An Introduction to Statistical Genetic Data Analysis

Name: An Introduction to Statistical Genetic Data Analysis
Brand: MIT Press
Price: 38.99 EUR
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Melinda C. Mills Nicola Barban Felix C. Tropf(Autor*in)

MIT Press

Erschienen am 18. Februar 2020

432 Seiten

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Inhalt

Intro
Title Page
Copyright
Table of Contents
Preface
I: Foundations
1. Introduction: Fundamental Concepts and the Human Genome
Objectives
1.1 Introduction
1.1.1 Motivation and aim of this book
1.1.2 Overview of topics covered in this book
1.1.3 What are DNA, the genome, a gene, and a chromosome?
1.2 Mendel's laws, sexual reproduction, and genetic recombination
1.3 Genetic polymorphisms
1.3.1 Alleles, single-nucleotide polymorphisms (SNPs), and minor allele frequency (MAF)
1.3.2 Monogenic, polygenic, and omnigenic effects
1.4 From genes to protein and the central dogma of molecular biology
1.4.1 From genes to protein: Genes, amino acids, nucleotides, and proteins
1.4.2 The central dogma of molecular biology: Transcription and translation
1.5 Homozygous and heterozygous alleles, dominant and recessive traits
1.6 Heritability
1.6.1 Defining heritability: Broad- and narrow-sense heritability
1.6.2 Common misconceptions about heritability
1.6.3 Twin, SNP, and GWAS heritability
1.6.4 Missing and hidden heritability
1.7 Conclusion
Exercises
Further reading and resources
References
2. A Statistical Primer for Genetic Data Analysis
Objectives
2.1 Introduction
2.2 Basic statistical concepts
2.2.1 Mean, standard deviation, and variance
2.2.2 Covariance and the variance-covariance matrix
2.3 Statistical models
2.3.1 Regression models
2.3.2 The null and alternative hypothesis and significance thresholds
2.4 Correlation, causation, and multivariate causal models
2.4.1 Correlation versus causation
2.4.2 Multivariate causal models
2.5 Fixed-effects models, random-effects models, and mixed models
2.6 Replication of results and overfitting
2.7 Conclusion
Exercises
Further reading
Software for mixed-model analyses
Appendix
References
3. A Primer in Human Evolution
Objectives
3.1 Introduction
3.2 Human dispersal out of Africa
3.3 Population structure and stratification
3.3.1 Population structure, genetic admixture, and Principal Component Analysis (PCA)
3.3.2 Common misnomers of population structure: Ancestry is not race
3.3.3 Genetic scores cannot be transferred across ancestry groups
3.3.4 How genes mirror geography
3.4 Human evolution, selection, and adaptation
3.4.1 Evolution, fitness, and natural selection
3.4.2 Genetic drift
3.5 The Hardy-Weinberg equilibrium
3.5.1 Assumptions of the HWE
3.5.2 Understanding the notation of the HWE
3.6 Linkage disequilibrium and haplotype blocks
3.7 Conclusion
Exercises
Further reading and resources
References
4. Genome-Wide Association Studies
Objectives
4.1 Introduction and background
4.2 GWAS research design and meta-analysis
4.2.1 GWAS research design
4.2.2 Data analysis plan
4.2.3 Meta-analysis
4.3 Statistical inference, methods, and heterogeneity
4.3.1 Nature of the phenotype
4.3.2 P-values and Z-scores
4.3.3 Correcting for multiple testing in a GWAS
4.3.4 Manhattan plots
4.3.5 Evaluating dichotomous versus quantitative traits
4.3.6 Fixed-effects versus random-effects models
4.3.7 Weighting, false discovery rate (FDR), and imputation
4.3.8 Sources of heterogeneity
4.4 Quality control (QC) of genetic data
4.5 The NHGRI-EBI GWAS Catalog
4.5.1 What is the NHGRI-EBI GWAS Catalog?
4.5.2 A brief history of the GWAS
4.5.3 Lack of diversity in GWASs
4.6 Conclusion and future directions
Exercises
Further reading
References
5. Introduction to Polygenic Scores and Genetic Architecture
Objectives
5.1 Introduction
5.1.1 What is a polygenic score?
5.1.2 The origins of polygenic scores
5.2 Construction of polygenic scores
5.2.1 Large sample sizes required in GWAS discovery
5.2.2 Selection of SNPs to include
5.3 Validation and prediction of polygenic scores
5.3.1 Independent target sample
5.3.2 Similar ancestry in target sample
5.3.3 Relatedness, population stratification, and differential bias
5.3.4 Variance explained only by common genetic markers missing rare variants
5.3.5 Missing and hidden heritability in prediction of phenotypes from genetic markers (SNPs)
5.3.6 Trade-off between prediction and understanding biological mechanisms
5.4 Shared genetic architecture of phenotypes
5.4.1 Predicting other phenotypes
5.4.2 Phenotypic and genetic correlation
5.4.3 Pleiotropy
5.4.4 Multitrait analysis
5.5 Causal modeling with polygenic scores
5.5.1 Genetic confounding
5.5.2 Mendelian Randomization
5.5.3 Controlling for confounders
5.5.4 Gene-environment interaction and heterogeneity
5.6 Conclusion
Exercises
Further reading
References
6. Gene-Environment Interplay
Objectives
6.1 Introduction: What is gene-environment (G×E) interplay?
6.2 Defining the environment in G×E research
6.2.1 Nature and scope of E: Multilevel, multidomain, and multitemporal
6.2.2 Interdependence of environmental risk factors
6.3 A brief history of G×E research
6.3.1 Classic approaches
6.3.2 Candidate gene cG×E approaches
6.3.3 Genome-wide polygenic score G×E approaches
6.4 Conceptual G×E models
6.4.1 Diathesis-stress, vulnerability, or contextual triggering model
6.4.2 Bioecological or social compensation model
6.4.3 Differential susceptibility model
6.4.4 Social control or social push model
6.4.5 Research designs to study G×E
6.5 Gene-environment correlation (rGE)
6.5.1 Passive gene-environment correlation (rGE)
6.5.2 Evocative (or reactive) rGE
6.5.3 Active rGE
6.5.4 Why are models of rGE important?
6.5.5 Research designs to study rGE
6.6 Conclusion and future directions
6.6.1 Why haven't many G×Es been identified?
Exercises
Further reading
References
II: Working with Genetic Data
7. Genetic Data and Analytical Challenges
Objectives
7.1 Introduction
7.2 Genotyping and sequencing array
7.2.1 Genotyping and sequencing technologies
7.2.2 Linkage disequilibrium and imputation
7.2.3 Limitations of genotyping arrays and next-generation sequencing
7.2.4 Drop in costs per genome
7.3 Overview of human genetic data for analysis
7.3.1 Prominently used genetic data
7.3.2 Sources that archive and distribute data
7.3.3 Obtaining GWAS summary statistics
7.4 Different formats in genomics data
7.4.1 Genomics data is big data
7.4.2 PLINK software and genotype formats
7.4.3 PLINK binary files
7.5 Genetic formats for imputed data
7.5.1 PLINK 2.0
7.5.2 Oxford file formats
7.5.3 The variant call format (VCF)
7.6 Data used in this book
7.7 Data transfer, storage, size, and computing power
7.7.1 Data storage
7.7.2 Data sharing, transfer across borders, and cloud storage
7.7.3 Size of data and computational power
7.8 Conclusion
Exercises
Further reading and resources
References
8. Working with Genetic Data, Part I: Data Management, Descriptive Statistics, and Quality Control
Objectives
8.1 Introduction: Working with genetic data
8.2 Getting started with PLINK
8.2.1 The command line
8.2.2 Calling PLINK and the PLINK command line
8.2.3 Running scripts in terminal
8.2.4 Opening PLINK files
8.2.5 Recode binary files to create new readable dataset with .ped and .map files
8.2.6 Import data from other formats
8.3 Data management
8.3.1 Select individuals and markers
8.3.2 Merge different genetic files and attaching a phenotype
8.4 Descriptive statistics
8.4.1 Allele frequency
8.4.2 Missing values
8.5 Quality control of genetic data
8.5.1 Per-individual QC
8.5.2 Per-marker QC
8.5.3 Genome-wide association meta-analysis QC
8.6 Conclusion
Exercises
Further reading and resources
References
9. Working with Genetic Data, Part II: Association Analysis, Population Stratification, and Genetic Relatedness
Objectives
9.1 Introduction
9.1.1 Aim of this chapter
9.1.2 Data and computer programs used in this chapter
9.2 Association analysis
9.3 Linkage disequilibrium
9.4 Population stratification
9.5 Genetic relatedness
9.6 Relatedness matrix and heritability with GCTA
9.7 Conclusion
Exercises
Further reading and resources
References
10. An Applied Guide to Creating and Validating Polygenic Scores
Objectives
10.1 Introduction
10.1.1 Creating a polygenic score
10.1.2 Data used in this chapter
10.2 How to construct a score with selected variants (monogenic)
10.3 Pruning and thresholding method
10.4 How to calculate a polygenic score using PRSice 2.0
10.5 Validating the PGS
10.6 LDpred: Accounting for LD in polygenic score calculations
10.6.1 Introduction and three steps
10.7 Conclusion
Exercises
Further reading and resources
References
III: Applications and Advanced Topics
11. Polygenic Score and Gene-Environment Interaction (G×E) Applications
Objectives
11.1 Introduction
11.2 Polygenic score applications: (Cross-trait) prediction and confounding
11.2.1 Out-of-sample prediction
11.2.2 Cross-trait prediction and genetic covariation
11.2.3 Genetic confounding
11.3 Gene-environment interaction
11.3.1 Application: BMI × birth cohort
11.4 Challenges in gene-environment interaction research
11.5 Conclusion and future directions
Exercises
Further reading
References
12. Applying Genome-Wide Association Results
Objectives
12.1 Introduction
12.2 Plotting association results
12.2.1 Manhattan plots
12.1.2 Regional association plots
12.1.3 Quantile-Quantile plots and the ? statistic
12.2 Estimating heritability from summary statistics
12.3 Estimating genetic correlations from summary statistics
12.4 MTAG: Multi-Trait Analysis of Genome-wide association summary statistics
12.5 Conclusion
Exercises
Further reading and resources
References
13. Mendelian Randomization and Instrumental Variables
Objectives
13.1 Introduction
13.2 Randomized control trials and causality
13.3 Mendelian Randomization
13.4 Instrumental variables and Mendelian Randomization
13.4.1 The IV model in an MR framework
13.4.2 Violation of statistical assumptions of the IV approach
13.5 Extensions of standard MR
13.5.1 Using multiple markers as independent instruments
13.5.2 Using polygenic scores as IVs
13.5.3 Bidirectional MR analyses
13.6 Applications of MR
13.6.1 Consequences of alcohol consumption
13.6.2 Body mass index and mortality
13.6.3 Causes of dementia and Alzheimer's disease
13.7 Conclusion
Exercises
Further reading
References
14. Ethical Issues in Genomics Research
Objectives
14.1 Introduction
14.2 Genetics is not destiny: Genetic determinism
14.2.1 Variation in traits and ability to use individual PGSs as predictors
14.2.2 Heritability and missing heritability
14.3 Clinical use of PGSs
14.3.1 Genetics and family history
14.3.2 Genetic scores for screening, intervention, and life planning
14.3.3 Pharmacogenetics
14.3.4 Public understanding of genetic information and information risks
14.4 Lack of diversity in genomics
14.4.1 Lack of diversity in GWASs
14.4.2 European ancestry bias related to PGS construction
14.5 Privacy, consent, legal issues, insurance, and General Data Protection Regulation
14.5.1 Privacy in the age of public genetics: Solving crimes and finding people
14.5.2 The changing nature of informed consent in genomic research
14.5.3 Insurance and genetics
14.5.4 GDPR and genetics
14.6 Conclusion and future directions
Further reading and resources
References
15. Conclusions and Future Directions
15.1 Summary and reflection
15.2 Future directions
References
Appendix 1: Software Used in This Book
A1.1 Introduction
A1.2 RStudio and R
A1.3 PLINK
A1.4 GCTA
A1.5 PRSice
A1.6 Python
A1.6.1 How to switch from Python 3 to Python 2
A1.6.2 Installing packages in Python
A1.7 Git
A1.8 LDpred
A1.9 LDSC
A1.10 MTAG
A1.11 Using Windows for this book
References
Appendix 2: Data Used in This Book
A2.1 Introduction
A2.2 Description of simulated data
A2.3 Health and Retirement Study
A2.4 Data used by chapter
References
Glossary
Index

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