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Статті в журналах з теми "Genetic theory"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Farley, John D. "Evolutionary genetic theory." British Journal of Psychiatry 160, no. 6 (June 1992): 861–62. http://dx.doi.org/10.1192/bjp.160.6.861.

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2

Ahad, Md Abdul. "The Neutral Theory (Theory of Genetic Drift) and the Nearly Neutral Theory of Molecular Evolution are Opposite to Evolution." International Journal of Bio-resource and Stress Management 14, July, 7 (July 22, 2023): 1016–27. http://dx.doi.org/10.23910/1.2023.3455a.

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Анотація:

The Neutral theory (also known as the theory of genetic drift) means genetic drift and vice-versa. But the Nearly Neutral theory means genetic drift plus natural selection. However, genetic drift changes the gene frequency randomly and thus it is non-additive, directionless and thus valueless for evolution. Again, genetic drift works only in small populations and thus, genetic drift means small population. But small populations have to inbreed and produce homozygous organisms. Consequently, those populations suffer from various diseases and abnormalities and finally may suddenly extinct. Moreover, any homozygous organism means zero variation, mutation-genetic drift equilibrium also creates zero variation. But variation is the raw material of evolution; so, no evolution occurs by the genetic drift. So, evolutionary biologists rejected both the genetic drift and the small populations for any kind of evolution. Hence, the Neutral theory is opposite to any kind of evolution. Again, recent experiments of ecological genetics with small populations, 12 biochemical tests, and the data of the DNA sequence, fossil evidence oppose the Neutral theory. Furthermore, the rate of evolution by the Neutral theory is equal to the rate of mutation. But mutations are opposite to any kind of evolution. So, biologists rejected the Neutral theory. As the natural selection is not justified in Nearly Neutral theory; so, Neutral theory=Nearly Neutral theory. Consequently, the rejection of the Neutral theory means the rejection of the Nearly Neutral theory. Thus, both the Neutral theory and the Nearly Neutral theory are opposite to any kind of evolution.

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3

Hendricks, M. "Experiments Challenge Genetic Theory." Science News 134, no. 11 (September 10, 1988): 166. http://dx.doi.org/10.2307/3972733.

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4

Schmitt, Lothar M. "Theory of genetic algorithms." Theoretical Computer Science 259, no. 1-2 (May 2001): 1–61. http://dx.doi.org/10.1016/s0304-3975(00)00406-0.

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5

Bürger, Reinhard. "Multilocus population-genetic theory." Theoretical Population Biology 133 (June 2020): 40–48. http://dx.doi.org/10.1016/j.tpb.2019.09.004.

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6

O’Malley, Maureen A. "Endosymbiosis and its implications for evolutionary theory." Proceedings of the National Academy of Sciences 112, no. 33 (April 16, 2015): 10270–77. http://dx.doi.org/10.1073/pnas.1421389112.

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Historically, conceptualizations of symbiosis and endosymbiosis have been pitted against Darwinian or neo-Darwinian evolutionary theory. In more recent times, Lynn Margulis has argued vigorously along these lines. However, there are only shallow grounds for finding Darwinian concepts or population genetic theory incompatible with endosymbiosis. But is population genetics sufficiently explanatory of endosymbiosis and its role in evolution? Population genetics “follows” genes, is replication-centric, and is concerned with vertically consistent genetic lineages. It may also have explanatory limitations with regard to macroevolution. Even so, asking whether population genetics explains endosymbiosis may have the question the wrong way around. We should instead be asking how explanatory of evolution endosymbiosis is, and exactly which features of evolution it might be explaining. This paper will discuss how metabolic innovations associated with endosymbioses can drive evolution and thus provide an explanatory account of important episodes in the history of life. Metabolic explanations are both proximate and ultimate, in the same way genetic explanations are. Endosymbioses, therefore, point evolutionary biology toward an important dimension of evolutionary explanation.

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7

Aravindan, PP. "Host genetics and tuberculosis: Theory of genetic polymorphism and tuberculosis." Lung India 36, no. 3 (2019): 244. http://dx.doi.org/10.4103/lungindia.lungindia_146_15.

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8

Aravindan, PP. "Host genetics and tuberculosis : Theory of genetic polymorphism and tuberculosis." Lung India 36, no. 3 (2019): 244. http://dx.doi.org/10.4103/0970-2113.257707.

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9

Newmeyer, Frederick J. "Genetic dysphasia and linguistic theory." Journal of Neurolinguistics 10, no. 2-3 (April 1997): 47–73. http://dx.doi.org/10.1016/s0911-6044(97)00002-x.

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10

Rowe, D. E. "Errors in genetic theory equations." Theoretical and Applied Genetics 71, no. 3 (December 1985): 451–54. http://dx.doi.org/10.1007/bf00251186.

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11

Tzafestas, Spyros. "Genetic Programming Theory and Practice." Journal of Intelligent and Robotic Systems 45, no. 1 (January 2006): 97–99. http://dx.doi.org/10.1007/s10846-005-9004-6.

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12

Dragovich, Branko. "Genetic code and number theory." Facta universitatis - series: Physics, Chemistry and Technology 14, no. 3 (2016): 225–41. http://dx.doi.org/10.2298/fupct1603225d.

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Анотація:

Living organisms are the most complex, interesting and significant objects regarding all substructures of the universe. Life science is regarded as a science of the 21st century and one can expect great new discoveries in the near futures. This article contains an introductory brief review of genetic information, its coding and translation of genes to proteins through the genetic code. Some theoretical approaches to the modelling of the genetic code are presented. In particular, connection of the genetic code with number theory is considered and the role of p-adic numbers is underlined.

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13

Mealey, Linda. "Comment on genetic similarity theory." Behavior Genetics 15, no. 6 (November 1985): 571–74. http://dx.doi.org/10.1007/bf01065452.

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14

PHILIPPERUSHTON, J., R. RUSSELL, and P. WELLS. "Personality and genetic similarity theory." Journal of Social and Biological Systems 8, no. 1 (January 1985): 63–86. http://dx.doi.org/10.1016/0140-1750(85)90062-4.

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15

Nevo, Eviatar. "Evolutionary Processes and Theory: The Ecological-Genetics Interface." Water Science and Technology 27, no. 7-8 (April 1, 1993): 489–96. http://dx.doi.org/10.2166/wst.1993.0586.

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The evolutionary process is reviewed in terms of the ecological-genetics interface based on genetic diversity in natural populations of plants and animals, using the environmental-genetic correlation methodology at three geographic levels: (1) Local, several species in Israeli microsites; (2) Regional, 21 species across Israel and 2 species in the Near East; and (3) Global, 1111; 184 and 189 species in three studies across the planet. The species analyzed are taxonomically unrelated, and vary in their ecologies, demographies, life histories, and other biological variables. They were mostly tested by horizontal starch gel electrophoresis for allozymic diversity, averaging 25 gene loci, and other genetic polymorphisms. In addition, ten studies involved DNA polymorphisms. The following results were found at all three geographic levels: (1) The levels of genetic diversity vary nonrandomly and are structured within and among populations, species, and higher taxa; and (2) Genetic diversity is correlated with niche width, and partly predictable, primarily by ecological factors. These results corroborate the adaptive, environmental theory of genetic diversity. They were also verified for several allozyme loci in controlled laboratory experiments in pollution biology. Natural selection in its various forms appears to be a major force maintaining, differentiating and orienting evolutionary change in protein and DNA polymorphisms.

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16

Hermisson, Joachim, and Günter P. Wagner. "The Population Genetic Theory of Hidden Variation and Genetic Robustness." Genetics 168, no. 4 (December 2004): 2271–84. http://dx.doi.org/10.1534/genetics.104.029173.

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17

Lundquist, Andrew L., Renee C. Pelletier, Courtney E. Leonard, Winfred W. Williams, Katrina A. Armstrong, Heidi L. Rehm, and Eugene P. Rhee. "From Theory to Reality: Establishing a Successful Kidney Genetics Clinic in the Outpatient Setting." Kidney360 1, no. 10 (August 12, 2020): 1097–104. http://dx.doi.org/10.34067/kid.0004262020.

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Анотація:

BackgroundGenetic testing in nephrology is increasingly described in the literature and several groups have suggested significant clinical benefit. However, studies to date have described experience from established genetic testing centers or from externally funded research programs.MethodsWe established a de novo kidney genetics clinic within an academic adult general nephrology practice. Key features of this effort included a pipeline for internal referrals, flexible scheduling, close coordination between the nephrologist and a genetic counselor, and utilization of commercial panel-based testing. Over the first year, we examined the outcomes of genetic testing, the time to return of genetic testing, and out-of-pocket cost to patients.ResultsThirty patients were referred and 23 were evaluated over the course of five clinic sessions. Nineteen patients underwent genetic testing with new diagnoses in nine patients (47%), inconclusive results in three patients (16%), and clearance for kidney donation in two patients (11%). On average, return of genetic results occurred 55 days (range 9–174 days) from the day of sample submission and the average out-of-pocket cost to patients was $155 (range $0–$1623).ConclusionsWe established a kidney genetics clinic, without a pre-existing genetics infrastructure or dedicated research funding, that identified a new diagnosis in approximately 50% of patients tested. This study provides a clinical practice model for successfully incorporating genetic testing into ambulatory nephrology care with minimal capital investment and limited financial effect on patients.

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18

Wagner, Gunter P., Ginger Booth, and Homayoun Bagheri-Chaichian. "A Population Genetic Theory of Canalization." Evolution 51, no. 2 (April 1997): 329. http://dx.doi.org/10.2307/2411105.

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19

Rushton, J. Philippe. "Evolution, altruism and genetic similarity theory." Mankind Quarterly 27, no. 4 (1987): 379–96. http://dx.doi.org/10.46469/mq.1987.27.4.2.

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20

Hench, Larry L. "A Genetic Theory of Bioactive Materials." Key Engineering Materials 192-195 (September 2000): 575–80. http://dx.doi.org/10.4028/www.scientific.net/kem.192-195.575.

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21

WRIGLEY, TERRY. "The Zombie Theory of Genetic Intelligence." FORUM 61, no. 1 (2019): 77. http://dx.doi.org/10.15730/forum.2019.61.1.77.

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22

Geraedts, Joep, and Karen Sermon. "Preimplantation genetic screening 2.0: the theory." Molecular Human Reproduction 22, no. 8 (June 2, 2016): 839–44. http://dx.doi.org/10.1093/molehr/gaw033.

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23

Banham Bridges, K. M. "A Genetic Theory of the Emotions." Journal of Genetic Psychology 152, no. 4 (December 1991): 487–500. http://dx.doi.org/10.1080/00221325.1991.9914709.

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24

Moore, Ginger A., and Jenae M. Neiderhiser. "Behavioral Genetic Approaches and Family Theory." Journal of Family Theory & Review 6, no. 1 (March 2014): 18–30. http://dx.doi.org/10.1111/jftr.12028.

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25

McAllister, Marion. "Grounded Theory in Genetic Counseling Research." Journal of Genetic Counseling 10, no. 3 (June 2001): 233–50. http://dx.doi.org/10.1023/a:1016628408498.

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26

Arazi, Arnon, Eshel Ben-Jacob, and Uri Yechiali. "Bridging genetic networks and queueing theory." Physica A: Statistical Mechanics and its Applications 332 (February 2004): 585–616. http://dx.doi.org/10.1016/j.physa.2003.07.009.

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27

Dunbar, R. I. M. "Genetic similarity theory needs more development." Behavioral and Brain Sciences 12, no. 3 (September 1989): 520–21. http://dx.doi.org/10.1017/s0140525x00057368.

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28

Snaselova, Petra, and Frantisek Zboril. "Genetic Algorithm using Theory of Chaos." Procedia Computer Science 51 (2015): 316–25. http://dx.doi.org/10.1016/j.procs.2015.05.248.

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29

Figureau, A. "Information theory and the genetic code." Origins of Life and Evolution of the Biosphere 17, no. 3-4 (September 1987): 439–49. http://dx.doi.org/10.1007/bf02386481.

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30

Wagner, Günter P., Ginger Booth, and Homayoun Bagheri-Chaichian. "A POPULATION GENETIC THEORY OF CANALIZATION." Evolution 51, no. 2 (April 1997): 329–47. http://dx.doi.org/10.1111/j.1558-5646.1997.tb02420.x.

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31

Brändas, Erkki J. "Molecular theory of the genetic code." Molecular Physics 116, no. 19-20 (May 17, 2018): 2622–32. http://dx.doi.org/10.1080/00268976.2018.1471227.

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32

Figureau, A. "Information theory and the genetic code." Origins of Life and Evolution of the Biosphere 16, no. 3-4 (September 1986): 520. http://dx.doi.org/10.1007/bf02422175.

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33

Zojaji, Zahra, and Mohammad Mehdi Ebadzadeh. "Semantic schema theory for genetic programming." Applied Intelligence 44, no. 1 (July 23, 2015): 67–87. http://dx.doi.org/10.1007/s10489-015-0696-4.

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34

Vagyn, Yu V. "Evolution of darwinism. Synthetic theory of evolutions: 1926 – 1975 years." Visnik ukrains'kogo tovaristva genetikiv i selekcioneriv 17, no. 1 (July 31, 2019): 51–56. http://dx.doi.org/10.7124/visnyk.utgis.17.1.1201.

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Анотація:

The process of combining Darwinism and genetics, which entered the history of biology as a synthetic theory of evolution, is considered.Key words: synthetic theory of evolution, neo-Darwinism, the concept of a biological species, population genetics, genetic polymorphism, the theory of dominance, gene drift.

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35

McGlothlin, Joel W., Erol Akçay, Edmund D. Brodie, Allen J. Moore, and Jeremy Van Cleve. "A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution." Journal of Heredity 113, no. 1 (January 1, 2022): 109–19. http://dx.doi.org/10.1093/jhered/esab064.

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Abstract Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory and, in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

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36

Hutchinson, Michael, Cleanthe Spanaki, Sergey Lebedev, and Andreas Plaitakis. "Genetic basis of common diseases: The general theory of Mendelian recessive genetics." Medical Hypotheses 65, no. 2 (January 2005): 282–86. http://dx.doi.org/10.1016/j.mehy.2005.02.034.

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37

Rao, Y. V. Subba. "NEW THEORY OF EVOLUTION FROM GENETIC COMPLEXITY OF DIVERSE PERSPECTIVES." International Journal of Research -GRANTHAALAYAH 9, no. 10 (November 12, 2021): 291–303. http://dx.doi.org/10.29121/granthaalayah.v9.i10.2021.4316.

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Анотація:

In this study, a new hypothesis of evolution is proposed. Genetic complexity provides a plausible hypothesis of the evolution of life on Earth and is supported by ample evidence from different perspectives. The current theory of evolution and natural selection proposed by Darwin is accepted in biology, plausibly, for want of a more viable alternative in based on the recent advances made in cell biology, molecular biology, and genetics. The proposed hypothesis of evolution based on the different perspectives of genetic complexity addresses the two critical areas of advanced complex life of Cambrian explosion and the development of even more complex and intricate human brain in contradistinction to the Evolution Theory envisaged by Charles Darwin.

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38

RUSHTON, ALAN R. "William Bateson and the chromosome theory of heredity: a reappraisal." British Journal for the History of Science 47, no. 1 (July 5, 2013): 147–71. http://dx.doi.org/10.1017/s0007087413000320.

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AbstractWilliam Bateson vigorously objected to the assumptions within the chromosome theory of heredity proposed by T. H. Morgan because he perceived inadequate experimental data that could substantiate the theory. Those objections were largely resolved by 1921, and Bateson reluctantly accepted the basic assumption that chromosomes carried the genetic factors from one generation to the next. Bateson's own research at that time on developmental genetics seemed out of touch with the general tone of the genetics field, and the chromosome theory did not provide illuminating mechanisms that elucidated phenomena such as plant variegations or chimeras. Bateson imagined a general theory of heredity and development based on vortices and waves, concepts he borrowed from contemporary physics. For decades he sought to devise an intellectually and aesthetically satisfying theory to eventually explain evolution in genetic terms, but his aspirations remained unfulfilled when he died in 1926.

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39

Seal, Nuananong. "Introduction to Genetics and Childhood Obesity: Relevance to Nursing Practice." Biological Research For Nursing 13, no. 1 (August 26, 2010): 61–69. http://dx.doi.org/10.1177/1099800410381424.

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Анотація:

Purpose: The aims for this article are to provide an overview of the current state of research on genetic contributions to the development of childhood obesity and to suggest genetic-focused nursing practices to prevent childhood obesity. Organizing Constructs: Genetic epidemiology of childhood obesity, modes to identifying obesity genes, types of human obesity genes, and nursing implications are discussed. Clinical Relevance: The successful integration of genetics into nursing practice will provide opportunities for nurses to participate fully as major agents and collaborators in the health care revolution. Conclusions: Practicing nurses across the profession will need to become knowledgeable about genetics and take part in obesity prevention through genetic assessment of susceptibility and appropriate environmental interventions.

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40

Wong, Jeffrey Tze-Fei. "Question 6: Coevolution Theory of the Genetic Code: A Proven Theory." Origins of Life and Evolution of Biospheres 37, no. 4-5 (July 5, 2007): 403–8. http://dx.doi.org/10.1007/s11084-007-9094-1.

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41

McGlothlin, Joel W., Jason B. Wolf, Edmund D. Brodie, and Allen J. Moore. "Quantitative genetic versions of Hamilton's rule with empirical applications." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1642 (May 19, 2014): 20130358. http://dx.doi.org/10.1098/rstb.2013.0358.

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Анотація:

Hamilton's theory of inclusive fitness revolutionized our understanding of the evolution of social interactions. Surprisingly, an incorporation of Hamilton's perspective into the quantitative genetic theory of phenotypic evolution has been slow, despite the popularity of quantitative genetics in evolutionary studies. Here, we discuss several versions of Hamilton's rule for social evolution from a quantitative genetic perspective, emphasizing its utility in empirical applications. Although evolutionary quantitative genetics offers methods to measure each of the critical parameters of Hamilton's rule, empirical work has lagged behind theory. In particular, we lack studies of selection on altruistic traits in the wild. Fitness costs and benefits of altruism can be estimated using a simple extension of phenotypic selection analysis that incorporates the traits of social interactants. We also discuss the importance of considering the genetic influence of the social environment, or indirect genetic effects (IGEs), in the context of Hamilton's rule. Research in social evolution has generated an extensive body of empirical work focusing—with good reason—almost solely on relatedness. We argue that quantifying the roles of social and non-social components of selection and IGEs, in addition to relatedness, is now timely and should provide unique additional insights into social evolution.

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42

González-García, José S., and José Díaz. "Information theory and the ethylene genetic network." Plant Signaling & Behavior 6, no. 10 (October 2011): 1483–98. http://dx.doi.org/10.4161/psb.6.10.16424.

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43

Parton, Kevin A. "A simple theory of induced genetic change." Frontiers in Ecology and the Environment 7, no. 5 (June 2009): 239. http://dx.doi.org/10.1890/09.wb.015.

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44

Casanova, JL. "IS-052 Genetic Theory Of Infectious Diseases." Archives of Disease in Childhood 99, Suppl 2 (October 2014): A17.1—A17. http://dx.doi.org/10.1136/archdischild-2014-307384.52.

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45

Cantú-Paz, Erick, and David E. Goldberg. "Efficient parallel genetic algorithms: theory and practice." Computer Methods in Applied Mechanics and Engineering 186, no. 2-4 (June 2000): 221–38. http://dx.doi.org/10.1016/s0045-7825(99)00385-0.

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46

BECKMAN, R., and L. LOEB. "Genetic instability in cancer: Theory and experiment." Seminars in Cancer Biology 15, no. 6 (December 2005): 423–35. http://dx.doi.org/10.1016/j.semcancer.2005.06.007.

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47

Koninckx, Philippe R., Anastasia Ussia, Leila Adamyan, Arnaud Wattiez, Victor Gomel, and Dan C. Martin. "Pathogenesis of endometriosis: the genetic/epigenetic theory." Fertility and Sterility 111, no. 2 (February 2019): 327–40. http://dx.doi.org/10.1016/j.fertnstert.2018.10.013.

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48

Buscema, Massimo. "Genetic doping algorithm (GenD): theory and applications." Expert Systems 21, no. 2 (May 2004): 63–79. http://dx.doi.org/10.1111/j.1468-0394.2004.00264.x.

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49

Visscher, D. W., F. H. Sarkar, S. R. Wolman, and C. W. M. Bedrossian. "Theory and methodology of evaluating genetic alterations." Diagnostic Cytopathology 10, no. 3 (May 1994): 289–98. http://dx.doi.org/10.1002/dc.2840100318.

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50

Megson, G. M., and I. M. Bland. "Generic systolic array for genetic algorithms." IEE Proceedings - Computers and Digital Techniques 144, no. 2 (1997): 107. http://dx.doi.org/10.1049/ip-cdt:19971126.

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