Update Meeting: 25-08-2021
Jenny Sjaarda
2021-08-24
Last updated: 2021-09-15
Checks: 7 0
Knit directory: proxyMR/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | f7fc22e | Jenny Sjaarda | 2021-09-15 | update all update meetings Rmds |
| Rmd | 8cd643a | jennysjaarda | 2021-08-24 | intial update meeeting 25-08-21 |
| html | 8cd643a | jennysjaarda | 2021-08-24 | intial update meeeting 25-08-21 |
1 Last meeting summary.
- Discussed summary of pathways from \(X\) in index (\(X_i\)) to \(Y\) in the partner (\(Y_p\)).
- Need for running the same individual MR (i.e. simply the standard MR) so we can compare the two pathways (see DAG below).
- Multiply perpendicular paths together and to calculate the variance between them use the formula here.
3 Results.
3.1 Heterogeneity amonst different groups.
3.1.1 Sex heterogeneity
For each proxyMR (\(Y_{p} \sim X_i\)), analyses were run in each sex separately. The results below display the results that differ among sexes, after BF correction (i.e. 0.05 / (131 * 131))
However in this case, it may be more sensible to only consider same traits (i.e. testing for more of an "assortative mating" effect), reducing the number of tests to 131. However, none pass BF-significance (p < 0.05/131 = 0.0003817). The most significant results is lifetime number of sexual partners.
3.1.2 Binned heterogeneity
For each proxyMR (\(Y_{p} \sim X_i\)), MR analyses were run in the full sample as well as in 5 roughly equal sized bins according to time_together and age. Specifically, the outcome data set was split into 5 bins, and the SNP-outcome effect was estimated in each bin separately. These outcome-SNP effects were then used to generate bin-specific MR estimates, using the same SNP-exposure effects from Neale.
To estimate the difference among bins, used two approaches:
- The Cochran's Q test to test for heterogeneity in meta-analyses.
- Tested the significance of the slope of linear model of median bin (either
ageortime together) versus the bin-specific MR estimate.
The two results are shown below, restricted to only same_traits. Note that each statistic was computed in each sex separately and meta-analyzed.
3.1.2.1 Q-statistic results
3.1.2.2 Slope results
3.2 Standard MR
To complete all MR anlayses in the DAG above, needed to generate same-person MR estimates (i.e. standard MR). These were also run sex-specific, using Neale summary statistics. As expected, there are many BF-significant summary MR results. Significance was determined based on the meta-analyzed result across sexes. Thus the number of tests was equal to: 131^2 - 131 = 17030 (number of traits =131`). The BF-significant results can be seen below.
Number of BF-signifcant MR pairs: 8542.
3.3 Paths from X-index to Y-partner
There are three distinct paths from \(X_{index}\) to \(Y_{partner}\) as illustrated in the DAG above:
- Assortative mating through \(X\): \(\gamma\)
- Assortative mating through \(Y\): \(\rho\)
- Indirectly (i.e. through a latent variable which either increases or decreases the observed effect of \(X_{index}\) to \(Y_{partner}\)): \(\omega\)
The estimates of \(\gamma\), \(\rho\) and \(\omega\) can be seen below, meta-analyzed across sex.
3.3.1 Heterogeneity among paths
Next, we wanted to compare the three paths from \(X_index\) to \(Y_partner\). This was done using a standard heterogeneity test.
3.3.2 Sex heterogeneity amongst paths
3.3.3 Hierarchical clustering
Working on a figure like the one here and here.
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