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J Exp Biol,
2007]
Probably all heritable traits, including disease susceptibility, are affected by interactions between mutations in multiple genes. We understand little, however, about how genes interact to produce phenotypes, and there is little power to detect interactions between genes in human population studies. An alternative approach towards understanding how mutations combine to produce phenotypes is to construct systematic genetic interaction networks in model organisms. Here I describe the methods that are being used to map genetic interactions in yeast and C. elegans, and the insights that these networks provide for human disease. I also discuss the mechanistic interpretation of genetic interaction networks, how genetic interactions can be used to understand gene function, and methods that have been developed to predict genetic interactions on a genome-wide scale.
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Genome Biol,
2011]
High-throughput phenotyping approaches (phenomics) are being combined with genome-wide genetic screens to identify alterations in phenotype that result from gene inactivation. Here we highlight promising technologies for 'phenome-scale' analyses in multicellular organisms.
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Trends in Genetics,
2004]
The complex relationship between the genotype and the phenotype constrains and biases phenotypic evolution. Indeed, random mutation can have non-random (anisotropic) effects on the phenotype. In this review, we propose an operational definition of the'phenotypic neighborhood' of a given genotype, as obtained after induced mutagenesis or in mutation accumulation lines, with examples of anisotropic distributions of phenotypes reached when exploring the vicinity of a genotype. We also compare the phenotypic neighborhood for a given developmental process among species, focusing on nematode vulva development. Finally, we compare the phenotypic neighborhood assessed by mutagenesis with the phenotypic spectrum of wild isolates of the same species and make inferences about the action of selection and/or drift on the same developmental process in
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Worm,
2012]
Model systems, including C. elegans, have been successfully studied to understand the genetic control of development. A genotype's phenotype determines its evolutionary fitness in natural environments, which are typically harsh, heterogeneous and dynamic. Phenotypic plasticity, the process by which one genome can produce different phenotypes in response to the environment, allows genotypes to better match their phenotype to their environment. Phenotypic plasticity is rife among nematodes, seen both as differences among life-cycles stages, perhaps best exemplified by parasitic nematodes, as well as developmental choices, such as shown by the C. elegans dauer/non-dauer developmental choice. Understanding the genetic basis of phenotypically plastic traits will probably explain the function of many genes whose function still remains unclear. Understanding the adaptive benefits of phenotypically plastic traits requires that we understand how plasticity differs among genotypes, and the effects of this in diverse, different environments.
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Methods,
2016]
With the development of bio-imaging techniques, an increasing number of studies apply these techniques to generate a myriad of image data. Its applications range from quantification of cellular, tissue, organismal and behavioral phenotypes of model organisms, to human facial phenotypes. The bio-imaging approaches to automatically detect, quantify, and profile phenotypic changes related to specific biological questions open new doors to studying phenotype-genotype associations and to precisely evaluating molecular changes associated with quantitative phenotypes. Here, we review major applications of bioimage-based quantitative phenotype analysis. Specifically, we describe the biological questions and experimental needs addressable by these analyses, computational techniques and tools that are available in these contexts, and the new perspectives on phenotype-genotype association uncovered by such analyses.
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Methods Cell Biol,
1995]
Geneticists like to point out that the ultimate test of a proposed function for a gene and its encoded product (or products) in a living organism involves making a mutant and analyzing its phenotype. This is the goal of reverse genetics: a gene is cloned and sequenced, its transcripts and protein coding sequence are analyzed, and a function may be proposed; one must then introduce a mutation in the gene in a living organism to see what the functional consequences are. The analysis of genetic mosaics takes this philosophy a step further. In mosaics, some cells of an individual are genotypically mutant and other cells are genotypically wild type. One then asks what the phenotypic consequences are for the living organism. This is not the same as asking what cells transcribe the gene or in what cells the protein product of the gene is to be found, but rather it is asking in what cells the wild-type gene is needed for a given function...
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Methods Cell Biol,
1995]
Caenorhabditis elegans is in all likelihood the first metazoan animal whose entire genome will be determined. In addition, a very detailed description of the animal's morphology, development, and physiology is available (see elsewhere in this book, and Wood, 1988). Thus, the complete phenotype and genotype of an animal will be known. What is not known is how genotype determines phenotype; to study this, one needs to establish connections between genome sequence and phenotypes. Much has been done by classic or forward genetics: mutagenesis experiments have identified loci involved in a specific trait. Many of these loci have already been defined at the molecular level, and the genome sequence will certainly aid in the identification of many more. The opposite approach, reverse genetics, becomes naturally more important when more of the genome sequence is determined: Given the sequence of a gene of which nothing else is know, how can the function of that gene be determined? Reverse genetics is more than targeted inactivation. One can study a gene's function by several approaches...|
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Circ Res,
2011]
The nematode Caenorhabditis elegans has become established as a major experimental organism with applications to many biomedical research areas. The body wall muscle cells are a useful model for the study of human cardiomyocytes and their homologous structures and proteins. The ability to readily identify mutations affecting these proteins and structures in C elegans and to be able to rigorously characterize their genotypes and phenotypes at the cellular and molecular levels permits mechanistic studies of the responsible interactions relevant to the inherited human cardiomyopathies. Future work in C elegans muscle holds great promise in uncovering new mechanisms in the pathogenesis of these cardiac disorders.
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Bioessays,
2008]
Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707-710, 2008. (c) 2008 Wiley Periodicals, Inc.
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Methods Mol Biol,
2006]
Single-nucleotide polymorphism (SNP) mapping is the easiest and most reliable way to map genes in Caenorhabditis elegans. SNPs are extremely dense and usually have no associated phenotype, making them ideal markers for mapping. SNP mapping has three steps. First, recombinant mutant animals are generated over a polymorphic strain (usually CB4856) using standard genetic techniques. Second, the genotype of these animals at SNP loci is determined using one of a variety of SNP detection technologies. Third, linkage between the mutant and one or more SNPs is used to position the mutant on the chromosome relative to the SNPs. This chapter presents a detailed procedure for generating recombinant animals, for assaying SNPs using restriction enzymes, and for analyzing mapping data.