Statistical Analysis
Two-sample Mendelian randomization on GWAS summaries returns IVW estimates, sensitivity plots, harmonized tables, R scripts, and a Methods draft [1].
| Category | Details |
|---|---|
| Supported platforms / instruments | OpenGWAS database IDs; client-supplied GWAS summary statistics (.tsv, .txt); UK Biobank, FinnGen, and GWAS Catalog exports when documented; one-sample MR from individual-level genotype and phenotype data when scoped separately |
| Input requirements | Per-SNP beta, standard error, effect allele, other allele, and sample size; several independent instruments after LD clumping for two-sample MR [1]; exposure and outcome GWAS ideally from non-overlapping cohorts or with overlap fraction documented [5] |
| Reference builds supported | Human GRCh38 / hg38 (primary); GRCh37 / hg19 with documented liftover when scoped; LD clumping against 1000 Genomes Phase 3 EUR by default |
| Primary tools (with versions) | TwoSampleMR 0.7.4; ieugwasr 1.1.0; MendelianRandomization 0.10.0; MR-PRESSO 1.0. Pinned per project |
| Typical turnaround time | 2–4 weeks (standard two-sample MR); 3–5 weeks (multivariable MR, multiple exposure–outcome pairs, or scoped extensions). Confirmed at kickoff |
| Deliverable formats | Harmonized GWAS .tsv; MR result tables (.csv, .xlsx); scatter, funnel, and leave-one-out plots (PDF/SVG); STROBE-MR mapping document; commented R scripts with renv.lock; Methods draft |
| Key cited best-practice reference | Burgess et al. [1], Wellcome Open Research; Skrivankova et al. [6], STROBE-MR, BMJ |
| Custom / bespoke analysis | MR-RAPS, multivariable or mediation MR, sample-overlap correction, colocalization extensions, client-specified clumping thresholds, pre-specified phenome-wide scans, and non-standard output formats scoped at kickoff |
Mendelian randomization treats germline genetic variants as instrumental variables to estimate causal exposure effects on outcomes from summary-level or individual-level association data [3]. Pepkio's default workflow is two-sample MR: SNP–exposure associations come from one GWAS and SNP–outcome associations from a separate study. IVW then combines per-SNP Wald ratios [4]. Allele allocation at conception is quasi-random, so MR can reduce confounding and reverse causation relative to observational regression. IV assumptions still need to be checked [1]. Custom entry points and estimators are agreed at kickoff. See the Mendelian randomization glossary.
Two-sample MR is a fit when you have genome-wide association summary data for a modifiable exposure and a disease or trait outcome, and you need a causal estimate that observational studies cannot support on their own [1].
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Two-sample Mendelian randomization | Causal effect of a modifiable exposure on an outcome using existing GWAS summary data | Requires valid IV assumptions; sensitive to horizontal pleiotropy, weak instruments, and sample overlap | Quote-based bioinformatics; no new wet-lab data required when public GWAS suffice |
| Multivariable observational epidemiology | Rich covariate adjustment when randomized trials are infeasible | Residual confounding and reverse causation limit causal interpretation | Lower per-analysis cost than new cohorts; higher risk of biased causal claims |
| Randomized controlled trial | Causal inference for interventions when a trial can be run | Cost, duration, and ethical constraints for many exposures | Highest per-study cost; often used as the reference design when feasible |
The scope records biological plausibility of a causal pathway, matched population ancestry between GWAS sources, and pre-specified primary and sensitivity estimators before data extraction [1, 6]. Sample overlap between exposure and outcome cohorts is documented because partial overlap biases estimates toward the observational association [5].
Tools used: Scope template; literature review notes
Output: Signed scope document with exposure, outcome, estimand, and planned sensitivity methods
OpenGWAS IDs, client summary-statistics files, genome build, ancestry descriptor, trait units, and case–control versus continuous outcome type are recorded. Missing required columns (beta, se, effect allele, other allele, sample size) are flagged before instrument selection.
Tools used: Custom validation scripts; ieugwasr 1.1.0 gwasinfo() when OpenGWAS IDs are used
Output: input_manifest.csv with GWAS accessions, build, ancestry, sample size, and overlap notes
Genome-wide significant SNPs (p < 5×10⁻⁸) associated with the exposure are extracted and LD-clumped (r² < 0.001 within a 10,000 kb window) against a European reference panel to obtain independent instruments [10]. Clumping parameters and the LD reference population are locked at kickoff.
Tools used: TwoSampleMR 0.7.4; ieugwasr 1.1.0
Output: exposure_instruments.tsv with SNP, beta, SE, effect allele, other allele, p-value, and clumping metadata
Per-SNP and aggregate F-statistics quantify instrument–exposure association strength. F-statistics are reported as weak-instrument diagnostics. They are not used to drop SNPs after the fact, which can introduce winner's-curse bias [3, 11].
Tools used: TwoSampleMR 0.7.4
Output: instrument_strength.csv with per-SNP F-statistics, exposure R², and aggregate F
The same instrument SNPs are queried in the outcome GWAS. SNPs absent from the outcome dataset are logged. LD-proxy lookup via OpenGWAS is attempted when appropriate and documented [10].
Tools used: TwoSampleMR 0.7.4; ieugwasr 1.1.0
Output: outcome_snps.tsv with SNP-level outcome beta, SE, and availability flags
Effect alleles are aligned so exposure and outcome betas refer to the same allele. Palindromic SNPs (A/T or C/G) are resolved using allele frequency checks or removed when strand cannot be determined. Every decision is logged [1].
Tools used: TwoSampleMR 0.7.4
Output: harmonized_dat.tsv; harmonization_log.csv listing flipped, removed, and ambiguous SNPs
The primary analysis uses the IVW multi-instrument estimator under an assumption of balanced pleiotropy across instruments [4]. Effect estimates are reported on the exposure scale documented in the scope (for example, log-odds per 1-SD exposure increase).
Tools used: MendelianRandomization 0.10.0 [12]; TwoSampleMR 0.7.4
Output: mr_primary_results.csv with columns method, exposure, outcome, nsnp, beta, se, pval
Weighted median, MR-Egger, and leave-one-out analyses test stability to horizontal pleiotropy and outlier SNPs [5, 13]. MR-PRESSO global and outlier tests detect pleiotropic distortion [14]. Cochran's Q quantifies heterogeneity across SNP-specific estimates. RadialMR outlier tests are included when scoped.
Tools used: MendelianRandomization 0.10.0; MR-PRESSO 1.0; TwoSampleMR 0.7.4; RadialMR when scoped
Output: mr_sensitivity_results.csv; pleiotropy_diagnostics.csv
Scatter plots display SNP-specific Wald ratios against instrument strength. Funnel plots and leave-one-out influence plots show whether single SNPs drive the aggregate estimate [1]. Per-SNP forest plots are included when scoped.
Tools used: TwoSampleMR 0.7.4; ggplot2 via MendelianRandomization 0.10.0
Output: MR scatter plot, funnel plot, leave-one-out plot, and optional forest plot (PDF/SVG)
Results are mapped to STROBE-MR checklist items covering instrument selection, assumption assessment, and sensitivity reporting [6]. An interpretation memo states where exclusion restriction or weak-instrument limitations may apply. Commented R scripts, lock files, README, and a Methods draft cite exact GWAS accessions and software versions.
Tools used: Custom STROBE-MR mapping template
Output: strobe_mr_mapping.csv; interpretation memo; final deliverable bundle; Methods draft
You need exposure and outcome GWAS summary statistics with per-SNP beta, standard error, effect allele, other allele, and sample size, or OpenGWAS database IDs we can query on your behalf. After LD clumping, most two-sample MR analyses use several independent instruments, though the count depends on trait polygenicity and outcome GWAS sample size [1, 11]. Population ancestry should match between exposure and outcome sources. Custom column formats are accepted when documented at kickoff.
Yes, within documented limits. Missing SNPs in the outcome GWAS are logged and LD-proxy lookup is attempted where appropriate [10]. Files lacking required columns, mixed genome builds without liftover, or summary statistics with undocumented allele coding need preprocessing scoped at kickoff. If too few instruments survive harmonization for stable inference, Pepkio reports this before finalizing interpretation.
Pepkio routinely works with OpenGWAS database IDs, UK Biobank summary-data exports, FinnGen release files, GWAS Catalog full summary statistics, and client in-house .tsv or .txt files. OpenGWAS authentication tokens are configured when protected endpoints are required (ieugwasr 1.1.0). Non-GRCh38 builds and non-European ancestry cohorts are supported when LD reference panels and harmonization rules are agreed at kickoff.
Standard two-sample MR for one pre-specified exposure–outcome pair typically completes in 2–4 weeks from data receipt. Projects involving multivariable MR, multiple exposure–outcome pairs, or scoped extensions may take 3–5 weeks. Exact timelines are confirmed at kickoff with milestone check-ins when scoped.
MR does not use batch correction in the sequencing sense. Pepkio matches ancestry between exposure and outcome GWAS and documents sample overlap from study metadata [5]. Partial overlap between cohorts biases estimates toward the confounded observational association. Overlap is flagged, and overlap-correction approaches are discussed when non-overlapping GWAS are unavailable. Mismatched ancestry without appropriate LD panels is flagged at kickoff.
Yes. You retain full ownership of all scripts and results. Pepkio delivers commented R scripts with renv.lock files listing exact package versions, organized by pipeline stage with a README. Delivery is via private Git repository or agreed secure file transfer.
Yes, when scoped at kickoff. Checkpoint reviews can occur after instrument selection, after harmonization, and before final delivery. You can review exposure–outcome pairs, clumping parameters, sensitivity methods, and interpretation within agreed scope. A dedicated PhD-level scientific contact leads the project.
Support includes clarification of MR methods, harmonization decisions, pleiotropy diagnostics, and minor figure or table revisions within agreed scope (typically ≤20% of project scope). Pepkio drafts Methods and Supplementary text for analyses we performed. New exposure–outcome pairs or sensitivity methods requested by reviewers are scoped as separate milestones.
No. Pepkio operates as a fee-for-service provider and does not require co-authorship unless discussed in advance. Acknowledgment of bioinformatics support in the Acknowledgments section is standard practice.
Horizontal pleiotropy violates the exclusion restriction when instruments affect the outcome through pathways other than the exposure [14]. Pepkio runs weighted median MR, MR-Egger, MR-PRESSO, and leave-one-out analyses alongside IVW. When estimates diverge across methods, the interpretation memo treats causal claims cautiously and documents outlier SNPs.
Yes, when scoped at kickoff. Multivariable MR for correlated exposures and two-step mediation MR are supported using TwoSampleMR 0.7.4 and MendelianRandomization 0.10.0, with sensitivity analyses per Burgess et al. [1]. Network MR, MR-RAPS, and complex overlap corrections require explicit scoping.
Partial overlap biases two-sample MR toward the observational association. With 50% overlap, relative bias is approximately half the one-sample bias under F = 10 and a null causal effect [5]. Overlap is documented from metadata. Non-overlapping sources are preferred when available; otherwise overlap-adjustment methods are scoped. Fully overlapping one-sample MR is scoped separately.
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