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.
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