Assortative mating MR summary
Jenny Sjaarda
2021-09-15
Last updated: 2022-04-20
Checks: 7 0
Knit directory: proxyMR/
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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/AM_MR_summary.Rmd) and HTML (docs/AM_MR_summary.html) files. If you've configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 978309b | Jenny Sjaarda | 2022-04-20 | wflow_publish("analysis/AM_MR_summary.Rmd") |
| html | 9afe690 | Jenny Sjaarda | 2021-09-15 | Build site. |
| Rmd | e8da48b | Jenny Sjaarda | 2021-09-15 | add AM MR summary |
| Rmd | 103c1b5 | jennysjaarda | 2021-09-15 | add AM MR summary |
1 Background
For certain analyses it is worth only looking at MR results between couples where the exposure trait is the same as the outcome trait. We've called these analyses assortative mating MR (AM MR).
| Version | Author | Date |
|---|---|---|
| 529020f | jennysjaarda | 2021-09-24 |
2 Results
- The number of analyses corresponds to the number of traits (118).
- The significant results after adjusting for multiple hypothesis testing are shown below.
IVW_meta_betaandIVW_meta_pvalcorresponds to the beta and p-value of the meta-analyzed MR across sexes, respectively (i.e. MR estimates were computed in each sex-seperately using sex-specific SNP-exposure and SNP-outcome results and then meta-analyzed).- After adjusting for multiple tests (p <
0.05/66), identified 64 significant AM MR relationships (see below on details for how number of effective tests was obtained).
A note on multiple testing adjustment:
- Adjustment was based on PCs of correlation matrix.
- Calculate correlation matrix of all 118 traits tested.
- Perform PCs on the resulting corr matrix.
- Number of tests = # of PCs needed until variance explained is > 99.5%.
- Resulting # of effective tests: 66.
2.1 Sex heterogeneity
- For each AM MR (\(X_{p} \sim X_i\)), analyses were run in each sex separately.
- Of the 64 significant results shown above, we tested to see if there was any difference in AM MR estimates between sexes.
- A similar adjustment for multiple hypothesis testing was applied as above, resulting in 29 number of effective tests.
- After adjusting for multiple hypothesis testing (p <
0.05/29), 0 traits showed significant differences amongst sexes. - The table below shows the nominally significant results at
p < 0.05(of which there are 15).
Paired t-test
data: nominally_sig_sex_differences$IVW_beta_male and nominally_sig_sex_differences$IVW_beta_female
t = -2.8203, df = 14, p-value = 0.01362
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.20384885 -0.02773588
sample estimates:
mean of the differences
-0.1157924 In general, found that female estimates are on average larger than males (result of the paired t-test shown below).
Paired t-test
data: nominally_sig_sex_differences$IVW_beta_male and nominally_sig_sex_differences$IVW_beta_female
t = -2.8203, df = 14, p-value = 0.01362
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.20384885 -0.02773588
sample estimates:
mean of the differences
-0.1157924 2.2 Binned results
- For each AM MR (\(X_{p} \sim X_i\)), analyses were run in the full sample as well as in 5 roughly equal sized bins according to time couple had been together (
time_together_even_bins) estimated using time at household variable, and median age (age_even_bins). - Of the 64 significant results shown above, we tested to see if there was any significant difference in AM MR estimates amongst the two grouping variables.
- Specifically, the outcome data set was split into 5 bins, and the SNP-outcome effect was estimated in each bin separately (and each sex seperately).
- 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, we 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.
2.2.1 Q-statistic across bins
The number of significant q-statistics significant after multiple hypothesis testing (p < 0.05/29 = 0.0017241) in each group was:
- Binned by median age of couples, number of significant results within meta = 1 and sex-specific = 1.
- Binned by time couple had been together, number of significant results within meta = 0 and sex-specific = 0.
All results are displayed below (sort by Q_pval to view significant results).
2.2.2 Trend across bins
Slopes were calculated by estimating the beta-coefficient of a linear regression between MR estimate within each bin (dependent variable) and median bin (independent variable, either age or time at same household). Linear models were run both unweighted and weighted for the by the inverse of the SE of the MR estimate.
The number of significant trends (\(\beta\) estimates from the model: \(\alpha_{bin} \sim median_{bin}\)) significant after multiple hypothesis testing (p < 0.05/29 = 0.0017241) in each group was:
- Binned by median age of couples, number of significant results after adjusting for number of tests:
- \(\beta\) unweighted, meta-anlayzed across sexes: 0
- \(\beta\) weighted by inverse of the SE, meta-anlayzed across sexes: 0
- \(\beta\) significantly different between sexes: 0
- Binned by time couple had been together, number of significant results after adjusting for number of tests:
- \(\beta\) unweighted, meta-anlayzed across sexes: 0
- \(\beta\) weighted by inverse of the SE, meta-anlayzed across sexes: 0
- \(\beta\) significantly different between sexes: 2.
All results are displayed below (sort by various p-values to view significant results).
2.2.3 Figures
A few figures of interest are shown below.
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| 9afe690 | Jenny Sjaarda | 2021-09-15 |
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