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Books on the topic 'Quantitative Genetics Model'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Silson, Roy G. A predictive additive model for quantitative genetics: Principles and results. Tring: Greenfield, 1993.

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2

Silson, Roy G. Additive gene systems: An explanation for problems in evolution and selection. Herts: Greenfield, 1988.

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3

1955-, Banzhaf Wolfgang, and Eeckman Frank H, eds. Evolution and biocomputation: Computational models of evolution. Berlin: Springer, 1995.

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4

E, Tollefson Ann, ed. Adenovirus Methods and Protocols: Volume 1: Adenoviruses, Ad Vectors, Quantitation, and Animal Models. Totowa, NJ: Humana Press, 2007.

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5

Jarnecke, Amber M., and Susan C. South. Behavior and Molecular Genetics of the Five Factor Model. Edited by Thomas A. Widiger. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199352487.013.25.

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Behavior and molecular genetics informs knowledge of the etiology, structure, and development of the Five Factor Model (FFM) of personality. Behavior genetics uses quantitative modeling to parse the relative influence of nature and nurture on phenotypes that vary within the population. Behavior genetics research on the FFM has demonstrated that each domain has a heritability (proportion of variation due to genetic influences) of 40–50%. Molecular genetic methods attempt to identify specific genetic mechanisms associated with personality variation. To date, findings from molecular genetics are tentative, with significant results failing to replicate and accounting for only a small percentage of the variance. However, newer techniques hold promise for finding the “missing heritability” of FFM and related personality domains. This chapter presents an overview of commonly used behavior and molecular genetic techniques, reviews the work that has been done on the FFM domains and facets, and offers a perspective for future directions.

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6

Walsh, Bruce, and Michael Lynch. The Infinitesimal Model and Its Extensions. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0024.

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One standard approximation in quantitative genetics is the infinitesimal model, which assumes a large number of loci, each of small effect. In such a setting, the distribution of breeding values in unselected descendants is roughly multivariate normal and most of the (short-term) change in the additive variance under selection is through Bulmer effects (the generation of linkage disequilibrium) rather than by allele-frequency change. A variety of different infinitesimal models are found in the literature, and this chapter examines these different versions and the connections between them. It also examines the theory for moving beyond the infinitesimal approximation. Finally, this chapter shows that the much-debated worry over “missing heritability” simply follows under the infinitesimal setting.

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7

Quantitative Genetics in the Wild. Oxford University Press, 2014.

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8

Charmantier, Anne, Dany Garant, and Loeske E. B. Kruuk. Quantitative Genetics in the Wild. Oxford University Press, 2014.

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9

Walsh, Bruce, and Michael Lynch. Maintenance of Quantitative Genetic Variation. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0028.

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One of the major unresolved issues in quantitative genetics is what accounts for the amount of standing genetic variation in traits. A wide range of models, all reviewed in this chapter, have been proposed, but none fit the data, either giving too much variation or too little apparent stabilizing selection.

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10

Walsh, Bruce, and Michael Lynch. Evolution and Selection of Quantitative Traits. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.001.0001.

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Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

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11

(Editor), Jeffrey C. Hall, Jay C. Dunlap (Editor), Theodore Friedmann (Editor), and Francesco Giannelli (Editor), eds. Advances in Genetics, Volume 41 (Advances in Genetics). Academic Press, 1999.

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12

(Editor), Wolfgang Banzhaf, and Frank H. Eeckman (Editor), eds. Evolution and Biocomputation: Computational Models of Evolution (Lecture Notes in Computer Science). Springer-Verlag, 1995.

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13

Láruson, Áki Jarl, and Floyd Allan Reed. Population Genetics with R. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198829539.001.0001.

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Population genetics is an inherently quantitative discipline. Because the focus of population genetics studies is usually on abstract concepts like the frequencies of genetic variants over time, it can at first glance be difficult to conceptualize and appropriately visualize. As more and more quantitative models and methods have become established in the discipline, it has become necessary for people just entering the field to quickly develop a good understanding of the many layers of complex approaches, so as to correctly interpret even basic results. An unfortunate side effect of the widespread implementation of ready-to-use quantitative software packages is that some facets of analysis can become rote, which at best might lead to implementation without the full understanding of the user and at worst, inappropriate application leading to misguided conclusions. In this book a “learning by doing” approach is employed to encourage readers to begin developing an intuitive understanding of population genetics concepts. The analytical software R, which has increasingly been the program of choice for early exposure to basic statistical programming, is freely available online, has cross-platform compatibility (Windows, Mac, and Linux all support distributions of R), and offers the potential for hands-on implementation by the students, in addition to using pre-packaged functions.

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14

(Editor), Jeffrey C. Hall, Jay C. Dunlap (Editor), Theodore Friedmann (Editor), and Francesco Giannelli (Editor), eds. Advances in Genetics, Volume 41 (Advances in Genetics). Academic Press, 1999.

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15

Walsh, Bruce, and Michael Lynch. Short-term Changes in the Variance: 2. Changes in the Environmental Variance. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0017.

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While classical quantitative genetics usually assumes that all genotypes have the same environmental variance (the assumption of homoscedasticity), in reality, genotypes can show heteroscedasticity in the environmental variance. When such variation is heritable (i.e., has an additive variance in an outbred population), then the environmental variance can change under selection. This can either be due to an indirect response (such as during directional selection on a trait), or through direct selection to increase the homogeneity of a trait (such as for increased uniformity during harvesting). This chapter reviews the existing data on the heritability of the environmental variance and examines several different genetic models for predicting its response.

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16

(Editor), Wolfgang Banzhaf, and Frank H. Eckman (Editor), eds. Evolution and Biocomputation: Computational Models of Evolution (Lecture Notes in Computer Science). Springer, 1995.

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17

Kretschmer, Tina, and Matt DeLisi. Heritability of Antisocial Behavior. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199935383.013.128.

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This chapter reviews important strands of research on the heritability of antisocial behavior and crime, including both quantitative genetic studies using twin or adoption designs as well as molecular genetic approaches. Study designs are introduced and findings discussed. Contemporary avenues including gene-environment interplay and developmental models are presented. Overall it is concluded that a significant amount of variance in antisocial behavior and crime is attributable to genetic factors but conclusive knowledge on involvement of specific genes still absent. We conclude with a discussion of usage of genetic information in the criminal justice system and note future tasks for the field of bio-criminology.

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18

Nielsen, François. Genes and Status Achievement. Edited by Rosemary L. Hopcroft. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190299323.013.22.

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A number of human traits that are predictive of socioeconomic success (e.g., intelligence, certain personality traits, and educational attainment) or reflective of success (e.g., occupational prestige and earnings) have been found to be substantially affected by individual genetic endowments; some outcomes, such as educational attainment, are also affected by the family environment, although usually to a lesser extent. The associations among status-related traits are themselves largely due to genetic causes. By reshuffling the genes of parents at each generation, sexual reproduction produces a regression of status-relevant traits of offspring toward the population mean—downward for high-status parents, upward for low-status parents—generating social mobility in an achievement-oriented society. Incorporating the quantitative genetic decomposition of trait variance into genetic, shared environmental, and nonshared environmental sources into the classic sociological model of status achievement allows for a better understanding and measurement of central social stratification concepts, such as opportunity and ascription.

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19

Xu, Shizhong. Principles of Statistical Genomics. Springer, 2014.

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20

Xu, Shizhong. Principles of Statistical Genomics. Springer London, Limited, 2012.

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21

Principles Of Statistical Genomics. Springer-Verlag New York Inc., 2008.

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22

W. Bessei, V. Lutz, J.B. Kjaer, M. Grashorn, and J. Bennewitz. Relationships between foraging and open-field activity in young chicks and feather pecking in adult birds: results of analyses using quantitative genetics and structural equation models. Verlag Eugen Ulmer, 2018. http://dx.doi.org/10.1399/eps.2018.242.

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