The expression tractability of biological traits shaped by natural selection

Understanding how gene expression is translated to phenotype is central to modern molecular biology,and the success is contingent on the intrinsic tractability of the specific traits under examination.However, an a priori estimate of trait tractability from the perspective of gene expression is unavailable.Motivated by the concept of entropy in a thermodynamic system, we here propose such an estimate(S_T)by gauging the number(N) of expression states that underlie the same trait abnormality, with large S_T corresponding to large N. By analyzing over 200 yeast morphological traits, we show that S_T predicts the tractability of an expression-trait relationship. We further show that S_T is ultimately determined by natural selection, which builds co-regulated gene modules to minimize possible expression states.     

Refining phenotype characterization in genetic linkage studies of schizophrenia.

A definitive replicable genetic linkage for a major locus underlying the susceptibility to schizophrenia has not been identified to date. Although there are several possible explanations for the failure to find linkage in schizophrenia, one major problem is that the range of phenotypic expressions of the genes for schizophrenia has not been clarified. A more refined understanding of the various phenotypic expressions of a gene related to schizophrenia would enhance the power of studies designed to detect a genetic linkage with a major chromosomal locus and would benefit other strategies for understanding the etiology of schizophrenia. The genes for schizophrenia may be frequently expressed in relatives of schizophrenic patients, although with less severe symptoms than those of chronic schizophrenia. Two series of findings support this notion. Nonschizophrenic relatives of schizophrenic patients demonstrate an increased incidence of nonpsychotic schizophrenia-like symptoms and traits, and they manifest deficit performances on several different laboratory tests of neurocognitive functioning. A more refined phenotypic expression of a schizophrenia-related gene may thus be indicated by personality traits and subclinical neurocognitive deficits. These personality traits and neurocognitive deficits are considered here as possible aids in the identification of affected cases in genetic linkage studies of schizophrenia. Terminology for different indicators of neurocognitive deficits is introduced, and the relative utility of personality traits and indicators of neurocognitive deficit for genetic linkage studies is discussed. As specific examples, schizophrenia-related personality traits that are unrelated to affective symptoms and performance deficits on tasks of eye tracking and continuous attention are considered for strategies for broadening phenotype characterization without reducing the specificity of affected case identification.     

Refining phenotype characterization in genetic linkage studies of schizophrenia

Abstract

A definitive replicable genetic linkage for a major locus underlying the susceptibility to schizophrenia has not been identified to date. Although there are several possible explanations for the failure to find linkage in schizophrenia, one major problem is that the range of phenotypic expressions of the genes for schizophrenia has not been clarified. A more refined understanding of the various phenotypic expressions of a gene related to schizophrenia would enhance the power of studies designed to detect a genetic linkage with a major chromosomal locus and would benefit other strategies for understanding the etiology of schizophrenia.

The genes for schizophrenia may be frequently expressed in relatives of schizophrenic patients, although with less severe symptoms than those of chronic schizophrenia. Two series of findings support this notion. Nonschizophrenic relatives of schizophrenic patients demonstrate an increased incidence of nonpsychotic schizophrenia‐like symptoms and traits, and they manifest deficit performances on several different laboratory tests of neurocognitive functioning. A more refined phenotypic expression of a schizophrenia‐related gene may thus be indicated by personality traits and subclinical neurocognitive deficits.

These personality traits and neurocognitive deficits are considered here as possible aids in the identification of affected cases in genetic linkage studies of schizophrenia. Terminology for different indicators of neurocognitive deficits is introduced, and the relative utility of personality traits and indicators of neurocognitive deficit for genetic linkage studies is discussed. As specific examples, schizophrenia‐related personality traits that are unrelated to affective symptoms and performance deficits on tasks of eye tracking and continuous attention are considered for strategies for broadening phenotype characterization without reducing the specificity of affected case identification.     

Structural and functional genomics in domestic animals: the way to understand the phenotype

Molecular approaches to genome analysis in livestock are reviewed by discussing the contribution of molecular genome analysis to the identification of the genetic variation underlying phenotypic variation (structural genome analysis) and to the definition of the trait-associated and environment-affected gene expression (functional genome analysis) as an important prerequisite to understanding the formation of a phenotype. Aspects of using mapped 'quantitative trait loci' (QTL) or gene variants as well as the identified trait-associated and environment-affected gene expression profile in livestock production are expounded.     

NEW TOOLS FOR STUDYING INTEGRATION AND MODULARITY

Abstract The study of phenotypic integration concerns the modular nature of organismal phenotypes. The concept provides a rationale for why certain subsets of phenotypic traits show particularly high levels of association over development and/or evolution. The techniques detailed in this report facilitate the generation and testing of hypotheses of phenotypic integration and trait interaction. The approach advocated for exploring patterns of interaction among traits is based on the statistical notion of conditional independence, incorporated in a technique known as graphical modeling. The use of graphical models is illustrated with an application to a well-known biological dataset of fowl skeletal measurements, previously analyzed by Sewall Wright. A definition of phenotypic modularity is given, based on a notion of mutual information, which provides a consistent criterion for recognizing and delimiting integrated subsets of traits and which can be related to standard models of multivariate selection.     

Clustering expressed genes on the basis of their association with a quantitative phenotype

Cluster analyses of gene expression data are usually conducted based on their associations with the phenotype of a particular disease. Many disease traits have a clearly defined binary phenotype (presence or absence), so that genes can be clustered based on the differences of expression levels between the two contrasting phenotypic groups. For example, cluster analysis based on binary phenotype has been successfully used in tumour research. Some complex diseases have phenotypes that vary in a continuous manner and the method developed for a binary trait is not immediately applicable to a continuous trait. However, understanding the role of gene expression in these complex traits is of fundamental importance. Therefore, it is necessary to develop a new statistical method to cluster expressed genes based on their association with a quantitative trait phenotype. We developed a model-based clustering method to classify genes based on their association with a continuous phenotype. We used a linear model to describe the relationship between gene expression and the phenotypic value. The model effects of the linear model (linear regression coefficients) represent the strength of the association. We assumed that the model effects of each gene follow a mixture of several multivariate Gaussian distributions. Parameter estimation and cluster assignment were accomplished via an Expectation-Maximization (EM) algorithm. The method was verified by analysing two simulated datasets, and further demonstrated using real data generated in a microarray experiment for the study of gene expression associated with Alzheimer's disease.     

Domestication rewired gene expression and nucleotide diversity patterns in tomato

Plant domestication has led to considerable phenotypic modifications from wild species to modern varieties. However, although changes in key traits have been well documented, less is known about the underlying molecular mechanisms, such as the reduction of molecular diversity or global gene co‐expression patterns. In this study, we used a combination of gene expression and population genetics in wild and crop tomato to decipher the footprints of domestication. We found a set of 1729 differentially expressed genes (DEG) between the two genetic groups, belonging to 17 clusters of co‐expressed DEG, suggesting that domestication affected not only individual genes but also regulatory networks. Five co‐expression clusters were enriched in functional terms involving carbohydrate metabolism or epigenetic regulation of gene expression. We detected differences in nucleotide diversity between the crop and wild groups specific to DEG. Our study provides an extensive profiling of the rewiring of gene co‐expression induced by the domestication syndrome in one of the main crop species.     

Population genetic variation in gene expression is associated with phenotypic variation in <Emphasis Type="Italic">Saccharomyces cerevisiae</Emphasis>

Background

The relationship between genetic variation in gene expression and phenotypic variation observable in nature is not well understood. Identifying how many phenotypes are associated with differences in gene expression and how many gene-expression differences are associated with a phenotype is important to understanding the molecular basis and evolution of complex traits.

Results

We compared levels of gene expression among nine natural isolates of Saccharomyces cerevisiae grown either in the presence or absence of copper sulfate. Of the nine strains, two show a reduced growth rate and two others are rust colored in the presence of copper sulfate. We identified 633 genes that show significant differences in expression among strains. Of these genes, 20 were correlated with resistance to copper sulfate and 24 were correlated with rust coloration. The function of these genes in combination with their expression pattern suggests the presence of both correlative and causative expression differences. But the majority of differentially expressed genes were not correlated with either phenotype and showed the same expression pattern both in the presence and absence of copper sulfate. To determine whether these expression differences may contribute to phenotypic variation under other environmental conditions, we examined one phenotype, freeze tolerance, predicted by the differential expression of the aquaporin gene AQY2. We found freeze tolerance is associated with the expression of AQY2.

Conclusions

Gene expression differences provide substantial insight into the molecular basis of naturally occurring traits and can be used to predict environment dependent phenotypic variation.

     

Harnessing gene expression to identify the genetic basis of drug resistance

The advent of cost‐effective genotyping and sequencing methods have recently made it possible to ask questions that address the genetic basis of phenotypic diversity and how natural variants interact with the environment. We developed Camelot (CAusal Modelling with Expression Linkage for cOmplex Traits), a statistical method that integrates genotype, gene expression and phenotype data to automatically build models that both predict complex quantitative phenotypes and identify genes that actively influence these traits. Camelot integrates genotype and gene expression data, both generated under a reference condition, to predict the response to entirely different conditions. We systematically applied our algorithm to data generated from a collection of yeast segregants, using genotype and gene expression data generated under drug‐free conditions to predict the response to 94 drugs and experimentally confirmed 14 novel gene–drug interactions. Our approach is robust, applicable to other phenotypes and species, and has potential for applications in personalized medicine, for example, in predicting how an individual will respond to a previously unseen drug.     

Genetic mapping of quantitative phenotypic traits in Saccharomyces cerevisiae

Saccharomyces cerevisiae has become a favorite production organism in industrial biotechnology presenting new challenges to yeast engineers in terms of introducing advantageous traits such as stress tolerances. Exploring subspecies diversity of S. cerevisiae has identified strains that bear industrially relevant phenotypic traits. Provided that the genetic basis of such phenotypic traits can be identified inverse engineering allows the targeted modification of production strains. Most phenotypic traits of interest in S. cerevisiae strains are quantitative, meaning that they are controlled by multiple genetic loci referred to as quantitative trait loci (QTL). A straightforward approach to identify the genetic basis of quantitative traits is QTL mapping which aims at the allocation of the genetic determinants to regions in the genome. The application of high-density oligonucleotide arrays and whole-genome re-sequencing to detect genetic variations between strains has facilitated the detection of large numbers of molecular markers thus allowing high-resolution QTL mapping over the entire genome. This review focuses on the basic principle and state of the art of QTL mapping in S. cerevisiae. Furthermore we discuss several approaches developed during the last decade that allow down-scaling of the regions identified by QTL mapping to the gene level. We also emphasize the particular challenges of QTL mapping in nonlaboratory strains of S. cerevisiae.     

Historical and philosophical perspectives on the study of developmental bias

Throughout the recent history of research at the intersection of evolution and development, notions such as developmental constraint, evolutionary novelty, and evolvability have been prominent, but the term “developmental bias” has scarcely been used. And one may even doubt whether a unique and principled definition of bias is possible. I argue that the concept of developmental bias can still play a vital scientific role by means of setting an explanatory agenda that motivates investigation and guides the formulation of integrative explanatory frameworks. Less crucial is a definition that would classify patterns of phenotypic variation and unify variational patterns involving different traits and taxa as all being “bias.” Instead, what we should want is a concept that generates intellectual identity across various researchers, and that unites the diverse fields and approaches relevant to the study of developmental bias, from paleontology to behavioral biology. I point to some advantages of conducting research specifically under the label of “developmental bias,” compared with employing other, more common terms such as “evolvability.”     

Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for high-throughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.     

What is a phenotype? History and new developments of the concept

Even though the word “phenotype”, as well as the expression “genotype–phenotype relationship”, are a part of the everyday language of biologists, they remain abstract notions that are sometimes misunderstood or misused. In this article, I begin with a review of  the genesis of the concept of phenotype and of the meaning of the genotype-phenotype “relationship" from a historical perspective. I then illustrate how the development of new approaches for exploring the living world has enabled us to phenotype organisms at multiple levels, with traits that can either be measures or parameters of functions, leading to a virtually unlimited amount of phenotypic data. Thus, pleiotropy becomes a central issue in the study of the genotype–phenotype relationship. Finally, I provide a few examples showing that important genetic and evolutionary features clearly differ with the phenotypic level considered. The way genotypic variation propagates across the phenotypic levels to shape fitness variation is an essential research program in biology.

     

Beyond the gene

This paper is a response to the increasing difficulty biologists find in agreeing upon a definition of the gene, and indeed, the increasing disarray in which that concept finds itself. After briefly reviewing these problems, we propose an alternative to both the concept and the word gene-an alternative that, like the gene, is intended to capture the essence of inheritance, but which is both richer and more expressive. It is also clearer in its separation of what the organism statically is (what it tangibly inherits) and what it dynamically does (its functionality and behavior). Our proposal of a genetic functor, or genitor, is a sweeping extension of the classical genotype/phenotype paradigm, yet it appears to be faithful to the findings of contemporary biology, encompassing many of the recently emerging-and surprisingly complex-links between structure and functionality.     

Bayesian association of haplotypes and non-genetic factors to regulatory and phenotypic variation in human populations

MOTIVATION: With the recent availability of large-scale data sets profiling single nucleotide polymorphisms (SNPs) and quantitative traits data across different human subpopulations, there has been much attention directed towards discovering patterns of genetic variation and their connection to gene regulation and the onset/progression of disease. While previous work has focused primarily on correlating individual SNP markers with gene expression and disease, it has been suggested that using haplotype blocks instead of individual markers can significantly increase statistical power. RESULTS: We present BlockMapper, a probabilistic generative model for genotype data and quantitative traits data, such as gene expression or phenotype measurements. BlockMapper discovers the block structure of genotype data and associates these inferred blocks to patterns of variation in quantitative traits data, whilst accounting for non-genetic factors. Our model achieves high accuracy for predicting Crohn's disease phenotype in Chromosome 5q31 and reveals novel cis-associations between two haplotype blocks in the ENm006 genomic region and GDI1, a gene implicated in X-linked mental retardation. Our results underscore the importance of accounting for the influence of large sets of SNPs on patterns of regulatory/phenotypic variation and represent a step towards an understanding of human genetic variation.