A picture is worth a thousand words: Genomics to phenomics in the yeastSaccharomyces cerevisiae

Genomic Adaptations of Saccharomyces Genus to Wine Niche

Wine yeast have been exposed to harsh conditions for millennia, which have led to adaptive evolutionary strategies. Thus, wine yeasts from Saccharomyces genus are considered an interesting and highly valuable model to study human-drive domestication processes. The rise of whole-genome sequencing technologies together with new long reads platforms has provided new understanding about the population structure and the evolution of wine yeasts. Population genomics studies have indicated domestication fingerprints in wine yeast, including nucleotide variations, chromosomal rearrangements, horizontal gene transfer or hybridization, among others. These genetic changes contribute to genetically and phenotypically distinct strains. This review will summarize and discuss recent research on evolutionary trajectories of wine yeasts, highlighting the domestication hallmarks identified in this group of yeast.

Synthetic biology for improving cell fate decisions and tissue engineering outcomes

Synthetic biology is a relatively new field of science that combines aspects of biology and engineering to create novel tools for the construction of biological systems. Using tools within synthetic biology, stem cells can then be reprogrammed and differentiated into a specified cell type. Stem cells have already proven to be largely beneficial in many different therapies and have paved the way for tissue engineering and regenerative medicine. Although scientists have made great strides in tissue engineering, there still remain many questions to be answered in regard to regeneration. Presented here is an overview of synthetic biology, common tools built within synthetic biology, and the way these tools are being used in stem cells. Specifically, this review focuses on how synthetic biologists engineer genetic circuits to dynamically control gene expression while also introducing emerging topics such as genome engineering and synthetic transcription factors. The findings mentioned in this review show the diverse use of stem cells within synthetic biology and provide a foundation for future research in tissue engineering with the use of synthetic biology tools. Overall, the work done using synthetic biology in stem cells is in its early stages, however, this early work is leading to new approaches for repairing diseased and damaged tissues and organs, and further expanding the field of tissue engineering.

Targeting the CINful genome: Strategies to overcome tumor heterogeneity

Genomic instability, and more specifically chromosomal instability (CIN), arises from a number of processes that are defective in cancer, such as aberrant mitotic cell division, replication stress, defective DNA damage repair, and ineffective telomere maintenance. CIN is an emerging hallmark of cancer that contributes to tumor heterogeneity through increased rates of genetic alterations. As genetic heterogeneity within a single tumor and between tumors is a key challenge leading to treatment failures, this brings to question, whether therapeutic approaches should aim at the genetic diversity or a specific mutation present within these tumors. Answering this question will determine the future of personalized targeted therapies. Here we discuss, how the genetic diversity associated with CIN in tumor cells can be used as a therapeutic advantage and targeted by exploiting the genetic concepts of synthetic lethality and synthetic dosage lethality. Given that a number of CIN-related pathways work together to fix the DNA damage within our genome and ensure proper segregation of chromosomes, we specifically focus on the genetic interactions amongst these pathways and their potential therapeutic applicability in cancer. We also discuss, how tumor genetic heterogeneity can be targeted in emerging immunotherapeutic approaches.

A Road Map to Personalizing Targeted Cancer Therapies Using Synthetic Lethality

Targeted therapies rely on the genetic and epigenetic status of the tumor cells and are seen as the most promising approach to treat cancer today. However, current targeted therapies focus on directly inhibiting those molecules that are altered in tumor cells. Unfortunately, targeting these molecules, even with specific inhibitors, is challenging as tumor cells rewire their genetic circuitry to eliminate genetic dependency on these targets. Here, we describe how synthetic lethality approaches can be used to identify genetic dependencies and develop personalized targeted therapies. We also discuss strategies to specifically target these genetic dependencies, using small molecule and biologic drugs.

Navigating yeast genome maintenance with functional genomics

Maintenance of genome integrity is a fundamental requirement of all organisms. To address this, organisms have evolved extremely faithful modes of replication, DNA repair and chromosome segregation to combat the deleterious effects of an unstable genome. Nonetheless, a small amount of genome instability is the driver of evolutionary change and adaptation, and thus a low level of instability is permitted in populations. While defects in genome maintenance almost invariably reduce fitness in the short term, they can create an environment where beneficial mutations are more likely to occur. The importance of this fact is clearest in the development of human cancer, where genome instability is a well-established enabling characteristic of carcinogenesis. This raises the crucial question: what are the cellular pathways that promote genome maintenance and what are their mechanisms? Work in model organisms, in particular the yeast Saccharomyces cerevisiae, has provided the global foundations of genome maintenance mechanisms in eukaryotes. The development of pioneering genomic tools inS. cerevisiae, such as the systematic creation of mutants in all nonessential and essential genes, has enabled whole-genome approaches to identifying genes with roles in genome maintenance. Here, we review the extensive whole-genome approaches taken in yeast, with an emphasis on functional genomic screens, to understand the genetic basis of genome instability, highlighting a range of genetic and cytological screening modalities. By revealing the biological pathways and processes regulating genome integrity, these analyses contribute to the systems-level map of the yeast cell and inform studies of human disease, especially cancer.

Global analysis of fungal morphology exposes mechanisms of host cell escape

Developmental transitions between single-cell yeast and multicellular filaments underpin virulence of diverse fungal pathogens. For the leading human fungal pathogen Candida albicans, filamentation is thought to be required for immune cell escape via induction of an inflammatory programmed cell death. Here we perform a genome-scale analysis of C. albicans morphogenesis and identify 102 negative morphogenetic regulators and 872 positive regulators, highlighting key roles for ergosterol biosynthesis and N-linked glycosylation. We demonstrate that C. albicans filamentation is not required for escape from host immune cells; instead, macrophage pyroptosis is driven by fungal cell-wall remodelling and exposure of glycosylated proteins in response to the macrophage phagosome. The capacity of killed, previously phagocytized cells to drive macrophage lysis is also observed with the distantly related fungal pathogen Cryptococcus neoformans. This study provides a global view of morphogenetic circuitry governing a key virulence trait, and illuminates a new mechanism by which fungi trigger host cell death.

Several pathogenic fungi such as Candida albicans undergo transitions between single-celled forms and multicellular filaments. Here the authors perform a genome-scale analysis of C. albicans and show that, contrary to common belief, filamentation is not required for escape from host immune cells.

Building high-resolution synthetic lethal networks: a 'Google map' of the cancer cell

The most commonly used therapies for cancer involve delivering high doses of radiation or toxic chemicals to the patient that also cause substantial damage to normal tissue. To overcome this, researchers have recently resorted to a basic biological concept called 'synthetic lethality' (SL) that takes advantage of interactions between gene pairs. The identification of SL interactions is of considerable therapeutic interest because if a particular gene is SL with a tumor-causing mutation, then the targeting that gene carries therapeutic advantages. Mapping these interactions in the context of human cancer cells could hold the key to effective, targeted cancer treatments. In this review, we cover the recent advances that aim to identify these SL interactions using unbiased genetic screens.

Functional genomics in the study of yeast cell polarity: moving in the right direction

The budding yeast Saccharomyces cerevisiae has been used extensively for the study of cell polarity, owing to both its experimental tractability and the high conservation of cell polarity and other basic biological processes among eukaryotes. The budding yeast has also served as a pioneer model organism for virtually all genome-scale approaches, including functional genomics, which aims to define gene function and biological pathways systematically through the analysis of high-throughput experimental data. Here, we outline the contributions of functional genomics and high-throughput methodologies to the study of cell polarity in the budding yeast. We integrate data from published genetic screens that use a variety of functional genomics approaches to query different aspects of polarity. Our integrated dataset is enriched for polarity processes, as well as some processes that are not intrinsically linked to cell polarity, and may provide new areas for future study.

The emergence of lipid droplets in yeast: current status and experimental approaches

The ‘discovery’ of lipid droplets as a metabolically highly active subcellular organelle has sparked great scientific interest in its research in recent years. The previous view of a rather inert storage pool of neutral lipids—triacylglycerol and sterols or steryl esters—has markedly changed. Driven by the endemic dimensions of lipid-associated disorders on the one hand, and the promising biotechnological application to generate oils (‘biodiesel’) from single-celled organisms on the other, multiple model organisms are exploited in basic and applied research to develop a better understanding of biogenesis and metabolism of this organelle. This article summarizes the current status of LD research in yeast and experimental approaches to obtain insight into the regulatory and structural components driving lipid droplet formation and their physiological and pathophysiological roles in lipid homeostasis.

Variance in epistasis links gene regulation and evolutionary rate in the yeast genetic interaction network

Organisms face a constantly shifting landscape of environmental conditions and internal physiological states. How gene regulation and cellular functions are maintained across genetic and environmental variation is therefore a fundamental question in biology. Here, we analyze the Saccharomyces cerevisiae genetic interaction network to understand how the yeast cell maintains regulatory capacity across genetic backgrounds and environmental conditions. We used the recently characterized synthetic sick/lethal network in yeast, which measures the fitness effects of knocking out pairs of genes, to analyze interactions among 4,364 genes. Genes with large variance in epistatic effects on fitness are highly and ubiquitously expressed (with open chromatin conformations in their promoter regions) and evolve more slowly than genes with weak effects on fitness. Thus, rather than being the elements responsible for the regulation and responsiveness of the genetic network, genes with large epistatic effects tend to be more mundane “housekeeping” genes whose consistent expression is critical to fitness under all environments and that are thereby deeply embedded within the regulatory structure of the network. Our analysis shows that the yeast cell has evolved a system whereby a physical mechanism of regulation (nucleosome occupancy) buffers key genes from the variability experienced by the cell as a whole.

Proteome-wide screens in Saccharomyces cerevisiae using the yeast GFP collection

The budding yeast is a simple and genetically tractable eukaryotic organism. It remains a leading system for functional genomic work and has been the focus of many pioneering efforts, including the systematic construction and analysis of gene deletion mutants. Over the past decade, many large-scale studies have made use of the deletion and other mutant collections to assay genetic interactions, chemical sensitivities, and other phenotypes, contributing enormously to our understanding of gene function. The deletion mutant collection has also been used in cell biological surveys to identify genes that control cell and organelle morphology. One valuable approach for systematic definition of gene function and biological pathways involves global assessment of the localization patterns of the proteins they encode and how these patterns are altered in response to environmental or genetic perturbation. However, proteome-wide, cell biological screens are extremely challenging, from both a technical and computational perspective. The yeast GFP collection, an elegant and unique strain set, is ideal for studying both protein localization and abundance across the proteome ( http://yeastgfp.yeastgenome.org/ ). In this chapter, we outline how the yeast GFP collection has been used to date and discuss approaches for conducting future surveys of the proteome.

Interactions of subunit CCT3 in the yeast chaperonin CCT/TRiC with Q/N-rich proteins revealed by high-throughput microscopy analysis

The eukaryotic chaperonin containing t-complex polypeptide 1 (CCT/TRiC) is an ATP-fueled machine that assists protein folding. It consists of two back-to-back stacked rings formed by eight different subunits that are arranged in a fixed permutation. The different subunits of CCT are believed to possess unique substrate binding specificities that are still mostly unknown. Here, we used high-throughput microscopy analysis of yeast cells to determine changes in protein levels and localization as a result of a Glu to Asp mutation in the ATP binding site of subunits 3 (CCT3) or 6 (CCT6). The mutation in subunit CCT3 was found to induce cytoplasmic foci termed P-bodies where mRNAs, which are not translated, accumulate and can be degraded. Analysis of the changes in protein levels and structural modeling indicate that P-body formation in cells with the mutation in CCT3 is linked to the specific interaction of this subunit with Gln/Asn-rich segments that are enriched in many P-body proteins. An in vitro gel-shift analysis was used to show that the mutation in subunit CCT3 interferes with the ability of CCT to bind a Gln/Asn-rich protein aggregate. More generally, the strategy used in this work can be used to unravel the substrate specificities of other chaperone systems.

Intensity quantile estimation and mapping--a novel algorithm for the correction of image non-uniformity bias in HCS data

MOTIVATION

Image non-uniformity (NU) refers to systematic, slowly varying spatial gradients in images that result in a bias that can affect all downstream image processing, quantification and statistical analysis steps. Image NU is poorly modeled in the field of high-content screening (HCS), however, such that current conventional correction algorithms may be either inappropriate for HCS or fail to take advantage of the information available in HCS image data.

RESULTS

A novel image NU bias correction algorithm, termed intensity quantile estimation and mapping (IQEM), is described. The algorithm estimates the full non-linear form of the image NU bias by mapping pixel intensities to a reference intensity quantile function. IQEM accounts for the variation in NU bias over broad cell intensity ranges and data acquisition times, both of which are characteristic of HCS image datasets. Validation of the method, using simulated and HCS microtubule polymerization screen images, is presented. Two requirements of IQEM are that the dataset consists of large numbers of images acquired under identical conditions and that cells are distributed with no within-image spatial preference.

AVAILABILITY AND IMPLEMENTATION

MATLAB function files are available at http://nadon-mugqic.mcgill.ca/.

PhenoM: a database of morphological phenotypes caused by mutation of essential genes in Saccharomyces cerevisiae

About one-fifth of the genes in the budding yeast are essential for haploid viability and cannot be functionally assessed using standard genetic approaches such as gene deletion. To facilitate genetic analysis of essential genes, we and others have assembled collections of yeast strains expressing temperature-sensitive (ts) alleles of essential genes. To explore the phenotypes caused by essential gene mutation we used a panel of genetically engineered fluorescent markers to explore the morphology of cells in the ts strain collection using high-throughput microscopy. Here, we describe the design and implementation of an online database, PhenoM (Phenomics of yeast Mutants), for storing, retrieving, visualizing and data mining the quantitative single-cell measurements extracted from micrographs of the ts mutant cells. PhenoM allows users to rapidly search and retrieve raw images and their quantified morphological data for genes of interest. The database also provides several data-mining tools, including a PhenoBlast module for phenotypic comparison between mutant strains and a Gene Ontology module for functional enrichment analysis of gene sets showing similar morphological alterations. The current PhenoM version 1.0 contains 78 194 morphological images and 1 909 914 cells covering six subcellular compartments or structures for 775 ts alleles spanning 491 essential genes. PhenoM is freely available at http://phenom.ccbr.utoronto.ca/.

Systematic exploration of essential yeast gene function with temperature-sensitive mutants

Conditional temperature-sensitive (ts) mutations are valuable reagents for studying essential genes in the yeast Saccharomyces cerevisiae. We constructed 787 ts strains, covering 497 (∼45%) of the 1,101 essential yeast genes, with ∼30% of the genes represented by multiple alleles. All of the alleles are integrated into their native genomic locus in the S288C common reference strain and are linked to a kanMX selectable marker, allowing further genetic manipulation by synthetic genetic array (SGA)-based, high-throughput methods. We show two such manipulations: barcoding of 440 strains, which enables chemical-genetic suppression analysis, and the construction of arrays of strains carrying different fluorescent markers of subcellular structure, which enables quantitative analysis of phenotypes using high-content screening. Quantitative analysis of a GFP-tubulin marker identified roles for cohesin and condensin genes in spindle disassembly. This mutant collection should facilitate a wide range of systematic studies aimed at understanding the functions of essential genes.

Protein complexes are central in the yeast genetic landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available.

Genetic interactions indicate functional dependencies between genes and are a powerful tool to predict gene function. Functionally related genes tend to have similar profiles of genetic interactions. Recently, global scale mapping of quantitative (positive and negative) genetic interactions has been performed. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, such as biological processes or protein complexes, or connections between modules, but it is not yet known how these patterns globally relate to known functional modules. Here we systematically evaluate the monochromatic nature of known biological processes and their connections in the yeast Saccharomyces cerevisiae. We find that 10% of biological processes and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these monochromatic groups. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms.

Advanced methods for high-throughput microscopy screening of genetically modified yeast libraries

High-throughput methodologies have created new opportunities for studying biological phenomena in an unbiased manner. Using automated cell manipulations and microscopy platforms, it is now possible to easily screen entire genomes for genes that affect any cellular process that can be visualized. The onset of these methodologies promises that the near future will bring with it a more comprehensive and richly integrated understanding of complex and dynamic cellular structures and processes. In this review, we describe how to couple systematic genetic tools in the budding yeast Saccharomyces cerevisiae alongside robotic visualization systems to attack biological questions. The combination of high-throughput microscopy screens with the powerful, yet simple, yeast model system for studying the eukaryotic cell should pioneer new knowledge in all areas of cell biology.

Charting the genetic interaction map of a cell

Genome sequencing projects have revealed a massive catalog of genes and astounding genetic diversity in a variety of organisms. We are now faced with the formidable challenge of assigning functions to thousands of genes, and how to use this information to understand how genes interact and coordinate cell function. Studies indicate that the majority of eukaryotic genes are dispensable, highlighting the extensive buffering of genomes against genetic and environmental perturbations. Such robustness poses a significant challenge to those seeking to understand the wiring diagram of the cell. Genome-scale screens for genetic interactions are an effective means to chart the network that underlies this functional redundancy. A complete atlas of genetic interactions offers the potential to assign functions to most genes identified by whole genome sequencing projects and to delineate a functional wiring diagram of the cell. Perhaps more importantly, mapping genetic networks on a large-scale will shed light on the general principles and rules governing genetic networks and provide valuable information regarding the important but elusive relationship between genotype and phenotype.

A Personal Perspective on High-Content Screening (HCS)

High-content screening (HCS) was introduced in 1997 based on light microscope imaging technologies to address the need for an automated platform that could analyze large numbers of individual cells with subcellular resolution using standard microplates. Molecular specificity based on fluorescence was a central element of the platform taking advantage of the growing list of reagent classes and the ability to multiplex. In addition, image analysis coupled to data management, data mining, and data visualization created a tool that focused on biological information and knowledge to begin exploring the functions of genes identified in the genomics revolution. This overview looks at the development of HCS, the evolution of the technologies, and the market up to the present day. In addition, the options for adopting uniform definitions is suggested along with a perspective on what advances are needed to continue building the value of HCS in biomedical research, drug discovery, and development and diagnostics.

Exploiting the determinants of stochastic gene expression in Saccharomyces cerevisiae for genome-wide prediction of expression noise

Gene regulation is a process with many steps allowing for stochastic biochemical reactions, which leads to expression noise-i.e., the cell-to-cell stochastic fluctuation in protein abundance. Such expression noise can give rise to drastically diverse phenotypes, even within isogenic cell populations. Although numerous biophysical approaches had been proposed to model the origin and propagation of expression noise in biological networks, these models essentially characterize the innate stochastic dynamics in gene regulation in a mechanistic way. In this work, by investigating expression noise in the context of yeast cellular networks, we place the biophysical formulism onto solid genetic ground. At the sequence level, we show that extremely noisy genes are highly conserved in their coding sequences. At the level of cellular networks where natural selection is manifested by the topological constraints, we show that genes with varying expression noise are modularly organized in the protein interaction network and are positioned orderly in the gene regulatory network. We demonstrate that these topological constraints are highly predictive of stochastic gene expression, with which we were able to confidently predict stochastic expression for more than 2,000 yeast genes whose expression noise was previously not known. We validated the predictions by high-content cell imaging. Our approach makes feasible genome-wide prediction of stochastic gene expression, and such predictability in turn suggests that expression noise is an evolvable genetic trait.

High-content, image-based screening for drug targets in yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.

Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe

A genetic interaction occurs when the combination of two mutations leads to an unexpected phenotype. Screens for synthetic genetic interactions have been used extensively to identify genes whose products are functionally related. In particular, synthetic lethal genetic interactions often identify genes that buffer one another or impinge on the same essential pathway. For the yeast Saccharomyces cerevisiae, we developed a method termed synthetic genetic array (SGA) analysis, which offers an efficient approach for the systematic construction of double mutants and enables a global analysis of synthetic genetic interactions. In a typical SGA screen, a query mutation is crossed to an ordered array of ~5000 viable gene deletion mutants (representing ~80% of all yeast genes) such that meiotic progeny harboring both mutations can be scored for fitness defects. This approach can be extended to all ~6000 genes through the use of yeast arrays containing mutants carrying conditional or hypomorphic alleles of essential genes. Estimating the fitness for the two single mutants and their corresponding double mutant enables a quantitative measurement of genetic interactions, distinguishing negative (synthetic lethal) and positive (within pathway and suppression) interactions. The profile of genetic interactions represents a rich phenotypic signature for each gene and clustering genetic interaction profiles group genes into functionally relevant pathways and complexes. This array-based approach automates yeast genetic analysis in general and can be easily adapted for a number of different genetic screens or combined with high-content screening systems to quantify the activity of specific reporters in genome-wide sets of single or more complex multiple mutant backgrounds. Comparison of genetic and chemical-genetic interaction profiles offers the potential to link bioactive compounds to their targets. Finally, we also developed an SGA system for the fission yeast Schizosaccharomyces pombe, providing another model system for comparative analysis of genetic networks and testing the conservation of genetic networks over millions of years of evolution.

Integrating high-throughput genetic interaction mapping and high-content screening to explore yeast spindle morphogenesis

A combination of yeast genetics, synthetic genetic array analysis, and high-throughput screening reveals that sumoylation of Mcm21p promotes disassembly of the mitotic spindle.

We describe the application of a novel screening approach that combines automated yeast genetics, synthetic genetic array (SGA) analysis, and a high-content screening (HCS) system to examine mitotic spindle morphogenesis. We measured numerous spindle and cellular morphological parameters in thousands of single mutants and corresponding sensitized double mutants lacking genes known to be involved in spindle function. We focused on a subset of genes that appear to define a highly conserved mitotic spindle disassembly pathway, which is known to involve Ipl1p, the yeast aurora B kinase, as well as the cell cycle regulatory networks mitotic exit network (MEN) and fourteen early anaphase release (FEAR). We also dissected the function of the kinetochore protein Mcm21p, showing that sumoylation of Mcm21p regulates the enrichment of Ipl1p and other chromosomal passenger proteins to the spindle midzone to mediate spindle disassembly. Although we focused on spindle disassembly in a proof-of-principle study, our integrated HCS-SGA method can be applied to virtually any pathway, making it a powerful means for identifying specific cellular functions.

Colloquium papers: Numbering the hairs on our heads: the shared challenge and promise of phenomics

Evolution and medicine share a dependence on the genotype-phenotype map. Although genotypes exist and are inherited in a discrete space convenient for many sorts of analyses, the causation of key phenomena such as natural selection and disease takes place in a continuous phenotype space whose relationship to the genotype space is only dimly grasped. Direct study of genotypes with minimal reference to phenotypes is clearly insufficient to elucidate these phenomena. Phenomics, the comprehensive study of phenotypes, is therefore essential to understanding biology. For all of the advances in knowledge that a genomic approach to biology has brought, awareness is growing that many phenotypes are highly polygenic and susceptible to genetic interactions. Prime examples are common human diseases. Phenomic thinking is starting to take hold and yield results that reveal why it is so critical. The dimensionality of phenotypic data are often extremely high, suggesting that attempts to characterize phenotypes with a few key measurements are unlikely to be completely successful. However, once phenotypic data are obtained, causation can turn out to be unexpectedly simple. Phenotypic data can be informative about the past history of selection and unexpectedly predictive of long-term evolution. Comprehensive efforts to increase the throughput and range of phenotyping are an urgent priority.