INTERACTING PHENOTYPES AND THE EVOLUTIONARY PROCESS: I. DIRECT AND INDIRECT GENETIC EFFECTS OF SOCIAL INTERACTIONS

Interacting phenotypes are traits whose expression is affected by interactions with conspecifics. Commonly-studied interacting phenotypes include aggression, courtship, and communication. More extreme examples of interacting phenotypes—traits that exist exclusively as a product of interactions—include social dominance, intraspecific competitive ability, and mating systems. We adopt a quantitative genetic approach to assess genetic influences on interacting phenotypes. We partition genetic and environmental effects so that traits in conspecifics that influence the expression of interacting phenotypes are a component of the environment. When the trait having the effect is heritable, the environmental influence arising from the interaction has a genetic basis and can be incorporated as an indirect genetic effect. However, because it has a genetic basis, this environmental component can evolve. Therefore, to consider the evolution of interacting phenotypes we simultaneously consider changes in the direct genetic contributions to a trait (as a standard quantitative genetic approach would evaluate) as well as changes in the environmental (indirect genetic) contribution to the phenotype. We then explore the ramifications of this model of inheritance on the evolution of interacting phenotypes. The relative rate of evolution in interacting phenotypes can be quite different from that predicted by a standard quantitative genetic analysis. Phenotypic evolution is greatly enhanced or inhibited depending on the nature of the direct and indirect genetic effects. Further, unlike most models of phenotypic evolution, a lack of variation in direct genetic effects does not preclude evolution if there is genetic variance in the indirect genetic contributions. The available empirical evidence regarding the evolution of behavior expressed in interactions, although limited, supports the predictions of our model.     

High incidence of later-onset fabry disease revealed by newborn screening

The classic phenotype of Fabry disease, X-linked alpha -galactosidase A (alpha -Gal A) deficiency, has an estimated incidence of approximately 1 in 50,000 males. The recent recognition of later-onset variants suggested that this treatable lysosomal disease is more frequent. To determine the disease incidence, we undertook newborn screening by assaying the alpha-Gal A activity in blood spots from 37,104 consecutive Italian male neonates. Enzyme-deficient infants were retested, and "doubly screened-positive" infants and their relatives were diagnostically confirmed by enzyme and mutation analyses. Twelve (0.03%) neonates had deficient alpha-Gal A activities and specific mutations, including four novel missense mutations (M51I, E66G, A73V, and R118C), three missense mutations (F113L, A143T, and N215S) identified previously in later-onset patients, and one splicing defect (IVS5(+1G-->T)) reported in a patient with the classic phenotype. Molecular modeling and in vitro overexpression of the missense mutations demonstrated structures and residual activities, which were rescued/enhanced by an alpha-Gal A-specific pharmacologic chaperone, consistent with mutations that cause the later-onset phenotype. Family studies revealed undiagnosed Fabry disease in affected individuals. In this population, the incidence of alpha-Gal A deficiency was 1 in approximately 3,100, with an 11 : 1 ratio of patients with the later-onset : classic phenotypes. If only known disease-causing mutations were included, the incidence would be 1 in approximately 4,600, with a 7 : 1 ratio of patients with the later-onset : classic phenotypes. These results suggest that the later-onset phenotype of Fabry disease is underdiagnosed among males with cardiac, cerebrovascular, and/or renal disease. Recognition of these patients would permit family screening and earlier therapeutic intervention. However, the higher incidence of the later-onset phenotype in patients raises ethical issues related to when screening should be performed--in the neonatal period or at early maturity, perhaps in conjunction with screening for other treatable adult-onset disorders.     

Statistical Estimation of Correlated Genome Associations to a Quantitative Trait Network

Many complex disease syndromes, such as asthma, consist of a large number of highly related, rather than independent, clinical or molecular phenotypes. This raises a new technical challenge in identifying genetic variations associated simultaneously with correlated traits. In this study, we propose a new statistical framework called graph-guided fused lasso (GFlasso) to directly and effectively incorporate the correlation structure of multiple quantitative traits such as clinical metrics and gene expressions in association analysis. Our approach represents correlation information explicitly among the quantitative traits as a quantitative trait network (QTN) and then leverages this network to encode structured regularization functions in a multivariate regression model over the genotypes and traits. The result is that the genetic markers that jointly influence subgroups of highly correlated traits can be detected jointly with high sensitivity and specificity. While most of the traditional methods examined each phenotype independently and combined the results afterwards, our approach analyzes all of the traits jointly in a single statistical framework. This allows our method to borrow information across correlated phenotypes to discover the genetic markers that perturb a subset of the correlated traits synergistically. Using simulated datasets based on the HapMap consortium and an asthma dataset, we compared the performance of our method with other methods based on single-marker analysis and regression-based methods that do not use any of the relational information in the traits. We found that our method showed an increased power in detecting causal variants affecting correlated traits. Our results showed that, when correlation patterns among traits in a QTN are considered explicitly and directly during a structured multivariate genome association analysis using our proposed methods, the power of detecting true causal SNPs with possibly pleiotropic effects increased significantly without compromising performance on non-pleiotropic SNPs.     

How to approach the study of syndromes in macroevolution and ecology

Syndromes, wherein multiple traits evolve convergently in response to a shared selective driver, form a central concept in ecology and evolution. Recent work has questioned the existence of some classic syndromes, such as pollination and seed dispersal syndromes. Here, we discuss some of the major issues that have afflicted research into syndromes in macroevolution and ecology. First, correlated evolution of traits and hypothesized selective drivers is often relied on as the only evidence for adaptation of those traits to those hypothesized drivers, without supporting evidence. Second, the selective driver is often inferred from a combination of traits without explicit testing. Third, researchers often measure traits that are easy for humans to observe rather than measuring traits that are suited to testing the hypothesis of adaptation. Finally, species are often chosen for study because of their striking phenotypes, which leads to the illusion of syndromes and divergence. We argue that these issues can be avoided by combining studies of trait variation across entire clades or communities with explicit tests of adaptive hypotheses and that taking this approach will lead to a better understanding of syndrome‐like evolution and its drivers.     

X-inactivation and human disease: X-linked dominant male-lethal disorders

X chromosome inactivation (XCI) is the process by which the dosage imbalance of X-linked genes between XX females and XY males is functionally equalized. XCI modulates the phenotype of females carrying mutations in X-linked genes, as observed in X-linked dominant male-lethal disorders such as oral-facial-digital type I (OFDI) and microphthalmia with linear skin-defects syndromes. The remarkable degree of heterogeneity in the XCI pattern among female individuals, as revealed by the recently reported XCI profile of the human X chromosome, could account for the phenotypic variability observed in these diseases. Furthermore, the recent characterization of a murine model for OFDI shows how interspecies differences in the XCI pattern between Homo sapiens and Mus musculus result in discrepancies between the phenotypes observed in patients and mice.     

MultiPhen: joint model of multiple phenotypes can increase discovery in GWAS

The genome-wide association study (GWAS) approach has discovered hundreds of genetic variants associated with diseases and quantitative traits. However, despite clinical overlap and statistical correlation between many phenotypes, GWAS are generally performed one-phenotype-at-a-time. Here we compare the performance of modelling multiple phenotypes jointly with that of the standard univariate approach. We introduce a new method and software, MultiPhen, that models multiple phenotypes simultaneously in a fast and interpretable way. By performing ordinal regression, MultiPhen tests the linear combination of phenotypes most associated with the genotypes at each SNP, and thus potentially captures effects hidden to single phenotype GWAS. We demonstrate via simulation that this approach provides a dramatic increase in power in many scenarios. There is a boost in power for variants that affect multiple phenotypes and for those that affect only one phenotype. While other multivariate methods have similar power gains, we describe several benefits of MultiPhen over these. In particular, we demonstrate that other multivariate methods that assume the genotypes are normally distributed, such as canonical correlation analysis (CCA) and MANOVA, can have highly inflated type-1 error rates when testing case-control or non-normal continuous phenotypes, while MultiPhen produces no such inflation. To test the performance of MultiPhen on real data we applied it to lipid traits in the Northern Finland Birth Cohort 1966 (NFBC1966). In these data MultiPhen discovers 21% more independent SNPs with known associations than the standard univariate GWAS approach, while applying MultiPhen in addition to the standard approach provides 37% increased discovery. The most associated linear combinations of the lipids estimated by MultiPhen at the leading SNPs accurately reflect the Friedewald Formula, suggesting that MultiPhen could be used to refine the definition of existing phenotypes or uncover novel heritable phenotypes.     

A robust multiple-locus method for quantitative trait locus analysis of non-normally distributed multiple traits

Linear regression-based quantitative trait loci/association mapping methods such as least squares commonly assume normality of residuals. In genetics studies of plants or animals, some quantitative traits may not follow normal distribution because the data include outlying observations or data that are collected from multiple sources, and in such cases the normal regression methods may lose some statistical power to detect quantitative trait loci. In this work, we propose a robust multiple-locus regression approach for analyzing multiple quantitative traits without normality assumption. In our method, the objective function is least absolute deviation (LAD), which corresponds to the assumption of multivariate Laplace distributed residual errors. This distribution has heavier tails than the normal distribution. In addition, we adopt a group LASSO penalty to produce shrinkage estimation of the marker effects and to describe the genetic correlation among phenotypes. Our LAD-LASSO approach is less sensitive to the outliers and is more appropriate for the analysis of data with skewedly distributed phenotypes. Another application of our robust approach is on missing phenotype problem in multiple-trait analysis, where the missing phenotype items can simply be filled with some extreme values, and be treated as outliers. The efficiency of the LAD-LASSO approach is illustrated on both simulated and real data sets.     

Monitoring congenital rubella embryopathy

BACKGROUND: Currently, all developed countries include rubella vaccination in their immunization programs, targeting the complete elimination of congenital rubella syndrome (CRS). In the underdeveloped world, where this severely disabling condition still exists, only a few countries have implemented vaccination policies, and almost no data on their effectiveness or on prevalence rates are available. The aims of the present work were to search for the best phenotype to be used as a sentinel for CRS in a large series of malformed newborns and to propose a CRS surveillance system, based only on clinical data. METHODS: A total of 43 infants diagnosed as having CRS were obtained from 19,184 multimalformed infants, ascertained by the Latin-American Collaborative Study of Congenital Malformations, World Health Organization (WHO) Collaborating Centre for the Prevention of Birth Defects (ECLAMC), over 3,883,165 consecutive births, between 1982 and 2003. They were distributed by country and the most frequent birth defects were identified. From the 19,184 multimalformed infants, all cases presenting the birth defects identified were selected. The sensitivity, specificity, and likelihood ratio (LR) in detecting CRS were determined for these birth defects, alone and in combination. The sample size of multimalformed infants required to detect different levels of increase in the rate of CRS was determined for three sentinel phenotypes. RESULTS: The rate of CRS was highest in Brazil. Based on the best possible combination of sensitivity, specificity, and LR, the dyad comprising eye anomalies and congenital heart defects was shown to be the most appropriate sentinel, with the lowest sample size required, to detect CRS in neonates. CONCLUSIONS: A surveillance system for CRS, based on clinical data in newborns, is being proposed, in an attempt to monitor ongoing vaccination policies, aimed at eliminating CRS in developing countries.     

Multivariate Dimensionality Reduction Approaches to Identify Gene-Gene and Gene-Environment Interactions Underlying Multiple Complex Traits

The elusive but ubiquitous multifactor interactions represent a stumbling block that urgently needs to be removed in searching for determinants involved in human complex diseases. The dimensionality reduction approaches are a promising tool for this task. Many complex diseases exhibit composite syndromes required to be measured in a cluster of clinical traits with varying correlations and/or are inherently longitudinal in nature (changing over time and measured dynamically at multiple time points). A multivariate approach for detecting interactions is thus greatly needed on the purposes of handling a multifaceted phenotype and longitudinal data, as well as improving statistical power for multiple significance testing via a two-stage testing procedure that involves a multivariate analysis for grouped phenotypes followed by univariate analysis for the phenotypes in the significant group(s). In this article, we propose a multivariate extension of generalized multifactor dimensionality reduction (GMDR) based on multivariate generalized linear, multivariate quasi-likelihood and generalized estimating equations models. Simulations and real data analysis for the cohort from the Study of Addiction: Genetics and Environment are performed to investigate the properties and performance of the proposed method, as compared with the univariate method. The results suggest that the proposed multivariate GMDR substantially boosts statistical power.     

An Effective Method to Identify Heritable Components from Multivariate Phenotypes

Multivariate phenotypes may be characterized collectively by a variety of low level traits, such as in the diagnosis of a disease that relies on multiple disease indicators. Such multivariate phenotypes are often used in genetic association studies. If highly heritable components of a multivariate phenotype can be identified, it can maximize the likelihood of finding genetic associations. Existing methods for phenotype refinement perform unsupervised cluster analysis on low-level traits and hence do not assess heritability. Existing heritable component analytics either cannot utilize general pedigrees or have to estimate the entire covariance matrix of low-level traits from limited samples, which leads to inaccurate estimates and is often computationally prohibitive. It is also difficult for these methods to exclude fixed effects from other covariates such as age, sex and race, in order to identify truly heritable components. We propose to search for a combination of low-level traits and directly maximize the heritability of this combined trait. A quadratic optimization problem is thus derived where the objective function is formulated by decomposing the traditional maximum likelihood method for estimating the heritability of a quantitative trait. The proposed approach can generate linearly-combined traits of high heritability that has been corrected for the fixed effects of covariates. The effectiveness of the proposed approach is demonstrated in simulations and by a case study of cocaine dependence. Our approach was computationally efficient and derived traits of higher heritability than those by other methods. Additional association analysis with the derived cocaine-use trait identified genetic markers that were replicated in an independent sample, further confirming the utility and advantage of the proposed approach.     

Autistic-like and schizotypal traits in a life history perspective: diametrical associations with impulsivity,sensation seeking,and sociosexual behavior

According to recent theoretical models, autistic-like and schizotypal traits can be regarded as opposite sides of a single continuum of variation in personality and cognition, and may be diametrically associated with individual differences in life history strategies. In this view, schizotypy is a psychological phenotype oriented toward high mating effort and reduced parenting, consistent with a fast life history strategy, whereas autistic-like traits contribute to a slow strategy characterized by reduced mating effort and high parental investment. In this study, we tested the hypothesis that autistic-like and schizotypal traits would be diametrically associated with unrestricted sociosexuality, impulsivity, and sensation seeking (three key behavioral correlates of fast life history strategies in humans) in a sample of 152 young adults (18–38 years). The results were consistent with a diametrical autism–schizotypy axis of individual variation. In line with our hypotheses, autism–schizotypy scores were uniquely associated with individual differences in impulsivity, sensation seeking, and sociosexual behavior, even after controlling for variation in Big Five personality traits. However, we found no significant associations with sociosexual attitude in the present sample. Our findings provide additional support for a life history model of autistic-like and schizotypal traits and demonstrate the heuristic value of this approach in the study of personality and psychopathology.     

Frequency-dependent sexual selection

Sexual selection by female choice is expected to give rise to a frequency-dependent sexual advantage in favour of preferred male phenotypes: the rarer the preferred phenotypes, the more often they are chosen as mates. This 'rare-male advantage' can maintain a polymorphism when two or more phenotypes are mated preferentially: each phenotype gains an advantage when it is rarer than the others; no preferred phenotype can then be lost from the population. Expression of preference may be complete or partial. In models of complete preference, females with a preference always mate preferentially. Models of partial preference are more realistic: in these models, the probability that a female mates preferentially depends on the frequency with which she encounters the males she prefers. Two different 'encounter models' of partial preference have been derived: the O'Donald model and the Charlesworth model. The encounter models contain the complete preference model as a limiting case. In this paper, the Charlesworth model is generalized to allow for female preference of more than one male phenotype. Levels of frequency dependence can then be compared in the O'Donald and Charlesworth models. The complete preference model and both encounter models are formulated in the same genetical terms of preferences for dominant and recessive male phenotypes. Polymorphic equilibria and conditions for stability are derived for each of the three models. The models are then fitted to data of frequencies of matings observed in experiments with the two-spot ladybird. The complete preference model gives as good a fit as the encounter models to the data of these and other experiments. The O'Donald and Charlesworth encounter models are shown to produce a very similar frequency-dependent relation. Generally, as females become less choosy, they express their preference with more dependence on male frequency, whereas the resulting selection of the males becomes less frequency dependent. More choosy females are more constant in expressing their preference, producing greater frequency dependence in the selection of the males.     

On the generalized poisson regression mixture model for mapping quantitative trait loci with count data

Statistical methods for mapping quantitative trait loci (QTL) have been extensively studied. While most existing methods assume normal distribution of the phenotype, the normality assumption could be easily violated when phenotypes are measured in counts. One natural choice to deal with count traits is to apply the classical Poisson regression model. However, conditional on covariates, the Poisson assumption of mean-variance equality may not be valid when data are potentially under- or overdispersed. In this article, we propose an interval-mapping approach for phenotypes measured in counts. We model the effects of QTL through a generalized Poisson regression model and develop efficient likelihood-based inference procedures. This approach, implemented with the EM algorithm, allows for a genomewide scan for the existence of QTL throughout the entire genome. The performance of the proposed method is evaluated through extensive simulation studies along with comparisons with existing approaches such as the Poisson regression and the generalized estimating equation approach. An application to a rice tiller number data set is given. Our approach provides a standard procedure for mapping QTL involved in the genetic control of complex traits measured in counts.     

Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model

Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.     

The evolution of dispersal rates in a heterogeneous time-periodic environment

A reaction-diffusion model for the evolution of dispersal rates is considered in which there is both spatial heterogeneity and temporal periodicity. The model is restricted to two phenotypes because of technical difficulties, but a wide range of mathematical techniques and computational effort are needed to obtain useful answers. We find that the question of selection is a great deal richer than in the autonomous case, where the phenotype with the lowest diffusion is selected for. In the current model either the lower or higher diffuser rate may be selected, or there may be coexistence of phenotypes. The paper raises several open questions and suggests in particular that a mutation-selection multi-phenotypic model would repay study. Received: 17 April 2000 / Revised version: 2 May 2001 / Published online: 12 October 2001     

Statistical models for trisomic phenotypes.

Certain genetic disorders are rare in the general population but more common in individuals with specific trisomies, which suggests that the genes involved in the etiology of these disorders may be located on the trisomic chromosome. As with all aneuploid syndromes, however, a considerable degree of variation exists within each phenotype so that any given trait is present only among a subset of the trisomic population. We have previously presented a simple gene-dosage model to explain this phenotypic variation and developed a strategy to map genes for such traits. The mapping strategy does not depend on the simple model but works in theory under any model that predicts that affected individuals have an increased likelihood of disomic homozygosity at the trait locus. This paper explores the robustness of our mapping method by investigating what kinds of models give an expected increase in disomic homozygosity. We describe a number of basic statistical models for trisomic phenotypes. Some of these are logical extensions of standard models for disomic phenotypes, and some are more specific to trisomy. Where possible, we discuss genetic mechanisms applicable to each model. We investigate which models and which parameter values give an expected increase in disomic homozygosity in individuals with the trait. Finally, we determine the sample sizes required to identify the increased disomic homozygosity under each model. Most of the models we explore yield detectable increases in disomic homozygosity for some reasonable range of parameter values, usually corresponding to smaller trait frequencies. It therefore appears that our mapping method should be effective for a wide variety of moderately infrequent traits, even though the exact mode of inheritance is unlikely to be known.     

The X-linked gene G4.5 is responsible for different infantile dilated cardiomyopathies.

Barth syndrome (BTHS) is an X-linked disorder characterized clinically by the associated features of cardiac and skeletal myopathy, short stature, and neutropenia. The clinical manifestations of the disease are, in general, quite variable, but cardiac failure as a consequence of cardiac dilatation and hypertrophy is a constant finding and is the most common cause of death in the first months of life. X-linked cardiomyopathies with clinical manifestations similar to BTHS have been reported, and it has been proposed that they may be allelic. We have recently identified the gene responsible for BTHS, in one of the Xq28 genes, G4.5. In this paper we report the sequence analysis of 11 additional familial cases: 8 were diagnosed as possibly affected with BTHS, and 3 were affected with X-linked dilated cardiomyopathies. Mutations in the G4.5 gene were found in nine of the patients analyzed. The molecular studies have linked together what were formerly considered different conditions and have shown that the G4.5 gene is responsible for BTHS (OMIM 302060), X-linked endocardial fibroelastosis (OMIM 305300), and severe X-linked cardiomyopathy (OMIM 300069). Our results also suggest that very severe phenotypes may be associated with null mutations in the gene, whereas mutations in alternative portions or missense mutations may give a "less severe" phenotype.     

Inferring causal phenotype networks from segregating populations

A major goal in the study of complex traits is to decipher the causal interrelationships among correlated phenotypes. Current methods mostly yield undirected networks that connect phenotypes without causal orientation. Some of these connections may be spurious due to partial correlation that is not causal. We show how to build causal direction into an undirected network of phenotypes by including causal QTL for each phenotype. We evaluate causal direction for each edge connecting two phenotypes, using a LOD score. This new approach can be applied to many different population structures, including inbred and outbred crosses as well as natural populations, and can accommodate feedback loops. We assess its performance in simulation studies and show that our method recovers network edges and infers causal direction correctly at a high rate. Finally, we illustrate our method with an example involving gene expression and metabolite traits from experimental crosses.     

X-Linked Gene Expression in the Virginia Opossum: Differences between the Paternally Derived Gpd and Pgk-A Loci

Expression of X-linked glucose-6-phosphate dehydrogenase (G6PD) and phosphoglycerate kinase-A (PGK-A) in the Virginia opossum (Didelphis virginiana) was studied electrophoretically in animals from natural populations and those produced through controlled laboratory crosses. Blood from most of the wild animals exhibited a common single-banded phenotype for both enzymes. Rare variant animals, regardless of sex, exhibited single-banded phenotypes different in mobility from the common mobility class of the respective enzyme. The laboratory crosses confirmed the allelic basis for the common and rare phenotypes. Transmission of PGK-A phenotypes followed the pattern of determinate (nonrandom) inactivation of the paternally derived Pgk-A allele, and transmission of G6PD also was consistent with this pattern. A survey of tissue-specific expression of G6PD phenotypes of heterozygous females revealed, in almost all tissues, three-banded patterns skewed in favor of the allele that was expressed in blood cells. Three-banded patterns were never observed in males or in putatively homozygous females. These patterns suggest simultaneous, but unequal, expression of the maternally and paternally derived Gpd alleles within individual cells (i.e., partial paternal allele expression). The absence of such partial expression was noted in a parallel survey of females heterozygous at the Pgk-A locus. Thus, it appears that Gpd and Pgk-A are X-linked in D. virginiana and subject to preferential paternal allele inactivation, but that dosage compensation may not be complete for all paternally derived X-linked genes. The data establish the similarity between the American and Australian marsupial patterns of X-linked gene regulation and, thus, support the hypothesis that this form of dosage compensation was present in the early marsupial lineage that gave rise to these modern marsupial divisions. In addition, the data provide the first documentation of the differential expression of two X-linked genes in a single marsupial species. Because of its combination of X-linked variation, high fecundity, and short generation time, D. virginiana is a unique model for pursuing questions about marsupial gene regulation that have been difficult to approach through studies of Australian species.     

Intraspecific phenotypic differences in fish affect ecosystem processes as much as bottom–up factors

Evolution of life history traits can occur rapidly and has the potential to influence ecological processes, which can also be shaped by abiotic and biotic factors. Few studies have shown that life history phenotype can affect ecological processes as much as commonly studied biotic ecological variables, but currently we do not know how the ecological effects of life history phenotype compare in size to the effects of abiotic factors, or whether the ecological effects of phenotypes are sensitive to variability in abiotic conditions. Using a factorial mesocosm experiment we compared the ecosystem effects of guppy Poecilia reticulata life history phenotypes in two light treatments representing a four‐fold difference in light levels, which was comparable to upstream downstream differences in light availability in Trinidadian streams. Light and phenotype had significant effects on similar aspects of ecosystem function. Whereas light had a stronger effect on ecosystem structure (algal and invertebrate stocks) than phenotype, phenotype and light had nearly equal effects on many ecosystem processes (nutrient recycling, nutrient fluxes, ecosystem metabolism and leaf litter decomposition). Light had a stronger effect on most guppy life history traits and guppy fitness than differences between phenotypes. The effect of light on these traits was consistent with higher availability of food resources in the high light treatments. Interactions between light and phenotype were weak for the majority of response variables suggesting that abiotic variability did not alter the mechanisms by which phenotypes affect ecosystem function. We conclude that subtle phenotypic differences in consumers can affect ecosystem processes as much as meaningful variability in abiotic factors which until recently were thought to be the primary drivers of ecosystem function in nature. However, despite its effects on traits and the ecosystem, light did not alter the effect of guppy phenotype on ecosystem function.