Learn about our projects.
- Life Under Stress
- Variance Analytics
- Systems Genetics: Complex Traits
- It's the Microbiome ...
- Turkana Genome Project
To find out about the main projects in the lab, please watch this space.
Why do some individuals appear to be more sensitive than others to environmental perturbation?
The answer to this question has broad implications ranging from our ability to make predictions about disease risk from genotype, to our ability to identify the drivers of inter-individual variability, and our understanding of toxicity mode-of-action. Exploring the contribution of genotype-by-environment interactions (GxE) to individual variation has been very challenging in humans, where epidemiological studies exploring GxE are generally underpowered, have difficulties quantifying environmental exposure. To address this problem we created a new community resource to study the genetic basis of complex trait variation in Drosophila melanogaster composed of large, synthetic outbred populations. The approach we outline enables us to break away from traditional, and often underpowered approaches that have relied on inbred strains or RILs. With this new and versatile community resource, we can rear thousands of genetically unique flies drawn from a common genetic pool, expose them to a range of different environments and contrast the ensuing genetic architectures
Data we have collected over the past couple of years indicate that differences in individual sensitivity emerge from the disruption of regulatory systems where individuals that are more sensitive to environmental stress have decreased transcriptional robustness for many genes and that this variation in robustness is under genetic control. We have developed an analytical framework to identify context-dependent transcriptional networks and the polymorphisms that control this variation. The impact of mutations that affect environmental sensitivity are heavily dependent on the interaction between genetic background and environmental stress simultaneously. As important as they are, the epistatic interactions associated with variation in penetrance have been notoriously difficult to identify. We are currently developing experiment and statistical approaches aimed at mapping and testing the contribution of such interaction of individual variation in environmental sensitivity.
It is well established in quantitative genetics that stressful environmental exposure tends to increase the phenotypic variance of a population, but how and why?
This 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.
Our 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)
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.
In 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.
In 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.
Can the microbiome influence host evolutionary trajectories?
The microbiome shapes many traits in hosts, but we still do not understand how it influences host evolution. To impact host evolution, the microbiome must be heritable and have phenotypic effects on the host. However, the complex inheritance and context-dependence of the microbiome challenges traditional models of organismal evolution. We take a multifaceted approach to identify conditions in which the microbiome influences host evolutionary trajectories.
We are currently exploring quantitative genetic models to study how microbial inheritance and phenotypic effects can modulate host evolutionary responses to selection. We are particularly interested in how hosts may leverage locally adapted microbes, increasing survivorship in stressful environments. As well as how microbial variation may increase host phenotypic variation, enabling exploration of novel fitness landscapes.
The complex interplay between host and microbial genetic variation is surprisingly understudied. We use a combination of approaches from experimental evolution in Drosophila to ecological sampling in humans across environmental gradients and lifestyle. We aim to incorporating microbial variation in standard evolutionary and quantitative genetics model to better understand how phenotypic variation is generated, and subsequently, how selection operates across ecological and evolutionary scales.
Do modern diseases arise from genomes living in the past?
In a blink of evolutionary time, humans have explored every corner of this planet and have shown an amazing capacity to adapt to extreme conditions. The Turkana, a seminomadic pastoralist tribe, live in northern Kenya in one of the most arid environments in the world. Having retained their traditional lifestyle, the Turkana provide a rare opportunity to address how ecological pressures and natural selection shape human genetic variation (a question we will explore using whole genome sequencing). Further, because of recent infrastructure developments, many Turkana are moving away from their ancestral lands and into cities. This unique situation allows us to ask another important question: what happens when a locally adapted population is transplanted to a novel urban environment? Such urban-rural migrations are commonly accompanied by an increased risk of chronic diseases; yet, our mechanistic understanding of how these transitions impact health is limited. To address this gap, we are contrasting transcriptomic and phenotypic data collected from traditional Turkana versus those that have moved, within their lifetime, to major cities. Together, this work not only allow us to retrace human evolutionary history, but to understand how the disruption of locally adapted systems may lead to disease.
About the Turkana people
The Turkana people inhabit one of the most arid ecosystems in East Africa, with year-round highs of 100F and low levels of seasonal, unpredictable rainfall. The Turkana are pastoral nomads, and 80% of their diet is derived from milk or other animal products. Daily protein intake is thus extremely high (300% of the WHO requirements), but total caloric intake is low (1,300-1,600 kcal/day for adults). Turkana are consequently very lean, yet they undertake the arduous task of collecting water on a daily basis. This process typically involves walking several kilometers (5 to 10 km is not unusual) to wells dug in dry river beds, and hauling water up from the bottom of a well (which can exceed 30 feet during the dry season). Water must then be carried back to the home and shared among family and livestock. As a result, the Turkana drink relatively little water on a daily basis, while tolerating extreme heat and exerting considerable energy; they do so despite limited caloric reserves and a protein-rich diet, which takes considerably more energy to digest than fats or carbohydrates. This extreme lifestyle has likely selected for numerous physiological adaptations in the Turkana people that we aim to uncover.
Download our Protocols and Methods.
If you’re interested in our protocols, please watch this space.
TM3’seq: a tagmentation-mediated 3’ sequencing approach for improving scalability of RNA-seq experiments
RNA-seq has become the standard tool for collecting genome-wide expression data in very diverse fields, from ecology and developmental biology to quantitative genetics and medical genomics. However, RNA-seq library preparation as well as its sequencing requirements are still prohibitive for many laboratories, in particular when large sample sizes are involved. Recently, the field of single-cell transcriptomics has been able to reduce costs and increase throughput by adopting an approach that barcodes individual samples during reverse transcription and pools them before cDNA synthesis, effectively processing a single sample for most of the library preparation procedure. In contrast, RNA-seq protocols where each sample is processed individually are significantly more expensive and lower throughput than single-cell approaches. Yet, many experimental approaches are designed around follow-up experiments on a subset of samples, and therefore require that individual libraries are generated for each sample. In order to fill this gap, we have developed TM3’seq, a 3’-enriched library preparation protocol that uses Tn5 transposase and preserves the sample identity at each step. TM3’seq is designed for the high-throughput processing of individual samples (96 samples in 6h, with only 3h hands-on time) at a fraction of the cost of commercial kits ($1.5 per sample), while recovering gene expression profiles of the same quality as the commercial kits. We expect that the cost- and time-efficient features of TM3’seq make large-scale RNA-seq experiments more permissive for the entire scientific community.
TM3’seq: a tagmentation-mediated 3’ sequencing approach for improving scalability of RNA-seq experiments Luisa F. Pallares, Serge Picard, Julien F. Ayroles. (2019) bioRxiv https://doi.org/10.1101/585810
Meet our lab members.
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.
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.
Amanda received her BS in Ecology and Evolutionary Biology from the University of California: Los Angeles, and her PhD in Ecology from Duke University. Her PhD was co-advised by Susan Alberts and Jenny Tung. At Princeton, she is a Helen Hay Whitney Foundation postdoctoral fellow working with Julien Ayroles and Josh Akey.
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?
Diogo has an undergraduate degree in biology from the University of São Paulo, where he also obtained a master’s degree (2012) and a Ph.D. (2019) in genetics and evolutionary biology, working with Prof. Gabriel Marroig. Diogo’s work is centered on the evolution of genetic correlations, a topic he explores using several different approaches, including QTL mapping, experimental evolution, computer simulations, and comparative data. He joins the Ayroles lab as a Princeton Presidential Postdoctoral Fellow.
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 Drosophila–Wolbachia-mitoch
Scott received his BS in mathematics along with minors in history and English from the University of Arkansas at Little Rock. As an undergraduate, he was involved extensively in software development in industry and academia. At Princeton, he is interested in how the foundations of mathematics, computer science, and statistics intersect with physiology, genomics, and neuroscience to give insight into complex biological systems.Email
Julie did her Bachelor in Medicine at Harbin Medical University, China and Ph.D. in Molecular and Cell Biology at SUNY-Downstate Medical Center under the supervision of Dr. Maureen McLeod. She studied signal transduction pathways regulating meiosis using fission yeast Schizosaccharomyces pombe as a model. Prior to joining the Ayroles lab, she worked as a research specialist in the Andolfatto lab at Princeton University. She applied various genomic technologies, especially Next Generation Sequencing (NGS) on understanding genome evolution and genetic mechanisms underlying adaptations in a variety of species. She is interested in developing novel genomic methods and high-throughput automation for large scale population genetic studies.
Michael recently completed his BA from UC Berkeley. He currently investigating the contribution of the microbiome to host phenotypic variation.Email
Sara is currently an undergraduate student, majoring in Ecology and Evolutionary Biology. She is also a member of the varsity Women’s Tennis Team. Her research interests include understanding the interaction between genotype and phenotype, as well as understanding how nutrition influences this relationship. Her senior thesis project will investigate how genotypic variation in Drosophila influences metabolites and their pathways, specifically glucose metabolism.
Minjia is currently an undergraduate student, majoring in Ecology and Evolutionary Biology. Her research interests include investigating the relation between genotype and phenotype, particularly in response to dietary changes in glucose. Her senior thesis research explores how genotypic variation and adaptation influence fitness levels and metabolic processes.
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.
Peruse our publications.
34- Henry L.P., Bruijning M., Forsberg K.G.S., Ayroles J.F. (2019). Can The Microbiome Influence Host Evolutionary Trajectories? BioRxiv 700237.
33- Amanda L., Gurven M., Kamau J, Martins D., Ayroles J.F. (2019). Market-integration and urbanization have strong, non-linear effects on metabolic health in the Turkana tribe. BioRxiv 756866.
32- Palares LF, Picard S, Ayroles JF. (2019). TM3’seq: a tagmentation-mediated 3’ sequencing approach for improving scalability of RNA-seq experiments. bioRxiv 585810 (under review Genome Biology).
31- Bruijning M, Metcalf J, Jongejans E and Ayroles JF. (2019). Exploring the fitness consequences of intra-genotypic variation. bioRxiv, 439659 (in press Trends in Ecology and Evolution).
30- A J Lea, M Subramaniam, A Ko, T Lehtimäki, E Raitoharju, MikaKähönen, I Seppälä, N Mononen, O Raitakari, M Ala-Korpela, P Pajukanta, N Zaitlen, Ayroles JF. (2019). Genetic and environmental perturbations lead to regulatory decoherence. eLife 2019;8:e40538
29- S Musharoff, DS Park, A Dahl, JM Galanter, X Liu, S Huntsman, C Eng, Burchard EG, Ayroles JF *, Zaitlen N* (2018) Existence and implications of population variance structure. bioRxiv, 439661 (under revision to AJHG). (*equal contribution)
28- Schrider DR, Ayroles JF, Matute DR, AD Kern AD. (2018). Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia. PLoS genetics 14 (4), e1007341.
27- Dumitrascu B, Darnell G, Ayroles JF, Engelhardt BE. (2018). Statistical tests for detecting variance effects in quantitative trait studies. Bioinformatics 1, 11.
24 – 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.
23 – 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. Proceedings of the National Academy of Sciences 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 Melanogaster. Plos 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 Melanogaster. BMC 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 traits 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 Melanogaster. Conservation 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.
1- Metcalf CJE*, Ayroles JF*. (2019). Chapter: “Why does intra-genotypic variance persist?” In book titled “Unsolved Problems in Ecology’. Princeton University Press. (*equal contribution)