Our lab studies the biological basis for individual variation. The genetic architecture of complex traits is not a static blueprint of the phenotype; rather, it is highly dynamic and context-dependent. In many ways, we study the ecology of the genome: how do genes interact with each other and their environment to shape variation between individuals? We address this question using a combination of approaches focused on studying natural genetic variation from the lab to the field and from Drosophila to humans.


We are always looking for curious and motivated graduate students and have various opportunities for postdocs. If you’re interested, please don’t hesitate to contact us.

Learn about our projects.

Our lab’s projects are divided into three main branches. Please click above to learn more.

 ocean floor

It is well established in quantitative genetics that stressful environmental exposure tends to increase the phenotypic variance of a population, but how and why? And why do some individuals appear to be more sensitive than others to such perturbations?
graphic1This is a fundamental question for any biologist interested in understanding the genetic basis of variation for complex traits.  Although studies of development, morphology and animal breeding have long noted the heterogeneity of variance among genotypes, this axis of variation has received little attention compared to the effect of genetic variation on trait means. There is now clear evidence for the importance of genetic control of variance and that variance itself is a quantitative trait. This has important implications both in medical genetics and evolutionary biology. If different genetic
 backgrounds differ in their
 propensity for phenotypic variability, then individuals derived from a high-variability genetic background may exhibit an extreme phenotype by chance alone. A property of that genotype that would not have been informed by traditional mean focused quantitative genetic approaches. In the context of evolutionary change, this could accelerate or slow down adaptation to new conditions. With respect to health, this could result in disease, changes in variance can affect the probability of individuals finding itself in the tails of the distribution. Therefore, by focusing primarily on the effect of genetic variation on trait averages and ignoring its effect on variance, we may be missing a very important axis contributing to phenotypic variation.
graphic2Our lab explores this problem both from an evolutionary perspective asking: under what scenario might variance control evolve? What evolutionary forces maintain variation for alleles controlling phenotypic variability?  And from a medical perspective: how does variance control affect our ability to make predictions from genotype to phenotype? Does the presence of variance increasing alleles increase the probability an individual being in the tail of distribution?


Evolutionary dynamics of variability  
Benjamin de Bivort Lab (Harvard)
Barbara Engelhardt (The BEE Lab)
Robustness As A Driver Of Disease Emergence
Paivi Pajukanta Lab (UCLA)
Noah Zaitlen Lab (UCSF)

Please check back soon for a complete description of this project.

selection_responseAllele Frequency

A major focus of the lab is to study how genotype-by-genotype and genotype-by-environment interaction modulates gene regulatory networks and ultimately shape individual variation.
The current quantitative genetic paradigm is driven by a prevailing view that additive genetic models – focused on the mean effect of alternative alleles – adequately explain variation for most phenotypes. Unfortunately, a decade after the popularization of GWAS and in spite of much effort, we have fallen short of the goal of explaining most of the heritability for complex traits in terms of allelic effects. This averaging approach is designed to describe the mean effect of an allele randomized over a large number of the genetic backgrounds and environments. But each individual has faced a unique trajectory of environmental insults, some of which may have quite large genotype-specific effects.
A fast-growing body of evidence indicates that the genotype-phenotype map is much more complicated than Fisher’s additive model would predict. When measurements can be made with reasonable control of the environment, complex, non-additive interrelationships between loci appear to be the rule and not the exception. Furthermore, these allelic effects are often environmentally sensitive. The paradigm derived from traditional quantitative genetics is at odds with a major goal of genetics as we often seek to understand the causal path from genotype to phenotype for individuals and not populations.
sys gen1In Drosophila, we have developed a unique resource for mapping variation in complex traits using large synthetic Drosophila outbred populations. These genetically diverse mapping panels allow us to control genetic background and allele frequency as well as the environment of each population. Notably, our method breaks away from traditional approaches that often rely on problematic inbred strains. This allows us to rear thousands of genetically unique flies, drawn from a common genetic pool, expose them to different environments and study the combined effect of genetic background and environment perturbation. We are currently focusing on metabolic traits.
sys gen2In humans, we are collaborating with the lab of Dr Paivi Pajukanta at UCLA and are taking a systems genetics approach to the study of metabolic syndromes (METSIN cohort). Our laboratory has developed fully automated approaches to perform transcriptional profiling at high-throughput for a fraction of the cost of currently available methods. This is allowing us to profile a large number of individuals and use a systems genetics approach to study metabolic variation, echoing our work in flies.
Understanding the genetic basis of complex traits demands that we go beyond describing the relationships between polymorphic DNA and phenotypic variation. To that end we take a system genetics approach, simultaneously measuring variation at multiple levels of biological organization is a necessary first step. Patterns of transcriptional correlation allow the construction of co-expression networks describing how genetic variation affects transcriptional variation (i.e. eQTL), and how directed transcriptional networks in turn correlate with phenotypic variation. Together, this information will allow us to draw the causal path from variation in allele frequency to a phenotypic differences between individuals. Such directionality indicates the flow of biological information and sets the framework through which perturbations can be predicted. This is the promise of systems genetics – the formulation of causal predictions painting a detailed picture of a dynamic genotype-phenotype map.

sys gen3

Download our protocols.

If you’re interested in our protocols, please watch this space.


Meet our lab members.

Julien Ayroles

Principal Investigator

Julien has taken a diverse path throughout his career. As an undergraduate at the University Paul Sabatier in Toulouse (France) and as a Masters student at UI Urbana-Champaign, his training was primarily in ecology and evolutionary biology. During this time, he developed a keen interest in conservation biology that later led him to genetics. He completed his Ph.D. at North Carolina State University under the mentorship of Drs Eric Stone and Trudy Mackay. During his doctorate, he developed various approaches that centered on using a systems genetics approach to dissect the genetic basis of complex traits in Drosophila. He was then elected to the Harvard Society of Fellows as a Junior Fellow, where he studied the relationship between standing natural genetic variation and phenotypic variation, bridging theoretical and empirical approaches. His background in ecology and evolution grounds him as an organismal biologist, and it is in that context that he approaches the molecular and functional work in the lab.

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Serge Picard

Research Associate

Serge received his BS in Biochemistry from UQTR (Université du Québec à Trois-Rivières) and a Grad diploma in Bioinformatics from UQAM (Université du Québec à Montréal). He began his career in Endocrinology focusing on hypertension in pregnancy at the Centre Hospitalier Universitaire Sainte-Justine. He then switched to immunology working on host response to Candida albicans and the development of immunodominant antigens for immunotherapeutic cancer treatment at the Biotechnology Research Institute of Montreal. He then moved on to Next Gen Sequencing providing innovative library prep and sequencing solutions for a variety of assays and organisms for Janelia Research Campus and Princeton University where he collaborates with the Andolfatto, Ayroles, and Kocher labs.

Sudarshan Chari


Sudarshan currently studies (1) the genetic architecture of complex traits using individual based whole genome sequencing (2) mechanisms of phenotypic plasticity and genetic assimilation using an ‘evolve and resequence’ approach and (3) the nature and degree of pleiotropy by performing meta-analysis on genome-wide association studies. He received a dual Ph.D. in Zoology and Ecology, Evolutionary Biology & Behavior from Michigan State University, working with Dr. Ian Dworkin investigating the role of genetic background effects in functional genetics and evolution. Before attending graduate school in the US, he received his BSc in Chemistry from Mumbai University and MSc in Biochemistry from Maharaja Sayajirao University in India. He is broadly interested in understanding the influence of genetic interactions and environmental variation in shaping genotype-phenotype relationships of developmental & behavioral traits and describing the evolutionary trajectories of such relationships.

Luisa F. Pallares


Luisa did her Bachelors in Biology at Universidad Nacional de Colombia in Bogotá. Under the supervision of Dr. Joao Muñoz she studied the ecology and evolution of social behavior in Canids. For her graduate studies, Luisa moved to Germany where she worked with Prof. Diethard Tautz at the Max Planck Institute for Evolutionary Biology and received her PhD in 2015. Her research focused on understanding the genomic architecture of craniofacial shape, and its implications for the evolution of between- and within-species variation in mice. She worked at the same institute as a postdoctoral researcher trying to get a mechanistic understanding on how and when mutations in candidate loci are reflected in adult phenotypes. Luisa is interested in the evolution of complex traits, and is broadly interested in the dynamic nature of the genotype-phenotype map.

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Amanda Lea


Amanda is a PhD student in the University Program in Ecology, co-advised by Jenny Tung and Susan Alberts. Her PhD work focuses on the impact of early life environment on gene regulation, behavior, and fitness. Amanda studies the links between environmental variation and epigenetic modifications, and the influence of kin and social experience early in life on trait variation later in life.

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Simon Forsberg


Simon did his BSc in biotechnology and MSc in bioinformatics at Uppsala University (Sweden). He went on to do his PhD studies in quantitative and computational genetics under the supervision of Örjan Carlborg. His PhD work focuses on genetic interactions and genetic control of phenotypic variability. Simon is broadly interested in the genetic architectures of complex traits, and in the prediction of individual phenotypes based on their genotype. In particular, he is interested in the topic of individual components versus entire systems: To what extent can we understand the genetics of complex traits by studying one gene at a time, and to what extent do we need to consider the daunting number of possible interactions between them?

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Luke Henry

Graduate Student

Luke received his BA in biology and BM in bassoon performance from Bard College. As an undergraduate, he worked with Dr. Felicia Keesing on the ecology of Lyme disease at multiple ecological scales. Following graduation, as a technician at the University of Virginia with Dr. Ben Blackman, he investigated adaptation to photoperiod in wild and domesticated sunflowers. He received his MS in Biology from Indiana University, working with Drs. Keith Clay and Irene Newton on the maintenance and ecology of maternal transmission in DrosophilaWolbachia-mitochondria symbiosis. At Princeton, he is interested in understanding how species interactions influence evolution to novel environments through using host-microbiome associations as a model ecological and evolutionary system.
Sara Godwin
Sara is currently an undergraduate student, majoring  in Ecology and Evolutionary Biology.

Minjia Tang
Minjia is currently an undergraduate student, majoring  in Ecology and Evolutionary Biology.

Michael Fernandez
Michael is a junior visiting from U.C. Berkeley where he will complete his undergraduate degree (before returning to Princeton for Graduate School – we hope!).

Brent Albertson
Brent is currently an undergraduate student, he will major in Ecology and Evolutionary Biology. He is also a member of the varsity Men’s Track and Field team. His research interests include understanding what drives phenotypic variation in populations and the relationship between genotype and phenotype. His senior thesis project is investigating how genotypic variation in Drosophila influences the trainability of flies undergoing an endurance exercise training regimen, with a specific focus on exercise-induced mitochondrial biogenesis.

deserve a restin cold roomproduction

Peruse our publications.

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Coming Soon:

  • Metcalf CJE*, Ayroles JF*. (2016). Chapter: “Why does intra-genotypic variance persist?” In book titled “Unsolved Problems in Ecology’. Princeton University Press. (under review)
  • Ayroles, JF and Clark AG. (2016). The misguided search for the missing heritability of complex traits. Nature Review Genetics. (under review)
  • Dumitrascu, B., Darnell, G., Ayroles, JF. and Engelhardt, B.E., 2016. A Bayesian test to identify variance effects. arXiv preprint arXiv:1512.01616.


  • 23 – Zwarts L, Broeck LV, Cappuyns E, Ayroles JF, Magwire MM, Vulsteke V, Clements J, Mackay TF, Callaerts P. (2015) The genetic basis of natural variation in mushroom body size in Drosophila melanogaster. Nature Communications.11:6.
  • 22 –  Ayroles JF, Buchanan SM, O’Leary C, Skutt-Kakaria K, Grenier JK, Clark AG, Hartl DL, de Bivort BL. (2015). Behavioral idiosyncrasy reveals genetic control of phenotypic variability. PNAS 112(21):6706-11.
  • 21 – Matute DR, Ayroles JF. (2014) Hybridization occurs between Drosophila simulans and D. sechellia in the Seychelles archipelago. Journal of Evolutionary Biology. 27(6):1057-68.
  • 20 –  Corbett-Detig RB,  Zhou J, Clark AG, Hartl DL, Ayroles JF. (2013). Genetic Incompatibilities Within Species are Widespread. Nature, 504, 135–137.
  • 19 – Huang W, Richards S, Carbone MA, Zhu D, Anholt RRH, Ayroles JF, et al. (2012) Epistasis Dominates The Genetic Architecture Of Drosophila Quantitative Traits. PNAS, 109:15553-15559.
  • 18 – Massouras A, Waszak SM, Albarca M, Hens K, Holcombe K, Ayroles JF, Dermitzakis ET, Eric A Stone EA,  Jensen J D, Mackay T.F.C, Deplancke B. (2012) Genomic Variation And Its Impact On Gene Expression In Drosophila melanogasterPlos Genetics.  8 (11): e1003055.
  • 17 – Mackay TFC*, Richards S*, Barbadilla A *, Stone EA*, Ayroles JF, Zhu D, Sònia Casillas. et. al. (2012) The Drosophila Genetics Reference Panel:A Community Resource for Analysis of  Population Genomics and Quantitative Traits.  Nature, 482(7384):173-8. Faculty of 1000, Biology 
  • 16 – Ober U*, Ayroles  JF*, Stone EA, Richards S, Zhu D,Gibbs RA, Stricker C, Gianola D, Schlather M, Mackay TFC, Simianer H. (2011) Using Whole Genome Sequence Data to Predict Quantitative Trait Phenotypes in Drosophila melanogaster. PLoS Genetics, 8(5): e1002685.  Faculty of 1000, Biology 
  • 15 – Rowe K, Singhal S, MacManes M, Ayroles JF, Morelli TL, Rubidge E, Bi K, Moritz C (2012). Museum Genomics: Low Cost And High Accuracy Genetic Data From Historical Specimens. Molecular Ecology Ressources, 11(6): 1082–1092.
  • 14 – Ayroles JF, Laflamme B , Wolfner MA, Mackay TFC. (2011) Sifting Through The Data: Identifying Top Candidates For Novelseminal Protein Genes From Drosophila Whole Genome Expression Data. Genetics Research, 93(6): 387-395.
  • 13 – Jumbo-Lucioni P*, Ayroles JF*, Chambers MM, Jordan KW, Leips J, Mackay TF, De Luca M. (2010) Systems Genetics Analysis Of Body Weight And Energy Metabolism Traits In Drosophila melanogasterBMC Genomics, 11(11): 297. (* Contributed equally)
  • 12 – Edwards, A, Ayroles JF, Stone EA, Mackay TFC. (2009) A Transcriptional Network Associated With Natural Variation In Drosophila Aggressive Behavior. Genome Biology, 10(7): R76.
  • 11 – Mackay TFC, Stone EA, Ayroles JF. (2009) Quantitative Genetics: Prospects And Challenges. Nature Review Genetics, 10(8): 565-577.
  • 10 – Morozova TV*, Ayroles JF*, Jordan KW, Duncan LH, Carbone MA, Lyman RF, Stone EA, Govindaraju DR, Ellison RC, Mackay TF, Anholt RR. (2009) Alcohol Sensitivity In Drosophila: Translational Potential Of Systems Genetics. Genetics, 183(2): 733-745  (* Contributed equally)
  • 9 – Harbison ST, Carbone MA, Ayroles JF, Stone EA, Lyman RF, Mackay TFC (2009) Co-Regulated Transcriptional Networks Contribute to Natural Genetic Variation in Drosophila Sleep. Nature Genetics, 41(3): 371-375.
  • 8 – Ayroles JF, Carbone MA, Stone EA, Jordan KW, Lyman RF, Magwire MM, Rollman SM, Duncan LH, Lawrence F, Anholt RH, Mackay TFC. (2009) Systems genetics of complex taits in Drosophila melanogaster.  Nature Genetics, 41(3): 299-307. Faculty of 1000, Biology
  • 7 – Kocher SD, Ayroles JF, Stone EA, Grozinger CM. (2009) Genomics Of Pheromone Response: Cooperation And Conflict In Honey Bees. Plos ONE,5(2): e9116.
  • 6 – Stone EA, Ayroles JF. (2009) Modulated Modularity Clustering As An Exploratory Tool For Functional Genomic Inference. PLoS Genetics, 5(5): e1000479.
  • 5 – Ayroles JF, Hughes KA, Reedy MM, Rodriguez-Zas SL, Drnevich JM, Rowe KC, Cáceres CE, Paige KN. (2009) Genome-Wide Assessment Of Inbreeding Depression In Drosophila melanogasterConservation Biology, 23(4): 920-930
  • 4 – Carbone MA, Ayroles JF, Yamamoto A, Morozova TV, West SA, Magwire MM, Mackay TF, Anholt RR. (2009) Overexpression Of Myocilin In The Drosophila Eye Activates The Unfolded Protein Response: Implications For Glaucoma. PLoS ONE, 4(1): e4216.
  • 3 – Ayroles JF, Gibson G. (2006) Analysis Of Variance Of Microarray Data. Methods Enzymol, 411: -33.
  • 2 – Hughes KA, Ayroles JF, Reedy MM, Drnevich JM, Rowe KC, Ruedi EA, Cáceres CE, Paige KN. (2006) Segregating Variation In The Transcriptome: Cis Regulation And Additivity Of Effects. Genetics 173(3): 1347-1355.
  • 1 – Dejean A, Solano PJ, Ayroles JF, Corbara B, Orivel J. (2005) Insect Behaviour: Arboreal Ants Build Traps to Capture Prey. Nature, (434):973.