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

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

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 | 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

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

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

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

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

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

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

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