Omic Association Studies with R and Bioconductor

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A01=Alejandro Cáceres
A01=Juan R. González
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Air Pollutant
Author_Alejandro Cáceres
Author_Juan R. González
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bioconductor community
Bioinformatics
Category1=Non-Fiction
Category=PBT
Category=PS
Category=UFM
Cell Type Composition
COP=United Kingdom
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DNA Methylation
Epigenome Wide Association Studies
Epigenomic
Epigenomic Data
eq_computing
eq_isMigrated=2
eq_non-fiction
eq_science
exposomic data
Freedom AIC
Freedom Residual Deviance
Genetic Score
Genome-wide association studies
Genomic
Geo Accession Number GSE8919
GWAS Catalog
Language_English
Metadata Columns
Methylomic Data
Min 1Q Median 3Q Max
NaN NaN
Negative ER
Omic Data
omic data analysis
Omic Datasets
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phenomic data
Post Bronchodilator FEV1
Price_€100 and above
PS=Active
R repositories
Reference Genome
RNA Seq Data
SNP Association
SNP Genotype
softlaunch
Surrogate Variable Analysis
Traits of interest
Transcriptomic Differences
Transcritomic

Product details

  • ISBN 9781138340565
  • Weight: 725g
  • Dimensions: 156 x 234mm
  • Publication Date: 11 Jun 2019
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
  • Language: English
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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

Juan R. González 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. González 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. Cáceres 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.