Meta-analysis and Combining Information in Genetics and Genomics

Rudy Guerra, Darlene R. Goldstein

June 14, 2017 by Chapman and Hall/CRC
Reference - 360 Pages - 55 B/W Illustrations
ISBN 9781138116115 - CAT# K35462
Series: Chapman & Hall/CRC Mathematical and Computational Biology


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  • Presents a wide-ranging survey of information combination methodology specific to genetic and genomic studies
  • Examines the problems that occur when identifying common disease or quantitative trait loci across different studies
  • Explores the combination of multiple, heterogeneous data sources, including genotype, gene expression, protein, and DNA sequence data
  • Covers both frequentist and Bayesian frameworks


Novel Techniques for Analyzing and Combining Data from Modern Biological Studies
Broadens the Traditional Definition of Meta-Analysis

With the diversity of data and meta-data now available, there is increased interest in analyzing multiple studies beyond statistical approaches of formal meta-analysis. Covering an extensive range of quantitative information combination methods, Meta-analysis and Combining Information in Genetics and Genomics looks at how to analyze multiple studies from a broad perspective.

After presenting the basic ideas and tools of meta-analysis, the book addresses the combination of similar data types: genotype data from genome-wide linkage scans and data derived from microarray gene expression experiments. The expert contributors show how some data combination problems can arise even within the same basic framework and offer solutions to these problems. They also discuss the combined analysis of different data types, giving readers an opportunity to see data combination approaches in action across a wide variety of genome-scale investigations.

As heterogeneous data sets become more common, biological understanding will be significantly aided by jointly analyzing such data using fundamentally sound statistical methodology. This book provides many novel techniques for analyzing data from modern biological studies that involve multiple data sets, either of the same type or multiple data sources.

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