, and each of these hypotheses can be inferred independent of network-wide patterns. At the same time, the network-scale interaction patterns consistently mapped pathway organization. The analysis therefore assigns functional relevance to individual genetic interactions while also revealing global genetic architecture.