ArticleFrontiers in genetics2014
Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Identifying coevolving loci using interspecific genetic correlations.Ecology and evolution · 2017Article
- No evidence for adaptation to local rhizobial mutualists in the legumeEcology and evolution · 2017Article
- Genotype specificity among hosts, pathogens, and beneficial microbes influences the strength of symbiont-mediated protection.Evolution; international journal of organic evolution · 2017Article
- Genotype-specific interactions between parasitic arthropods.Heredity · 2017Article
- Specialization for resistance in wild host-pathogen interaction networks.Frontiers in plant science · 2015Article
- Epidemiological and Evolutionary Outcomes in Gene-for-Gene and Matching Allele Models.Frontiers in plant science · 2015Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting how species interactions evolve requires that we understand the mechanistic basis of coevolution, and thus the functional genotype-by-genotype interactions (G × G) that drive reciprocal natural selection. Theory on host-parasite coevolution provides testable hypotheses for empiricists, but depends upon models of functional G × G that remain loosely tethered to the molecular details of any particular system. In practice, reciprocal cross-infection studies are often used to partition the variation in infection or fitness in a population that is attributable to G × G (statistical G × G). Here we use simulations to demonstrate that within-population statistical G × G likely tells us little about the existence of coevolution, its strength, or the genetic basis of functional G × G. Combined with studies of multiple populations or points in time, mapping and molecular techniques can bridge the gap between natural variation and mechanistic models of coevolution, while model-based statistics can formally confront coevolutionary models with cross-infection data. Together these approaches provide a robust framework for inferring the infection genetics underlying statistical G × G, helping unravel the genetic basis of coevolution.
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