Updated 2nd edition just published: Statistical Learning in Genetics - An Introduction Using R
In September 2023, one of the grand old men of statistical genetics, professor emeritus Daniel Sorensen from Center for Quantitative Genetics and Genomics (QGG), published the 1st edition of his latest book, in which he is sharing his experience from a long career in research. Now an updated 2nd edition has been published, which benefits from many clarifications and extensions of themes discussed in the first edition.
The overall structure on the book, however, is the same. It introduces computer-based methods for the analysis of genomic data and is divided in three parts.
- Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models.
- Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods.
- Part III consists of exercises and their solutions.
The target group is numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, the book contains considerably more detail in explanations and derivations.
Daniel Sorensen Statistical Learning in Genetics - An Introduction Using R. Springer Publishing. The 2nd edition has just been released.
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