Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies
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, bu...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-04-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00077/full |
| _version_ | 1818364345775554560 |
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| author | Katy Denise Heath Scott eNuismer |
| author_facet | Katy Denise Heath Scott eNuismer |
| author_sort | Katy Denise Heath |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T22:02:54Z |
| format | Article |
| id | doaj.art-2579d2ae4b5f4699860d0715ae5b9d33 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-13T22:02:54Z |
| publishDate | 2014-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-2579d2ae4b5f4699860d0715ae5b9d332022-12-21T23:29:56ZengFrontiers Media S.A.Frontiers in Genetics1664-80212014-04-01510.3389/fgene.2014.0007781816Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studiesKaty Denise Heath0Scott eNuismer1University of Illinois at Urbana ChampaignUniversity of IdahoPredicting 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.http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00077/fullSymbiosispathogenEpistasisCoevolutionintergenomic epistasis |
| spellingShingle | Katy Denise Heath Scott eNuismer Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies Frontiers in Genetics Symbiosis pathogen Epistasis Coevolution intergenomic epistasis |
| title | Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| title_full | Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| title_fullStr | Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| title_full_unstemmed | Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| title_short | Connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| title_sort | connecting functional and statistical definitions of genotype by genotype interactions in coevolutionary studies |
| topic | Symbiosis pathogen Epistasis Coevolution intergenomic epistasis |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00077/full |
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