Genotype to Phenotype: A Complex Problem

Dowell, Robin D.; Ryan, Owen; Jansen, An; Cheung, Doris; Agarwala, Sudeep D.; Danford, Timothy; Bernstein, Douglas A.; Rolfe, P. Alexander; Heisler, Lawrence E.; Chin, Brian; Nislow, Corey; Giaever, Guri; Phillips, Patrick C.; Fink, Gerald R.; Gifford, David K.; Boone, Charles

doi:10.1126/science.1189015

Cited by 380 publications

(408 citation statements)

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“…The kar1 mutants are yeast strains that are deficient in nuclear fusion during mating and thus exchange cytoplasmic but not nuclear content (22) (Methods). The chromosomal mutations we studied were strain-specific: single gene deletions that confer growth defects in the Sigma strain but not in the reference strain S288c (23). Fig.…”

Section: Significancementioning

confidence: 99%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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The measurement of any nonchromosomal genetic contribution to the heritability of a trait is often confounded by the inability to control both the chromosomal and nonchromosomal information in a population. We have designed a unique system in yeast where we can control both sources of information so that the phenotype of a single chromosomal polymorphism can be measured in the presence of different cytoplasmic elements. With this system, we have shown that both the source of the mitochondrial genome and the presence or absence of a dsRNA virus influence the phenotype of chromosomal variants that affect the growth of yeast. Moreover, by considering this nonchromosomal information that is passed from parent to offspring and by allowing chromosomal and nonchromosomal information to exhibit nonadditive interactions, we are able to account for much of the heritability of growth traits. Taken together, our results highlight the importance of including all sources of heritable information in genetic studies and suggest a possible avenue of attack for finding additional missing heritability.A fundamental problem in genetics is unraveling the link between genotype and phenotype. Ascertaining the heritability of a trait is a key step toward harnessing the predictive capacity of genetic information for human disease risk assessment and therapy (1). Knowledge of all of the elements contributing to heritability would facilitate the establishment of a causal relationship between the information that is passed down from generation to generation and the resulting phenotype. Genomewide association studies (GWASs) have successfully identified many human polymorphisms that are associated with traits such as height, eye color, or susceptibility to common diseases, but these variants typically explain only a small proportion of the observed heritability of a trait (2, 3).A number of explanations for missing heritability have been suggested (2), including the existence of many weak variants with effects too small to achieve statistical significance (4), interactions between variants that cannot be identified with current studies (5), rare variants that were not identified by GWAS, and epigenetic effects (6-8). The contribution of nonchromosomal information to the missing heritability is rarely considered, despite the fact that there is a long history documenting the effect in many organisms of diverse cytoplasmic elements on phenotype. Recent work on a mouse model of Crohn disease supports a combinatorial model of complex disease traits in which the pathology requires the interaction between a specific mutation in the mouse and a specific strain of virus (9). Another recent study showed strong effects on the plant metabolome stemming from variation in mitochondrial and chloroplast genomes (10). In humans, the importance of nonchromosomal information has been supported by targeted analyses, but these studies have not analyzed its impact on heritability in a well-controlled context (11-13). Such nonchromosomal interactions might help e...

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Section: Significancementioning

confidence: 99%

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Edwards

Symbor-Nagrabska

Dollard

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…Instead, these networks occur in the context of all the alleles in the genome, which usually vary among individuals. There is substantial evidence that wild-type genetic background almost always modulates the phenotypic effects of mutations (e.g., McKenzie et al 1982;Threadgill et al 1995;Atallah et al 2004;Milloz et al 2008;Chandler 2010;Dowell et al 2010;Gerke et al 2010). The influence of wild-type genetic backgrounds also extends to interactions among mutations (Remold and Lenski 2004;Dworkin et al 2009;Wang et al 2013b), altering patterns of epistasis, and these complex interactions are likely widespread .…”

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confidence: 99%

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

et al. 2014

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The phenotypic consequences of individual mutations are modulated by the wild-type genetic background in which they occur. Although such background dependence is widely observed, we do not know whether general patterns across species and traits exist or about the mechanisms underlying it. We also lack knowledge on how mutations interact with genetic background to influence gene expression and how this in turn mediates mutant phenotypes. Furthermore, how genetic background influences patterns of epistasis remains unclear. To investigate the genetic basis and genomic consequences of genetic background dependence of the scalloped E3 allele on the Drosophila melanogaster wing, we generated multiple novel genome-level datasets from a mapping-byintrogression experiment and a tagged RNA gene expression dataset. In addition we used whole genome resequencing of the parental lines-two commonly used laboratory strains-to predict polymorphic transcription factor binding sites for SD. We integrated these data with previously published genomic datasets from expression microarrays and a modifier mutation screen. By searching for genes showing a congruent signal across multiple datasets, we were able to identify a robust set of candidate loci contributing to the background-dependent effects of mutations in sd. We also show that the majority of background-dependent modifiers previously reported are caused by higher-order epistasis, not quantitative noncomplementation. These findings provide a useful foundation for more detailed investigations of genetic background dependence in this system, and this approach is likely to prove useful in exploring the genetic basis of other traits as well. GENETICISTS often strictly control their organisms' wildtype genetic backgrounds when experimentally dissecting genetic pathways. Although this tight control is necessary to avoid faulty inferences caused by confounding variables (e.g., Burnett et al. 2011), it can often paint an incomplete or even incorrect picture; no genetic pathway or network exists in a vacuum. Instead, these networks occur in the context of all the alleles in the genome, which usually vary among individuals. There is substantial evidence that wild-type genetic background almost always modulates the phenotypic effects of mutations (e.g., McKenzie et al. 1982;Threadgill et al. 1995;Atallah et al. 2004;Milloz et al. 2008;Chandler 2010;Dowell et al. 2010;Gerke et al. 2010). The influence of wild-type genetic backgrounds also extends to interactions among mutations (Remold and Lenski 2004;Dworkin et al. 2009;Wang et al. 2013b), altering patterns of epistasis, and these complex interactions are likely widespread . Alleles that influence many mutant phenotypes segregate in most natural populations, representing a potential source of cryptic genetic variation Félix 2007;Vaistij et al. 2013). In many cases, this cryptic variation has been described phenomenologically, or via the partitioning of genetic variance components (Gibson et al. 1999;Dworkin et al. 2003;McGuigan e...

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“…The reactions in the isoprenoid and mevalonate pathways mentioned above provide one example. Another example is the existence of essential genes unique to particular strains of S. cerevisiae (16).…”

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confidence: 99%

Superessential reactions in metabolic networks

Barve

Rodrigues²,

Wagner³

2012

Proc. Natl. Acad. Sci. U.S.A.

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The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.drug resistance | drug target identification | essential reactions

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Genotype to Phenotype: A Complex Problem

Abstract: In yeast, the impact of gene knockouts depends on genetic background.

Cited by 380 publications

References 6 publications

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Interactions between chromosomal and nonchromosomal elements reveal missing heritability

Causes and Consequences of Genetic Background Effects Illuminated by Integrative Genomic Analysis

Superessential reactions in metabolic networks

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