self-report asthma
Yunqi Yang
4/16/2024
Last updated: 2024-06-06
Checks: 7 0
Knit directory: survival-data-analysis/
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library(survival)
library(susieR)
devtools::load_all("/Users/nicholeyang/Downloads/logisticsusie")ℹ Loading logisticsusieRegion 1
res = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res_susie/fit.susie.chr11_61000000_63000000.rds")
gwas = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res202404/gwas_chr11_61000000_63000000.rds")fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10] [1] 0.048345837 0.018942668 0.017804861 0.017581518 0.016594471 0.009652823
[7] 0.008519408 0.008456946 0.008387958 0.008320214class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs$cs
NULL
$coverage
NULL
$requested_coverage
[1] 0.95plot(-log10(gwas[, "p.value.spa"]), xlab = "SNP", ylab = "-log10(p-value)",
ylim = c(0, 8), cex = 0.8)
| Version | Author | Date |
|---|---|---|
| c6dd36b | yunqiyang0215 | 2024-04-16 |
Region 2
res = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res_susie/fit.susie.chr11_1113000_1750000.rds")
gwas = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res202404/gwas_chr11_1113000_1750000.rds")fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10] [1] 0.020313888 0.020143342 0.019259236 0.016483365 0.012727759 0.011996254
[7] 0.009421512 0.009018887 0.009014567 0.008883730class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs$cs
NULL
$coverage
NULL
$requested_coverage
[1] 0.95plot(-log10(gwas[, "p.value.spa"]), xlab = "SNP", ylab = "-log10(p-value)",
ylim = c(0, 8), cex = 0.8)
| Version | Author | Date |
|---|---|---|
| c6dd36b | yunqiyang0215 | 2024-04-16 |
Region 3
res = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res_susie/fit.susie.chr11_75000000_77000000.rds")
gwas = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res202404/gwas_chr11_75000000_77000000.rds")
gwas.cond = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res202404/chr11_75000000_77000000.conditional.rds")fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10] [1] 0.83918080 0.26044924 0.24549898 0.21465913 0.18439119 0.15293021
[7] 0.03706393 0.01280900 0.01168736 0.01047847class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs$cs
$cs$L1
[1] 3196 3199 3200 3203 3225 3245
$cs$L2
[1] 3064 3235
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.9423496 0.9786286 0.9854458
L2 0.9414139 0.9414139 0.9414139
$cs_index
[1] 1 2
$coverage
[1] 0.9500171 0.9908003
$requested_coverage
[1] 0.95par(mfrow = c(2,2))
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20, main = "CS 1")
snps2 = colnames(X)[cs$cs$L2]
colors <- ifelse(rownames(gwas) %in% snps2, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20, main = "CS 2")
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.6, pch = 20, ylim = c(34, 38), main = "Zoom in CS 1")
plot(-log10(gwas.cond[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(conditional p-value)", cex = 0.6, pch = 20, ylim = c(23, 26), main = "Conditional association of CS 1")
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)]) MAF missing.rate p.value.spa p.value.norm Stat
rs7936070_T 0.4766971 0 1.503789e-37 1.452115e-37 1612.065
rs7936312_T 0.4766166 0 1.251399e-37 1.208159e-37 1613.890
rs7936323_A 0.4765950 0 1.070192e-37 1.033056e-37 1615.204
rs7936434_C 0.4768852 0 2.342714e-37 2.263324e-37 1607.740
rs10160518_G 0.4973644 0 1.555762e-35 1.515029e-35 -1567.527
rs7931483_A 0.4765866 0 2.109275e-36 2.041818e-36 1579.912
Var z
rs7936070_T 15838.20 12.80942 0.21465913
rs7936312_T 15838.79 12.82369 0.24549898
rs7936323_A 15834.63 12.83582 0.26044924
rs7936434_C 15838.51 12.77494 0.18439119
rs10160518_G 15868.56 -12.44361 0.01280900
rs7931483_A 15716.01 12.60264 0.03706393cbind(gwas.cond[rownames(gwas.cond) %in% snps1, ], pip[sort(cs$cs$L1)]) MAF missing.rate p.value.spa p.value.norm Stat
rs7936070_T 0.4766971 0 4.823822e-26 4.717842e-26 1287.957
rs7936312_T 0.4766166 0 4.131520e-26 4.040066e-26 1289.747
rs7936323_A 0.4765950 0 3.605439e-26 3.525156e-26 1291.143
rs7936434_C 0.4768852 0 6.942573e-26 6.792836e-26 1283.831
rs10160518_G 0.4973644 0 8.093681e-25 7.981116e-25 -1260.409
rs7931483_A 0.4765866 0 4.547058e-25 4.456734e-25 1257.133
Var z
rs7936070_T 14884.23 10.55694 0.21465913
rs7936312_T 14884.58 10.57149 0.24549898
rs7936323_A 14880.83 10.58427 0.26044924
rs7936434_C 14885.54 10.52266 0.18439119
rs10160518_G 15009.29 -10.28801 0.01280900
rs7931483_A 14770.25 10.34397 0.03706393gwas.cond[-log10(gwas.cond[, "p.value.spa"]) > 24, ] MAF missing.rate p.value.spa p.value.norm Stat
rs61893460_A 0.4520203 0 8.707685e-26 8.432591e-26 1273.523
rs7126418_T 0.4519984 0 1.395756e-25 1.352595e-25 1267.156
rs7110818_T 0.4512180 0 1.292825e-25 1.252389e-25 1267.093
rs7114362_T 0.4968866 0 5.279875e-25 5.205427e-25 -1263.234
rs7936070_T 0.4766971 0 4.823822e-26 4.717842e-26 1287.957
rs7936312_T 0.4766166 0 4.131520e-26 4.040066e-26 1289.747
rs7936323_A 0.4765950 0 3.605439e-26 3.525156e-26 1291.143
rs7936434_C 0.4768852 0 6.942573e-26 6.792836e-26 1283.831
rs11236791_A 0.4518935 0 8.942057e-26 8.659252e-26 1272.741
rs10160518_G 0.4973644 0 8.093681e-25 7.981116e-25 -1260.409
rs11236797_A 0.4510802 0 6.732786e-26 6.514237e-26 1275.455
rs7931483_A 0.4765866 0 4.547058e-25 4.456734e-25 1257.133
Var z
rs61893460_A 14704.39 10.50227
rs7126418_T 14682.42 10.45758
rs7110818_T 14660.50 10.46488
rs7114362_T 14956.97 -10.32909
rs7936070_T 14884.23 10.55694
rs7936312_T 14884.58 10.57149
rs7936323_A 14880.83 10.58427
rs7936434_C 14885.54 10.52266
rs11236791_A 14693.35 10.49977
rs10160518_G 15009.29 -10.28801
rs11236797_A 14680.93 10.52660
rs7931483_A 14770.25 10.34397cbind(gwas[rownames(gwas) %in% snps2, ], pip[sort(cs$cs$L2)]) MAF missing.rate p.value.spa p.value.norm Stat
rs61894547_T 0.05155540 0 3.916273e-24 8.543620e-25 570.9379
rs55646091_A 0.05086819 0 5.110427e-25 9.672793e-26 572.9645
Var z
rs61894547_T 3083.681 10.28145 0.8391808
rs55646091_A 2983.742 10.48931 0.1529302Comment: those not in the CS either have larger pvalue or lower MAF. For example MAF around 0.45 vs. 0.47.
SNP rs61894547 is on the intron of gene EMSY, a known gene associated with risk of asthma.
Region 4
res = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res_susie/fit.susie.chr17_37500000_39000000.rds")
gwas = readRDS("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/res202404/gwas_chr17_37500000_39000000.rds")fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10] [1] 0.15054878 0.14440487 0.12546579 0.10494243 0.09420209 0.09279236
[7] 0.08840848 0.08325030 0.08116618 0.02647503class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs$cs
$cs$L1
[1] 1215 1218 1219 1226 1227 1232 1239 1241 1249
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.9946412 0.9984299 0.9992943
$cs_index
[1] 1
$coverage
[1] 0.9515849
$requested_coverage
[1] 0.95#par(mfrow = c(1,))
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20)
| Version | Author | Date |
|---|---|---|
| c6dd36b | yunqiyang0215 | 2024-04-16 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)]) MAF missing.rate p.value.spa p.value.norm Stat
rs11651596_C 0.4711579 0 9.947331e-36 9.618739e-36 -1570.315
rs12949100_A 0.4709323 0 7.665040e-36 7.409160e-36 -1572.964
rs8069176_A 0.4712393 0 1.199672e-35 1.160289e-35 -1568.498
rs4795399_C 0.4712166 0 8.096519e-36 7.827701e-36 -1573.030
rs2305480_A 0.4712096 0 9.136234e-36 8.833860e-36 -1571.808
rs11078926_A 0.4711895 0 9.466887e-36 9.153782e-36 -1571.440
rs11078927_T 0.4710100 0 1.059990e-35 1.024952e-35 -1570.176
rs12939832_A 0.4710054 0 8.304129e-36 8.027765e-36 -1572.232
rs4795400_T 0.4712255 0 7.079374e-36 6.843430e-36 -1573.472
Var z
rs11651596_C 15832.73 -12.47983 0.15054878
rs12949100_A 15833.46 -12.50060 0.14440487
rs8069176_A 15834.00 -12.46489 0.09279236
rs4795399_C 15845.87 -12.49623 0.12546579
rs2305480_A 15845.64 -12.48661 0.10494243
rs11078926_A 15845.40 -12.48378 0.09420209
rs11078927_T 15842.77 -12.47478 0.08325030
rs12939832_A 15834.88 -12.49422 0.08840848
rs4795400_T 15827.70 -12.50691 0.08116618
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin20.6.0 (64-bit)
Running under: macOS Monterey 12.0.1
Matrix products: default
BLAS: /usr/local/Cellar/openblas/0.3.18/lib/libopenblasp-r0.3.18.dylib
LAPACK: /usr/local/Cellar/r/4.1.1_1/lib/R/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] logisticsusie_0.0.0.9004 testthat_3.1.0 susieR_0.12.35
[4] survival_3.2-11 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rcpp_1.0.8.3 lattice_0.20-44 prettyunits_1.1.1 ps_1.6.0
[5] rprojroot_2.0.2 digest_0.6.28 utf8_1.2.2 R6_2.5.1
[9] plyr_1.8.6 RcppZiggurat_0.1.6 evaluate_0.14 highr_0.9
[13] ggplot2_3.4.3 pillar_1.9.0 rlang_1.1.1 rstudioapi_0.13
[17] irlba_2.3.5 whisker_0.4 callr_3.7.3 jquerylib_0.1.4
[21] Matrix_1.5-3 rmarkdown_2.11 desc_1.4.0 devtools_2.4.2
[25] splines_4.1.1 stringr_1.4.0 munsell_0.5.0 mixsqp_0.3-43
[29] compiler_4.1.1 httpuv_1.6.3 xfun_0.27 pkgconfig_2.0.3
[33] pkgbuild_1.2.0 htmltools_0.5.5 tidyselect_1.2.0 tibble_3.1.5
[37] matrixStats_0.63.0 reshape_0.8.9 fansi_0.5.0 crayon_1.4.1
[41] dplyr_1.0.7 withr_2.5.0 later_1.3.0 grid_4.1.1
[45] jsonlite_1.7.2 gtable_0.3.0 lifecycle_1.0.3 git2r_0.28.0
[49] magrittr_2.0.1 scales_1.2.1 Rfast_2.0.6 cli_3.6.1
[53] stringi_1.7.5 cachem_1.0.6 fs_1.5.0 promises_1.2.0.1
[57] remotes_2.4.2 bslib_0.4.1 ellipsis_0.3.2 generics_0.1.2
[61] vctrs_0.6.3 tools_4.1.1 glue_1.4.2 purrr_0.3.4
[65] parallel_4.1.1 processx_3.8.1 pkgload_1.2.3 fastmap_1.1.0
[69] yaml_2.2.1 colorspace_2.0-2 sessioninfo_1.1.1 memoise_2.0.1
[73] knitr_1.36 usethis_2.1.3 sass_0.4.4