White Blood - WholeBlood
sheng Qian
2022-10-18
Last updated: 2023-01-26
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
[1] 13069[1] 11858
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1206 901 655 485 593 787 624 536 368 631 707 583 336 351 390 545
17 18 19 20 21 22
707 160 529 312 179 273 [1] 1Load ctwas results
Check convergence of parameters
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior) SNP gene
0.0002296 0.0136450 #estimated group prior variance
estimated_group_prior_var <- estimated_group_prior_var_all[,ncol(group_prior_var_rec)]
print(estimated_group_prior_var) SNP gene
18.16 22.91 #estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)[1] 59.42#report sample size
print(sample_size)[1] 350470#report group size
print(group_size) SNP gene
8696600 11858 #estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve) SNP gene
0.10348 0.01058 #total PVE
sum(estimated_group_pve)[1] 0.1141#attributable PVE
estimated_group_pve/sum(estimated_group_pve) SNP gene
0.90726 0.09274 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag id susie_pip mu2 PVE
2797 <NA> 6_24 cg05505711 0.9871 163.38 4.602e-04
10391 ACVRL1 12_32 cg21236262 0.9860 54.44 1.532e-04
6048 NLRC5 16_30 cg11987321 0.9791 45.92 1.283e-04
1346 MYO1C 17_2 cg02622416 0.9771 44.76 1.248e-04
11516 ITGAL 16_24 cg24033122 0.9681 152.53 4.213e-04
12041 <NA> 21_18 cg25269340 0.9601 62.06 1.700e-04
7147 AC034220.3;SLC22A5 5_79 cg14196790 0.9584 131.36 3.592e-04
5896 RP11-351M16.3 10_20 cg11671940 0.9545 125.03 3.405e-04
7197 CTC-503J8.4;MLLT1 19_6 cg14319541 0.9470 23.06 6.230e-05
11617 CTD-2330K9.3;MST1R 3_35 cg24308560 0.9463 67.85 1.832e-04
2492 <NA> 6_34 cg04903159 0.9461 24.08 6.500e-05
12817 CD6 11_34 cg27098804 0.9456 25.48 6.875e-05
4228 <NA> 20_38 cg08181326 0.9364 26.24 7.012e-05
2069 AP000908.1;ZBTB16 11_67 cg04049253 0.9357 77.08 2.058e-04
2428 <NA> 4_40 cg04797359 0.9142 34.54 9.008e-05
5455 LAPTM5 1_20 cg10709350 0.9132 38.28 9.975e-05
194 <NA> 8_83 cg00395667 0.9092 29.76 7.722e-05
2544 <NA> 4_8 cg05012661 0.9029 25.77 6.638e-05
10473 EOMES 3_20 cg21473142 0.8851 60.43 1.526e-04
4952 RP11-136O12.2 8_83 cg09660151 0.8820 35.71 8.986e-05
7938 <NA> 5_35 cg15854679 0.8709 40.59 1.009e-04
6315 CCT6A;PSPH 7_40 cg12555334 0.8686 49.07 1.216e-04
12390 <NA> 17_23 cg26106178 0.8549 65.49 1.597e-04
3814 PSMD3 17_23 cg07427276 0.8543 612.47 1.493e-03
7910 LSM4 19_15 cg15796753 0.8142 24.47 5.684e-05
z num_eqtl
2797 -1.928 1
10391 -7.281 1
6048 -6.699 1
1346 6.588 1
11516 12.478 1
12041 -7.930 1
7147 -13.657 1
5896 -11.496 1
7197 -4.440 1
11617 -8.414 1
2492 -4.669 1
12817 -4.279 1
4228 3.735 1
2069 -8.003 1
2428 -7.206 1
5455 6.601 1
194 5.231 1
2544 4.861 1
10473 7.702 1
4952 5.719 1
7938 -5.997 1
6315 -5.881 1
12390 12.877 1
3814 -37.218 1
7910 3.864 1Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag id susie_pip mu2
6148 HCP5;MICA 6_25 cg12182649 0.000e+00 6856.0
4857 YBEY 21_24 cg09417038 0.000e+00 5919.7
7897 PCNT 21_24 cg15764445 0.000e+00 4906.4
3051 PCNT 21_24 cg05998816 0.000e+00 2803.4
8 RNU6-1133P 6_25 cg00020172 0.000e+00 2013.3
10750 LSS;MCM3AP-AS1 21_24 cg22203077 0.000e+00 1830.9
5431 PRMT2 21_24 cg10657630 0.000e+00 1737.3
5940 SPATC1L 21_24 cg11766577 0.000e+00 1636.1
8353 DHFRP2 6_25 cg16655385 0.000e+00 1626.0
4931 HLA-B;HLA-C;XXbac-BPG248L24.12 6_25 cg09588770 0.000e+00 1502.9
3918 CCHCR1 6_25 cg07613268 0.000e+00 1057.1
12721 CDSN;PSORS1C1 6_25 cg26858414 0.000e+00 977.1
2645 <NA> 21_24 cg05200811 0.000e+00 902.2
8563 HLA-C 6_25 cg17096289 0.000e+00 874.5
10711 <NA> 6_25 cg22110654 2.325e-12 834.0
10735 CCHCR1;TCF19 6_25 cg22158929 0.000e+00 669.3
1951 HLA-C;XXbac-BPG248L24.13 6_25 cg03813170 0.000e+00 652.5
3814 PSMD3 17_23 cg07427276 8.543e-01 612.5
1946 CCHCR1;TCF19 6_25 cg03809032 0.000e+00 551.6
5740 <NA> 6_26 cg11294950 7.787e-01 540.0
PVE z num_eqtl
6148 0.000e+00 9.5997 1
4857 0.000e+00 -0.9051 1
7897 0.000e+00 4.4107 1
3051 0.000e+00 -3.9025 1
8 0.000e+00 16.6715 1
10750 0.000e+00 -3.3519 1
5431 0.000e+00 -3.1832 1
5940 0.000e+00 0.9963 1
8353 0.000e+00 10.9478 1
4931 0.000e+00 27.9121 1
3918 0.000e+00 6.8884 1
12721 0.000e+00 6.1315 1
2645 0.000e+00 0.2651 1
8563 0.000e+00 28.7994 1
10711 5.533e-15 -25.9180 1
10735 0.000e+00 -8.1247 1
1951 0.000e+00 -18.7958 1
3814 1.493e-03 -37.2183 1
1946 0.000e+00 -5.0970 1
5740 1.200e-03 -29.6301 1Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag id susie_pip mu2 PVE
3814 PSMD3 17_23 cg07427276 0.8543 612.47 0.0014930
5740 <NA> 6_26 cg11294950 0.7787 539.99 0.0011998
2797 <NA> 6_24 cg05505711 0.9871 163.38 0.0004602
315 ZFP57 6_23 cg00588198 0.6029 266.14 0.0004579
11516 ITGAL 16_24 cg24033122 0.9681 152.53 0.0004213
7147 AC034220.3;SLC22A5 5_79 cg14196790 0.9584 131.36 0.0003592
5896 RP11-351M16.3 10_20 cg11671940 0.9545 125.03 0.0003405
8546 TAPBP;ZBTB22 6_28 cg17055704 0.6555 148.72 0.0002782
2069 AP000908.1;ZBTB16 11_67 cg04049253 0.9357 77.08 0.0002058
10529 RPL13P;XXbac-BPG308K3.6 6_22 cg21598044 0.7513 90.17 0.0001933
11617 CTD-2330K9.3;MST1R 3_35 cg24308560 0.9463 67.85 0.0001832
11743 RP11-202G18.1 9_56 cg24562906 0.7508 79.37 0.0001700
12041 <NA> 21_18 cg25269340 0.9601 62.06 0.0001700
12390 <NA> 17_23 cg26106178 0.8549 65.49 0.0001597
10391 ACVRL1 12_32 cg21236262 0.9860 54.44 0.0001532
10473 EOMES 3_20 cg21473142 0.8851 60.43 0.0001526
7718 CERS5;RP4-605O3.4 12_31 cg15405652 0.7814 64.27 0.0001433
12033 HLA-F;HLA-F-AS1 6_23 cg25252977 0.1880 257.82 0.0001383
6048 NLRC5 16_30 cg11987321 0.9791 45.92 0.0001283
1346 MYO1C 17_2 cg02622416 0.9771 44.76 0.0001248
z num_eqtl
3814 -37.218 1
5740 -29.630 1
2797 -1.928 1
315 21.618 1
11516 12.478 1
7147 -13.657 1
5896 -11.496 1
8546 9.168 1
2069 -8.003 1
10529 14.802 1
11617 -8.414 1
11743 -10.604 1
12041 -7.930 1
12390 12.877 1
10391 -7.281 1
10473 7.702 1
7718 -9.154 1
12033 20.622 1
6048 -6.699 1
1346 6.588 1Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag id susie_pip mu2
3814 PSMD3 17_23 cg07427276 8.543e-01 612.5
10390 GSDMA 17_23 cg21230266 1.636e-11 473.7
9278 GSDMA 17_23 cg18632152 4.541e-08 339.0
5740 <NA> 6_26 cg11294950 7.787e-01 540.0
8563 HLA-C 6_25 cg17096289 0.000e+00 874.5
7137 SKIV2L 6_26 cg14178705 6.117e-14 474.2
4931 HLA-B;HLA-C;XXbac-BPG248L24.12 6_25 cg09588770 0.000e+00 1502.9
12413 GSDMA 17_23 cg26162295 5.256e-06 246.2
9055 BTNL2 6_26 cg18067840 0.000e+00 402.1
10711 <NA> 6_25 cg22110654 2.325e-12 834.0
2316 DPCR1;HCG21 6_24 cg04559908 5.488e-05 483.6
8374 HCG9 6_24 cg16690356 2.549e-08 445.9
3587 <NA> 6_26 cg07007382 0.000e+00 291.1
8307 SLC44A4 6_26 cg16553272 0.000e+00 373.3
3780 SLC44A4 6_26 cg07363637 0.000e+00 373.3
315 ZFP57 6_23 cg00588198 6.029e-01 266.1
12636 SFTA2 6_25 cg26672776 0.000e+00 467.4
6792 SFTA2 6_25 cg13561028 0.000e+00 467.4
11249 <NA> 6_24 cg23397584 2.845e-10 363.7
11864 HCG4B;HLA-K 6_24 cg24838316 1.105e-03 328.4
PVE z num_eqtl
3814 1.493e-03 -37.22 1
10390 2.211e-14 35.66 1
9278 4.392e-11 -30.49 1
5740 1.200e-03 -29.63 1
8563 0.000e+00 28.80 1
7137 8.276e-17 -28.30 1
4931 0.000e+00 27.91 1
12413 3.693e-09 -27.47 1
9055 0.000e+00 26.85 1
10711 5.533e-15 -25.92 1
2316 7.571e-08 23.82 1
8374 3.243e-11 23.76 1
3587 0.000e+00 23.31 1
8307 0.000e+00 22.39 1
3780 0.000e+00 22.39 1
315 4.579e-04 21.62 1
12636 0.000e+00 21.34 1
6792 0.000e+00 21.34 1
11249 2.953e-13 -21.34 1
11864 1.035e-06 -21.20 1Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
#number of significant z scores
sum(abs(ctwas_gene_res$z) > sig_thresh)[1] 501#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.04225#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag id susie_pip mu2
3814 PSMD3 17_23 cg07427276 8.543e-01 612.5
10390 GSDMA 17_23 cg21230266 1.636e-11 473.7
9278 GSDMA 17_23 cg18632152 4.541e-08 339.0
5740 <NA> 6_26 cg11294950 7.787e-01 540.0
8563 HLA-C 6_25 cg17096289 0.000e+00 874.5
7137 SKIV2L 6_26 cg14178705 6.117e-14 474.2
4931 HLA-B;HLA-C;XXbac-BPG248L24.12 6_25 cg09588770 0.000e+00 1502.9
12413 GSDMA 17_23 cg26162295 5.256e-06 246.2
9055 BTNL2 6_26 cg18067840 0.000e+00 402.1
10711 <NA> 6_25 cg22110654 2.325e-12 834.0
2316 DPCR1;HCG21 6_24 cg04559908 5.488e-05 483.6
8374 HCG9 6_24 cg16690356 2.549e-08 445.9
3587 <NA> 6_26 cg07007382 0.000e+00 291.1
8307 SLC44A4 6_26 cg16553272 0.000e+00 373.3
3780 SLC44A4 6_26 cg07363637 0.000e+00 373.3
315 ZFP57 6_23 cg00588198 6.029e-01 266.1
12636 SFTA2 6_25 cg26672776 0.000e+00 467.4
6792 SFTA2 6_25 cg13561028 0.000e+00 467.4
11249 <NA> 6_24 cg23397584 2.845e-10 363.7
11864 HCG4B;HLA-K 6_24 cg24838316 1.105e-03 328.4
PVE z num_eqtl
3814 1.493e-03 -37.22 1
10390 2.211e-14 35.66 1
9278 4.392e-11 -30.49 1
5740 1.200e-03 -29.63 1
8563 0.000e+00 28.80 1
7137 8.276e-17 -28.30 1
4931 0.000e+00 27.91 1
12413 3.693e-09 -27.47 1
9055 0.000e+00 26.85 1
10711 5.533e-15 -25.92 1
2316 7.571e-08 23.82 1
8374 3.243e-11 23.76 1
3587 0.000e+00 23.31 1
8307 0.000e+00 22.39 1
3780 0.000e+00 22.39 1
315 4.579e-04 21.62 1
12636 0.000e+00 21.34 1
6792 0.000e+00 21.34 1
11249 2.953e-13 -21.34 1
11864 1.035e-06 -21.20 1SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag id.1 susie_pip mu2 PVE
31374 rs6679677 1_70 rs6679677 1.0000 147.63 4.212e-04
35617 rs34599082 1_78 rs34599082 1.0000 185.66 5.297e-04
118778 rs3835963 2_110 rs3835963 1.0000 108.00 3.082e-04
222971 rs1371794 4_51 rs1371794 1.0000 478.82 1.366e-03
225794 rs529050451 4_56 rs529050451 1.0000 754.06 2.152e-03
382085 rs56388170 7_24 rs56388170 1.0000 370.92 1.058e-03
401128 rs79912251 7_56 rs79912251 1.0000 147.20 4.200e-04
634150 rs79490353 13_7 rs79490353 1.0000 193.91 5.533e-04
787253 rs12461480 19_4 rs12461480 1.0000 219.56 6.265e-04
800100 rs73036520 19_31 rs73036520 1.0000 234.53 6.692e-04
835202 rs13049759 21_24 rs13049759 1.0000 21500.25 6.135e-02
835204 rs13049893 21_24 rs13049893 1.0000 21593.63 6.161e-02
901161 rs200599211 5_79 rs200599211 1.0000 2066.61 5.897e-03
932297 rs201369106 6_25 rs201369106 1.0000 13326.40 3.802e-02
63486 rs12239046 1_131 rs12239046 1.0000 100.16 2.858e-04
73211 rs780093 2_16 rs780093 1.0000 139.87 3.991e-04
325911 rs1233385 6_23 rs1233385 1.0000 291.24 8.310e-04
9003 rs3917932 1_22 rs3917932 1.0000 211.24 6.027e-04
786406 rs74253465 19_2 rs74253465 1.0000 69.89 1.994e-04
401101 rs740047 7_56 rs740047 1.0000 82.25 2.347e-04
327568 rs41258084 6_27 rs41258084 1.0000 191.23 5.456e-04
467073 rs7462429 8_92 rs7462429 1.0000 93.87 2.678e-04
176143 rs372491637 3_80 rs372491637 1.0000 95.08 2.713e-04
443238 rs4738814 8_46 rs4738814 1.0000 223.03 6.364e-04
381946 rs177448 7_23 rs177448 1.0000 67.37 1.922e-04
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800077 -4.8148
787244 -1.1756
737345 7.8304
326117 13.1014
527812 6.8721
501554 -6.7645
384639 -3.9061
432856 -9.2035
779704 -5.7062
358935 -2.5202
301033 -5.1644
225741 -12.5319
750807 -5.2449
775488 7.5188
318417 7.6921
921835 -11.0071
407888 5.7559
677994 6.7202
801230 5.8812
443269 -9.7350
615577 -9.0244
261657 -7.6311
109903 -6.6131
511803 5.8663
321587 -8.1734
587170 -10.0269
411742 -7.0822
799759 -5.2550
411752 6.0194
865402 7.4448
661877 5.3312
92903 -6.8900
448662 -5.7235
634152 8.8376
816179 6.5292
738037 5.0321
331263 -4.9938
622870 -5.3274
25352 -5.5066
835836 -5.6685
366006 7.2656
563969 10.5731
7850 6.5043
435466 -5.4102
9006 9.8609
306000 -5.2876
998493 8.8972
802140 -3.9295
726075 5.1851
477419 -9.6543
534747 2.5648
428360 -5.0532
207186 -5.2962
128969 -6.1809
9695 6.4568
742691 -5.8528
703049 -8.5754
59605 -14.3656
325943 2.0906
154921 -5.2736
231885 5.4852
685357 5.8530
232573 5.8609
402617 -5.9039
387417 6.6561
481327 -4.5565
373475 7.1263
882499 -6.0848
489795 -7.2362
7092 -7.0539
211307 6.3327
787947 -5.4390
401205 -5.2273
83140 5.6673
750371 7.4417
78668 4.2466
36123 5.7613
746035 -8.3444
705096 -5.1295
761396 5.3438
791650 -9.2524
552602 -5.7716
35616 5.9213
193708 4.6439
176045 -6.2026
324837 -8.0370
604509 -5.5136
808726 -5.4026
318411 5.5926
382082 7.7822
159397 -5.4082
755913 4.7536
671949 -4.7561
448622 -9.4469
107620 -8.7371
462370 7.7217
677825 6.6888
79886 5.2992
784557 4.7579
118771 8.2415
759182 -6.3725
404576 4.8593
750515 5.8237
403154 -3.9742
138436 -4.7395
578947 6.2129
797123 5.1593
1069369 6.9349
786403 6.1367
565369 -5.3299
432477 -4.7407
639265 4.6345
412775 -5.5257
462893 -13.9906
725738 5.2069
663809 10.8893
739909 -6.1756
326074 -3.0302
583902 6.6195
660635 -4.9404
758646 8.3819
804696 4.5892
547711 11.2616
625236 -5.3471
490123 -4.4269
59281 7.0545
739317 5.4841
128869 4.7805
696258 -12.5555
212287 7.4784
701240 -4.5367
737688 -5.3814
325815 9.2044
49988 -5.3338
540236 -5.0133
689249 6.8797
50502 10.2518
297180 -7.5808
222403 -8.0809
123822 5.2549
331730 -4.6455
416990 4.9752
612867 -5.2670
626271 6.4063
322931 4.1644
462923 -6.9790
427217 6.6410
323909 -4.7844
142269 4.5317
543916 5.9897
377042 -4.6325
323668 5.0209
719780 3.8732
788677 -8.0320
610139 -4.7285
470385 -6.5608
534799 -4.4662
216819 -9.0312
547710 14.0583
823859 -4.8715
696257 0.8149
589127 -4.5946
608569 6.8687
502925 5.2746
388392 -4.6874
470419 7.5243
300678 -4.8858
326148 -5.8011
36414 -8.1097
714132 4.7238
152747 5.4477
593119 4.5310
212065 -4.4771
584080 4.8481
441691 -6.4066
586371 -4.7591
835588 4.4313
677846 -6.2664
373028 5.3596
732167 4.7348
909363 8.5058
426812 3.0103
817341 -4.6724
789659 7.1676
73236 3.3681
88428 -5.2861
698623 4.7005
703177 -4.5684
837817 -5.3105
318234 4.5511
715531 3.5242
824541 5.1986
462905 -6.2508
79015 -6.1157
287452 -4.8603
95086 5.5006
118761 -1.8327
949107 7.7213
5828 4.8663
86930 4.9226
232619 -4.4658
350229 4.7105
20760 4.5431
905144 -8.3518
585110 7.9835
151749 -10.1402
72716 6.0653
434021 -5.0223
328719 4.3281
279111 -5.0365
711572 -4.5188
26318 4.8992
380301 6.5229
849846 -6.3056
166604 5.2481
784560 -4.4409
609321 -5.3758
742660 4.3527
328051 6.8084
526287 -5.4996
193627 -4.0308
799241 8.2187
649437 -4.5407
39351 -4.3837
470074 -4.1988
98403 6.3095
359624 -4.4391
193745 -4.5603
688294 -4.9442
807994 -3.8163
323250 -5.4925
374130 -6.4972
622691 -7.5514
583229 4.1932
176112 -5.9429
835666 5.0353
49792 5.4575
401121 -11.6557
112355 -8.3440SNPs with largest effect sizes
#plot PIP vs effect size
#plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag id.1 susie_pip mu2 PVE z
835204 rs13049893 21_24 rs13049893 1.000e+00 21594 6.161e-02 4.818
835202 rs13049759 21_24 rs13049759 1.000e+00 21500 6.135e-02 4.970
835189 rs13052701 21_24 rs13052701 0.000e+00 18195 0.000e+00 4.944
835192 rs147757092 21_24 rs147757092 0.000e+00 16599 0.000e+00 4.614
835235 rs11702800 21_24 rs11702800 0.000e+00 15944 0.000e+00 -5.560
835248 rs2268523 21_24 rs2268523 0.000e+00 15904 0.000e+00 -5.586
835245 rs9981445 21_24 rs9981445 0.000e+00 15640 0.000e+00 -5.570
835265 rs2839257 21_24 rs2839257 0.000e+00 15533 0.000e+00 -5.710
835229 rs11909986 21_24 rs11909986 0.000e+00 13972 0.000e+00 6.006
835183 rs35679325 21_24 rs35679325 0.000e+00 13612 0.000e+00 -5.090
835200 rs4819221 21_24 rs4819221 0.000e+00 13565 0.000e+00 5.060
835208 rs4818832 21_24 rs4818832 0.000e+00 13465 0.000e+00 5.082
835207 rs62226478 21_24 rs62226478 0.000e+00 13463 0.000e+00 5.087
835205 rs34586434 21_24 rs34586434 0.000e+00 13438 0.000e+00 5.094
932295 rs3869131 6_25 rs3869131 1.874e-01 13390 7.159e-03 14.047
932294 rs3869130 6_25 rs3869130 3.170e-01 13390 1.211e-02 14.055
932291 rs3899823 6_25 rs3899823 4.323e-01 13389 1.652e-02 14.063
932307 rs34259803 6_25 rs34259803 4.038e-02 13386 1.542e-03 14.034
932292 rs3869129 6_25 rs3869129 2.288e-02 13382 8.738e-04 14.056
932284 rs73404245 6_25 rs73404245 1.582e-08 13343 6.024e-10 13.998
932297 rs201369106 6_25 rs201369106 1.000e+00 13326 3.802e-02 1.719
932303 rs7769942 6_25 rs7769942 0.000e+00 13212 0.000e+00 14.605
932281 rs566631013 6_25 rs566631013 0.000e+00 12252 0.000e+00 12.820
932279 rs373478096 6_25 rs373478096 0.000e+00 12252 0.000e+00 12.820
932280 rs200256874 6_25 rs200256874 0.000e+00 12252 0.000e+00 12.820
835196 rs2032226 21_24 rs2032226 0.000e+00 11617 0.000e+00 5.361
835190 rs4535149 21_24 rs4535149 0.000e+00 11518 0.000e+00 5.360
931939 rs9501109 6_25 rs9501109 0.000e+00 11156 0.000e+00 16.607
835180 rs2001809 21_24 rs2001809 0.000e+00 11011 0.000e+00 3.733
932006 rs60539855 6_25 rs60539855 0.000e+00 10504 0.000e+00 16.465
932373 rs4458721 6_25 rs4458721 0.000e+00 10381 0.000e+00 16.419
932309 rs73404265 6_25 rs73404265 0.000e+00 10106 0.000e+00 13.695
932302 rs7769403 6_25 rs7769403 0.000e+00 10105 0.000e+00 13.693
835185 rs4819218 21_24 rs4819218 0.000e+00 9960 0.000e+00 4.926
835184 rs2280959 21_24 rs2280959 0.000e+00 9946 0.000e+00 4.934
835270 rs2073381 21_24 rs2073381 0.000e+00 9882 0.000e+00 4.936
932081 rs114921825 6_25 rs114921825 0.000e+00 9681 0.000e+00 13.705
932051 rs117813801 6_25 rs117813801 0.000e+00 9680 0.000e+00 13.870
932052 rs143741838 6_25 rs143741838 0.000e+00 9680 0.000e+00 13.871
932076 rs4090415 6_25 rs4090415 0.000e+00 9676 0.000e+00 13.768
932074 rs4090417 6_25 rs4090417 0.000e+00 9675 0.000e+00 13.767
932075 rs4090416 6_25 rs4090416 0.000e+00 9675 0.000e+00 13.767
932072 rs9501116 6_25 rs9501116 0.000e+00 9675 0.000e+00 13.764
932071 rs9501115 6_25 rs9501115 0.000e+00 9674 0.000e+00 13.765
932276 rs28575156 6_25 rs28575156 0.000e+00 9670 0.000e+00 13.707
932109 rs2394999 6_25 rs2394999 0.000e+00 9666 0.000e+00 13.725
932108 rs2394998 6_25 rs2394998 0.000e+00 9666 0.000e+00 13.725
932103 rs2894212 6_25 rs2894212 0.000e+00 9666 0.000e+00 13.727
932105 rs4631325 6_25 rs4631325 0.000e+00 9666 0.000e+00 13.727
932101 rs80339222 6_25 rs80339222 0.000e+00 9666 0.000e+00 13.726SNPs with highest PVE
#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50) id region_tag id.1 susie_pip mu2 PVE z
835204 rs13049893 21_24 rs13049893 1.00000 21593.6 0.0616133 4.818
835202 rs13049759 21_24 rs13049759 1.00000 21500.3 0.0613469 4.970
932297 rs201369106 6_25 rs201369106 1.00000 13326.4 0.0380244 1.719
932291 rs3899823 6_25 rs3899823 0.43233 13388.9 0.0165161 14.063
932294 rs3869130 6_25 rs3869130 0.31703 13389.7 0.0121120 14.055
932295 rs3869131 6_25 rs3869131 0.18738 13389.7 0.0071590 14.047
927120 rs2524079 6_25 rs2524079 1.00000 2422.4 0.0069118 32.392
901161 rs200599211 5_79 rs200599211 1.00000 2066.6 0.0058967 2.454
933594 rs2229094 6_25 rs2229094 0.99999 1621.4 0.0046264 19.438
932524 rs9267123 6_25 rs9267123 1.00000 917.2 0.0026171 -28.182
225794 rs529050451 4_56 rs529050451 1.00000 754.1 0.0021516 1.632
622682 rs653178 12_67 rs653178 0.99999 581.0 0.0016579 -26.382
932307 rs34259803 6_25 rs34259803 0.04038 13386.5 0.0015422 14.034
222971 rs1371794 4_51 rs1371794 1.00000 478.8 0.0013662 -23.338
382085 rs56388170 7_24 rs56388170 1.00000 370.9 0.0010584 20.722
225797 rs9654217 4_56 rs9654217 0.43615 756.3 0.0009412 7.152
225798 rs9654218 4_56 rs9654218 0.43166 756.3 0.0009315 7.151
225741 rs6814526 4_56 rs6814526 0.99980 319.9 0.0009125 -12.532
932292 rs3869129 6_25 rs3869129 0.02288 13381.7 0.0008738 14.056
326102 rs2736155 6_26 rs2736155 1.00000 291.9 0.0008329 20.551
325911 rs1233385 6_23 rs1233385 1.00000 291.2 0.0008310 -22.798
901175 rs13361914 5_79 rs13361914 0.13082 2056.6 0.0007677 -1.754
901182 rs77753417 5_79 rs77753417 0.12692 2056.6 0.0007448 -1.754
325652 rs67040724 6_21 rs67040724 0.73094 353.6 0.0007375 -18.453
901178 rs113694337 5_79 rs113694337 0.12001 2056.6 0.0007042 -1.752
800100 rs73036520 19_31 rs73036520 1.00000 234.5 0.0006692 -14.726
922264 rs1264349 6_24 rs1264349 0.45594 505.0 0.0006570 -24.787
443238 rs4738814 8_46 rs4738814 1.00000 223.0 0.0006364 -10.895
787253 rs12461480 19_4 rs12461480 1.00000 219.6 0.0006265 -7.689
9003 rs3917932 1_22 rs3917932 1.00000 211.2 0.0006027 -15.876
326220 rs1265888 6_26 rs1265888 0.77081 269.7 0.0005931 -25.469
634150 rs79490353 13_7 rs79490353 1.00000 193.9 0.0005533 11.936
327568 rs41258084 6_27 rs41258084 1.00000 191.2 0.0005456 12.311
696258 rs72726030 15_15 rs72726030 0.94193 201.6 0.0005419 -12.555
358910 rs56293029 6_89 rs56293029 0.61074 307.3 0.0005355 -16.997
59605 rs10803164 1_122 rs10803164 0.99288 187.7 0.0005319 -14.366
35617 rs34599082 1_78 rs34599082 1.00000 185.7 0.0005297 -14.011
514089 rs112955547 10_18 rs112955547 0.47423 387.1 0.0005239 -18.443
921835 rs3131063 6_24 rs3131063 0.99961 183.2 0.0005225 -11.007
327497 rs9276625 6_27 rs9276625 0.54195 328.7 0.0005083 -20.936
916461 rs9260620 6_24 rs9260620 0.65479 272.0 0.0005082 -21.715
326148 rs115487693 6_26 rs115487693 0.90389 191.7 0.0004945 -5.801
787244 rs2041128 19_4 rs2041128 0.99995 167.9 0.0004791 -1.176
326117 rs1266074 6_26 rs1266074 0.99994 162.3 0.0004631 13.101
327505 rs4947258 6_27 rs4947258 0.45805 328.3 0.0004291 -20.922
31374 rs6679677 1_70 rs6679677 1.00000 147.6 0.0004212 -12.101
401128 rs79912251 7_56 rs79912251 1.00000 147.2 0.0004200 -10.952
514086 rs9804265 10_18 rs9804265 0.37773 386.7 0.0004167 -18.438
73211 rs780093 2_16 rs780093 1.00000 139.9 0.0003991 -11.914
663809 rs9323580 14_4 rs9323580 0.95592 145.5 0.0003970 10.889SNPs with largest z scores
#histogram of (abs) SNP z scores
hist(abs(ctwas_snp_res$z))
#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50) id region_tag id.1 susie_pip mu2 PVE z
1039597 rs8078723 17_23 rs8078723 9.637e-03 608.5 1.673e-05 37.33
1039634 rs709592 17_23 rs709592 1.111e-02 609.6 1.933e-05 37.32
1039635 rs709591 17_23 rs709591 9.442e-03 608.9 1.641e-05 37.31
1039589 rs55771023 17_23 rs55771023 7.256e-03 607.5 1.258e-05 37.30
1039588 rs55736351 17_23 rs55736351 6.861e-03 607.3 1.189e-05 37.30
1039553 rs4795414 17_23 rs4795414 4.977e-03 605.6 8.600e-06 37.30
1039554 rs4795415 17_23 rs4795415 4.970e-03 605.6 8.588e-06 37.30
1039555 rs4795416 17_23 rs4795416 4.946e-03 605.6 8.547e-06 37.30
1039564 rs8065519 17_23 rs8065519 4.933e-03 605.6 8.525e-06 37.30
1039549 rs56922390 17_23 rs56922390 4.664e-03 605.4 8.057e-06 37.29
1039575 rs12602172 17_23 rs12602172 4.551e-03 605.6 7.864e-06 37.29
1039548 rs4794822 17_23 rs4794822 4.288e-03 605.2 7.404e-06 37.29
1039583 rs55846942 17_23 rs55846942 4.522e-03 605.6 7.813e-06 37.29
1039573 rs12600856 17_23 rs12600856 4.452e-03 605.5 7.691e-06 37.29
1039594 rs4795419 17_23 rs4795419 4.560e-03 605.7 7.880e-06 37.29
1039595 rs4794823 17_23 rs4794823 4.314e-03 605.5 7.454e-06 37.29
1039567 rs8070454 17_23 rs8070454 3.899e-03 605.0 6.729e-06 37.29
1039586 rs4795418 17_23 rs4795418 4.014e-03 605.3 6.932e-06 37.29
1039569 rs6503530 17_23 rs6503530 3.779e-03 604.9 6.522e-06 37.28
1039590 rs7221894 17_23 rs7221894 3.446e-03 604.7 5.946e-06 37.28
1039631 rs2227336 17_23 rs2227336 3.358e-03 605.0 5.796e-06 37.27
1039618 rs2227322 17_23 rs2227322 2.952e-03 604.5 5.091e-06 37.27
1039629 rs1042658 17_23 rs1042658 3.029e-03 604.6 5.226e-06 37.27
1039639 rs11871747 17_23 rs11871747 2.096e-03 604.8 3.617e-06 37.26
1039607 rs7208815 17_23 rs7208815 3.078e-03 604.6 5.310e-06 37.26
1039600 rs4795420 17_23 rs4795420 2.441e-03 603.7 4.204e-06 37.26
1039599 rs62065177 17_23 rs62065177 2.408e-03 603.6 4.147e-06 37.26
1039609 rs7214085 17_23 rs7214085 2.336e-03 603.5 4.023e-06 37.26
1039604 rs57065891 17_23 rs57065891 2.320e-03 603.5 3.994e-06 37.26
1039612 rs59925331 17_23 rs59925331 2.309e-03 603.5 3.975e-06 37.26
1039613 rs2227315 17_23 rs2227315 2.167e-03 603.2 3.730e-06 37.25
1039540 rs7021 17_23 rs7021 1.603e-03 601.4 2.750e-06 37.24
1039531 rs4795412 17_23 rs4795412 1.415e-03 600.7 2.426e-06 37.23
1039544 rs8081692 17_23 rs8081692 1.416e-03 601.0 2.428e-06 37.23
1039507 rs57968500 17_23 rs57968500 1.129e-03 599.3 1.930e-06 37.22
1039646 rs4794824 17_23 rs4794824 5.135e-04 600.7 8.801e-07 37.13
1039644 rs3213762 17_23 rs3213762 1.389e-04 595.3 2.359e-07 37.07
1039570 rs111848559 17_23 rs111848559 2.509e-04 597.5 4.278e-07 37.06
1039661 rs6503533 17_23 rs6503533 6.071e-05 605.9 1.050e-07 -36.96
1039530 rs3826331 17_23 rs3826331 3.890e-07 589.6 6.545e-10 -36.93
1039632 rs1045929 17_23 rs1045929 8.074e-06 586.6 1.351e-08 36.89
1039518 rs60666652 17_23 rs60666652 1.231e-06 582.7 2.047e-09 36.72
1039517 rs60082402 17_23 rs60082402 1.155e-06 582.5 1.920e-09 36.72
1039603 rs60005225 17_23 rs60005225 2.605e-07 577.4 4.292e-10 36.67
1039561 rs12941811 17_23 rs12941811 2.759e-12 591.5 4.656e-15 -36.55
1039535 rs8070444 17_23 rs8070444 7.288e-12 586.4 1.219e-14 -35.99
1039503 rs12450688 17_23 rs12450688 7.989e-12 588.5 1.341e-14 -35.98
1039486 rs2305482 17_23 rs2305482 1.045e-11 591.4 1.763e-14 -35.98
1039536 rs8071037 17_23 rs8071037 6.789e-12 584.8 1.133e-14 -35.97
1039506 rs9915252 17_23 rs9915252 7.546e-12 587.3 1.264e-14 -35.96
sessionInfo()R version 4.1.0 (2021-05-18)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] cowplot_1.1.1 ggplot2_3.4.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] tidyselect_1.2.0 xfun_0.35 bslib_0.4.1 generics_0.1.3
[5] colorspace_2.0-3 vctrs_0.5.1 htmltools_0.5.4 yaml_2.3.6
[9] utf8_1.2.2 blob_1.2.3 rlang_1.0.6 jquerylib_0.1.4
[13] later_1.3.0 pillar_1.8.1 withr_2.5.0 glue_1.6.2
[17] DBI_1.1.3 bit64_4.0.5 lifecycle_1.0.3 stringr_1.5.0
[21] munsell_0.5.0 gtable_0.3.1 evaluate_0.19 memoise_2.0.1
[25] labeling_0.4.2 knitr_1.41 callr_3.7.3 fastmap_1.1.0
[29] httpuv_1.6.7 ps_1.7.2 fansi_1.0.3 highr_0.9
[33] Rcpp_1.0.9 promises_1.2.0.1 scales_1.2.1 cachem_1.0.6
[37] jsonlite_1.8.4 farver_2.1.0 fs_1.5.2 bit_4.0.5
[41] digest_0.6.31 stringi_1.7.8 processx_3.8.0 dplyr_1.0.10
[45] getPass_0.2-2 rprojroot_2.0.3 grid_4.1.0 cli_3.4.1
[49] tools_4.1.0 magrittr_2.0.3 sass_0.4.4 tibble_3.1.8
[53] RSQLite_2.2.19 whisker_0.4.1 pkgconfig_2.0.3 data.table_1.14.6
[57] assertthat_0.2.1 rmarkdown_2.19 httr_1.4.4 rstudioapi_0.14
[61] R6_2.5.1 git2r_0.30.1 compiler_4.1.0