Gene–Environment Interactions at Nucleotide Resolution

Gerke, Justin P.; Lorenz, Kimberly; Ramnarine, Shelina; Cohen, Barak A.

doi:10.1371/journal.pgen.1001144

Cited by 57 publications

(54 citation statements)

References 37 publications

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“…Here, we extend that finding by providing evidence that these modifiers' background dependence is, in most cases, due to higher-order epistasisinteractions between the focal mutation (in this case, sd E3 ), the modifier itself (in this case, a deletion), and alleles elsewhere in the genome. This finding is consistent with growing evidence that higher-order epistasis is prevalent (Weinreich et al 2013) and that what may initially seem to be a two-way interaction is often a more complex interaction involving additional loci or environmental influences (Whitlock and Bourguet 2000;Gerke et al 2010;Wang et al 2013b;Lalić and Elena 2013). To our knowledge this is the first attempt at combining such genetic and genomic data to infer the order of epistatic interactions (or at least to rule out lower-order interactions).…”

Section: Majority Of Background-dependent Modifiers Cannot Be Explainsupporting

confidence: 86%

“…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 .…”

mentioning

confidence: 99%

“…That is, we need to understand both the causes and consequences of this genetic background dependence of mutational effects (Chandler et al 2013). For instance, one study showed that specific quantitative trait nucleotide (QTN) alleles in naturally occurring yeast (Saccharomyces cerevisiae) isolates have complex phenotypic effects depending on both genetic background and environmental context (Gerke et al 2010). However, to make predictions about how a particular QTN may influence trait variation in a novel genetic (or environmental) context requires a more mechanistic understanding of how the QTN allele interacts with the genetic background to influence phenotypes, whether at the level of gene expression, cellular changes, morphology, behavior, etc.…”

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confidence: 99%

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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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Section: Majority Of Background-dependent Modifiers Cannot Be Explainsupporting

confidence: 86%

mentioning

confidence: 99%

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confidence: 99%

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

et al. 2014

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“…Small-effect QTL are dependent on large-effect QTN We had previously observed that the genetic background of a strain influences the effect sizes of the four large-effect QTNs on sporulation efficiency (Gerke et al 2009(Gerke et al , 2010. We therefore asked whether the small-effect QTL we identified would be influenced by the allelic status of the largeeffect QTN.…”

Section: Resultsmentioning

confidence: 96%

“…BC240 contains the natMX4 marker, conferring resistance to nourseothricin, BC248 contains the hygMX4 marker, conferring resistance to hygromycin, while BC713 and BC728 contain the kanMX4 marker, conferring resistance to G418 (Wach et al 1994;Goldstein and McCusker 1999). BC713 and BC728 were created by single-nucleotide replacement followed by multiple rounds of intercrossing and backcrossing to ensure that phenotypes were not affected by second-site mutations (Gerke et al 2010). The oak strain containing all four vineyard QTN (oak(vvvv), strain BC728) sporulates at 7.7%, while the vineyard strain containing the four oak QTN (vineyard (oooo), strain BC713) sporulates at 68.8% (Gerke et al 2009).…”

Section: Strainsmentioning

confidence: 99%

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Lorenz

Cohen

2012

Genetics

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Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small-and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL. COMPLEX traits exhibit non-Mendelian inheritance patterns, which arise from the segregation of multiple quantitative trait loci (QTL) (Lander and Schork 1994). A QTL is a region of the genome containing an allelic difference that causes a change in phenotype. Many medically and agriculturally important traits exhibit complex genetic architecture, including phenotypes ranging from diabetes and cancer penetrance to meat quality and frost tolerance in crops (Glazier et al. 2002;Heuven et al. 2009;Dumont et al. 2009;Gaudet et al. 2010). QTL with relatively large effects are the easiest to identify and analyze, yet most QTL have small average effects on complex traits (Mackay 2001). Thus, while theory and experiment suggest that a large fraction of the variation of many phenotypes will be explained by QTL with smaller effect sizes (Fisher 1930;Lango Allen et al. 2010;Yang et al. 2011), our current understanding of complex traits is based primarily on analyses of QTL with the largest effect sizes. Because small-effect QTL are necessarily more difficult to detect and analyze, a large fraction of the genetic architecture of most complex traits is not well understood. A more complete model of complex traits should include an understanding of the numbers, effect sizes, and interactions of small-effect QTL.To identify and study small-effect QTL, we used sporulation efficiency in the yeast Saccharomyces cerevisiae as a model complex trait (Gerke et al. 2006). This system offers several advantages for the study of QTL with relatively small-effect sizes. Sporulation efficiency is a highly heritable trait in yeast (Gerke et al. 2006). The measurements of sporulation efficiency can be performed in controlled environments that provide the statistical power to detect QTL with small-effect sizes. With this system, we previously identified four quantitative trait nucleotides (QTN) that have large effects on sporulation efficiency (Gerke et al. 2009). Here we describe crosses designed to uncover additional QTL that account for phenotypic variation not explained by the major-effect QTN. For the purposes of this study we define these addition...

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