Uploading a GWAS | Fetching your results | Interpreting your results | Study extractions | Coloc groups and coloc pairs | Deleting an upload

Genetic effects | Model 1 - simple linear relationship between x and y with large numbers of instruments of small effect | Model 2 - Heterogeneous treatment effect of x on y based on covariate | Model 3 - The interacting variable from (2) is proxied by a large number of proxies | Model 4 - Heterogeneous treatment effect at the instrument level

Data setup | Running Tryx | Step-by-step analysis | Significant candidate traits | Adjustment | Visualisation | Adjustment plot | Network plot | Volcano plot | Manhattan plot

Introduction | Initialise data | Check phenotype scales across ancestries | Extract instruments | Evaluate instrument heterogeneity across populations | Extract outcome data | Harmonise exposure and outcome data | Perform analysis using raw instruments | Perform regional scan to obtain LD agnostic instruments | Re-perform analysis using regional instruments | Evaluate similarity of pleiotropy across ancestry | MR GxE | Saving and loading data | Overview of methods

Generating the data manually | Generating the data using CAMERA_local

About midoc | Step 1 Specify the analysis and missingness models using a directed acyclic graph | Step 2 Check whether complete records analysis is likely to be a valid strategy | Step 3 Check whether multiple imputation is likely to be a valid strategy | Step 4 Check that all relationships are correctly specified | Step 5 Perform MI using the proposed imputation model | Illustration using our worked example

Introduction | Fit MR-LDP using simulated data | Fit MR-LDP using CAD-CAD study. | Fit MR-LDP using available LD information.

Introduction | MR methods | Sensitivity analyses | Heterogeneity statistics | Horizontal pleiotropy | Single SNP analysis | Leave-one-out analysis | Plots | Scatter plot | Forest plot | Forest plot with categories | RadialMR outlier example | MR-PRESSO outlier example | Leave-one-out plot | Funnel plot | 1-to-many forest plot | Step 1: Generate 1-to-many MR results | Step 2: Make the 1-to-many forest plot | Example 1. Effect of multiple risk factors on coronary heart disease | Example 2. MR results for multiple MR methods grouped by multiple exposures | Example 3. Stratify results on a grouping variable | Example 4. Effect of BMI on 103 diseases | MR-RAPS: Many weak instruments analysis | MR-GRIP | Reports | MR Steiger directionality test | Multivariable MR | Note about multivariable methods | Note about visualisation | Using your own summary data | From local files | From data frames | Mixing local and OpenGWAS data | Converting to MVMR format | MR estimates when instruments are correlated | MR-MoE: Using a mixture of experts machine learning approach | Post MR results management | Split outcome names | Split exposure names | Generate odds ratios with 95% confidence intervals | Subset on method | Combine all results | References

Comparison fits

Run fsw() on ivreg() model object | Run fsw() on AER::ivreg() model object | Run fsw() on estimatr::iv_robust() model object | Run fsw() on fixest::feols() model object | Comparison with F-statistic from lfe package | Comparison with output from ivreg2

Introduction | Reading in from a file | Example 1: The default column names are used | Example 2: The text file has non-default column names | Using an existing data frame | Obtaining instruments from existing catalogues | GWAS catalog | Metabolites | Proteins | Gene expression levels | DNA methylation levels | IEU OpenGWAS database | Clumping

Introduction | Dealing with strand issues | Correct, unambiguous | Incorrect reference, unambiguous | Ambiguous | Palindromic SNP, inferrable | Palindromic SNP, not inferrable | Options | Drop duplicate exposure-outcome summary sets

Background | Installation | Overview | Authentication | References

What has changed | Dataset IDs | Authentication | UKBiobank data has been curated | All data is now harmonised | LD reference panel is now harmonised | Instrument lists are up-to-date | dbSNP rs IDs | Everything is faster | What is new | Browse available datasets online | Chromosome and position | INDELs are retained | Multi-allelic variants are retained | More data | Error messages are more informative | Easier programmatic access to the database | Local LD operations | Access the data directly | Connect the data to different analytical tools | Key links | How to request new data

Available studies in IEU GWAS database | Extracting particular SNPs from particular studies | LD proxies | Using local GWAS summary data | Outcome data format | More advanced use of local data

Introduction | Features of the bpbounds package | Vitamin A supplementation example | Entering the data as conditional probabilities | Treating the data as bivariate | Mendelian randomization example | Simulated example that does not satisfy the IV conditions | Conclusion | References

Create Omiprep object | Run batch normalisation | Accessing data | Raw input data | Batch normalised data | Now any quality control step should be run on the "batch_normalised" layer. | QC data | Quick view of omiprep object | data array layers | omiprep summary

Setup | load the omiprep library | Read in the data and make a Omiprep object | Run the quality control | Export Omiprep | Export to Comets format | Export to Metaboanalyst format

Create Omiprep object | Summary of Omiprep object | Run standard quality control | Feature Summary | View feature summary from the QC pipeline | Manually run feature summary | Table of feature summary | Run feature summary on subset | Table of feature summary for subset | Additional feature_summary() attributes | hierarchical cluster dendrogram | Run sample & feature summaries together | Table of feature summary from summarise() function

Import example metabolomics data | Identify the Xenobiotics to exclude from the QC steps | QC the example Metabolon data | Generate the Omiprep report

Import Metabolon data | Quick look at data structure of the imported data | Create Omiprep object | Quick summary of the Omiprep object | Identify the Xenobiotics to exclude from the QC steps | QC Metabolon | Quick summary of the Omiprep object following QC

Identify the Xenobiotics to exclude from the QC steps | Run the quality control pipeline | View a summary of the Omiprep object

Create Omiprep object | Summary of Omiprep object | Run standard quality control | Sample Summary | View sample summary from the QC pipeline | Manually run sample summary | Table of sample summary | Run sample summary on subset | Table of sample summary on subset | Principal Componet Analysis | View PCs from the QC pipeline | Manually run PCA analysis | Table of PCA analysis results | PCA plot | Additional pc_and_outliers() attributes | Run sample & feature summaries together

Covariance and Phenotypic correlations in MVMR | References

Overview | Workflow | Step 1: Obtain summary data | Estimating pairwise covariances between SNP associations | Step 2: Format summary data | Step 3: Test for weak instruments | Step 4: Test for horizontal pleiotropy using conventional Q-statistic estimation | Step 5: Estimate causal effects | Step 6: Robust causal effect estimation. | References

Authentication | Deprecated Google authentication | Allowance | General API queries | Get API status | Get list of all available studies | Get list of a specific study | Extract particular associations from particular studies | Get the tophits from a study | Perform PheWAS | LD clumping | LD matrix | Variant information | Extracting GWAS summary data based on gene region | 1000 genomes annotations

Introduction | Getting started | Understanding info | Variant search returns proxy variants | Diving in | Trait example | How study_extractions, coloc_groups, and rare_results relate | Gene example | Coloc pairs example | Target–indication prioritisation workflow: Alzheimer's disease | Step 1: Genes mapped to Alzheimer's disease | Step 2: Rank by gene-level pleiotropy | Step 3: Variant-level specificity and consistency across variants and rare results | Diving deeper: TREM2 | Putting it together | Accessing summary statistics

Import Nightingale data directly into a Omiprep object | Quick look at data structure of the imported data | QC Nightingale | Quick summary of the Omiprep object following QC

Import Olink data | Quick look to identify the types of data imported | Create Omiprep object | Quick summary of the Omiprep object | QC Olink data | Quick summary of the Omiprep object following QC

Import SomaLogic data | Quick look to identify the types of data imported | Create Omiprep object | Quick summary of the Omiprep object | QC Olink data | Quick summary of the Omiprep object following QC

Upper limit of quantification (skewness) | 1) Simulate Data (1000 Samples x 500 Features) | 2) What skewness means (per feature) | What the threshold means | 3) Apply a skewness filtering rule | 4) Post-filtering impact on distributions | 4a) Feature-skewness distribution before vs after filtering | 4b) Feature retention by simulated type | 4c) Skewness profiles by feature type (pre vs post)

Overview | Quick start | Using generate_summary() | Using generate_individual() | Built-in presets | Plotting simulated data | Output

Creating a toy data set | Implementing MR-SimSS with mr_simss | Comparing MR-SimSS with classical Two-Sample MR methods | Additional mr_simss parameters | Performing mr.simss with real data | Same-trait empirical analysis with TwoSampleMR package

Trait colocalisations | Genome view | Complex trait colocalisations | Investigating a specific colocalisation group | TMPRSS6: eQTL/mQTL/sQTL and physiological measures | Region plot: scaled LBF values across all studies in the coloc group | Trait clustering

1. Constrained Instrumental Variable Methods | 2. Package Contents | 2.1 Datasets | 2.2 MR Methods | 3. Example

As a Contributor | Prerequisites | Summary stats file format | OpenGWAS ID | Setup | Upload a single dataset | 1. Create metadata (two methods) | 1a. Using the web portal | 1b. Using R/GwasDataImport alone | 2. Modify the metadata (only when necessary) | 3. Format the file and upload for QC | 4. Check QC pipeline state and report, and submit for approval | Upload in bulk | What's next | Known issues

1. Download the summary dataset | 2. Initialise | 3. Specify columns in dataset | 4. Input the meta-data | 5. Check that the meta-data are correct | 6. Process the summary dataset | 6. Upload meta-data | 7. Upload processed summary dataset | 8. Wait for the API pipeline to convert to VCF format, annotate and create a report | Summary | Example

Installation | Handling ComplexHeatmap installation errors

Plot | Shared Axis Limits

Lines and Bars | Legends | 1. Track Legend (legend_track) | 2. Section Legend (legend_section)

Circos Plots | Why Circular? | EpiViz | How it works

Introduction | 1. Load CHD Dataset | 2. Merge with RoB Assessments | 3. Format & Validate Input | 4. Add Indirectness & Adjust Effect Estimate for It | 5. Apply Bias Priors & Estimate Adjusted Effects | 5.1 Define Custom Priors | 5.2 Append priors and prepare data, and estimate adjusted effects | 6. Final data adjusted for both bias and indirectness | 7. Generate Bias-Adjusted Plot

Import Nightingale data directly into a Metaboprep object | Quick look at data structure of the imported data | QC Nightingale | Quick summary of the metaboprep object following QC

Absolute directions of bias/indirectness | Point estimate below NULL | Point estimate above NULL - Bias towards the NULL | Adding the adjustment values | Example | Additive - Favours comparator - right - positive sign | Additive - Favours intervention - left - negative sign | Proportional - Point estimate above NULL - Towards the NULL - left - negative sign | Proportional - Point estimate below NULL - Away from NULL - right - positive sign | Proportional - Point estimate below NULL - Towards the NULL - right - positive sign | Proportional - Point estimate below NULL - Away from NULL - left - negative sign

Introduction | Step 1: Create the example dataset | Step 2: Convert to long format | Step 3: Add absolute direction | Step 4: Append bias priors

Introduction | Example Data | Launch Interactive App | Conclusion

Introduction | Create example dataset | Define Default Bias and Indirectness Priors | Run bias adjustment | Stricter sensitivity scenario | Compare results | Conclusion

Import SomaLogic data | Quick look to identify the types of data imported | Create Metaboprep object | Quick summary of the metaboprep object | QC Olink data | Quick summary of the metaboprep object following QC

Create Metaboprep object | Summary of Metaboprep object | Run sample summary | Table of sample summary | Run sample summary on subset | Table of sample summary on subset | Run PCA analysis | Table of PCA analysis results | Additional attributes | Run sample & feature summaries together

Create Metaboprep object | Summary of Metaboprep object | Run feature summary | Table of feature summary | Feature summary attributes | Run feature summary on subset | Table of feature summary for subset | Run sample & feature summaries together

Import example metabolomics data | Identify the Xenobiotics to exclude from the QC steps | QC the example Metabolon data | Generate the metaboprep report

Import Metabolon data | Quick look at data structure of the imported data | Create Metaboprep object | Quick summary of the metaboprep object | Identify the Xenobiotics to exclude from the QC steps | QC Metabolon | Quick summary of the metaboprep object following QC

Create Metaboprep object | Run batch normalisation | Accessing data | Raw input data | Batch normlalised data

Setup | load the metaboprep library | Read in the data and make a Metaboprep object | Run the quality control | Export Metaboprep

Import Olink data | Quick look to identify the types of data imported | Create Metaboprep object | Quick summary of the metaboprep object | QC Olink data | Quick summary of the metaboprep object following QC

Introduction | Intuition for MR-SimSS framework | Individual-level: | How does MR-SimSS work? | Assumptions made by MR-SimSS | Notation

Run the quality control pipeline | View a summary of the Metaboprep object

Simulate Data | Outcome Regression | Inverse Propensity Weighting | Doubly Robust Approach | Maximum Likelihood | Comparison of Results

Simulation | Fitting Models | Other families

Example (Poisson distribution) | Treatment model | Outcome model | Custom Link Functions

Families

Heterogeneous treatment effects | Example | Continuous case

Introduction | Dataset | Model | Automatic parameter generation | Other copula families | Strength of relationships

About finemapr | Tool-independent scheme of analysis workflow | Installation | Load packages | Example data | Explore z-scores | Run tools | Run FINEMAP | Run CAVIAR | Run PAINTOR | Conclusions

Using TwoSampleMR | Using GWAS VCF files | Other options | Clumping vcf files | Extracting outcome data with LD proxies | Further MR methods | Bluecrystal4 users

ieugwasr | gwasvcf

External tools | Reading in everything | Converting to simple dataframes | Reading in with filters | Indexing rsid values | Indexing p-values | A note about chrompos | LD proxies | Using an LD reference panel | Compiling a list of tagging variants | Creating the VCF object from a data frame | Creating a gwasglue2 SummarySet object from a vcf file

LD clumping | LD matrix | LD proxies

Roadmap | Choice of language | Representation of GWAS data | GWAS data specification | Data type considerations | S7 | Simple S7 class | S7 property validation | S7 constructor | GWAS class | Load a GWAS | Run an analysis | MR object | Other things | Harmonisation | RSID mapping | Which subsetting files method? | genepi.utils::chrpos_to_rsid | RSID annotation for 10M rows | Index event bias | Other packages | MungeSumstats | Future directions | Discussion

Manhattan | Miami | EAF plot

Winner's Curse in MR | What is Winner's Curse bias? | How and why does Winner's Curse bias affect MR causal effect estimates? | Empirical evidence for the impact of Winner's Curse on MR estimates | How is Winner's Curse bias typically avoided in summary-level MR studies? | Weak Instrument Bias in MR | What is weak instrument bias? | How does weak instrument bias impact MR causal effect estimates? | Summary

Incorporating discrete variables | Categorical and Ordinal Variables

Set Up the Model

Overview | Workflow

MendelianRandomization package | The Input | The data | Univariable estimation methods | Inverse-variance weighted method | Median-based method | MR-Egger method | Maximum likelihood method | Mode-based estimation method | Heterogeneity-penalized method | Other univariable estimation methods | Multivariable Mendelian randomization | Summaries of multiple methods | Graphical summaries of results | Applied to MRInput object - display of data | Applied to MRMVInput object - display of data | Applied to MRAll object - comparison of estimates | Other graphical functions | Extracting association estimates from PhenoScanner | Final note of caution

Introduction | Introduction to sim_mv | Basic Usage | Input | Output | Simplest Usage | Specifying Causal Relationships Between Traits | Specifying Allele Frequencies | Simulating Data with LD | LD-Pruning, LD-Proxies, and LD Matrix Extraction | Specifying Sample Size, Sample Overlap, and Environmental Correlation | Specifying Sample Size and Sample Overlap | Using Sample Size 0 to Omit Traits | Understanding Genetic and Environmental Covariance

Overview | Functions | Data | Plots | Manhattan plot | PheWAS plot | QQ plot | Simple | Categories | Regional plot | Regional plot | Stacked regional plot

Genetic architecture | Linkage disequilibrium | Sample overlap | Synthesising sample overlap, LD and architecture

Setup

Slope-hunter | Background | Run | Visualise | The b-slope | Overall assessment - multiple methods | Applying the correction factor

Introduction | Default Behavior | Controlling which Variants are Effect Variants | Controlling Effect Size Distribution | Drawing Effects from a Mixture of Normals | Providing a fixed list of relative effect sizes | Providing an Exact Set of Direct Effects | Different effect distributions for different traits | Example Using Pre-Specified Effects | Custom Effect Size Distributions

Introduction | Resampling Summary Statistics or Individual Level Data from the Same Population | Resampling Summary Statistics from the Same Population | Resampling Individual Level Data from the Same Population | Generating genotypes only | Generating Genotypes and Phenotypes | Generating Phenotypes Only | Resampling Data from a Different Population | Understanding Effect Size Units | Changing LD and Allele Frequencies | Changing Environmental Variance or Covariance | Rescaling Effect Size Units

Available builds | Monitoring progress | Function options | Alt RSID output | Allele specification / matching | Evaluation speed

Setup | Harmonise data against LD matrix alleles

Column mapping | As data.table | Custom quality control

Set Up the Model | Simulate data and check distributions | Wrong models and inverse probability weighting | Maximum likelihood approach | Including the latent variable

PCA robust to outliers | Outliers and missing value imputation

Algorithms | Getting started | Some examples | Cross validation | Visualisation of the results

Overview | Citation | Source code | Installation | Update log | Tutorial | 1. Prepare data for GSMR analysis | 1.1 Load the GWAS summary data | 1.2 Estimate the LD correlation matrix | 2. Standardization | 3. GSMR analysis | 4. Bi-directional GSMR analysis | 5. Visualization

Overview | Citation | Installation | Update log | Tutorial | 1. Prepare data for GSMR analysis | 1.1 Load the GWAS summary data | 1.2 Estimate the LD correlation matrix | 2. Standardization | 3. GSMR analysis | 4. HEIDI-outlier analysis | 5. Bi-directional GSMR analysis | 6. Visualization

SummarySet | Create a Summaryset from a GWAS vcf file | DataSet

Introduction | Data input and preprocessing | QC of input pre-colocalization | Colocalization step with MRLocus | Colocalization with eCAVIAR (optional alternative) | Slope fitting step | Normalized allelic spread | Examine MRLocus estimates | Prior predictive check | Session info | References

1. Setting the metadata | 2. HMGCR (3-hydroxy-3-methylglutaryl-CoA reductase) gene region | 2.1. Meta-analysis for the chd trait | 2.2. Creating a dataset for all the traits | 2.3. Finemapping with SusieR | 2.4. Colocolisation with hypercoloc | 3. PCSK9 (proprotein convertase subtilisin/kexin type 9) gene region | 3.1. Meta-analysis for the chd trait | 3.2. Creating a dataset for all the traits | 3.3. Finemapping with SusieR | 3.4. Colocolisation with hypercoloc | 4. NPC1L1 (NPC1 like intracellular cholesterol transporter 1) gene region | 4.1. Meta-analysis for the chd trait | 4.2. Creating a dataset for all the traits | 4.3. Finemapping with SusieR | 4.4. Colocolisation with hypercoloc | 5. LPA (Lipoprotein(A)) gene region | 5.1. Meta-analysis for the chd trait | 5.2. Creating a dataset for all the traits | 5.3. Finemapping with SusieR | 5.4. Colocolisation with hypercoloc

Objectives | Data structures | Work flow | Implementation | Controlled fields | Design | Variantid

Install and load library | OpenSNP data | All Genotypes | All Phenotypes | Annotations | Download | Genotype user data | Phenotype user data | All known variations | NCBI SNP data | dbSNP

Introduction | Loading your data | Example data sets | Summary plots (rob_summary()) | Examples: | RoB2.0 tool for randomized controlled trials | ROBINS-I tool for non-randomized studies of interventions | QUADAS-2 tool for diagnostic test accuracy studies | rob_summary() options | Overall risk-of-bias judgments (overall) | Weighted or un-weighted bar plots (weighted) | Colour scheme (colour) | Traffic light plots (rob_traffic_light()) | rob_traffic_light() options | Point size (psize) | The "Generic" template | Motivation | Varying numbers of domains | Domain names

The GWAS Catalog | GWAS Catalog Entities | References

Objectives | Complete workflow for one dataset | Running each step individually | Workflow for all datasets | Lookup build | Suhre proteins | EBI Ignore list

1 | How to be sure that I can establish a connection to the GWAS Catalog server? | 2 | What resources is the GWAS Catalog database currently mapped against? | 3 | How to perform batch search with gwasrapidd? | 4 | What is the difference between a trait and a reported trait? | 5 | Genomic coordinates of genomic contexts seem to be wrong? | 6 | How to search for variants within a certain genomic region? | Single genomic range | Multiple genomic ranges | Searching variants by cytogenetic regions | 7 | Genomic range for an entire chromosome? | 8 | How to keep track of which queries generated which results? | 9 | How to combine results from multiple queries?

Estimate bias due to sample overlap | Complex Example | Estimate F-statistic

EpiGraphDB resources

Part 1: Using EpiGraphDB to obtain biological mappings | Mapping genes to proteins | Mapping proteins to pathways | Get pathway info | Part 2: Epidemiological relationships analysis | Look up GWAS studies | Explore Mendelian randomization studies | Specify exposure trait | Specify outcome trait | Specify both exposure and outcome traits | Part 3. Looking for literature evidence | EpiGraphDB node search | Advanced examples

Metadata | Meta nodes | Meta relationships and connections | Search for specific node | Fuzzy matching | Exact matching | Cypher (advanced) | sessionInfo

Change the API URL | Suppress start up message

Introduction | Using httr | Using curl | Other methods

Methods to query EpiGraphDB | mr | GET /mr | Returned data format | mode = "table" | mode = "raw"

Compare summary level simulations against individual level simulations | Compare two different populations

Clumping | Data from OpenGWAS | Data from VCF | Finemapping | Finemapping across the whole dataset | Multi-population finemapping

Expected standard error | Sampling the effect estimate | Sampling a set of effect estimates | Sensitivitiy to HWE

Obtain independent LD regions | Generate LD matrix objects | Try it out for generating summary data

Generating summary data from individual level data | More complex example | Generating summary data directly | No sample overlap case | Sample overlap case

Weak instrument bias | Winner's curse schematic

Finemapping pipeline | Conditional analysis pipeline

Reihenfolge der Funktionen | Szenario mit 1 Genvariante | Szenario mit 2 Genvarianten

What has changed | Dataset IDs | Authentication | UKBiobank data has been curated | All data is now harmonised | LD reference panel is now harmonised | Instrument lists are up-to-date | dbSNP rs IDs | Everything is faster | What is new | Browse available datasets online | Chromosome and position | INDELs are retained | Multi-allelic variants are retained | More data | Error messages are more informative | Easier programmatic access to the database | Local LD operations | Access the data directly | Connect the data to different analytical tools | Key links | How to request new data