After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data.

Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data

Uses up-to-date methods to exploit omic data

Presents methods through specific examples and computing sessions

Supplemented by a website, including code, datasets, and solutions

About the Author

Juan R. Gonz�lez is an Associate Research Professor leading the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has published extensively on methods and bioinformatics tools to detect structural variants from genomic data and to perform different types of omic association studies. Dr. Gonz�lez is the author of a large number of R and Bioconductor packages including state-of-the-art libraries such as SNPassoc or MAD that have been used to discover new susceptibility genetic factor for complex diseases.

Alejandro Caceres is a Senior Statistician in the Bioinformatics Research Group in Epidemiology at Barcelona Institute for Global Health. He has large experience in developing new statistical methods to exploit genomic, transcriptomic and epigenomic data obtained from public repositories. Dr. C�ceres is the author of several R and Bioconductor packages that have been used, for instance, to study the role of polymorphic genomic inversions in complex diseases or to investigate how the downregulation of chromosome Y may affect age-related diseases.

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