Last updated: 2021-09-24
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Knit directory: proxyMR/
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File | Version | Author | Date | Message |
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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 |
Version | Author | Date |
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fda4843 | jennysjaarda | 2021-09-14 |
Version | Author | Date |
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529020f | jennysjaarda | 2021-09-24 |
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?).
0.9
,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.
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.
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 |
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: \(X \rightarrow Z \rightarrow Y\).
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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