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The book gives an overview of developments in Quantitative Genetics
and variance component analysis in an era of Big Data and Sequenced
Genomes. It provides a detailed description of a direct method of
estimation that will be a useful means of extracting information
from a large set of data that was inconceivable 10 to 20 years
ago.The book is a combination of a history of variance component
analysis and a forward looking view as to how direct methods of
estimation arise from the availability of big data sets and
sequenced genomes of each individual in the sample.Many papers and
books on quantitative genetics versions of the general linear model
from statistics are useful for analyzing the data, using relatively
small sets of data. In this book, new methods of direct estimation
are introduced and analyzed that are appropriate for an era of big
sets of data and sequences genomes. These direct methods of
estimation are based on taking conditional expectations rather the
methods of least squares that characterize many applications of the
general linear model of statistics.
The book gives an overview of developments in Quantitative Genetics
and variance component analysis in an era of Big Data and Sequenced
Genomes. It provides a detailed description of a direct method of
estimation that will be a useful means of extracting information
from a large set of data that was inconceivable 10 to 20 years
ago.The book is a combination of a history of variance component
analysis and a forward looking view as to how direct methods of
estimation arise from the availability of big data sets and
sequenced genomes of each individual in the sample.Many papers and
books on quantitative genetics versions of the general linear model
from statistics are useful for analyzing the data, using relatively
small sets of data. In this book, new methods of direct estimation
are introduced and analyzed that are appropriate for an era of big
sets of data and sequences genomes. These direct methods of
estimation are based on taking conditional expectations rather the
methods of least squares that characterize many applications of the
general linear model of statistics.
The scope of this book is the field of evolutionary genetics. The
book contains new methods for simulating evolution at the genomic
level. It sets out applications using up to date Monte Carlo
simulation methods applied in classical population genetics, and
sets out new fields of quantifying mutation and selection at the
Mendelian level. A serious limitation of Wright-Fisher process, the
assumption that population size is constant, motivated the
introduction of self regulating branching processes in this book.
While providing a short review of the principles of probability and
its application and using computer intensive methods whilst
applying these principles, this book explains how it is possible to
derive new formulas expressed in terms of matrix algebra providing
new insights into the classical Wright-Fisher processes of
evolutionary genetics. Also covered are the development of new
methods for studying genetics and evolution, simulating nucleotide
substitutions of a DNA molecule and on self regulating branching
processes. Components of natural selection are studied in terms of
reproductive success of each genotype whilst also studying the
differential ability of genotypes to compete for resources and
sexual selection. The concept of the gene is also reviewed in this
book, and it provides a current definition of a gene based on very
recent experiments with micro-array technologies. A development of
stochastic models for simulating the evolution of model genomes
concludes the studies in this book. Deserving of a place on the
book shelves of workers in biomathematics, applied probability,
stochastic processes and statistics, as well as in bioinformatics
and phylogenetics, it will also be relevant to those interested in
computer simulation, and evolutionary biologists interested in
quantitative methods.
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