Genetic equilibrium

Genetic equilibrium is the condition of an allele or genotype in a gene pool (such as a population) where the frequency does not change from generation to generation.[1] Genetic equilibrium describes a theoretical state that is the basis for determining whether and in what ways populations may deviate from it. Hardy–Weinberg equilibrium is one theoretical framework for studying genetic equilibrium. It is commonly studied using models that take as their assumptions those of Hardy-Weinberg, meaning:

It can describe other types of equilibrium as well, especially in modeling contexts. In particular, many models use a variation of the Hardy–Weinberg principle as their basis. Instead of all of the Hardy–Weinberg characters being present, these instead assume a balance between the diversifying effects of genetic drift and the homogenizing effects of migration between populations.[2] A population not at equilibrium suggests that one of the assumptions of the model in question has been violated.

Theoretical models of genetic equilibrium

The Hardy–Weinberg principle provides the mathematical framework for genetic equilibrium. Genetic equilibrium itself, whether Hardy-Weinberg or otherwise, provides the groundwork for a number of applications, in including population genetics, conservation and evolutionary biology. With the rapid increase in whole genome sequences available as well as the proliferation of anonymous markers, models have been used to extend the initial theory to all manner of biological contexts.[3] Using data from genetic markers such as ISSRs and RAPDs as well as the predictive potential of statistics, studies have developed models to infer what processes drove the lack of equilibrium. This includes local adaptation, range contraction and expansion and lack of gene flow due to geographic or behavioral barriers, although equilibrium modeling has been applied to a wide range of topics and questions.

Equilibrium modeling have led to developments in the field. Because allelic dominance can disrupt predictions of equilibrium,[4] some models have moved away from using genetic equilibrium as an assumption. Instead of the traditional F-statistics, they make use of Bayesian estimates.[5] Holsinger et al. developed an analog to FST, called theta.[6] Studies have found Bayesian estimates to be better predictors of the patterns observed.[7] However, genetic equilibrium-based modeling remains a tool in population and conservation genetics-it can provide invaluable information about previous historical processes.[4]

Biological study systems

Genetic equilibrium has been studied in a number of taxa. Some marine species in particular have been used as study systems. The life history of marine organisms like sea urchins appear to fulfill the requirements of genetic equilibrium modeling better than terrestrial species.[8] They exist in large, panmictic populations that don’t appear to be strongly affected by geographic barriers. In spite of this, some studies have found considerable differentiation across the range of a species. Instead, when looking for genetic equilibrium, studies found large, widespread species complexes.[9] This indicates that genetic equilibrium may be rare or difficult to identify in the wild, due to considerable local demographic changes on shorter time scales.[10]

In fact, although a large population size is a required condition for genetic equilibrium according to Hardy–Weinberg, some have argued that a large population size can actually slow the approach to genetic equilibrium.[11] This can have implications for conservation, where genetic equilibrium can be used as a marker of a healthy and sustainable population.

References
  1. "Genetic equilibrium".
  2. Duvernell, D. D.; Lindmeier, J. B.; Faust, K. E.; Whitehead, A. (2008). "Relative influences of historical and contemporary forces shaping the distribution of genetic variation in the Atlantic killifish, Fundulus heteroclitus". Molecular Ecology. 17 (5): 1344–60. doi:10.1111/j.1365-294X.2007.03648.x. PMID 18302693. S2CID 27306569.
  3. Shriner, D. (2011). "Approximate and exact tests of Hardy-Weinberg equilibrium using uncertain genotypes". Genetic Epidemiology. 35 (7): 632–7. doi:10.1002/gepi.20612. PMC 4141651. PMID 21922537.
  4. Kramer, Koen; van der Werf, D. C. (2010). "Equilibrium and non-equilibrium concepts in forest genetic modeling: population- and individually-based approaches," Forest Systems, 19(SI): 100–112.
  5. Wilson, G. A.; Rannala, B. (2003). "Bayesian inference of recent migration rates using multilocus genotypes" (PDF). Genetics. 163 (3): 1177–91. doi:10.1093/genetics/163.3.1177. PMC 1462502. PMID 12663554.
  6. Holsinger, K. E.; Lewis, P. O.; Dey, D. K. (2002). "A Bayesian approach to inferring population structure from dominant markers". Molecular Ecology (Submitted manuscript). 11 (7): 1157–64. doi:10.1046/j.1365-294X.2002.01512.x. PMID 12074723. S2CID 11461448.
  7. Ramp Neale, Jennifer M.; Ranker, TOM A.; Collinge, Sharon K. (2008). "Conservation of rare species with island-like distributions: A case study of Lasthenia conjugens(Asteraceae) using population genetic structure and the distribution of rare markers". Plant Species Biology. 23 (2): 97–110. doi:10.1111/j.1442-1984.2008.00211.x.
  8. Palumbi, Stephen R.; Grabowsky, Gail; Duda, Thomas; Geyer, Laura; Tachino, Nicholas (1997). "Speciation and Population Genetic Structure in Tropical Pacific Sea Urchins". Evolution. 51 (5): 1506–1517. doi:10.1111/j.1558-5646.1997.tb01474.x. PMID 28568622..
  9. Knowlton, Nancy (1993). "Sibling Species in the Sea". Annual Review of Ecology and Systematics. 24: 189–216. doi:10.1146/annurev.es.24.110193.001201..
  10. Whitlock, M. C. (1992). "Temporal Fluctuations in Demographic Parameters and the Genetic Variance Among Populations". Evolution; International Journal of Organic Evolution. 46 (3): 608–615. doi:10.1111/j.1558-5646.1992.tb02069.x. PMID 28568658. S2CID 20983027.
  11. Birky Jr, C. W.; Maruyama, T.; Fuerst, P. (1983). "An approach to population and evolutionary genetic theory for genes in mitochondria and chloroplasts, and some results" (PDF). Genetics. 103 (3): 513–27. doi:10.1093/genetics/103.3.513. PMC 1202037. PMID 6840539.
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