Bioinformatics analysis service
GWAS summary statistics and pilot count matrices support two-sample Mendelian randomization or prospective power memos with sensitivity checks, R scripts, and a Methods draft.
| Category | Details |
|---|---|
| Data types supported | GWAS summary statistics (OpenGWAS IDs or client .tsv/.txt files); exposure and outcome GWAS metadata; pilot RNA-seq count matrices; cohort sample manifests; pre-specified instrument SNP lists; grant or protocol design briefs |
| Reference builds or standards used | STROBE-MR reporting checklist [5]; Burgess MR guidelines (2023); GRCh38 for variant harmonization; Conesa et al. [6] RNA-seq replicate guidance where omics design applies |
| Primary tools (with versions) | TwoSampleMR 0.7.4; ieugwasr 1.1.0; MendelianRandomization 0.9.2; MR-PRESSO 1.0; PROPER 1.43.0; RNASeqDesign 0.1.0; scPower 1.0.0; RNASeqPower 1.52.0; DESeq2 1.52.0 |
| Typical turnaround range | 1-2 weeks (experimental design memo from pilot or public priors); 2-4 weeks (standard two-sample MR); 3-5 weeks (multivariable MR, colocalization extensions, or multi-contrast design optimization). Confirmed at kickoff |
| Deliverable formats | Harmonized GWAS .tsv; MR result tables (.csv, .xlsx); scatter, funnel, and leave-one-out plots (PDF/SVG); power-vs-sample-size curves; design memo; STROBE-MR mapping document; commented R scripts; Methods draft |
| Regulatory/reproducibility standards followed | STROBE-MR checklist mapping; Burgess MR sensitivity-analysis recommendations; version-pinned software with sessionInfo() or conda lock files; private Git or Zenodo archival on request |
| Custom / bespoke analysis | Non-standard MR methods (MR-RAPS, multivariable MR, mediation MR), custom FDR or clumping thresholds, alternative power models, client-specified figure or table formats, and analyses beyond standard two-sample MR, scoped at kickoff |
Mendelian randomization (MR) uses genetic variants as instrumental variables to estimate causal effects of modifiable exposures. An instrumental variable must associate with the exposure, be independent of confounders, and affect the outcome only through the exposure. The F-statistic measures instrument strength. False discovery rate (FDR) controls expected false positives across many tests. A negative binomial model accounts for RNA-seq count overdispersion in power simulation.
Statistical analysis uses probability models to quantify uncertainty in biological conclusions. On this service that mainly means causal inference from genetic instruments and prospective study design for omics experiments. Mendelian randomization (MR) uses random allele allocation to estimate whether a modifiable exposure causally affects a disease outcome, rather than relying on observational regression alone [1]. Prospective design analysis asks whether a planned RNA-seq or scRNA-seq study has enough power before sequencing spend [2]. The NHGRI-EBI GWAS Catalog hosts 176,855 full summary-statistics datasets across 7,714 publications [3]. MR submissions to European Journal of Epidemiology rose from 3.1% of all submissions in 2020 to 13.0% in 2024 [4].
Published examples of questions this work can address:
Holmes et al. [11] applied weighted allele scores in 17 studies (62,199 participants; 12,099 CHD events) and found both unrestricted and restricted LDL-C instrument sets associated with CHD, supporting a causal role for LDL-C.
Millard et al. [12] performed an MR phenome-wide association study (MR-pheWAS) in 334,968 UK Biobank participants, identifying 587 associations at 5% FDR, including adverse effects on diabetes and hypertension.
Ching et al. [10] showed that increasing sample size yields more power than increasing sequencing depth once depth exceeds approximately 20 million reads per sample.
Schmid et al. [13] modelled power as a function of donors, cells per donor, and sequencing depth, finding that shallow sequencing of more cells often outperforms deep sequencing of fewer cells for cell-type-specific DE.
Lin et al. [12] developed RNASeqDesign to optimize sample size and depth jointly from pilot RNA-seq data under a fixed budget constraint.
This category covers Mendelian randomization and experimental design consulting. Each has a dedicated page for inputs, tools, and deliverables.
| Service | Description | Primary tools |
|---|---|---|
| Mendelian randomization | Two-sample MR from GWAS summary data with pleiotropy sensitivity analyses and STROBE-MR-aligned reporting | TwoSampleMR 0.7.4, MendelianRandomization 0.9.2, MR-PRESSO 1.0 |
| Experimental design | Pre-study power, sample-size, and replicate planning for bulk RNA-seq, scRNA-seq, and related omics | PROPER 1.43.0, RNASeqDesign 1.0.0, scPower 1.0.0, DESeq2 1.52.0 |
Each project includes processed tables, figures, scripts, and a Methods draft. Summary slides are not the only output.
Non-standard MR extensions, custom power models, or client-specified table formats are scoped at kickoff.
Confirm the exposure-outcome pair for MR, or the contrast, effect size, and target FDR for design. Assess biological plausibility and pre-register sensitivity analyses where feasible [1].
Output: signed scope document with primary and secondary estimands
For MR: collect OpenGWAS IDs or client summary-stat files, population ancestry, and genome build. For design: collect a pilot count matrix, sample manifest, and budget constraints.
Tools used: sample manifest template
Output: input_manifest.csv
MR: extract genome-wide significant SNPs, LD clump (r² < 0.001, 10,000 kb per TwoSampleMR convention), and compute per-SNP F-statistics [1]. Design: estimate mean dispersion and effect-size priors from pilot counts.
Tools used: TwoSampleMR 0.7.4; PROPER 1.43.0 estParam()
Output: instrument list or simulation parameter file
MR: align effect alleles, resolve palindromic SNPs with allele frequency checks, and log harmonization decisions [1]. Design: simulate negative-binomial counts under proposed replicate counts and depths.
Tools used: TwoSampleMR 0.7.4; PROPER 1.43.0 or RNASeqDesign 0.1.0
Output: harmonized GWAS tables or simulation draws
MR: inverse-variance weighted two-sample MR as the primary estimator [7]. Design: compute power at the target FDR across replicate and depth grids.
Tools used: MendelianRandomization 0.9.2; PROPER 1.43.0; scPower 1.0.0 (scRNA-seq)
Output: mr_primary_results.csv or power summary table
MR: weighted median, MR-Egger, MR-PRESSO outlier removal, and leave-one-out analysis [8, 9]. Design: sweep depth vs replicate tradeoffs [2, 10].
Tools used: MR-PRESSO 1.0; MendelianRandomization 0.9.2; RNASeqPower 1.52.0
Output: sensitivity tables and diagnostic plots
Map results to STROBE-MR checklist items and state where exclusion restriction or instrument-strength assumptions may be violated [5]. For design memos, document pilot-data limitations and recommend minimum viable replicate counts.
Output: interpretation memo with assumption audit
Export figures at publication resolution; bundle scripts, lock files, README, and Methods draft. Transfer via agreed secure channels. Post-delivery support covers methods clarification. Substantial new exposure-outcome pairs or design revisions are scoped separately.
Output: final deliverable bundle
Pepkio pins software versions at kickoff and cites primary references in the Methods draft. Tools commonly used for MR and experimental design:
| Tool | Version | Role | Primary citation |
|---|---|---|---|
| TwoSampleMR | 0.7.4 | Two-sample MR orchestration and OpenGWAS retrieval | https://doi.org/10.7554/eLife.34408 |
| ieugwasr | 1.1.0 | Programmatic access to OpenGWAS summary statistics | https://mrcieu.github.io/ieugwasr/ |
| MendelianRandomization | 0.9.2 | IVW, weighted median, MR-Egger estimators | https://doi.org/10.1093/ije/dyx034 |
| MR-PRESSO | 1.0 | Pleiotropy outlier detection and distortion test | https://doi.org/10.1038/s41588-018-0304-7 |
| PROPER | 1.43.0 | Simulation-based RNA-seq power evaluation | https://doi.org/10.1093/bioinformatics/btu640 |
| RNASeqDesign | 0.1.0 | Multi-dimensional sample-size and depth optimization from pilot data | https://doi.org/10.1111/rssc.12330 |
| scPower | 1.0.0 | scRNA-seq cell-type DE and eQTL power planning | https://doi.org/10.1038/s41467-021-26779-7 |
| RNASeqPower | 1.52.0 | Closed-form RNA-seq sample-size estimation | https://doi.org/10.1089/cmb.2012.0283 |
| DESeq2 | 1.52.0 | Pilot dispersion estimation from count matrices | https://doi.org/10.1186/s13059-014-0550-8 |
MR projects follow Burgess et al. [1] for harmonization and sensitivity reporting, TwoSampleMR conventions for LD clumping (r² < 0.001, 10,000 kb) and F-stat > 10 instrument strength, and STROBE-MR [5] for reporting. Design projects follow Jeon et al. [2] tool recommendations and Conesa et al. [6] guidance of ≥3 biological replicates per condition where feasible.
Common issues include weak instruments, pleiotropy, harmonization errors, underpowered designs, and automated scans with little biological grounding. Pepkio documents sensitivity checks and agrees scope at kickoff.
Instruments with F < 10 (a conventional weak-IV threshold) can bias MR estimates; Burgess et al. [1] recommends reporting F-statistics. Pepkio reports per-SNP and aggregate F-statistics, applies strict clumping, and documents leave-one-out stability before interpretation.
SNPs may influence outcomes through pathways independent of the exposure [9]. Pepkio runs MR-Egger, weighted median, and MR-PRESSO outlier tests and reports when estimates diverge materially.
Allele flipping, strand mismatches, and palindromic SNPs can produce spurious MR associations [1]. Pepkio logs every harmonization decision and flags SNPs removed for ambiguity.
Many RNA-seq studies are planned without simulation-based power assessment [2]. Pepkio runs PROPER or RNASeqDesign sweeps and recommends replicate count before library prep.
Automated exposure-outcome scans can yield technically valid but biologically nonsensical pairs [4]. Pepkio scopes MR questions with an explicit biological rationale at kickoff and discourages undirected phenome scans unless they are pre-specified.
For MR: exposure and outcome GWAS summary statistics (OpenGWAS IDs or .tsv files), population ancestry, and genome build. For experimental design: a pilot count matrix or public reference dataset, target fold change, FDR, and budget constraints. Pepkio confirms scope at kickoff. Custom summary-stat formats are accepted when documented in advance.
Experimental design memos typically take 1-2 weeks. Standard two-sample MR typically takes 2-4 weeks. Multivariable MR, colocalization extensions, or multi-contrast design optimization typically take 3-5 weeks. Exact timelines are confirmed at kickoff.
MR projects receive harmonized GWAS tables, primary and sensitivity MR result spreadsheets, scatter/funnel/leave-one-out plots, STROBE-MR mapping, commented R scripts, and a Methods draft. Design projects receive power curves, recommended replicate counts and sequencing depth, a simulation log, and a design memo suitable for grant Methods or Power sections.
Yes. Provide per-SNP beta, SE, effect allele, other allele, and sample size in a documented format. Pepkio harmonizes in-house files against the same allele-alignment rules used for OpenGWAS retrieval [1]. Mixed-ancestry or non-GRCh38 builds are supported when scoped at kickoff.
It depends on effect size, dispersion, and sequencing depth; there is no fixed number. Ching et al. [10] showed replicate count dominates power once depth exceeds about 20 million reads. Pepkio runs PROPER or RNASeqDesign simulations and recommends replicate count at your target FDR.
Mendelian randomization estimates causal effects of an exposure on an outcome using genetic instruments and existing GWAS summary data, typically after data collection. Experimental design estimates whether a planned omics experiment can detect specified effects before you sequence. Both deliver version-pinned R code and a Methods draft.
Yes, when scoped at kickoff. Pepkio supports multivariable MR for correlated exposures and mediation-style extensions using TwoSampleMR and MendelianRandomization, with sensitivity analyses per Burgess et al. [1]. Complex network MR or sample-overlap corrections need explicit scoping.
Yes. Pepkio delivers a design memo with power curves, assumed effect sizes, FDR targets, recommended replicate counts, and cited tool versions suitable for grant Methods or Power sections. Simulations use pilot data when available or published reference datasets when not [12, 14].
Yes. You retain full ownership. Pepkio delivers commented R scripts with conda or renv lock files via private Git or agreed file transfer. You can rerun, extend, or publish the code without restriction.
Yes. Checkpoint reviews follow instrument selection or simulation setup, after primary analysis, and before final delivery. You can review exposure-outcome pairs, clumping thresholds, FDR targets, and power assumptions within agreed scope.
Methods clarification and minor revisions within agreed scope (typically up to 20% of deliverables) are covered. Adding new exposure-outcome pairs, rerunning with different GWAS releases, or expanding design to new modalities are scoped and priced separately.
Yes, when scoped at kickoff: MR-RAPS, multivariable or mediation MR, custom clumping or FDR thresholds, alternative power simulators (ssizeRNA, powsimR), non-RNA-seq count models, or client-specified output formats.
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