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Journal of Integrative Neuroscience  2018, Vol. 17 Issue (1): 1-10    DOI: 10.31083/JIN-170034
Research article | Next articles
The origin of complex human diversity: Stochastic epistatic modules and the intrinsic compatibility between distributional robustness and phenotypic changeability
Shinji Ijichi1, 2, *(), Naomi Ijichi2, Yukina Ijichi2, Chikako Imamura3, Hisami Sameshima1, Yoichi Kawaike1, Hirofumi Morioka1
1 Health Service Center, Kagoshima University, 1-21-24 Korimoto, Kagoshima 890-8580, Japan
2 Institute for Externalization of Gifts and Talents, 7421-1 Shimofukumoto, Kagoshima 891-0144, Japan
3 Support Center for Students with Disabilities, Kagoshima University, 1-21-30 Korimoto, Kagoshima 890-0065, Japan
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Abstract  

The continuing prevalence of a highly heritable and hypo-reproductive extreme tail of a human neurobehavioral quantitative diversity suggests the reproductive majority retains the genetic mechanisms for extremes. From the perspective of stochastic epistasis, the effect of an epistatic modifier variant can randomly vary in both phenotypic value and effect direction among carriers depending on the genetic identity and the modifier carriers are ubiquitous in the population. The neutrality of the mean genetic effect in carriers ensures the survival of the variant under selection pressures. Functionally or metabolically related modifier variants make an epistatic network module and dozens of modules may be involved in the phenotype. To assess the significance of stochastic epistasis, a simplified module-based model was simulated. The individual repertoire of the modifier variants in a module also contributes in genetic identity, which determines the genetic contribution of each carrier modifier. Because the entire contribution of a module to phenotypic outcome is unpredictable in the model, the module effect represents the total contribution of related modifiers as a stochastic unit in simulations. As a result, the intrinsic compatibility between distributional robustness and quantitative changeability could mathematically be simulated using the model. The artificial normal distribution shape in large-sized simulations was preserved in each generation even if the lowest fitness tail was non-reproductive. The robustness of normality across generations is analogous to the real situation of complex human diversity, including neurodevelopmental conditions. The repeated regeneration of a non-reproductive extreme tail may be essential for survival and change of the reproductive majority, implying extremes for others. Further simulation to illustrate how the fitness of extreme individuals can be low across generations may be necessary to increase the plausibility of this stochastic epistasis model.

Key words:  Neurodevelopmental conditions      autism      schizophrenia      stochastic variation      complex trait      population diversity      quantitative change     
Submitted:  23 January 2017      Accepted:  05 April 2017      Published:  15 February 2018     
*Corresponding Author(s):  Shinji Ijichi     E-mail:  jiminy@hsc.kagoshima-u.ac.jp

Cite this article: 

Shinji Ijichi, Naomi Ijichi, Yukina Ijichi, Chikako Imamura, Hisami Sameshima, Yoichi Kawaike, Hirofumi Morioka. The origin of complex human diversity: Stochastic epistatic modules and the intrinsic compatibility between distributional robustness and phenotypic changeability. Journal of Integrative Neuroscience, 2018, 17(1): 1-10.

URL: 

https://jin.imrpress.com/EN/10.31083/JIN-170034     OR     https://jin.imrpress.com/EN/Y2018/V17/I1/1

Fig. 1.  Population size-dependent stability of the simulated phenotypic diversity. A simulation is illustrated as a sequence of boxplot diagrams. The phenotypic values (Xo) were automatically calculated using the formula (y-axis title) for each generation (G1 - G100) as described in Methods. Simulations were repeated five times with the population size varying from 10 to 500, and a representative simulation for each population size is shown. Small arrow heads indicate generations whose population was not normally distributed by an assessment of the absolute value for skewness and/or kurtosis ($\geqq$ 2.0). The module number was fixed (m = 10) for these representative simulations.

Fig. 2.  Conditional effects of each coefficient of the formula (y-axis title). A simulation is illustrated as a sequence of boxplot diagrams. The phenotypic values ($Xo$) were automatically calculated using the formula for each generation ($G1-G100$) as described in Methods. Simulations for each condition were repeated five times and three representative simulations are given for each condition (300 boxplots per condition) with $a$ = 0.5, $b$ = 0.5, and $c$ = 0 for the condition a + b $\leqq$ 1 and $c = 0, a = 0.5, b = 0.5$, and $c = 0.1$ for the condition $a + b$ $\leqq$ 1, $a + b$ $ > $ |c|, and $c \neq $ 0, $a = 0.5, b = 0.5$, and $c$ = 1.0 for the condition $a + b \leqq$ 1, $a + b \leqq$ |c|, and $c \neq $ 0, and $a = 0.53, b = 0.53$, and $c = 1.0$ for the condition $a + b > $ 1 and $c \neq $ 0, respectively. To exclude the contamination of the population size effect, the population size was fixed (n = 1, 000). The module number was also fixed ($m$ = 10).

Fig. 3.  Phenotypic changeability in a selection pressure where the lowest extremes in the population cannot leave offspring. A simulation is illustrated as a line graph (the mean value $ \pm $ one standard deviation). The phenotypic values ($Xo$) were automatically calculated using the formula (y-axis title) for each generation ($G1-G100$) as described in Methods. The percentage of nonreproductive extreme cases is shown at the right end of the simulation (from 0.2% to 10% with a minus symbol). To exclude the contamination of population size effect, the population size was fixed (n=1,000).

  
[1] Plomin R, Owen MJ, McGuffin P ( 1994) The genetic basis of complex human behaviors. Science 264( 5166), 1733-1739.
doi: 10.1126/science.8209254 pmid: 8209254
[2] Plomin R, DeFries JC, Knopik VS, Neiderhiser JM ( 2016) Top 10 replicated findings from behavioral genetics. Perspectives on Psychological Science 11( 1), 3-23.
doi: 10.1177/1745691615617439 pmid: 4739500
[3] Fischer B, Taborsky B, Dieckmann U ( 2009) Unexpected patterns of plastic energy allocation in stochastic environments. The American Naturalist 173( 3), e108-e120.
doi: 10.1086/596536
[4] Raser JM, O’shea EK ( 2005) Noise in gene expression: origins, consequences, and control. Science 309( 5743), 2010-2013.
doi: 10.1126/science.1105891
[5] Shin T, Kraemer D, Pryor J, Liu L, Rugila J, Howe L, Buck S, Murphy K, Lyons L, Westhusin M ( 2002) Cell biology: a cat cloned by nuclear transplantation. Nature 415( 6874), 859.
doi: 10.1038/nature723 pmid: 11859353
[6] Lark KG, Chase K, Adler F, Mansur LM, Orf JH ( 1995) Interactions between quantitative trait loci in soybean in which trait variation at one locus is conditional upon a specific allele at another. Proceedings of the National Academy of Sciences of the United States of America 92( 10), 4656-4660.
doi: 10.1073/pnas.92.10.4656 pmid: 7753859
[7] Kroymann J, Mitchell-Olds T ( 2005) Epistasis and balanced polymorphism influencing complex trait variation. Nature 435( 7038), 95-98.
doi: 10.1038/nature03480 pmid: 15875023
[8] Pettersson M, Besnier F, Siegel PB, Carlborg ¨O ( 2011) Replication and explorations of high-order epistasis using a large advanced intercross line pedigree. Plos Genetics 7( 7), e1002180.
doi: 10.1371/journal.pgen.1002180 pmid: 3140984
[9] Ijichi S, Ijichi N, Ijichi Y, Kawamura Y, Hashiguchi T, Morioka H ( 2008) For others: Epistasis and the evolutionary survival of an extreme tail of the quantitative distribution of autistic assets. Medical Hypotheses 70( 3), 515-521.
doi: 10.1016/j.mehy.2007.07.016 pmid: 17765402
[10] Beaudet AL ( 2007) Autism: highly heritable but not inherited. Nature Medicine 13( 5), 534-536.
doi: 10.1038/nm0507-534 pmid: 17479094
[11] Ijichi S, Ijichi N, Ijichi Y, Nagata J, Imamura C, Sameshima H, Kawaike Y, Morioka H ( 2015) The origin of population diversity: Stochastic interactions between a modifier variant and the individual genetic background. Natural Science 7( 5), 255-265.
doi: 10.4236/ns.2015.75029
[12] Ijichi S, Ijichi N, Ijichi Y, Sameshima H, Morioka H ( 2011) The genetic basis of phenotypic diversity: autism as an extreme tail of a complex dimensional trait. In, Amaral D et al.(Eds.) Autism Spectrum Disorders: The Role of Genetics in Diagnosis and Treatment. New York, Oxford University Press.
[13] Ijichi S, Ijichi N, Ijichi Y, Sameshima H, Saeki Y, Hall W, Morioka H ( 2011) Non-additive interactions between monomorphic and polymorphic loci: a theoretical explanation of the genetic underpinning for complex traits. Advances in Medicine and Biology 13, 133-148.
[14] Raser JM, O’shea EK ( 2004) Control of stochasticity in eukaryotic gene expression. Science 304( 5678), 1811-1814.
doi: 10.1126/science.1098641 pmid: 15166317
[15] Ansel J, Bottin H, Rodriguez-Beltran C, Damon C, Nagarajan M, Fehrmann S, Franc¸ois J, Yvert G ( 2008) Cell-to-cell stochastic variation in gene expression is a complex genetic trait. Plos Genetics 4( 4), e1000049.
doi: 10.1371/journal.pgen.1000049 pmid: 18404214
[16] Chalancon G, Ravarani CN, Balaji S, Martinez-Arias A, Aravind L, Jothi R, Babu MM ( 2012) Interplay between gene expression noise and regulatory network architecture. Trends in Genetics 28( 5), 221-232.
doi: 10.1016/j.tig.2012.01.006 pmid: 3340541
[17] Feinberg AP, Irizarry RA ( 2010) Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proceedings of the National Academy of Sciences of the United States of America 107( 1), 1757-1764.
doi: 10.1073/pnas.0906183107
[18] Magklara A, Lomvardas S ( 2013) Stochastic gene expression in mammals: lessons from olfaction. Trends in Cell Biology 23( 9), 449-456.
doi: 10.1016/j.tcb.2013.04.005 pmid: 3755038
[19] Lander ES ( 2011) Initial impact of the sequencing of the human genome. Nature 470( 7333), 187-197.
doi: 10.1038/nature09792
[20] Van Dongen J, Boomsma DI ( 2013) The evolutionary paradox and the missing heritability of schizophrenia. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics 162( 2), 122-136.
doi: 10.1002/ajmg.b.32135 pmid: 23355297
[21] Taub DR, Page J ( 2016) Molecular signatures of natural selection for polymorphic genes of the human dopaminergic and serotonergic systems: a review. Frontiers in Psychology 7, 857.
doi: 10.3389/fpsyg.2016.00857 pmid: 4896960
[22] Willi Y, Van Buskirk J, Schmid B, Fischer M ( 2007) Genetic isolation of fragmented populations is exacerbated by drift and selection. Journal of Evolutionary Biology 20( 2), 534-542.
doi: 10.1111/j.1420-9101.2006.01263.x pmid: 17305819
[23] Nesse RM, Williams GC ( 1996) Why We Get Sick: The New Science of Darwinian Medicine. New York, Vintage.
doi: 10.5015/utmj.v82i1.413
[24] Lynch M, Walsh B ( 1998) Genetics and Analysis of Quantitative Traits. Sunderland, MA: Sinauer Associates, Inc.
[25] Caspermeyer J ( 2015) An evolutionary approach reveals new clues toward understanding the roots of schizophrenia. Molecular Biology and Evolution 32(6), 1657, 1658.
doi: 10.1093/molbev/msv066 pmid: 25819396
[26] Baron-Cohen S ( 2002) The extreme male brain theory of autism. Trends in Cognitive Sciences 6( 6), 248-254.
doi: 10.1016/S1364-6613(02)01904-6
[27] Fitzgerald M ( 2004) Autism and Creativity: Is there a Link between Autism in Men and Exceptional Ability? British Medical Journal 328( 7448), 1139.
[28] Keller MC, Miller G ( 2006) Resolving the paradox of common, harmful, heritable mental disorders: which evolutionary genetic models work best? Behavioral and Brain Sciences 29( 4), 385-404.
doi: 10.1017/S0140525X06009095 pmid: 17094843
[29] Badcock C, Crespi B ( 2006) Imbalanced genomic imprinting in brain development: an evolutionary basis for the aetiology of autism. Journal of Evolutionary Biology 19( 4), 1007-1032.
doi: 10.1111/j.1420-9101.2006.01091.x pmid: 16780503
[30] Pearlson GD, Folley BS ( 2007) Schizophrenia, psychiatric genetics, and Darwinian psychiatry: an evolutionary framework. Schizophrenia Bulletin 34( 4), 722-733.
doi: 10.1093/schbul/sbm130 pmid: 18033774
[31] Reser JE ( 2011) Conceptualizing the autism spectrum in terms of natural selection and behavioral ecology: the solitary forager hypothesis. Evolutionary Psychology 9( 2), 207-238.
doi: 10.1177/147470491100900209 pmid: 22947969
[32] Dudley JT, Kim Y, Liu L, Markov GJ, Gerold K, Chen R, Butte AJ, Kumar S ( 2012) Human genomic disease variants: a neutral evolutionary explanation. Genome Research 22( 8), 1383-1394.
doi: 10.1101/gr.133702.111 pmid: 22665443
[33] Xu K, Schadt EE, Pollard KS, Roussos P, Dudley JT ( 2015) Genomic and network patterns of schizophrenia genetic variation in human evolutionary accelerated regions. Molecular Biology and Evolution 32( 5), 1148-1160.
doi: 10.1093/molbev/msv031 pmid: 4408416
[34] Gibson G, Dworkin I ( 2004) Uncovering cryptic genetic variation. Nature Reviews Genetics 5( 9), 681-690.
[35] Breen MS, Kemena C, Vlasov PK, Notredame C, Kondrashov FA ( 2012) Epistasis as the primary factor in molecular evolution. Nature 490( 7421), 535-538.
doi: 10.1038/nature11510 pmid: 23064225
[36] Paaby AB, Rockman MV ( 2014) Cryptic genetic variation: evolution’s hidden substrate. Nature Reviews Genetics 15( 4), 247-258.
doi: 10.1038/nrg3688 pmid: 24614309
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