LDL - Liver
sheng Qian
2022-10-18
Last updated: 2023-01-23
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
[1] 11502[1] 3347
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
324 225 166 135 135 217 173 111 134 134 210 187 61 100 109 185 195 46 258 94
21 22
48 100 [1] 1Load ctwas results
Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| 4d68754 | sq-96 | 2023-01-23 |
#estimated group prior
estimated_group_prior <- estimated_group_prior_all[,ncol(group_prior_rec)]
print(estimated_group_prior) SNP gene
0.000144 0.035782 #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
13.96 15.68 #estimated enrichment
estimated_enrichment <- estimated_enrichment_all[ncol(group_prior_var_rec)]
print(estimated_enrichment)[1] 248.4#report sample size
print(sample_size)[1] 343621#report group size
print(group_size) SNP gene
8696600 3347 #estimated group PVE
estimated_group_pve <- estimated_group_pve_all[,ncol(group_prior_rec)]
print(estimated_group_pve) SNP gene
0.050907 0.005465 #total PVE
sum(estimated_group_pve)[1] 0.05637#attributable PVE
estimated_group_pve/sum(estimated_group_pve) SNP gene
0.90305 0.09695 Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| 4d68754 | sq-96 | 2023-01-23 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z num_eqtl
4433 PSRC1 1_67 1.0000 823.87 2.398e-03 -41.793 1
9251 ZNF329 19_39 0.9942 106.53 3.082e-04 10.436 1
10708 NYNRIN 14_3 0.9921 53.33 1.540e-04 7.679 1
1597 PLTP 20_28 0.9921 56.81 1.640e-04 -5.732 1
NA.396 <NA> 8_12 0.9902 77.05 2.220e-04 10.465 1
11257 CYP2A6 19_28 0.9891 30.54 8.790e-05 5.407 1
9365 GAS6 13_62 0.9873 65.49 1.882e-04 -8.924 1
6774 PKN3 9_66 0.9859 45.73 1.312e-04 -6.621 1
5988 FADS1 11_34 0.9711 145.75 4.119e-04 12.675 1
NA.392 <NA> 1_121 0.9682 189.43 5.337e-04 -15.074 1
9046 KLHDC7A 1_13 0.9634 19.65 5.510e-05 4.124 1
9054 SPTY2D1 11_13 0.9459 31.38 8.637e-05 -5.587 1
1309 FMO2 1_84 0.9347 25.01 6.802e-05 4.838 1
2092 SP4 7_19 0.9196 93.93 2.514e-04 10.701 1
9827 PALM3 19_11 0.9143 18.64 4.960e-05 3.839 1
2454 ST3GAL4 11_77 0.9113 73.45 1.948e-04 11.734 1
6855 ALDH16A1 19_34 0.9009 27.97 7.334e-05 -4.119 1
10459 PRMT6 1_66 0.9008 30.36 7.958e-05 -5.374 1
8418 GNB2 7_62 0.9002 27.56 7.220e-05 5.813 1
1320 CWF19L1 10_64 0.8948 32.86 8.558e-05 5.707 1
7350 BRI3 7_60 0.8845 26.39 6.793e-05 -5.067 1
697 PIGB 15_24 0.8805 17.59 4.507e-05 3.665 1
7918 PDHB 3_40 0.8665 25.15 6.342e-05 3.304 1
3714 SLC2A4RG 20_38 0.8626 30.41 7.635e-05 -5.563 1
3659 GNMT 6_33 0.8575 26.56 6.628e-05 5.058 1
7542 LIPC 15_26 0.8485 63.23 1.561e-04 -7.731 1
1114 SRRT 7_62 0.8396 28.50 6.963e-05 5.938 1
10429 PNP 14_1 0.8284 17.49 4.216e-05 -3.575 1
NA.395 <NA> 5_78 0.8280 17.74 4.276e-05 -3.817 1
4669 SCYL2 12_59 0.8229 17.54 4.200e-05 -3.564 1
7092 NEK10 3_20 0.8183 21.58 5.140e-05 -4.089 1Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$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 susie_pip mu2 PVE z num_eqtl
10399 LPA 6_104 0.000e+00 13352.87 0.000e+00 6.319 1
5797 SLC22A3 6_104 0.000e+00 9690.02 0.000e+00 -6.225 1
4433 PSRC1 1_67 1.000e+00 823.87 2.398e-03 -41.793 1
NA.139 <NA> 6_104 0.000e+00 430.46 0.000e+00 -8.475 1
3270 ALDH6A1 14_34 2.732e-01 263.13 2.092e-04 4.361 1
5166 PTGR2 14_34 4.395e-04 256.75 3.284e-07 -3.091 1
5375 GEMIN7 19_31 0.000e+00 251.13 0.000e+00 14.336 1
8026 PCSK9 1_34 9.971e-05 238.01 6.907e-08 16.079 1
4315 ANGPTL3 1_39 9.475e-02 204.00 5.625e-05 15.169 1
NA.392 <NA> 1_121 9.682e-01 189.43 5.337e-04 -15.074 1
NA.397 <NA> 8_83 1.112e-01 164.21 5.314e-05 17.282 1
8700 ABO 9_70 7.330e-02 149.53 3.190e-05 12.100 1
10549 HLA-DMA 6_27 5.937e-03 145.77 2.518e-06 -2.364 1
5988 FADS1 11_34 9.711e-01 145.75 4.119e-04 12.675 1
2077 ATP13A1 19_15 5.775e-01 134.05 2.253e-04 -13.541 1
10926 FADS3 11_34 2.877e-02 111.20 9.310e-06 11.074 1
9251 ZNF329 19_39 9.942e-01 106.53 3.082e-04 10.436 1
2092 SP4 7_19 9.196e-01 93.93 2.514e-04 10.701 1
9025 EFCAB13 17_27 3.299e-01 92.58 8.889e-05 10.027 1
4047 NECTIN2 19_31 0.000e+00 91.26 0.000e+00 5.825 1Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z num_eqtl
4433 PSRC1 1_67 1.0000 823.87 2.398e-03 -41.793 1
NA.392 <NA> 1_121 0.9682 189.43 5.337e-04 -15.074 1
5988 FADS1 11_34 0.9711 145.75 4.119e-04 12.675 1
9251 ZNF329 19_39 0.9942 106.53 3.082e-04 10.436 1
2092 SP4 7_19 0.9196 93.93 2.514e-04 10.701 1
2077 ATP13A1 19_15 0.5775 134.05 2.253e-04 -13.541 1
NA.396 <NA> 8_12 0.9902 77.05 2.220e-04 10.465 1
3270 ALDH6A1 14_34 0.2732 263.13 2.092e-04 4.361 1
2454 ST3GAL4 11_77 0.9113 73.45 1.948e-04 11.734 1
9365 GAS6 13_62 0.9873 65.49 1.882e-04 -8.924 1
6090 CSNK1G3 5_75 0.7883 72.62 1.666e-04 8.881 1
1597 PLTP 20_28 0.9921 56.81 1.640e-04 -5.732 1
7542 LIPC 15_26 0.8485 63.23 1.561e-04 -7.731 1
10708 NYNRIN 14_3 0.9921 53.33 1.540e-04 7.679 1
6774 PKN3 9_66 0.9859 45.73 1.312e-04 -6.621 1
9025 EFCAB13 17_27 0.3299 92.58 8.889e-05 10.027 1
11257 CYP2A6 19_28 0.9891 30.54 8.790e-05 5.407 1
9054 SPTY2D1 11_13 0.9459 31.38 8.637e-05 -5.587 1
1320 CWF19L1 10_64 0.8948 32.86 8.558e-05 5.707 1
10459 PRMT6 1_66 0.9008 30.36 7.958e-05 -5.374 1Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z num_eqtl
4433 PSRC1 1_67 1.000e+00 823.87 2.398e-03 -41.79 1
NA.397 <NA> 8_83 1.112e-01 164.21 5.314e-05 17.28 1
8026 PCSK9 1_34 9.971e-05 238.01 6.907e-08 16.08 1
4315 ANGPTL3 1_39 9.475e-02 204.00 5.625e-05 15.17 1
NA.392 <NA> 1_121 9.682e-01 189.43 5.337e-04 -15.07 1
5375 GEMIN7 19_31 0.000e+00 251.13 0.000e+00 14.34 1
2077 ATP13A1 19_15 5.775e-01 134.05 2.253e-04 -13.54 1
5988 FADS1 11_34 9.711e-01 145.75 4.119e-04 12.67 1
11016 APOC2 19_31 0.000e+00 88.82 0.000e+00 -12.21 1
8700 ABO 9_70 7.330e-02 149.53 3.190e-05 12.10 1
2454 ST3GAL4 11_77 9.113e-01 73.45 1.948e-04 11.73 1
10926 FADS3 11_34 2.877e-02 111.20 9.310e-06 11.07 1
6183 POC5 5_44 1.441e-02 75.86 3.181e-06 10.86 1
2092 SP4 7_19 9.196e-01 93.93 2.514e-04 10.70 1
NA.396 <NA> 8_12 9.902e-01 77.05 2.220e-04 10.46 1
9251 ZNF329 19_39 9.942e-01 106.53 3.082e-04 10.44 1
2094 DNAH11 7_19 5.080e-02 88.49 1.308e-05 10.23 1
8523 TNKS 8_12 1.744e-01 66.86 3.394e-05 10.13 1
9025 EFCAB13 17_27 3.299e-01 92.58 8.889e-05 10.03 1
9910 RHCE 1_18 7.008e-02 89.44 1.824e-05 10.02 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$z) > sig_thresh)[1] 113#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.03376#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z num_eqtl
4433 PSRC1 1_67 1.000e+00 823.87 2.398e-03 -41.79 1
NA.397 <NA> 8_83 1.112e-01 164.21 5.314e-05 17.28 1
8026 PCSK9 1_34 9.971e-05 238.01 6.907e-08 16.08 1
4315 ANGPTL3 1_39 9.475e-02 204.00 5.625e-05 15.17 1
NA.392 <NA> 1_121 9.682e-01 189.43 5.337e-04 -15.07 1
5375 GEMIN7 19_31 0.000e+00 251.13 0.000e+00 14.34 1
2077 ATP13A1 19_15 5.775e-01 134.05 2.253e-04 -13.54 1
5988 FADS1 11_34 9.711e-01 145.75 4.119e-04 12.67 1
11016 APOC2 19_31 0.000e+00 88.82 0.000e+00 -12.21 1
8700 ABO 9_70 7.330e-02 149.53 3.190e-05 12.10 1
2454 ST3GAL4 11_77 9.113e-01 73.45 1.948e-04 11.73 1
10926 FADS3 11_34 2.877e-02 111.20 9.310e-06 11.07 1
6183 POC5 5_44 1.441e-02 75.86 3.181e-06 10.86 1
2092 SP4 7_19 9.196e-01 93.93 2.514e-04 10.70 1
NA.396 <NA> 8_12 9.902e-01 77.05 2.220e-04 10.46 1
9251 ZNF329 19_39 9.942e-01 106.53 3.082e-04 10.44 1
2094 DNAH11 7_19 5.080e-02 88.49 1.308e-05 10.23 1
8523 TNKS 8_12 1.744e-01 66.86 3.394e-05 10.13 1
9025 EFCAB13 17_27 3.299e-01 92.58 8.889e-05 10.03 1
9910 RHCE 1_18 7.008e-02 89.44 1.824e-05 10.02 1SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag susie_pip mu2 PVE z
70338 rs1042034 2_13 1.0000 244.90 7.127e-04 16.5730
70344 rs934197 2_13 1.0000 414.29 1.206e-03 33.0609
72074 rs780093 2_16 1.0000 170.40 4.959e-04 -14.1426
324246 rs115740542 6_20 1.0000 164.34 4.783e-04 -12.5323
367370 rs12208357 6_103 1.0000 256.54 7.466e-04 12.2823
367473 rs60425481 6_104 1.0000 56798.34 1.653e-01 -7.1125
677728 rs369107859 14_34 1.0000 1054.21 3.068e-03 -2.2480
756650 rs113408695 17_39 1.0000 151.01 4.395e-04 12.7688
790508 rs73013176 19_9 1.0000 247.13 7.192e-04 -16.2327
800345 rs62117204 19_31 1.0000 826.47 2.405e-03 -44.6722
800363 rs111794050 19_31 1.0000 784.25 2.282e-03 -33.5996
800396 rs814573 19_31 1.0000 2276.75 6.626e-03 55.5379
800398 rs113345881 19_31 1.0000 795.97 2.316e-03 -34.3186
800401 rs12721109 19_31 1.0000 1379.27 4.014e-03 -46.3258
857431 rs11591147 1_34 1.0000 1261.29 3.671e-03 -39.1649
930791 rs67138090 6_27 1.0000 954.13 2.777e-03 4.4111
793039 rs3794991 19_15 1.0000 425.19 1.237e-03 -21.4921
756676 rs8070232 17_39 1.0000 165.98 4.830e-04 -8.0915
961976 rs28601761 8_83 1.0000 343.47 9.996e-04 -25.2552
70289 rs11679386 2_12 1.0000 146.11 4.252e-04 11.9094
70424 rs1848922 2_13 1.0000 234.65 6.829e-04 25.4123
70347 rs548145 2_13 1.0000 679.15 1.976e-03 33.0860
494792 rs2437818 9_53 1.0000 73.44 2.137e-04 6.3340
503009 rs115478735 9_70 1.0000 315.06 9.169e-04 19.0118
1072634 rs1800961 20_28 1.0000 74.44 2.166e-04 -8.8970
800736 rs150262789 19_32 1.0000 78.19 2.276e-04 -10.8985
755734 rs1801689 17_38 1.0000 83.33 2.425e-04 9.3964
800059 rs73036721 19_30 1.0000 60.20 1.752e-04 -7.7879
729965 rs12149380 16_38 1.0000 115.92 3.373e-04 -4.1646
442304 rs4738679 8_45 1.0000 110.81 3.225e-04 -11.6999
275773 rs1499279 5_30 1.0000 64.00 1.862e-04 -8.3746
78139 rs72800939 2_28 1.0000 57.36 1.669e-04 -7.8457
790546 rs137992968 19_9 1.0000 117.64 3.424e-04 -10.7526
387614 rs217396 7_32 1.0000 79.20 2.305e-04 -9.4286
7646 rs79598313 1_18 1.0000 48.08 1.399e-04 7.0246
440909 rs140753685 8_42 1.0000 57.09 1.662e-04 7.7992
800104 rs62115478 19_30 1.0000 188.25 5.478e-04 -14.3262
53653 rs2807848 1_112 1.0000 56.76 1.652e-04 -7.8828
348807 rs9496567 6_67 1.0000 39.84 1.159e-04 -6.3402
324225 rs72834643 6_20 0.9999 46.24 1.346e-04 -6.0487
961992 rs112875651 8_83 0.9999 304.15 8.851e-04 -24.2936
320517 rs11376017 6_13 0.9999 67.28 1.958e-04 -8.5079
790572 rs4804149 19_10 0.9998 47.41 1.379e-04 6.5194
793070 rs113619686 19_15 0.9997 57.28 1.667e-04 0.5939
324979 rs454182 6_22 0.9997 38.89 1.131e-04 4.7791
857494 rs499883 1_34 0.9997 203.10 5.909e-04 16.1075
78003 rs139029940 2_27 0.9997 39.73 1.156e-04 6.8150
367561 rs374071816 6_104 0.9996 10391.13 3.023e-02 16.2541
730008 rs57186116 16_38 0.9995 69.52 2.022e-04 7.7146
790537 rs1569372 19_9 0.9995 294.46 8.565e-04 10.0055
790625 rs322144 19_10 0.9994 60.64 1.764e-04 3.9466
540086 rs17875416 10_71 0.9994 38.89 1.131e-04 -6.2663
606070 rs7397189 12_36 0.9991 34.92 1.015e-04 -5.7710
282225 rs7701166 5_44 0.9991 35.36 1.028e-04 -2.4848
494765 rs2297400 9_53 0.9990 41.87 1.217e-04 6.6057
790529 rs147985405 19_9 0.9988 2357.05 6.851e-03 -48.9352
792679 rs2302209 19_14 0.9984 43.94 1.277e-04 6.6360
430636 rs1495743 8_20 0.9981 41.86 1.216e-04 -6.5160
582921 rs3135506 11_70 0.9960 151.40 4.389e-04 12.3730
582926 rs75542613 11_70 0.9958 36.39 1.054e-04 -6.5344
815140 rs76981217 20_24 0.9956 35.66 1.033e-04 7.6925
442272 rs56386732 8_45 0.9955 35.08 1.016e-04 -7.0123
735460 rs2255451 16_48 0.9948 37.91 1.097e-04 -6.3628
325416 rs3130253 6_23 0.9943 30.54 8.837e-05 5.6415
622062 rs653178 12_67 0.9938 98.63 2.853e-04 11.0501
610436 rs148481241 12_44 0.9911 27.97 8.067e-05 5.0955
282166 rs10062361 5_44 0.9896 210.17 6.053e-04 20.3206
801656 rs838145 19_33 0.9881 101.19 2.910e-04 -11.8738
790532 rs3745677 19_9 0.9881 94.85 2.728e-04 9.3358
140939 rs709149 3_9 0.9874 37.15 1.068e-04 -6.7820
326201 rs28780090 6_24 0.9870 50.85 1.461e-04 6.8714
147585 rs9834932 3_24 0.9825 66.67 1.906e-04 -8.4816
815091 rs6029132 20_24 0.9824 40.06 1.145e-04 -6.7625
729741 rs4396539 16_37 0.9817 27.62 7.890e-05 -5.2329
626151 rs11057830 12_76 0.9772 26.35 7.493e-05 4.9296
403432 rs3197597 7_61 0.9771 29.60 8.418e-05 -5.0452
815144 rs73124945 20_24 0.9761 32.33 9.185e-05 -7.7754
800719 rs34942359 19_32 0.9738 64.16 1.818e-04 -7.0096
245580 rs114756490 4_100 0.9714 26.44 7.474e-05 4.9889
387664 rs141379002 7_33 0.9684 26.15 7.368e-05 4.8970
469253 rs7024888 9_3 0.9630 26.10 7.315e-05 -5.0558
222851 rs1458038 4_54 0.9621 53.64 1.502e-04 -7.4179
591553 rs11048034 12_9 0.9611 36.81 1.030e-04 6.1337
477117 rs1556516 9_16 0.9603 75.21 2.102e-04 -8.9921
822861 rs62219001 21_2 0.9601 26.60 7.432e-05 -4.9484
759809 rs4969183 17_44 0.9533 50.03 1.388e-04 7.1693
927792 rs1064173 6_26 0.9499 52.05 1.439e-04 -8.8560
620155 rs1196760 12_63 0.9492 26.28 7.260e-05 -4.8667
625016 rs1169300 12_74 0.9475 69.45 1.915e-04 8.6855
78019 rs4076834 2_27 0.9436 442.31 1.215e-03 -20.1086
78016 rs13430143 2_27 0.9435 83.77 2.300e-04 -3.3445
324064 rs75080831 6_19 0.9422 57.91 1.588e-04 -7.9067
325387 rs28986304 6_23 0.9373 42.43 1.157e-04 7.3825
730006 rs9652628 16_38 0.9277 129.58 3.499e-04 11.9505
367364 rs9456502 6_103 0.9179 33.68 8.998e-05 5.9640
746193 rs117859452 17_17 0.9175 24.78 6.617e-05 -3.8517
426313 rs117037226 8_11 0.9160 24.54 6.543e-05 4.1922
800636 rs377297589 19_32 0.9157 51.49 1.372e-04 -6.7865
566550 rs6591179 11_36 0.9154 25.26 6.730e-05 4.8933
70341 rs78610189 2_13 0.9130 60.60 1.610e-04 -8.3855
195374 rs5855544 3_120 0.9113 24.38 6.465e-05 -4.5937
171757 rs189174 3_74 0.9056 42.02 1.107e-04 6.7678
507959 rs10905277 10_8 0.9020 28.31 7.430e-05 5.1258
197161 rs36205397 4_4 0.9000 40.00 1.048e-04 6.1594
725849 rs821840 16_30 0.8993 168.52 4.410e-04 -13.4753
351543 rs12199109 6_73 0.8992 25.50 6.673e-05 4.8570
539797 rs12244851 10_70 0.8962 37.68 9.827e-05 -4.8831
806414 rs74273659 20_5 0.8961 24.89 6.491e-05 4.6468
790613 rs322125 19_10 0.8921 106.99 2.778e-04 -7.4704
635038 rs1012130 13_10 0.8902 41.65 1.079e-04 -2.7810
830102 rs2835302 21_16 0.8845 25.69 6.613e-05 -4.6537
494785 rs2777788 9_53 0.8840 60.76 1.563e-04 -5.7370
579190 rs201912654 11_59 0.8771 40.82 1.042e-04 -6.3056
199386 rs2002574 4_10 0.8765 25.08 6.396e-05 -4.5583
746102 rs3032928 17_17 0.8682 34.31 8.670e-05 6.1119
818643 rs10641149 20_32 0.8662 27.58 6.952e-05 5.0758
123036 rs7569317 2_120 0.8656 43.94 1.107e-04 7.9007
485103 rs11144506 9_35 0.8568 27.27 6.800e-05 5.0427
70141 rs6531234 2_12 0.8466 42.67 1.051e-04 -7.1708
282189 rs3843482 5_44 0.8451 411.33 1.012e-03 25.0344
815109 rs6102034 20_24 0.8450 98.60 2.425e-04 -11.1900
751321 rs4793601 17_28 0.8449 30.91 7.601e-05 -6.2095
813885 rs11167269 20_21 0.8437 59.11 1.451e-04 -7.7950
790582 rs58495388 19_10 0.8431 34.55 8.476e-05 5.5313
357746 rs9321207 6_86 0.8339 31.20 7.572e-05 5.4016
756661 rs9303012 17_39 0.8332 160.79 3.899e-04 2.2591
98706 rs138192199 2_69 0.8266 26.18 6.297e-05 4.6708
903214 rs9884390 4_48 0.8254 70.41 1.691e-04 8.9764
842190 rs145678077 22_17 0.8219 25.69 6.145e-05 -4.8686
635030 rs1799955 13_10 0.8215 73.57 1.759e-04 -6.6936
801631 rs62130338 19_33 0.8116 45.35 1.071e-04 8.4694
831239 rs149577713 21_19 0.8034 30.13 7.044e-05 3.3168
729946 rs12708919 16_38 0.8026 146.26 3.416e-04 11.3028
234747 rs138204164 4_77 0.8016 27.00 6.299e-05 -4.8489
497363 rs2762469 9_56 0.8003 25.88 6.029e-05 -4.5317SNPs 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 susie_pip mu2 PVE z
367469 rs3106169 6_104 6.626e-01 56850 1.096e-01 11.139
367470 rs3127598 6_104 4.734e-01 56850 7.833e-02 11.135
367478 rs3106167 6_104 4.782e-01 56850 7.911e-02 11.136
367462 rs11755965 6_104 5.697e-02 56833 9.422e-03 11.140
367473 rs60425481 6_104 1.000e+00 56798 1.653e-01 -7.113
367453 rs12194962 6_104 2.482e-10 56713 4.096e-11 11.106
367471 rs3127597 6_104 5.989e-12 56676 9.877e-13 11.145
367432 rs3119311 6_104 0.000e+00 41129 0.000e+00 8.031
367426 rs3127579 6_104 0.000e+00 29946 0.000e+00 7.568
367420 rs10945658 6_104 0.000e+00 26200 0.000e+00 8.309
367419 rs3119308 6_104 0.000e+00 26136 0.000e+00 8.274
367415 rs3103352 6_104 0.000e+00 26135 0.000e+00 8.522
367411 rs3101821 6_104 0.000e+00 26044 0.000e+00 8.528
367417 rs12205178 6_104 0.000e+00 25988 0.000e+00 8.297
367409 rs148015788 6_104 0.000e+00 25657 0.000e+00 8.351
367520 rs3124784 6_104 0.000e+00 21492 0.000e+00 9.680
367521 rs3127596 6_104 0.000e+00 19498 0.000e+00 9.556
367514 rs3127599 6_104 0.000e+00 19405 0.000e+00 9.259
367484 rs2481030 6_104 0.000e+00 18643 0.000e+00 4.811
367449 rs2504949 6_104 0.000e+00 15359 0.000e+00 2.937
367502 rs388170 6_104 0.000e+00 14217 0.000e+00 3.833
367424 rs316013 6_104 0.000e+00 13630 0.000e+00 -3.002
367425 rs316012 6_104 0.000e+00 13466 0.000e+00 -3.074
367505 rs9355288 6_104 0.000e+00 13158 0.000e+00 6.319
367413 rs610206 6_104 0.000e+00 12446 0.000e+00 -2.944
367414 rs595374 6_104 0.000e+00 12422 0.000e+00 -2.921
367421 rs315995 6_104 0.000e+00 12119 0.000e+00 -3.207
367418 rs543435 6_104 0.000e+00 12073 0.000e+00 -3.250
367467 rs452867 6_104 0.000e+00 11366 0.000e+00 -7.124
367476 rs367334 6_104 0.000e+00 11358 0.000e+00 -7.106
367464 rs589931 6_104 0.000e+00 11356 0.000e+00 -7.116
367465 rs600584 6_104 0.000e+00 11356 0.000e+00 -7.113
367466 rs434953 6_104 0.000e+00 11356 0.000e+00 -7.111
367472 rs380498 6_104 0.000e+00 11356 0.000e+00 -7.115
367440 rs3119312 6_104 0.000e+00 10900 0.000e+00 3.771
367561 rs374071816 6_104 9.996e-01 10391 3.023e-02 16.254
367499 rs2872317 6_104 0.000e+00 9987 0.000e+00 6.746
367496 rs2313453 6_104 0.000e+00 9980 0.000e+00 6.718
367566 rs4252185 6_104 3.618e-04 9573 1.008e-05 15.878
367487 rs146184004 6_104 0.000e+00 9536 0.000e+00 6.534
367490 rs624319 6_104 0.000e+00 9395 0.000e+00 -6.291
367489 rs637614 6_104 0.000e+00 9381 0.000e+00 -6.362
367491 rs486339 6_104 0.000e+00 9317 0.000e+00 -6.311
367436 rs316036 6_104 0.000e+00 9142 0.000e+00 -7.009
367488 rs555754 6_104 0.000e+00 9076 0.000e+00 -6.593
367567 rs12212146 6_104 0.000e+00 7282 0.000e+00 -2.410
367434 rs582280 6_104 0.000e+00 7051 0.000e+00 2.635
367433 rs497039 6_104 0.000e+00 7049 0.000e+00 2.634
367620 rs1247539 6_104 0.000e+00 5711 0.000e+00 -4.294
367517 rs9346818 6_104 0.000e+00 5710 0.000e+00 7.950SNPs with highest PVE
#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
367473 rs60425481 6_104 1.00000 56798.3 0.1652936 -7.1125
367469 rs3106169 6_104 0.66256 56850.4 0.1096180 11.1387
367478 rs3106167 6_104 0.47818 56849.8 0.0791115 11.1356
367470 rs3127598 6_104 0.47345 56849.9 0.0783293 11.1347
367561 rs374071816 6_104 0.99964 10391.1 0.0302291 16.2541
367462 rs11755965 6_104 0.05697 56832.9 0.0094222 11.1396
790529 rs147985405 19_9 0.99882 2357.0 0.0068514 -48.9352
800396 rs814573 19_31 1.00000 2276.8 0.0066258 55.5379
800401 rs12721109 19_31 1.00000 1379.3 0.0040139 -46.3258
857431 rs11591147 1_34 1.00000 1261.3 0.0036706 -39.1649
677728 rs369107859 14_34 1.00000 1054.2 0.0030679 -2.2480
930791 rs67138090 6_27 1.00000 954.1 0.0027767 4.4111
800345 rs62117204 19_31 1.00000 826.5 0.0024052 -44.6722
800398 rs113345881 19_31 1.00000 796.0 0.0023164 -34.3186
800363 rs111794050 19_31 1.00000 784.3 0.0022823 -33.5996
70347 rs548145 2_13 1.00000 679.1 0.0019764 33.0860
677737 rs2159704 14_34 0.47971 1048.1 0.0014632 1.2852
930681 rs9275698 6_27 0.50855 931.1 0.0013780 -0.6590
793039 rs3794991 19_15 1.00000 425.2 0.0012374 -21.4921
78019 rs4076834 2_27 0.94362 442.3 0.0012146 -20.1086
70344 rs934197 2_13 1.00000 414.3 0.0012056 33.0609
282189 rs3843482 5_44 0.84507 411.3 0.0010116 25.0344
961976 rs28601761 8_83 1.00000 343.5 0.0009996 -25.2552
677745 rs7144134 14_34 0.71924 444.1 0.0009296 4.3724
677725 rs7156583 14_34 0.30166 1048.2 0.0009202 1.2485
503009 rs115478735 9_70 1.00000 315.1 0.0009169 19.0118
961992 rs112875651 8_83 0.99990 304.2 0.0008851 -24.2936
790537 rs1569372 19_9 0.99946 294.5 0.0008565 10.0055
367370 rs12208357 6_103 1.00000 256.5 0.0007466 12.2823
790508 rs73013176 19_9 1.00000 247.1 0.0007192 -16.2327
70338 rs1042034 2_13 1.00000 244.9 0.0007127 16.5730
931247 rs2859088 6_27 0.25401 925.4 0.0006841 -0.7222
70424 rs1848922 2_13 1.00000 234.6 0.0006829 25.4123
931229 rs2858883 6_27 0.23764 924.7 0.0006395 -0.7327
282166 rs10062361 5_44 0.98964 210.2 0.0006053 20.3206
857494 rs499883 1_34 0.99970 203.1 0.0005909 16.1075
800104 rs62115478 19_30 1.00000 188.3 0.0005478 -14.3262
72074 rs780093 2_16 1.00000 170.4 0.0004959 -14.1426
756676 rs8070232 17_39 1.00000 166.0 0.0004830 -8.0915
324246 rs115740542 6_20 1.00000 164.3 0.0004783 -12.5323
367384 rs3818678 6_103 0.77137 208.6 0.0004683 -9.9478
725849 rs821840 16_30 0.89926 168.5 0.0004410 -13.4753
756650 rs113408695 17_39 1.00000 151.0 0.0004395 12.7688
582921 rs3135506 11_70 0.99605 151.4 0.0004389 12.3730
70289 rs11679386 2_12 1.00000 146.1 0.0004252 11.9094
756661 rs9303012 17_39 0.83316 160.8 0.0003899 2.2591
677735 rs72627160 14_34 0.12361 1046.8 0.0003766 1.2216
306767 rs12657266 5_92 0.75928 163.1 0.0003604 13.8948
730006 rs9652628 16_38 0.92771 129.6 0.0003499 11.9505
790546 rs137992968 19_9 1.00000 117.6 0.0003424 -10.7526SNPs 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 susie_pip mu2 PVE z
800396 rs814573 19_31 1.000e+00 2276.8 6.626e-03 55.54
790529 rs147985405 19_9 9.988e-01 2357.0 6.851e-03 -48.94
790524 rs73015020 19_9 7.011e-04 2344.2 4.783e-06 -48.80
790522 rs138175288 19_9 3.239e-04 2342.4 2.208e-06 -48.78
790523 rs138294113 19_9 7.623e-05 2338.7 5.188e-07 -48.75
790525 rs77140532 19_9 4.559e-05 2338.8 3.103e-07 -48.74
790526 rs112552009 19_9 2.317e-05 2335.4 1.575e-07 -48.71
790527 rs10412048 19_9 8.951e-06 2335.5 6.083e-08 -48.70
790521 rs55997232 19_9 1.369e-09 2315.5 9.228e-12 -48.52
800401 rs12721109 19_31 1.000e+00 1379.3 4.014e-03 -46.33
800345 rs62117204 19_31 1.000e+00 826.5 2.405e-03 -44.67
800332 rs1551891 19_31 0.000e+00 493.9 0.000e+00 -42.27
871774 rs12740374 1_67 2.233e-03 811.9 5.276e-06 -41.79
871770 rs7528419 1_67 5.512e-03 816.9 1.310e-05 -41.74
871781 rs646776 1_67 2.200e-03 812.1 5.199e-06 41.73
871780 rs629301 1_67 1.222e-03 808.7 2.877e-06 41.69
871792 rs583104 1_67 5.479e-04 788.9 1.258e-06 41.09
871795 rs4970836 1_67 5.346e-04 786.6 1.224e-06 41.05
871797 rs1277930 1_67 5.504e-04 785.0 1.257e-06 40.98
871798 rs599839 1_67 5.749e-04 785.9 1.315e-06 40.96
790530 rs17248769 19_9 3.312e-09 1773.3 1.709e-11 -40.84
790531 rs2228671 19_9 2.438e-09 1762.2 1.250e-11 -40.70
871778 rs3832016 1_67 3.170e-04 743.0 6.855e-07 40.40
871775 rs660240 1_67 3.126e-04 738.5 6.718e-07 40.29
871793 rs602633 1_67 3.708e-04 734.5 7.926e-07 39.96
857431 rs11591147 1_34 1.000e+00 1261.3 3.671e-03 -39.16
790520 rs9305020 19_9 2.531e-14 1351.7 9.957e-17 -34.84
800392 rs405509 19_31 0.000e+00 974.3 0.000e+00 -34.64
871761 rs4970834 1_67 7.465e-04 578.3 1.256e-06 -34.62
800398 rs113345881 19_31 1.000e+00 796.0 2.316e-03 -34.32
800316 rs62120566 19_31 0.000e+00 1356.3 0.000e+00 -33.74
800363 rs111794050 19_31 1.000e+00 784.3 2.282e-03 -33.60
70347 rs548145 2_13 1.000e+00 679.1 1.976e-03 33.09
800369 rs4802238 19_31 0.000e+00 980.9 0.000e+00 33.08
70344 rs934197 2_13 1.000e+00 414.3 1.206e-03 33.06
800310 rs188099946 19_31 0.000e+00 1299.9 0.000e+00 -33.04
800380 rs2972559 19_31 0.000e+00 1322.0 0.000e+00 32.29
800304 rs201314191 19_31 0.000e+00 1204.9 0.000e+00 -32.07
871782 rs3902354 1_67 3.652e-04 479.9 5.100e-07 32.00
871771 rs11102967 1_67 3.566e-04 477.2 4.952e-07 31.94
871796 rs4970837 1_67 4.386e-04 481.6 6.148e-07 31.86
800371 rs56394238 19_31 0.000e+00 976.9 0.000e+00 31.55
800348 rs2965169 19_31 0.000e+00 351.4 0.000e+00 -31.38
800372 rs3021439 19_31 0.000e+00 867.1 0.000e+00 31.05
871766 rs611917 1_67 3.102e-04 445.4 4.021e-07 -30.98
70374 rs12997242 2_13 1.061e-11 380.1 1.173e-14 30.82
800379 rs12162222 19_31 0.000e+00 1130.9 0.000e+00 30.50
70348 rs478588 2_13 5.033e-11 626.7 9.180e-14 30.49
800309 rs62119327 19_31 0.000e+00 1060.3 0.000e+00 -30.42
70349 rs56350433 2_13 1.875e-12 350.4 1.912e-15 30.23
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