Update Meeting: 23-09-2021
Jenny Sjaarda
2021-09-20
Last updated: 2021-09-24
Checks: 7 0
Knit directory: proxyMR/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | fd45d13 | Jenny Sjaarda | 2021-09-24 | publish update meeting 24-09-21 |
| Rmd | 71c67df | jennysjaarda | 2021-09-24 | select the right columns for DT |
| Rmd | 529020f | jennysjaarda | 2021-09-24 | add update meeting 23-09-2021 |
| html | 529020f | jennysjaarda | 2021-09-24 | add update meeting 23-09-2021 |
1 Last meeting summary.
- Because the results comparing \(\omega\) to \(\rho + \gamma\) were slightly confusing (see last meeting summary here), decided we should explore the impact of \(X_i\) on the \(Y_i \rightarrow Y_p\) causal effects.
| Version | Author | Date |
|---|---|---|
| fda4843 | jennysjaarda | 2021-09-14 |
- Two things to explore:
- Identify overlap between \(X_i \rightarrow X_p\) IVs and \(Y_i \rightarrow Y_p\).
- Adjust the \(Y_i \rightarrow Y_p\) MR results for effects of \(X_i\) (as depicted below).
| Version | Author | Date |
|---|---|---|
| 529020f | jennysjaarda | 2021-09-24 |
2 Results.
2.1 Overlap between IVs for \(X\) and \(Y\).
Is simply calculating the SNP overlap good enough? Better to calculate signal overlap using LD at some \(r^2\) cutoff (what threshold would be useful?).
- For each IV used to estimate the causal effect \(Y_i \rightarrow Y_p\),
- Test if they are in LD with any IV used to estimate the causal effect \(X_i \rightarrow X_p\) at \(r^2\) >
0.9, - Repeat for all IVs for \(Y_i\) to calcualte the percentage overlap between the two sets of IVs.
- Define percentage overlap as: (# of \(Y_i\) IVs are in LD with at least one \(X_i\) IV at \(r^2\) >
0.9) / (total number of \(Y_i\) IVs used).
The results can be seen below. The YiXi_IV_exact_overlap column indicates the percentage of \(Y_i\) IVs that are an exact match with \(X_i\) IVs (same rs#). The YiXi_IV_sig_overlap column denotes the proportion overlap of signals, as defined above.
The results with the most overlap involve traits that are nearly identical.
2.2 Adjust \(Y_i \rightarrow Y_p\).
Performed MV MR \(Y_i \rightarrow Y_p\) relationships: \(Y_p \sim Y_i + X_i\). Where IVs were the same as the two trait MR (\(Y_p \sim Y_i\)) based on \(Y\), but the effect of the IVs on \(X_i\) were also included in the model.
We can compare the \(\alpha_{y_{i}\rightarrow y_{p}}\) before and after adjustment, and clearly see a band of estimates centered around \(y = 0\), which matches nearly perfectly the instances where \(Y\) IVs share more than 50% of \(X\) IVs. There are several cases where the adjusted results caused the \(abs(\alpha_{y_{i}\rightarrow y_{p}})\) to increase.
2.3 Updated two trait results with adjusted \(Y_i\).
The results below show the significant difference between \(\omega\) vs \(\rho\), \(\gamma\) and their sum. Concerned that most of the significant results involve cases where the adjusted \(abs(\alpha_{y_{i}\rightarrow y_{p}})\) is greater than the unadjusted case.
| omega_vs_gam_BF_sig_meta | Frq |
|---|---|
| FALSE | 1459 |
| TRUE | 438 |
| omega_vs_rho_BF_sig_meta | Frq |
|---|---|
| FALSE | 1366 |
| TRUE | 531 |
| omega_vs_gam_rho_BF_sig_meta | Frq |
|---|---|
| FALSE | 1477 |
| TRUE | 420 |
2.4 MVMR.
Have so far run / am running all MVMR models but need to sum up along all \(\beta\) from the MV model to create the \(\gamma_{mv}\). (Having some computing issues with pruning.)
Should we use the adjusted \(Y_i \rightarrow Y_p\) for calculating \(\rho\)?
MV MR: \(X \rightarrow Z \rightarrow Y\).
- Drop Z's if not significant in MV MR (i.e. \(Y \sim X + Z_1 + Z_2 + Z_3...\))
- Do we drop from the model itself and recompute the beta-coefficients? Or simply not include them in the result sum?
| Version | Author | Date |
|---|---|---|
| 1afb033 | jennysjaarda | 2021-09-14 |
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
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