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Recently, medicine began moving to more informed healthcare management and medical treatment guided by diagnostics, termed, theranostics. In the last decade, the search for markers for disease subtypes and more personalized treatment responses has been accelerated by academic researchers and pharmaceutical companies. Currently used high-throughput genome-wide methodology is resulting in a shift to a hypothesis-free approach to identify appropriate genes. This, in turn, has resulted in the identification of a multitude of biomarker candidates that explain a small fraction of patients’ variations in drug response therapy or disease clinical course. These research initiatives need to be followed by functional studies to determine the molecular mode of action and clinical utility of these markers. Here, the status of identification of biomarkers for diagnosis, disease course, treatment response, and related factors involved in development and response to treatment for multiple sclerosis are reviewed.

This chapter discusses the role of genetic variation in modulating gene expression and how this can help resolve functionally important regulatory variants. The successful application of genetic mapping techniques to define expression quantitative trait loci in model organisms including yeast and mice is discussed, together with evidence from studies of human populations. The need to take into account transcript isoform diversity resulting from alternative splicing is highlighted, together with the value of analysis based on allele-specific gene expression and at the protein level. The synergy between genome-wide disease association studies and analysis of the genetics of gene expression, also at a genome-wide level in terms of markers and expression traits, is illustrated by review of recent studies in asthma. The context specificity of regulatory variants is demonstrated, noting the importance of analysis in primary cells or tissues in conditions relevant to the disease or other trait of interest.

Microarray technology can simultaneously monitor the expression levels of thousands of genes across many experimental conditions or treatments, providing us with unique opportunities to investigate the evolutionary pattern of gene regulation. This chapter focuses on how to model the evolution of gene family expression with three goals: (i) statistical methods such as the likelihood ratio test can be applied for exploring the evolutionary pattern of gene expression; (ii) evolutionary tracing of expression changes can be predicted by the Bayesian method; and (iii) the statistical model can be utilized to study the expression-motif association. Several statistical models have been developed, most of which viewed gene expression data as continuous so that the modeling was based on the random-walk (Brownian) model. The chapter discusses these models and their applications.

This chapter addresses two issues in which molecular analyses are important for our understanding of life history evolution. First, information on the genomic regulation of trade-offs – a central assumption of life history theory and evolutionary theory in general – promises to facilitate the production of mechanistic models of trade-offs, which may contribute to the development of theories that combine quantitative genetic and mechanistic approaches. Second, genomic analyses can address the question of the extent to which evolutionary trajectories are deterministic versus stochastic (the ‘skinning the cat problem’). In this regard, the chapter presents an experimental framework within which to investigate this problem.

States of arrested development and reproductive quiescence have widespread effects on life histories in many taxa. Insects that inhabit seasonal environments often express a diapause syndrome which affects a suite of traits including energy allocation, resistance to environmental stress, patterns of reproduction, and age specific mortality rates. Recent work has begun to elucidate the genetic architecture of insect diapause, and candidate genes for the regulation of diapause have been identified in multiple organisms. This research has the potential to outline common pathways and mechanisms of seasonal adaptations, as well as the molecular basis of variation in the expression of a pleiotropic life history syndrome.

This chapter reviews recent theoretical advances in relation to maternal-effect genes and the theory of relaxed selective constraint (RSC). It explains how to test theoretical predictions using sequence data and discusses how genetic questions on the evolution of maternal effects can be approached using a combination of microarray expression data and DNA sequence data. This chapter also highlights the importance of genetic models of maternal effects and maternal zygotic interactions in improving our understanding of how maternal effects evolve.

The usual analysis of gene expression data ignores the correlation between gene expression values. Biologically, this assumption is unreasonable. The approach presented in this chapter allows for correlation between genes through a sparse Gaussian graphical model: sparse inverse covariance matrices and their associated graphical representations are used to capture the notion of gene networks. Existing methods find their limitations in the issue posed by the identification of the pattern of zeroes in such inverse covariance matrices. A workable solution for determining the zero pattern is provided in this chapter. Two other important contributions of this chapter are a method for very high-dimensional model fitting and a distribution-free approach to hypothesis testing. Such tests address assessment of differential expression and of differential connection, a novel notion introduced in this chapter. An example dealing with real data is presented.