Basics of Modern Statistical Genetics

Over the past 40 years we have seen significant advances in the use of genetics to study subjects from evolution and ecology to medicine and human health. Early protein and restriction analysis studies have given way to high-throughput sequencing and genome-wide studies. With these advances has come the need for increasingly complex statistical analyses. This has come in the form of multifaceted algorithms such as eigenanalysis and Bayesian analysis. Bayesian analysis has become one of the most widely used techniques in modern statistical genetics as the use of Markov chain Monte Carlo (MCMC) methods have made it relatively quick and simple to incorporate prior information into the model. Gene mapping studies have also become increasingly widespread as candidate genes are being found for a number of phenotypes, conditions, and diseases. In this book we look at a number of novel techniques used to study populations, phenotypes, and gene expression in organisms as diverse as humans and plants. We end with three case studies applying statistical genetics to identify genes associated with human diseases.