This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.

Features: Functional and systems mapping models to characterize the genetic architecture of multiple medication processes Statistical methods for analyzing informative missing data in pharmacogenetic association studies Functional graph theory of inferring genetic interaction networks from association data Leveraging the concept of epistasis to capture its bidirectional, signed and weighted properties Modeling gene-induced cell-cell crosstalk and its impact on drug response A graph model of drug-drug interactions in combination therapies Critical methodological issues to improve pharmacogenomic research as the cornerstone of precision medicine

Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science. Statistical and Computational Pharmacogenomics presents recent developments in statistical methodology with a number of detailed worked examples that outline how these methods can be applied. This comprehensive volume provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with a thorough understanding of this complex field and how computational skills can be employed.

Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science. Statistical and Computational Pharmacogenomics presents recent developments in statistical methodology with a number of detailed worked examples that outline how these methods can be applied. This comprehensive volume provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with a thorough understanding of this complex field and how computational skills can be employed.

This book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of the DNA-based marker and phenotypic data that arise in agriculture, forestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping, and assumes a background in regression analysis and maximum likelihood approaches. The strength of this book lies in the construction of general models and algorithms for linkage analysis, as well as in QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops.