TUM Library

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

Cover; Half Title; Title Page; Copyright Page; Dedication; Contents; Preface; 1 Introduction; 1.1 Book overview; 1.2 Overview of omic data; 1.2.1 Genomic data; 1.2.1.1 Genomic SNP data; 1.2.1.2 SNP arrays; 1.2.1.3 Sequencing methods; 1.2.2 Genomic data for other structural variants; 1.2.3 Transcriptomic data; 1.2.3.1 Microarrays; 1.2.3.2 RNA-seq; 1.2.4 Epigenomic data; 1.2.5 Exposomic data; 1.3 Association studies; 1.3.1 Genome-wide association studies; 1.3.2 Whole transcriptome pro ling; 1.3.3 Epigenome-wide association studies; 1.3.4 Exposome-wide association studies

1.4 Publicly available resources1.4.1 dbGaP; 1.4.2 EGA; 1.4.3 GEO; 1.4.4 1000 Genomes; 1.4.5 GTEx; 1.4.6 TCGA; 1.4.7 Others; 1.5 Bioconductor; 1.5.1 R; 1.5.2 Omic data in Bioconductor; 1.6 Book's outline; 2 Case examples; 2.1 Chapter overview; 2.2 Reproducibility: The case for public data repositories; 2.3 Case 1: dbGaP; 2.4 Case 2: GEO; 2.5 Case 3: GTEx; 2.6 Case 4: TCGA; 2.7 Case 5: NHANES; 3 Dealing with omic data in Bioconductor; 3.1 Chapter overview; 3.2 snpMatrix; 3.3 ExpressionSet; 3.4 SummarizedExperiment; 3.5 GRanges; 3.6 RangedSummarizedExperiment; 3.7 ExposomeSet

3.8 MultiAssayExperiment3.9 MultiDataSet; 4 Genetic association studies; 4.1 Chapter overview; 4.2 Genetic association studies; 4.2.1 Analysis packages; 4.2.2 Association tests; 4.2.3 Single SNP analysis; 4.2.4 Hardy{Weinberg equilibrium; 4.2.5 SNP association analysis; 4.2.6 Gene environment and gene gene interactions; 4.3 Haplotype analysis; 4.3.1 Linkage disequilibrium heatmap plots; 4.3.2 Haplotype estimation; 4.3.3 Haplotype association; 4.3.4 Sliding window approach; 4.4 Genetic score; 4.5 Genome-wide association studies; 4.5.1 Quality control of SNPs

4.5.2 Quality control of individuals4.5.3 Population ancestry; 4.5.4 Genome-wide association analysis; 4.5.5 Adjusting for population strati cation; 4.6 Post-GWAS visualization and interpretation; 4.6.1 Genome-wide associations for imputed data; 5 Genomic variant studies; 5.1 Chapter overview; 5.2 Copy number variants; 5.2.1 CNV calling; 5.3 Single CNV association; 5.3.1 Inferring copy number status from signal data; 5.3.2 Measuring uncertainty of CNV calling; 5.3.3 Assessing the association between CNVs and traits; 5.3.3.1 Modeling association; 5.3.3.2 Global test of associations

5.3.4 Whole genome CNV analysis5.4 Genetic mosaicisms; 5.4.1 Calling genetic mosaicisms; 5.4.2 Calling the loss of chromosome Y; 5.5 Polymorphic inversions; 5.5.1 Inversion detection; 5.5.2 Inversion calling; 5.5.3 Inversion association; 6 Addressing batch e ects; 6.1 Chapter overview; 6.2 SVA; 6.3 ComBat; 7 Transcriptomic studies; 7.1 Chapter overview; 7.2 Microarray data; 7.2.1 Normalization; 7.2.2 Filter; 7.2.3 Di erential expression; 7.3 Next generation sequencing data; 7.3.1 Normalization; 7.3.2 Gene ltering; 7.3.3 Di erential expression; 8 Epigenomic studies; 8.1 Chapter overview

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