On the meaning of non-epistatic selection

Puniyani, Amit; Liberman, Uri; Feldman, Marcus W.

doi:10.1016/j.tpb.2004.05.001

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…Thus, the choice of definition alters conclusions relevant to the adaptive value of sex and recombination. Fitness measures based on growth rates of isogenic cultures can be mapped to fitness measures based on allele frequencies within a population (22), so that the choice of definition should also be of great concern to population geneticists.…”

Section: Discussionmentioning

confidence: 99%

“…Although fitness was originally measured in terms of population allele frequencies (1,22,23), it can also be measured by using growth rates of isogenic microbial cultures. Genetic interaction studies have used different measures of fitness, including: (i) the exponential growth rate of the mutant strain relative to that of wild type (4,9,15,19) (the relative-growthrate measure); (ii) the increase in mutant population relative to wild type in one wild-type generation (the relative-population measure) (6); and (iii) the number of progeny per mutant organism relative to the number of progeny for wild type in one wild-type generation (the relative-progeny measure) (24).…”

Section: G Enetic Interactions Have Long Been Studied In Model Organismsmentioning

confidence: 99%

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Defining genetic interaction

Mani

St.Onge²,

Hartman³

et al. 2008

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

435

457

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Sometimes mutations in two genes produce a phenotype that is surprising in light of each mutation's individual effects. This phenomenon, which defines genetic interaction, can reveal functional relationships between genes and pathways. For example, double mutants with surprisingly slow growth define synergistic interactions that can identify compensatory pathways or protein complexes. Recent studies have used four mathematically distinct definitions of genetic interaction (here termed Product, Additive, Log, and Min). Whether this choice holds practical consequences has not been clear, because the definitions yield identical results under some conditions. Here, we show that the choice among alternative definitions can have profound consequences. Although 52% of known synergistic genetic interactions in Saccharomyces cerevisiae were inferred according to the Min definition, we find that both Product and Log definitions (shown here to be practically equivalent) are better than Min for identifying functional relationships. Additionally, we show that the Additive and Log definitions, each commonly used in population genetics, lead to differing conclusions related to the selective advantages of sexual reproduction.epistasis ͉ fitness ͉ gene function G enetic interactions have long been studied in model organisms as a means of identifying functional relationships among genes or their corresponding gene products, with the nature of these relationships depending on the types of interactions (1-3). Additionally, the extent and nature of genetic interaction are important to theoretical explanations for the selective advantage of sexual reproduction and recombination (4-7). The study of genetic interaction has become increasingly systematic and large-scale, especially in the yeast Saccharomyces cerevisiae (6,(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21). This provides an opportunity to examine properties of different quantitative definitions of genetic interaction and their impact on biological interpretation.A quantitative genetic interaction definition has two components: a quantitative phenotypic measure and a neutrality function that predicts the phenotype of an organism carrying two noninteracting mutations. Interaction is then defined by deviation of a double-mutant organism's phenotype from the expected neutral phenotype. A double mutant with a more extreme phenotype than expected defines a synergistic (or synthetic) interaction between the corresponding mutations (synthetic lethality, in the extreme case). Alleviating or "diminishing returns" interactions, in which the double-mutant phenotype is less severe than expected, often result when gene products operate in concert or in series within the same pathway. Alleviating interactions arise, for example, when a mutation in one gene impairs the function of a whole pathway, thereby masking the consequence of mutations in additional members of that pathway.One class of phenotype, fitness, has been central to many large-scale genetic interaction studies. Althou...

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Section: Discussionmentioning

confidence: 99%

Section: G Enetic Interactions Have Long Been Studied In Model Organismsmentioning

confidence: 99%

Defining genetic interaction

Mani

St.Onge²,

Hartman³

et al. 2008

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

435

457

View full text Add to dashboard Cite

show abstract

“…These studies identified GIs based on fitness measurements ( Figure 1B ), a class of phenotype that is measured in terms of population allele frequency (Wolf et al, 2000; Otto and Lenormand, 2002; Puniyani et al, 2004), growth rate, or number of progeny of mutant strain relative to wild-type (Elena and Lenski, 1997; Szafraniec et al, 2003; Segre et al, 2005; Sanjuan and Elena, 2006; St Onge et al, 2007). The additive and multiplicative models, originally used by developmental geneticists ( Figure 1A ) and fitness measurements in yeast ( Figure 1B ) respectively, consider the expected phenotype of a double mutant to be the sum (or the product) of the phenotypes measured for the single mutants if the two mutated genes function independently one from the other (Mani et al, 2008).…”

Section: What Is a Genetic Interaction?mentioning

confidence: 99%

Genetic interaction networks: better understand to better predict

Boucher

Jenna

2013

Front. Genet.

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A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances.

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“…To this end, we consider a ''baseline'' model of additive fitnesses that entails no additive interactions between the loci and frames the problem as the evolution of modifier alleles that cause departure from additivity (see, e.g., ref. 18). In an earlier study of this kind with the LewontinKojima (19) model of fitness interactions, we found that an allele that increased epistatic interactions would increase in frequency when introduced near a stable state of linkage disequilibrium (13).…”

mentioning

confidence: 71%

Evolutionary theory for modifiers of epistasis using a general symmetric model

Liberman

Feldman

2006

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

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Genetic interactions in fitness are studied by using modifier theory. The effects on fitness of two linked genes are perturbed by alleles at a third linked locus that controls the extent of epistasis in fitness between the first two. This epistasis is determined by a symmetric interaction matrix, and it is shown that a modifier allele that increases epistasis will invade when the linkage between the other two genes is sufficiently tight and these genes are in linkage disequilibrium. With linkage equilibrium among the major loci, increased or decreased epistasis may evolve depending on the allele frequencies at these loci.modifier theory ͉ linkage disequilibrium ͉ symmetric viabilities ͉ interaction matrix ͉ external stability

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On the meaning of non-epistatic selection

Cited by 10 publications

References 11 publications

Defining genetic interaction

Defining genetic interaction

Genetic interaction networks: better understand to better predict

Evolutionary theory for modifiers of epistasis using a general symmetric model

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