• Description:
    • Region 1
    • Region 2
    • Region 3
    • Region 4

Last updated: 2024-06-27

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Description:

Coxph Susie result on all asthma/ AOA/ COA in UKBiobank.

library(survival)
library(susieR)
devtools::load_all("/Users/nicholeyang/Downloads/logisticsusie")
ℹ Loading logisticsusie

Region 1

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.

1. All asthma cases

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)

2. COA

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)

3. AOA

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)

Region 2

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.

1. All asthma cases

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)

2. COA

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)

3. AOA

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)

Region 3

No significant signals for COA, marginal significant signals for AOA.

1. All asthma cases

region = "chr2_143400001_147900000"
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 
71863.82 24439.76 11045.23 
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10]
 [1] 0.04544534 0.04473008 0.04449596 0.04117635 0.04089321 0.04065538
 [7] 0.04018651 0.03991979 0.03925386 0.03923206
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
 [1] 815 817 820 828 831 832 848 849 850 852 854 855 869 871 872 875 876 877 881
[20] 902 911 918 920 922 931 937 951 952 954 958 961 966 970


$purity
   min.abs.corr mean.abs.corr median.abs.corr
L1    0.9331464     0.9711805        0.989993

$cs_index
[1] 1

$coverage
[1] 0.9564943

$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$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
b700347 yunqiyang0215 2024-06-27
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
                   MAF missing.rate  p.value.spa p.value.norm      Stat
rs961414_T   0.4668426            0 1.181296e-05 1.180891e-05 -550.9762
rs7596680_G  0.4668163            0 1.153933e-05 1.153535e-05 -551.6466
rs4422110_T  0.4668991            0 1.226399e-05 1.225982e-05 -550.0536
rs1474011_A  0.4621279            0 6.734099e-06 6.731454e-06 -566.5605
rs1533426_A  0.4618248            0 6.133450e-06 6.130990e-06 -568.9658
rs6430080_A  0.4617999            0 6.167679e-06 6.165208e-06 -568.8491
rs13009175_T 0.4620989            0 6.926270e-06 6.923567e-06 -565.9236
rs7571606_A  0.4618362            0 6.019053e-06 6.016629e-06 -569.5354
rs10803511_C 0.4622585            0 6.846865e-06 6.844186e-06 -566.0754
rs2138445_C  0.4620487            0 7.082199e-06 7.079448e-06 -565.4754
rs9287372_G  0.4620392            0 6.763720e-06 6.761066e-06 -566.7569
rs10427255_T 0.4620517            0 7.085743e-06 7.082990e-06 -565.5130
rs1949330_G  0.4619910            0 6.964304e-06 6.961588e-06 -565.6939
rs7421123_A  0.4619928            0 7.207146e-06 7.204356e-06 -564.7179
rs1516135_T  0.4620021            0 7.416861e-06 7.414009e-06 -563.8953
rs1996287_T  0.4619772            0 7.358687e-06 7.355851e-06 -564.0431
rs1996286_T  0.4619781            0 7.309687e-06 7.306866e-06 -564.2206
rs2381726_C  0.4619797            0 7.299379e-06 7.296562e-06 -564.2531
rs2138448_G  0.4619789            0 7.353206e-06 7.350372e-06 -564.0179
rs6756212_T  0.4618215            0 8.740699e-06 8.737455e-06 -558.8317
rs13393501_C 0.4823639            0 2.274611e-05 2.273959e-05 -534.0060
rs1516145_T  0.4823836            0 2.249026e-05 2.248380e-05 -534.3840
rs2381712_G  0.4825300            0 1.801863e-05 1.801317e-05 -540.2935
rs2063862_G  0.4823982            0 2.334894e-05 2.334229e-05 -533.3349
rs34338764_T 0.4822215            0 2.434462e-05 2.433776e-05 -532.1429
rs4662420_A  0.4823360            0 2.303219e-05 2.302561e-05 -533.8011
rs1516141_G  0.4823333            0 2.282209e-05 2.281556e-05 -534.2390
rs10175039_T 0.4823268            0 2.268138e-05 2.267488e-05 -534.4205
rs10201277_C 0.4823297            0 2.292250e-05 2.291594e-05 -534.1428
rs10204857_G 0.4823336            0 2.206225e-05 2.205588e-05 -535.1424
rs6745444_G  0.4823034            0 2.040591e-05 2.039991e-05 -537.1892
rs6721116_A  0.4822490            0 2.032906e-05 2.032307e-05 -537.2026
rs12617922_A 0.4822474            0 1.963027e-05 1.962444e-05 -538.0827
                  Var         z           
rs961414_T   15816.20 -4.381086 0.02430087
rs7596680_G  15817.84 -4.386190 0.02475122
rs4422110_T  15822.23 -4.372915 0.02348297
rs1474011_A  15837.26 -4.502008 0.04117635
rs1533426_A  15832.36 -4.521821 0.04473008
rs6430080_A  15834.11 -4.520643 0.04449596
rs13009175_T 15843.76 -4.496025 0.04018651
rs7571606_A  15836.16 -4.525804 0.04544534
rs10803511_C 15834.98 -4.498477 0.04065538
rs2138445_C  15852.08 -4.491286 0.03925386
rs9287372_G  15854.81 -4.501075 0.04089321
rs10427255_T 15854.93 -4.491179 0.03923206
rs1949330_G  15839.11 -4.494860 0.03991979
rs7421123_A  15835.89 -4.487560 0.03874554
rs1516135_T  15832.93 -4.481443 0.03777104
rs1996287_T  15829.36 -4.483123 0.03807728
rs1996286_T  15829.26 -4.484548 0.03832149
rs2381726_C  15828.96 -4.484849 0.03837502
rs2138448_G  15826.82 -4.483282 0.03812738
rs6756212_T  15796.85 -4.446269 0.03278549
rs13393501_C 15891.02 -4.236139 0.02002611
rs1516145_T  15894.45 -4.238680 0.02016356
rs2381712_G  15874.89 -4.288194 0.02468269
rs2063862_G  15895.21 -4.230258 0.01952405
rs34338764_T 15894.82 -4.220855 0.01872521
rs4662420_A  15899.91 -4.233330 0.01976524
rs1516141_G  15910.52 -4.235390 0.01990042
rs10175039_T 15910.89 -4.236779 0.02001044
rs10201277_C 15912.21 -4.234403 0.01982594
rs10204857_G 15907.21 -4.242993 0.02052037
rs6745444_G  15897.91 -4.260468 0.02197360
rs6721116_A  15892.41 -4.261312 0.02206268
rs12617922_A 15886.24 -4.269121 0.02277305
rm(res, gwas, X, fit)

2. COA

region = "chr2_143400001_147900000"
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
NULL

$coverage
NULL

$requested_coverage
[1] 0.95
rm(res, gwas, X, fit)

3. AOA

region = "chr2_143400001_147900000"
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] 815 817 820 828 831 832 848 849 850 852 854 855 869 871 872 875 876 877 881
[20] 902 911 918 920 922 937 951 952 954 958 961 966 970


$purity
   min.abs.corr mean.abs.corr median.abs.corr
L1    0.9333614     0.9716784       0.9901346

$cs_index
[1] 1

$coverage
[1] 0.9573009

$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$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
b700347 yunqiyang0215 2024-06-27
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
                   MAF missing.rate  p.value.spa p.value.norm      Stat
rs961414_T   0.4667480            0 3.587785e-09 3.575483e-09 -567.2621
rs7596680_G  0.4667207            0 3.327163e-09 3.315650e-09 -568.4859
rs4422110_T  0.4668022            0 3.585143e-09 3.572852e-09 -567.3869
rs1474011_A  0.4620587            0 3.104126e-09 3.093126e-09 -569.9077
rs1533426_A  0.4617573            0 2.761546e-09 2.751611e-09 -571.6736
rs6430080_A  0.4617327            0 2.822795e-09 2.812667e-09 -571.3597
rs13009175_T 0.4620295            0 3.222931e-09 3.211561e-09 -569.4308
rs7571606_A  0.4617688            0 2.702730e-09 2.692982e-09 -572.0808
rs10803511_C 0.4621893            0 3.194542e-09 3.183262e-09 -569.4129
rs2138445_C  0.4619782            0 3.213446e-09 3.202104e-09 -569.6267
rs9287372_G  0.4619688            0 3.191584e-09 3.180309e-09 -569.7842
rs10427255_T 0.4619800            0 3.335727e-09 3.324006e-09 -569.0889
rs1949330_G  0.4619177            0 3.084471e-09 3.073529e-09 -570.0443
rs7421123_A  0.4619175            0 3.037673e-09 3.026878e-09 -570.2262
rs1516135_T  0.4619258            0 3.096824e-09 3.085844e-09 -569.8690
rs1996287_T  0.4619006            0 3.030006e-09 3.019233e-09 -570.1497
rs1996286_T  0.4619016            0 3.004928e-09 2.994233e-09 -570.2794
rs2381726_C  0.4619032            0 2.997264e-09 2.986593e-09 -570.3143
rs2138448_G  0.4619020            0 2.993832e-09 2.983173e-09 -570.2948
rs6756212_T  0.4617370            0 3.049255e-09 3.038412e-09 -569.4825
rs13393501_C 0.4822528            0 5.929335e-09 5.910926e-09 -560.5937
rs1516145_T  0.4822710            0 5.583078e-09 5.565618e-09 -561.6246
rs2381712_G  0.4824146            0 3.529489e-09 3.517830e-09 -568.6085
rs2063862_G  0.4822842            0 5.736962e-09 5.719080e-09 -561.2017
rs4662420_A  0.4822219            0 5.579847e-09 5.562396e-09 -561.7330
rs1516141_G  0.4822184            0 5.321629e-09 5.304891e-09 -562.6852
rs10175039_T 0.4822115            0 5.211446e-09 5.195013e-09 -563.0280
rs10201277_C 0.4822145            0 5.324454e-09 5.307708e-09 -562.7069
rs10204857_G 0.4822188            0 5.006536e-09 4.990674e-09 -563.6046
rs6745444_G  0.4821912            0 5.056727e-09 5.040721e-09 -563.2797
rs6721116_A  0.4821369            0 5.146031e-09 5.129776e-09 -562.8979
rs12617922_A 0.4821366            0 5.038611e-09 5.022653e-09 -563.1278
                  Var         z           
rs961414_T   9235.545 -5.902724 0.03077770
rs7596680_G  9236.497 -5.915154 0.03289158
rs4422110_T  9239.232 -5.902845 0.03081744
rs1474011_A  9247.003 -5.926578 0.03571079
rs1533426_A  9244.431 -5.945769 0.03966456
rs6430080_A  9245.459 -5.942174 0.03889306
rs13009175_T 9250.805 -5.920402 0.03455088
rs7571606_A  9246.632 -5.949296 0.04043099
rs10803511_C 9245.679 -5.921857 0.03482715
rs2138445_C  9255.655 -5.920887 0.03465308
rs9287372_G  9257.262 -5.922010 0.03485663
rs10427255_T 9257.397 -5.914740 0.03352549
rs1949330_G  9248.177 -5.927622 0.03593673
rs7421123_A  9246.242 -5.930134 0.03642080
rs1516135_T  9244.540 -5.926965 0.03579409
rs1996287_T  9242.470 -5.930549 0.03651092
rs1996286_T  9242.418 -5.931914 0.03677726
rs2381726_C  9242.245 -5.932333 0.03686159
rs2138448_G  9241.025 -5.932522 0.03689379
rs6756212_T  9224.086 -5.929509 0.03623298
rs13393501_C 9280.280 -5.819259 0.02361723
rs1516145_T  9282.343 -5.829313 0.02487404
rs2381712_G  9271.020 -5.905404 0.03712859
rs2063862_G  9282.826 -5.824771 0.02430319
rs4662420_A  9285.619 -5.829409 0.02487356
rs1516141_G  9291.907 -5.837314 0.02590142
rs10175039_T 9292.123 -5.840802 0.02636998
rs10201277_C 9292.906 -5.837226 0.02588728
rs10204857_G 9289.902 -5.847483 0.02730284
rs6745444_G  9284.468 -5.845822 0.02709423
rs6721116_A  9281.142 -5.842907 0.02670461
rs12617922_A 9277.564 -5.846420 0.02721545
rm(res, gwas, X, fit)

Region 4

Both very significant signals for AOA and COA, pval = 1e-20.

1. All asthma cases

region = "chr6_30500001_32100000"
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 
307736.2 114117.0  53094.4 
pip <- logisticsusie:::get_pip(fit$alpha)
effect_estimate <- colSums(fit$alpha * fit$mu)
pip.sorted = sort(pip, decreasing = TRUE)
pip.sorted[1:10]
 [1] 1.00000000 0.42929209 0.40183240 0.05935401 0.05427636 0.05280927
 [7] 0.05210378 0.04896134 0.04692130 0.04571521
class(fit) = "susie"
cs <- susie_get_cs(fit, X)
cs
$cs
$cs$L1
[1] 2501

$cs$L2
[1]   58 1262 1421 2056 2123 2136


$purity
   min.abs.corr mean.abs.corr median.abs.corr
L1    1.0000000     1.0000000       1.0000000
L2    0.8454127     0.9208507       0.9038682

$cs_index
[1] 1 2

$coverage
[1] 1.0000000 0.9522076

$requested_coverage
[1] 0.95
par(mfrow = c(1,2))
snps1 = colnames(X)[cs$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
b700347 yunqiyang0215 2024-06-27
print(snps1)
[1] "rs2428494_A"
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
                     [,1] [,2]
MAF          4.770845e-01    1
missing.rate 0.000000e+00    1
p.value.spa  2.359694e-51    1
p.value.norm 2.196762e-51    1
Stat         1.894944e+03    1
Var          1.579054e+04    1
z            1.507988e+01    1
cbind(gwas[rownames(gwas) %in% snps2, ], pip[sort(cs$cs$L2)])
                     MAF missing.rate  p.value.spa p.value.norm      Stat
rs4713451_C   0.08872864            0 3.316825e-13 2.852047e-13 -522.9695
rs9468965_A   0.07710370            0 1.821596e-13 1.512565e-13 -457.4414
rs114444221_G 0.06422248            0 2.759637e-12 2.328671e-12 -375.7347
rs113169753_A 0.08208212            0 1.052982e-11 9.428462e-12 -466.3420
rs113511111_A 0.08211077            0 1.016966e-11 9.101730e-12 -466.8795
rs111606016_C 0.08202514            0 1.144260e-11 1.025736e-11 -465.6304
                   Var         z           
rs4713451_C   5130.548 -7.301204 0.40183240
rs9468965_A   3835.717 -7.386049 0.42929209
rs114444221_G 2870.289 -7.013237 0.05427636
rs113169753_A 4682.540 -6.814968 0.02484358
rs113511111_A 4686.365 -6.820037 0.02549773
rs111606016_C 4684.914 -6.802844 0.02331646
rm(res, gwas, X, fit)

2. COA

region = "chr6_30500001_32100000"
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] 2501


$purity
   min.abs.corr mean.abs.corr median.abs.corr
L1            1             1               1

$cs_index
[1] 1

$coverage
[1] 0.9999816

$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$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
b700347 yunqiyang0215 2024-06-27
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
                     [,1]      [,2]
MAF          4.764637e-01 0.9999816
missing.rate 0.000000e+00 0.9999816
p.value.spa  1.580844e-23 0.9999816
p.value.norm 1.441063e-23 0.9999816
Stat         6.312377e+02 0.9999816
Var          3.980201e+03 0.9999816
z            1.000554e+01 0.9999816
rm(res, gwas, X, fit)

3. AOA

region = "chr6_30500001_32100000"
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] 2501


$purity
   min.abs.corr mean.abs.corr median.abs.corr
L1            1             1               1

$cs_index
[1] 1

$coverage
[1] 0.9999593

$requested_coverage
[1] 0.95
snps1 = colnames(X)[cs$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
b700347 yunqiyang0215 2024-06-27
cbind(gwas[rownames(gwas) %in% snps1, ], pip[sort(cs$cs$L1)])
                     [,1]      [,2]
MAF          4.753661e-01 0.9999593
missing.rate 0.000000e+00 0.9999593
p.value.spa  1.519018e-23 0.9999593
p.value.norm 1.472299e-23 0.9999593
Stat         9.597374e+02 0.9999593
Var          9.204670e+03 0.9999593
z            1.000342e+01 0.9999593
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