With the detailed genomic information that is now becoming
available, we have a plethora of data that allows researchers to
address questions in a variety of areas. Genome-wide association
studies (GWAS) have become a vital approach to identify candidate
regions associated with complex diseases in human medicine,
production traits in agriculture, and variation in wild
populations. Genomic prediction goes a step further, attempting to
predict phenotypic variation in these traits from genomic
information. Genome-Wide Association Studies and Genomic Prediction
pulls together expert contributions to address this important area
of study. The volume begins with a section covering the phenotypes
of interest as well as design issues for GWAS, then moves on to
discuss efficient computational methods to store and handle large
datasets, quality control measures, phasing, haplotype inference,
and imputation. Later chapters deal with statistical approaches to
data analysis where the experimental objective is either to confirm
the biology by identifying genomic regions associated to a trait or
to use the data to make genomic predictions about a future
phenotypic outcome (e.g. predict onset of disease). As part of the
Methods in Molecular Biology series, chapters provide helpful,
real-world implementation advice.
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