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Coxph Susie result on all asthma/ AOA/ COA in UKBiobank.
library(survival)
library(susieR)
devtools::load_all("/Users/nicholeyang/Downloads/logisticsusie")
ℹ Loading logisticsusie
Marginal significant signals for COA, weak signals for AOA.
rs11071559_T was the one with smallest pvalue in all asthma, and PIP = 0.24. Carole’s paper also reported this one as the top signal. But in AOA, it’s not the one with smallest pval, the pip is a lot smaller.
region = "chr15_59000001_63400000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/all/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/all_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
print(res[[3]])
user system elapsed
48021.738 27994.163 4110.945
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10]
[1] 0.24346967 0.10867456 0.10424352 0.10165664 0.08854406 0.06504377
[7] 0.05878656 0.04595972 0.04407223 0.04344761
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
[1] 428 438 442 444 446 453 454 455 460 478 480 482 485 490 492 497 498 499 501
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.7701707 0.9518411 0.965287
$cs_index
[1] 1
$coverage
[1] 0.9584117
$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20, main = "CS 1")
Version | Author | Date |
---|---|---|
a4d10d1 | yunqiyang0215 | 2024-06-20 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
MAF missing.rate p.value.spa p.value.norm Stat
rs7183955_C 0.1857994 0 2.047135e-12 1.965395e-12 -686.9818
rs922783_G 0.1348981 0 7.063027e-12 6.654735e-12 -589.8232
rs12900122_T 0.1334614 0 2.805769e-12 2.621221e-12 -598.6492
rs12903966_T 0.1334992 0 2.479238e-12 2.313825e-12 -600.2511
rs16943087_G 0.1333576 0 4.033002e-12 3.778604e-12 -594.3751
rs2279294_C 0.1335081 0 1.268415e-11 1.199169e-11 -581.5727
rs2279293_G 0.1333651 0 1.466795e-11 1.388085e-11 -579.6246
rs2279292_C 0.1345950 0 5.626978e-12 5.290515e-12 -593.7446
rs8025689_C 0.1352865 0 8.350735e-12 7.879322e-12 -590.3153
rs12905602_A 0.1333781 0 7.234467e-12 6.809929e-12 -588.6392
rs11633029_C 0.1349144 0 1.688390e-11 1.601014e-11 -580.5839
rs11637671_G 0.1349347 0 1.629390e-11 1.544692e-11 -581.0378
rs11639084_T 0.1321305 0 1.409666e-11 1.332602e-11 -577.8021
rs10519067_A 0.1268612 0 2.796316e-12 2.598234e-12 -588.6569
rs10519068_A 0.1281122 0 1.097777e-12 1.012287e-12 -601.5405
rs11071557_C 0.1300621 0 1.018211e-12 9.399860e-13 -606.1218
rs34753162_C 0.1300892 0 9.385815e-13 8.658258e-13 -607.0330
rs34986765_C 0.1298710 0 1.199225e-12 1.108639e-12 -603.5777
rs11071559_T 0.1282230 0 4.231213e-13 3.864963e-13 -613.6394
Var z
rs7183955_C 9530.716 -7.036917 0.02401601
rs922783_G 7382.065 -6.864880 0.02646692
rs12900122_T 7320.864 -6.996668 0.05878656
rs12903966_T 7323.492 -7.014131 0.06504377
rs16943087_G 7324.006 -6.945224 0.04407223
rs2279294_C 7357.135 -6.780312 0.01930962
rs2279293_G 7353.770 -6.759145 0.01772081
rs2279292_C 7409.842 -6.897555 0.03527074
rs8025689_C 7446.700 -6.840725 0.02459909
rs12905602_A 7359.514 -6.861588 0.02795568
rs11633029_C 7423.555 -6.738435 0.01539183
rs11637671_G 7423.697 -6.743638 0.01567289
rs11639084_T 7294.842 -6.765053 0.01741284
rs10519067_A 7076.016 -6.997902 0.04595972
rs10519068_A 7120.226 -7.128826 0.10424352
rs11071557_C 7208.462 -7.139020 0.10165664
rs34753162_C 7207.339 -7.150309 0.10867456
rs34986765_C 7193.795 -7.116299 0.08854406
rs11071559_T 7143.786 -7.260208 0.24346967
rm(res, gwas, X, fit)
region = "chr15_59000001_63400000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/coa/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/coa_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
[1] 399 402 404 412 413 414 418 420 421 422 427 438 442 444 446 453 454 455 460
[20] 478 480 482 485 490 492 497 498 499 501
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.9257579 0.9692791 0.9758275
$cs_index
[1] 1
$coverage
[1] 0.9594382
$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.5, pch = 20, main = "CS 1")
Version | Author | Date |
---|---|---|
a4d10d1 | yunqiyang0215 | 2024-06-20 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
MAF missing.rate p.value.spa p.value.norm Stat
rs1351544_T 0.1341295 0 4.461386e-10 3.713514e-10 -269.0891
rs1817479_C 0.1344601 0 3.808552e-10 3.158273e-10 -270.4024
rs8025324_A 0.1344176 0 3.725567e-10 3.087450e-10 -270.5693
rs16943064_A 0.1364784 0 5.377680e-10 4.518186e-10 -269.8478
rs9920526_T 0.1343697 0 3.189260e-10 2.631108e-10 -270.7226
rs9920610_C 0.1348054 0 4.116391e-10 3.422020e-10 -269.6961
rs9920560_A 0.1338591 0 5.835716e-10 4.889988e-10 -267.0437
rs9920592_T 0.1339444 0 5.579596e-10 4.670002e-10 -267.6067
rs9920593_T 0.1339534 0 5.553709e-10 4.647776e-10 -267.6369
rs1020730_T 0.1339552 0 5.392532e-10 4.509357e-10 -267.8550
rs7162065_A 0.1339407 0 4.841433e-10 4.036654e-10 -268.7056
rs922783_G 0.1351647 0 6.584186e-11 5.189163e-11 -283.4299
rs12900122_T 0.1337288 0 8.097954e-11 6.399853e-11 -280.9145
rs12903966_T 0.1337666 0 7.256387e-11 5.715275e-11 -281.6921
rs16943087_G 0.1336174 0 2.102903e-10 1.710490e-10 -274.5713
rs2279294_C 0.1337616 0 1.231272e-10 9.858842e-11 -278.7902
rs2279293_G 0.1336176 0 1.649137e-10 1.331822e-10 -276.7600
rs2279292_C 0.1348538 0 1.395559e-10 1.124347e-10 -278.9279
rs8025689_C 0.1355442 0 1.067680e-10 8.547625e-11 -281.4096
rs12905602_A 0.1336304 0 4.023675e-10 3.334537e-10 -270.7834
rs11633029_C 0.1351697 0 5.155685e-10 4.315266e-10 -270.2153
rs11637671_G 0.1351902 0 5.001905e-10 4.183294e-10 -270.4283
rs11639084_T 0.1323729 0 3.352193e-10 2.756701e-10 -270.8541
rs10519067_A 0.1271050 0 1.167764e-10 9.181477e-11 -273.8719
rs10519068_A 0.1283646 0 1.319142e-10 1.044645e-10 -273.9002
rs11071557_C 0.1303221 0 1.019793e-10 8.049745e-11 -277.2735
rs34753162_C 0.1303492 0 1.012173e-10 7.988084e-11 -277.3011
rs34986765_C 0.1301288 0 8.559017e-11 6.714989e-11 -278.1494
rs11071559_T 0.1284794 0 9.641085e-11 7.556718e-11 -276.4440
Var z
rs1351544_T 1844.440 -6.265617 0.02106368
rs1817479_C 1847.604 -6.290803 0.02338094
rs8025324_A 1847.817 -6.294323 0.02372920
rs16943064_A 1873.125 -6.234986 0.01829921
rs9920526_T 1835.440 -6.319088 0.02661092
rs9920610_C 1845.268 -6.278342 0.02221628
rs9920560_A 1841.711 -6.222595 0.01785386
rs9920592_T 1845.204 -6.229810 0.01827960
rs9920593_T 1845.178 -6.230558 0.01833191
rs1020730_T 1845.380 -6.235292 0.01864945
rs7162065_A 1846.851 -6.252604 0.01989128
rs922783_G 1863.667 -6.565405 0.09146328
rs12900122_T 1848.325 -6.534087 0.07795816
rs12903966_T 1848.987 -6.551001 0.08590416
rs16943087_G 1849.039 -6.385311 0.03510412
rs2279294_C 1857.237 -6.469100 0.05450854
rs2279293_G 1856.368 -6.423493 0.04278518
rs2279292_C 1870.557 -6.449210 0.04876977
rs8025689_C 1879.766 -6.490633 0.06000053
rs12905602_A 1857.793 -6.282368 0.02189398
rs11633029_C 1873.906 -6.242175 0.01838518
rs11637671_G 1873.945 -6.247031 0.01872130
rs11639084_T 1841.423 -6.311878 0.02482659
rs10519067_A 1786.345 -6.479848 0.05514732
rs10519068_A 1797.518 -6.460345 0.04934584
rs11071557_C 1819.845 -6.499668 0.05895974
rs34753162_C 1819.559 -6.500825 0.05941099
rs34986765_C 1816.117 -6.526889 0.06868704
rs11071559_T 1803.695 -6.509170 0.06395982
rm(res, gwas, X, fit)
region = "chr15_59000001_63400000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/aoa/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/aoa_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
NULL
$coverage
NULL
$requested_coverage
[1] 0.95
rm(res, gwas, X, fit)
Very significant signals for COA, marginal significant signals for AOA.
All asthma has a very weird CS. One All asthma CS overlap with COA CS. AOA CS has no overlap with other CSs.
region = "chr2_102100001_105300000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/all/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/all_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
print(res[[3]])
user system elapsed
370760.90 210483.81 30824.19
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10]
[1] 0.45753680 0.05677487 0.05635246 0.05510944 0.05506850 0.05371545
[7] 0.04598294 0.04059181 0.03899255 0.03087550
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L2
[1] 2205 2210 2212 2213 2214 2216 2220 2223 2224 2226 2233 2237 2238 2240 2241
[16] 2247 2248 2250 2251 2254 2255 2256 2257 2258 2260 2262 2264 2265 2266 2270
[31] 2273 2274 2276 2277 2279 2281 2282 2283 2287 2292 2295 2296 2299 2301 2302
[46] 2306 2307 2309 2311 2317 2328 2329 2330 2331 2332 2333 2336 2342 2344 2350
[61] 2352
$cs$L1
[1] 2261 2267 2275 2280 2284 2285 2300 2304 2314 2323 2324 2362 2365 2367 2368
[16] 2369 2375 2384 2409
$purity
min.abs.corr mean.abs.corr median.abs.corr
L2 0.9919537 0.9983229 0.9987347
L1 0.9862647 0.9966765 0.9973417
$cs_index
[1] 2 1
$coverage
[1] 0.9524084 0.9600456
$requested_coverage
[1] 0.95
par(mfrow = c(1,2))
snps1 = colnames(X)[cs$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$L2]
colors <- ifelse(rownames(gwas) %in% snps2, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20, main = "CS 2")
Version | Author | Date |
---|---|---|
aa454ea | yunqiyang0215 | 2024-06-21 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
MAF missing.rate p.value.spa p.value.norm Stat
rs72823635_C 0.1380777 0 9.576229e-41 1.324452e-41 -1176.896
rs950881_T 0.1380726 0 9.748399e-41 1.349239e-41 -1176.738
rs10179458_T 0.1381013 0 7.403775e-41 1.012584e-41 -1178.446
rs72823641_A 0.1373666 0 4.732089e-42 5.654617e-43 -1193.554
rs10189154_T 0.1380466 0 6.148579e-41 8.334263e-42 -1179.481
rs10189526_T 0.1380464 0 6.114220e-41 8.285568e-42 -1179.512
rs11679893_A 0.1380527 0 6.375127e-41 8.656649e-42 -1179.217
rs10865050_A 0.1380920 0 5.368975e-41 7.237633e-42 -1180.414
rs12053429_T 0.1380637 0 6.694631e-41 9.108477e-42 -1178.781
rs59185885_G 0.1353869 0 2.241854e-41 2.845097e-42 -1167.877
rs58815545_T 0.1380667 0 6.716752e-41 9.140149e-42 -1178.782
rs3771180_T 0.1381264 0 5.608771e-41 7.588367e-42 -1180.484
rs72823646_A 0.1380350 0 3.541638e-41 4.678987e-42 -1183.517
rs13431828_T 0.1380081 0 3.139351e-41 4.123458e-42 -1184.197
rs13408569_C 0.1377062 0 2.707921e-41 3.529341e-42 -1182.912
rs13408661_A 0.1379078 0 2.726747e-41 3.557209e-42 -1184.521
rs10173081_T 0.1380032 0 2.770821e-41 3.618302e-42 -1184.931
rs3771175_A 0.1377430 0 5.047224e-41 6.738070e-42 -1180.195
rs10197862_G 0.1383215 0 5.426446e-41 7.337032e-42 -1181.441
Var z
rs72823635_C 7586.161 -13.51223 0.01274015
rs950881_T 7585.660 -13.51087 0.01250895
rs10179458_T 7583.970 -13.53198 0.01756912
rs72823641_A 7543.225 -13.74243 0.45753680
rs10189154_T 7581.257 -13.54628 0.02173272
rs10189526_T 7581.182 -13.54672 0.02188418
rs11679893_A 7580.990 -13.54350 0.02098567
rs10865050_A 7581.669 -13.55664 0.02550848
rs12053429_T 7579.567 -13.53976 0.01950383
rs59185885_G 7347.221 -13.62497 0.05677487
rs58815545_T 7579.865 -13.53951 0.01943143
rs3771180_T 7586.448 -13.55317 0.02007143
rs72823646_A 7585.757 -13.58861 0.03899255
rs13431828_T 7584.152 -13.59786 0.04598294
rs13408569_C 7555.054 -13.60923 0.05506850
rs13408661_A 7576.263 -13.60866 0.05510944
rs10173081_T 7582.898 -13.60741 0.05371545
rs3771175_A 7572.989 -13.56189 0.01948692
rs10197862_G 7595.980 -13.55564 0.01154937
cbind(gwas[rownames(gwas) %in% snps2, ], pip[sort(cs$cs$L2)])
MAF missing.rate p.value.spa p.value.norm Stat
rs1420091_C 0.4741893 0 0.04343415 0.04343426 -254.3545
rs4399750_C 0.4742024 0 0.04219033 0.04219043 -255.9176
rs2110660_G 0.4739445 0 0.04202196 0.04202206 -256.1081
rs1420090_C 0.4741980 0 0.04195790 0.04195801 -256.2307
rs7565653_A 0.4741983 0 0.04197044 0.04197054 -256.2175
rs7568913_C 0.4726704 0 0.03376352 0.03376353 -267.3910
rs10179654_G 0.4725454 0 0.03006019 0.03006016 -273.1701
rs4090473_G 0.4741994 0 0.04287977 0.04287988 -255.1204
rs12476925_T 0.4738162 0 0.04398899 0.04398911 -253.7171
rs12476968_T 0.4738706 0 0.04252796 0.04252807 -255.4688
rs6721346_C 0.4737530 0 0.04447458 0.04447471 -253.1289
rs10178436_C 0.4726547 0 0.03684838 0.03684842 -262.9061
rs11679191_T 0.4737891 0 0.04481519 0.04481532 -252.7314
rs11685424_A 0.4737905 0 0.04494659 0.04494673 -252.5758
rs11685480_A 0.4743119 0 0.04743351 0.04743351 -249.7420
rs6733174_C 0.4738143 0 0.04755366 0.04755366 -249.5822
rs6543118_A 0.4727228 0 0.04026244 0.04026251 -258.3334
rs1558622_A 0.4742182 0 0.04659919 0.04659919 -250.6703
rs1558621_G 0.4738770 0 0.04687411 0.04687411 -250.3313
rs10189202_G 0.4741925 0 0.04793089 0.04793089 -249.1883
rs10191914_C 0.4742151 0 0.04757686 0.04757686 -249.5846
rs10189711_G 0.4738113 0 0.04764314 0.04764314 -249.4869
rs12712135_G 0.4726901 0 0.03887182 0.03887189 -260.1599
rs1558620_C 0.4742148 0 0.04802466 0.04802466 -249.0875
rs1558619_T 0.4740595 0 0.04795157 0.04795157 -249.1596
rs12996505_G 0.4730879 0 0.03883659 0.03883666 -260.2388
rs13020793_T 0.4742242 0 0.04813716 0.04813716 -248.9577
rs10183388_T 0.4730986 0 0.03983078 0.03983086 -258.9293
rs953934_T 0.4729525 0 0.04103713 0.04103721 -257.3664
rs1968171_T 0.4742123 0 0.04794220 0.04794220 -249.1836
rs4613307_G 0.4742125 0 0.04796262 0.04796262 -249.1609
rs1968170_A 0.4742026 0 0.04758388 0.04758388 -249.5872
rs11123918_C 0.4741587 0 0.05052438 0.05052438 -246.3781
rs10182639_A 0.4743496 0 0.05203128 0.05203128 -244.7823
rs11690443_A 0.4744681 0 0.05095669 0.05095669 -245.9098
rs12712136_C 0.4744596 0 0.05064872 0.05064872 -246.2421
rs974389_A 0.4740822 0 0.05216000 0.05216000 -244.6285
rs4142132_A 0.4744654 0 0.05064178 0.05064178 -246.2548
rs971764_T 0.4739114 0 0.05168196 0.05168196 -245.1564
rs1420088_C 0.4743646 0 0.04918105 0.04918105 -247.8470
rs11123920_T 0.4743140 0 0.04989757 0.04989757 -247.0801
rs6706844_C 0.4739374 0 0.05133120 0.05133120 -245.5290
rs11675988_C 0.4762069 0 0.05698309 0.05698309 -239.4453
rs11679900_T 0.4745015 0 0.05533114 0.05533114 -241.2578
rs11676075_C 0.4742919 0 0.05031902 0.05031902 -246.6278
rs11123921_G 0.4742925 0 0.05032595 0.05032595 -246.6205
rs12992762_C 0.4743465 0 0.05600560 0.05600560 -240.5810
rs12998412_C 0.4745086 0 0.05516205 0.05516205 -241.4267
rs11123922_C 0.4745087 0 0.05516879 0.05516879 -241.4203
rs12725988_T 0.4742123 0 0.05333701 0.05333701 -243.1924
rs76520363_A 0.4743312 0 0.05234657 0.05234657 -244.4862
rs76278109_G 0.4743494 0 0.05592508 0.05592508 -240.6635
rs76886731_T 0.4746917 0 0.05296045 0.05296045 -243.5965
rs150341880_T 0.4746447 0 0.05611893 0.05611893 -240.4858
rs138087973_G 0.4745021 0 0.05349105 0.05349105 -243.1116
rs76498201_G 0.4745064 0 0.05347515 0.05347515 -243.1284
rs12996772_T 0.4742928 0 0.05040735 0.05040735 -246.5351
rs1420102_T 0.4741610 0 0.05231199 0.05231199 -244.5174
rs12466380_G 0.4742917 0 0.05051025 0.05051025 -246.4174
rs1997467_G 0.4743260 0 0.04768922 0.04768922 -249.4460
rs1997466_G 0.4743715 0 0.05098088 0.05098088 -245.9699
Var z
rs1420091_C 15863.06 -2.019510 0.020567980
rs4399750_C 15867.49 -2.031637 0.022447600
rs2110660_G 15865.11 -2.033302 0.022180323
rs1420090_C 15870.38 -2.033936 0.022911552
rs7565653_A 15870.70 -2.033812 0.022885254
rs7568913_C 15865.02 -2.122885 0.040591813
rs10179654_G 15857.26 -2.169297 0.056352463
rs4090473_G 15874.23 -2.024878 0.021416219
rs12476925_T 15867.06 -2.014195 0.018767742
rs12476968_T 15863.71 -2.028315 0.021014149
rs6721346_C 15866.06 -2.009589 0.018115441
rs10178436_C 15862.57 -2.087440 0.030875498
rs11679191_T 15866.84 -2.006384 0.017834895
rs11685424_A 15866.78 -2.005153 0.017675726
rs11685480_A 15870.72 -1.982409 0.015798015
rs6733174_C 15867.59 -1.981336 0.014815661
rs6543118_A 15863.92 -2.051046 0.023503809
rs1558622_A 15868.37 -1.989925 0.016641187
rs1558621_G 15865.14 -1.987436 0.015141082
rs10189202_G 15871.25 -1.977981 0.015177251
rs10191914_C 15871.21 -1.981129 0.015520890
rs10189711_G 15868.26 -1.980538 0.014732695
rs12712135_G 15864.02 -2.065541 0.026154895
rs1558620_C 15871.75 -1.977150 0.015064340
rs1558619_T 15870.54 -1.977797 0.014930782
rs12996505_G 15867.91 -2.065913 0.027628580
rs13020793_T 15871.17 -1.976156 0.014872891
rs10183388_T 15868.21 -2.055499 0.025444259
rs953934_T 15867.24 -2.043154 0.022750435
rs1968171_T 15872.26 -1.977880 0.015170485
rs4613307_G 15872.28 -1.977699 0.015149208
rs1968170_A 15872.55 -1.981066 0.015516929
rs11123918_C 15874.12 -1.955497 0.012995327
rs10182639_A 15873.42 -1.942875 0.012086474
rs11690443_A 15873.08 -1.951844 0.013046980
rs12712136_C 15873.70 -1.954444 0.013324062
rs974389_A 15870.85 -1.941811 0.011567172
rs4142132_A 15874.38 -1.954503 0.013322996
rs971764_T 15874.57 -1.945773 0.011583486
rs1420088_C 15876.33 -1.967019 0.014296830
rs11123920_T 15877.82 -1.960841 0.013555044
rs6706844_C 15875.06 -1.948701 0.011960113
rs11675988_C 15824.65 -1.903441 0.011531797
rs11679900_T 15850.78 -1.916267 0.010290338
rs11676075_C 15877.97 -1.957242 0.013213798
rs11123921_G 15877.98 -1.957183 0.013208009
rs12992762_C 15849.11 -1.910992 0.009792043
rs12998412_C 15850.96 -1.917598 0.010402649
rs11123922_C 15851.01 -1.917544 0.010396758
rs12725988_T 15841.77 -1.932182 0.010272990
rs76520363_A 15877.54 -1.940273 0.011192302
rs76278109_G 15849.57 -1.911619 0.009830752
rs76886731_T 15844.22 -1.935243 0.011286261
rs150341880_T 15851.18 -1.910111 0.009961313
rs138087973_G 15851.71 -1.930935 0.011417901
rs76498201_G 15851.78 -1.931064 0.011446676
rs12996772_T 15878.23 -1.956491 0.013134177
rs1420102_T 15876.94 -1.940557 0.011449107
rs12466380_G 15877.24 -1.955617 0.012973333
rs1997467_G 15869.63 -1.980127 0.014645578
rs1997466_G 15884.16 -1.951641 0.012393843
rm(res, gwas, X, fit)
region = "chr2_102100001_105300000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/coa/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/coa_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
[1] 2261 2267 2275 2280 2284 2285 2300 2304 2314 2318 2323 2324 2362 2365 2367
[16] 2368 2369 2375 2384 2409 2441 2469 2496
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.9808287 0.993961 0.9959249
$cs_index
[1] 1
$coverage
[1] 0.9571007
$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.5, pch = 20, main = "CS 1")
Version | Author | Date |
---|---|---|
aa454ea | yunqiyang0215 | 2024-06-21 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
MAF missing.rate p.value.spa p.value.norm Stat
rs72823635_C 0.1384102 0 3.640657e-36 3.586463e-38 -565.3715
rs950881_T 0.1384052 0 3.650732e-36 3.596916e-38 -565.3431
rs10179458_T 0.1384360 0 3.414756e-36 3.344086e-38 -565.5294
rs72823641_A 0.1377047 0 5.234953e-37 4.132563e-39 -571.0181
rs10189154_T 0.1383818 0 2.548608e-36 2.419064e-38 -566.5161
rs10189526_T 0.1383816 0 2.532777e-36 2.402474e-38 -566.5365
rs11679893_A 0.1383882 0 2.425671e-36 2.291413e-38 -566.6889
rs10865050_A 0.1384279 0 2.126636e-36 1.983525e-38 -567.2004
rs12053429_T 0.1383980 0 2.452400e-36 2.318132e-38 -566.5963
rs4625927_C 0.1360328 0 3.103566e-36 2.879335e-38 -558.4277
rs59185885_G 0.1357253 0 3.083702e-36 2.866879e-38 -557.1747
rs58815545_T 0.1384010 0 2.441741e-36 2.307155e-38 -566.6235
rs3771180_T 0.1384616 0 4.257845e-37 3.374196e-39 -573.2972
rs72823646_A 0.1383742 0 7.893597e-37 6.626818e-39 -571.0281
rs13431828_T 0.1383453 0 1.069497e-36 9.257254e-39 -569.8501
rs13408569_C 0.1380420 0 6.873934e-37 5.670846e-39 -570.3877
rs13408661_A 0.1382438 0 6.675565e-37 5.497518e-39 -571.2896
rs10173081_T 0.1383423 0 9.488544e-37 8.108849e-39 -570.2477
rs3771175_A 0.1380877 0 1.381360e-36 1.215203e-38 -568.5501
rs10197862_G 0.1386554 0 8.108652e-37 6.876994e-39 -571.2970
rs145573519_T 0.1386368 0 1.596594e-36 1.459462e-38 -564.9405
rs56179005_A 0.1386146 0 1.019437e-36 8.876248e-39 -570.0474
rs72823669_T 0.1384047 0 4.251175e-37 3.357369e-39 -572.6834
Var z
rs72823635_C 1915.629 -12.91749 0.01062986
rs950881_T 1915.503 -12.91727 0.01059881
rs10179458_T 1915.103 -12.92287 0.01146301
rs72823641_A 1905.031 -13.08274 0.10723183
rs10189154_T 1914.411 -12.94776 0.01590902
rs10189526_T 1914.392 -12.94829 0.01602472
rs11679893_A 1914.348 -12.95192 0.01687270
rs10865050_A 1914.531 -12.96299 0.01966084
rs12053429_T 1913.985 -12.95103 0.01665957
rs4625927_C 1863.986 -12.93438 0.01394483
rs59185885_G 1855.535 -12.93471 0.01386645
rs58815545_T 1914.062 -12.95140 0.01674456
rs3771180_T 1915.756 -13.09814 0.13722082
rs72823646_A 1915.608 -13.04681 0.06482151
rs13431828_T 1915.191 -13.02131 0.04476226
rs13408569_C 1907.841 -13.05867 0.07789837
rs13408661_A 1913.187 -13.06104 0.08058440
rs10173081_T 1914.890 -13.03142 0.05191360
rs3771175_A 1912.565 -13.00052 0.03332018
rs10197862_G 1918.242 -13.04398 0.05188656
rs145573519_T 1892.435 -12.98650 0.02369409
rs56179005_A 1915.573 -13.02452 0.03957983
rs72823669_T 1911.546 -13.09852 0.11447487
rm(res, gwas, X, fit)
region = "chr2_102100001_105300000"
res = readRDS(paste0("/Users/nicholeyang/Downloads/survivalsusie/result/asthma_self_report/result/aoa/fit.susie.", region, ".rds"))
gwas = readRDS(paste0("/Users/nicholeyang/downloads/survivalsusie/result/gwas_surv/aoa_gwas_", region, ".rds"))
fit = res[[1]]
X = res[[2]]
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
[1] 2234 2263 2268 2291 2340 2345 2348 2377 2386 2388 2397 2401 2422
$purity
min.abs.corr mean.abs.corr median.abs.corr
L1 0.9237923 0.9613092 0.9408626
$cs_index
[1] 1
$coverage
[1] 0.9553805
$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$cs$L1]
colors <- ifelse(rownames(gwas) %in% snps1, "red", "black")
plot(-log10(gwas[, "p.value.spa"]), col = colors, xlab = "SNP", ylab = "-log10(p-value)", cex = 0.8, pch = 20, main = "CS 1")
Version | Author | Date |
---|---|---|
aa454ea | yunqiyang0215 | 2024-06-21 |
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
MAF missing.rate p.value.spa p.value.norm Stat Var
rs12470864_A 0.3863606 0 3.271588e-13 3.219111e-13 682.5887 8779.527
rs13020553_G 0.3862615 0 3.197035e-13 3.145611e-13 682.9466 8781.230
rs950880_A 0.3863995 0 3.257407e-13 3.205116e-13 682.9326 8786.959
rs13001325_T 0.3861511 0 2.619143e-13 2.576125e-13 685.5612 8783.714
rs1420104_A 0.3861597 0 2.750791e-13 2.705829e-13 684.9582 8784.109
rs12479210_T 0.3864228 0 3.037822e-13 2.988743e-13 683.8086 8786.786
rs13019081_C 0.3862194 0 3.119249e-13 3.068887e-13 683.4134 8785.215
rs1420101_T 0.3817173 0 6.321158e-13 6.221672e-13 673.4475 8759.543
rs13001714_G 0.3936798 0 1.126760e-11 1.114961e-11 637.9647 8825.688
rs12712142_A 0.3936688 0 1.068219e-11 1.056966e-11 638.6946 8825.863
rs6543119_T 0.3934532 0 1.292892e-11 1.279536e-11 636.2281 8829.345
rs13017455_T 0.3934435 0 1.197511e-11 1.185034e-11 637.2591 8829.038
rs11123923_A 0.3936404 0 1.173727e-11 1.161501e-11 637.7911 8836.236
z
rs12470864_A 7.284899 0.114354967
rs13020553_G 7.288012 0.116707204
rs950880_A 7.285486 0.114628070
rs13001325_T 7.314879 0.139194856
rs1420104_A 7.308280 0.133206898
rs12479210_T 7.294903 0.121686457
rs13019081_C 7.291339 0.119155594
rs1420101_T 7.195534 0.073043494
rs13001714_G 6.790822 0.007546732
rs12712142_A 6.798524 0.007857954
rs6543119_T 6.770934 0.006868286
rs13017455_T 6.782024 0.007266150
rs11123923_A 6.784921 0.007488015
rm(res, gwas, X, fit)
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