Science, Nature & Maths      Biological Sciences

Genes, Environments and Interactions: Evolutionary and Quantitative Genetics Brought Up-to-date

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Book Details
Language
English
Publishers
Springer; 1st ed. 2023 edition (19 Jan. 2024)
Weight
0.52 KG
Publication Date
24/02/2024
ISBN-10
3031411587
Pages
241 pages
ISBN-13
9783031411588
Dimensions
15.6 x 1.42 x 23.39 cm
SKU
9783031411588
Author Name
José M Álvarez-Castro (Author)
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Genetic effects are the core concepts from which quantitative genetics and the evolutionary synthesis emerged. The groundbreaking theory of genetic effects was first proposed over a century ago.

This book revises that theory, both conceptually and mathematically, and brings it up-to-date. The theory here compiled is supplemented with non-previously-published developments covering the broadest spectrum of simultaneously multiallelic and multilocus architectures with autosomal and sex-linked lociArbitrary interactions (dominance, gene-gene, gene-environment, gene-sex, and parent-of-origin interactions) are accounted forBoth effects of allele substitutions from the reference of individual genotypes and in the context of populations are worked outPopulations are considered regardless of any departures from equilibrium frequencies (including both departures from Hardy-Weinberg, departures from linkage equilibrium, and non-random associationsbetween/among genes and environments)All developments are derived under the same mathematical framework, so that transformations of genetic effects between different contexts are easily allowedIn brief, this book enables novel applications to current empirical paradigms (like gene-mapping and genomic prediction) while adhering to the classical conceptualization of genetic effects and variance decomposition that let quantitative genetics and the evolutionary synthesis flourish.

All relevant concepts are carefully clarified and discussed from a historical perspective. The theoretical developments presented in the book are illustrated by built-in cases and applications with real data.

Reassuringly, the adequacy of the theory here presented is corroborated based on the fundamentals of model development. .

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