Publisher's Synopsis
Statistical genetics is a scientific field concerned with the development and application of statistical methods for drawing inferences from genetic data. The term is most commonly used in the context of human genetics. Rapid advances in genomic technologies have galvanized the development of new techniques to interpret genetics and genomics data sets. Fundamentals of Modern Statistical Genetics emphasizes on the statistical models and methods that are used to understand genetics, following the historical and recent developments of genetics. First chapter presents an efficient statistical model for genome-wide estimating and testing the cytoplasmic effect, nuclear DNA imprinting effect as well as the interaction between them under reciprocal backcross and F2 designs derived from inbred lines. Second chapter provides an overview of the range of statistical methods that can be used to answer different immunological study questions. We discuss specific aspects of immunological studies and give examples of typical scientific questions related to immunological data. We review classical bivariate and multivariate statistical techniques (factor analysis, cluster analysis, discriminant analysis) and more advanced methods aimed to explore causal relationships and illustrate their application to immunological data. Third chapter aims at identifying genes in biochemical and physiological processes to reveal genetic causes of rare and common diseases. In fourth chapter, we propose a statistical design for detecting imprinted loci that control quantitative traits based on a random set of three-generation families from a natural population in humans. This design provides a pathway for characterizing the effects of imprinted genes on a complex trait or disease at different generations and testing transgenerational changes of imprinted effects. In fifth chapter, we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Sixth chapter aims to investigate fine-scale patterns of genetic heterogeneity in modern humans from a geographic perspective, a genetic geostatistical approach framed within a geographic information system is presented. Seventh chapter reveals the underlying importance of genetic diversity and reviews useable statistical techniques for identifying and grouping genotypes for intraspecies crop improvement. In eighth chapter, we focus on rheumatoid arthritis, systemic lupus erythematosus and ankylosing spondylitis and describe some of the recently described genes that underlie these conditions and the extent to which they overlap. Ninth chapter reveals on statistical design of personalized medicine interventions. Chapter ten provides an introductory review on how genes interact to produce biological functions. In eleventh chapter, we present the challenges and methods from a statistical perspective and focus on genetic association studies. In last chapter, we propose using eQTL weights as prior information in SNP based association tests to improve test power while maintaining control of the family-wise error rate (FWER) or the false discovery rate (FDR).
