Ref. # | Contribution | Sample | BP data | GE and genetic data | Method | Software | Main findings |
---|---|---|---|---|---|---|---|
[8] | Cao et al. [2015] | n = 397 individuals in 46 families, from family data set | Real data: SBP at time point 3 | GE and SNP data: k = 11,522 transcripts, l = 354,893 SNPs | SRVS | Matlab-toolbox SRVS | Of top 1000 variables associated with BP, 575 are SNPs and 425 are GE, 302 have plausible relevance for BP, 173 are associated with body weight, and 84 associated with left ventricular contractility |
[9] | Konigorski et al. [2015] | n = 81 unrelated individuals, from family data set | Real data: SBP at time point 1 | GE and WGS data on chromosome 19: k = 848 transcripts, l = 68,727 SNVs | Copula | R functions, available upon request | Higher power of bivariate copula models compared to univariate regression and univariate SKAT, SKAT-O |
Identification of 5 SNVs in CEACAM5 gene relevant for SBP, and 1075 cis-eQTLs relevant for GE | |||||||
[10] | Song et al. [2015] | n = 1389 individuals from family data set | Real data: SBP and DBP at time points 1–3 | SNP data: l = 460,359 SNPs | SEM | R-package strum | The 2 tested models (autoregressive and latent growth curve) show similar ranking of relevant SNPs |
Identification of 10 SNPs related to both SBP and DBP, mostly on chromosome 1 | |||||||
[11] | Sun et al. [2015] | n = 1851 unrelated individuals, from unrelated data set | real data: SBP and DBP | WES data: l = 152,337 SNVs | MURAT | R functions, available upon request | Multivariate tests tend to give smaller p values than the univariate SKAT, and can improve power |
Identification of 2 SNPs in CYP4A22 and near APOC4, which were previously reported to be associated with BP |