This book offers a unified resource on heterogeneity in statistical genetics. It provides an overview of past developments, as well as a comprehensive look at new methodological techniques and worked examples that are didactically useful. The book begins with an introduction to heterogeneity as mixture models used in statistics. It highlights formulas for important genetic concepts that are used extensively throughout the book. Next it looks at genomic misclassification in statistical genetics and includes examples of how this may arise in practice. The book then gives an overview of phenotype misclassification in statistical genetics. It covers association tests that can be used to allow for heterogeneity. In addition, the book presents a genetic association analysis of longitudinal data. The volume concludes by examining how to design genetic linkage and association studies that maintain desired statistical power in the presence of mixtures.This work surveys several high profile applications of heterogeneity and mixture models in these areas. It should appeal to established investigators active in several fields of statistics and genetics, as well as advanced students that are interested in the topic.weiterlesen