Union of Deepsea catagories significant in S3 or S4.

S3: Results of RBP dysregulation effect on psychiatric disorder risk

  • ASD, SCZ, MDD, BD, ADHD

S4: Results of top psychiatric disorder-associated RBPs dysregulation effect after conditioning QTLs

  • ASD, SCZ=MDD=BD=ADHD

CHD

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setwd("/storage11_7T/fuy/TADA-A/CHD/rds/")
prv=readRDS("2021-05-28_selected13_CHD_sf_allinfo_rr.rds")
df=readRDS("N2645_all_30annota_sep_rr.rds")
df2=df[df$rr.lst>0,]
df2 = df2[!(df2$idx %in% 27:29),]
df2$annota0=c(prv$annota,paste0("RBP dysreg eff on psych risk---Deepsea union of ",df2$annota[14:17]),
              paste0("top psych RBPs dysreg eff aft con QTLs---Deepsea union of ",df2$annota[18]),
              paste0("top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD"),
                  "stop gained")
df2=df2[,c(6,1:4)]
new=data.frame(annota0="prior",rr.lst=0.05,upper.lst=NA,lower.lst=NA,idx=NA)
df3=rbind(df2,new)

e=readRDS("2021-08-27_selected20_CHD_sf_uni_prior_joint_rr.rds")
f=readRDS("2021-08-27_selected20_CHD_sf_fix_joint_rr.rds")
df3$`joint_pi=0.021 (estim)` = e$par_estimate
df3$`joint_pi=0.05 (fixed)` = c(f,.05)
colnames(df3)[2]="separate_pi=0.05 (fixed)"
df3=df3[,c(1,6,7,2,3,4,5)]
options(digits=4)
df3
A data.frame: 21 × 7
annota0joint_pi=0.021 (estim)joint_pi=0.05 (fixed)separate_pi=0.05 (fixed)upper.lstlower.lstidx
<chr><dbl><dbl><dbl><dbl><dbl><int>
1coding constraint >= 90 0.12880 0.15041.6882.1921.1832 1
4RADAR: top5% variants in the post-transcriptional regulome of RBP 0.76410 0.65481.8152.3391.2906 4
6dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure 1.45302 1.27582.1952.7351.6543 6
8deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus 0.85090 0.58002.4313.0741.7866 8
9deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA9-0.45526-0.38501.7393.1550.3223 9
111<= mpc score < 2 0.31246 0.32411.8132.1401.487011
12mpc score >= 2 0.74773 0.51842.0592.5541.563112
13interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2 0.16213 0.20562.3082.8041.811513
14pathogenic missense: primateAI_MVP_mix 1.95994 1.57022.0352.2251.845114
15spidex_low3_spliceai_mix-ptv 0.78055 0.55401.4932.5810.404415
17ptv in [0,0.5) -5.39205 1.21922.2842.9441.623517
18ptv in [0.5,995) 5.98345 2.68583.0403.7702.309718
19ptv in [0.995,1) 6.49970 3.34513.7024.1613.242619
20RBP dysreg eff on psych risk---Deepsea union of ASD_S3 1.11500 0.96892.3022.6671.938320
21RBP dysreg eff on psych risk---Deepsea union of SCZ_S3 0.14760 0.34021.5041.8401.167921
22RBP dysreg eff on psych risk---Deepsea union of ADHD_S3 -0.43548-0.53501.3201.7660.873822
23RBP dysreg eff on psych risk---Deepsea union of BD_S3 -0.05300-0.17681.4381.8431.032823
25top psych RBPs dysreg eff aft con QTLs---Deepsea union of ASD_S4 0.32617 0.31861.9642.4661.462325
26top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD -0.27452-0.10111.8252.2411.408226
30stop gained -2.56679-0.11012.0032.2901.715330
16prior 0.02077 0.05000.050 NA NANA

ASD

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setwd("/storage11_7T/fuy/TADA-A/cell_WES/DNM/")
df=readRDS("ASD_all_27annota_sep_rr.rds")
df2=df[df$rr.lst>0,]
# df2 = df2[!(df2$idx %in% 27:29),]
df2=df2[,c(5,1,2,3,4)]
new=data.frame(annota="prior",rr.lst=0.05,upper.lst=NA,lower.lst=NA,idx=NA)
df3=rbind(df2,new)

e=readRDS("2021-08-26_selected_ASD_sf_uni_prior_joint_rr.rds")
f=readRDS("2021-08-26_selected_ASD_sf_fix_joint_rr.rds")
df3$`joint_pi=0.168 (estim)` = e$par_estimate
df3$`joint_pi=0.05 (fixed)` = c(f,.05)
colnames(df3)[2]="separate_pi=0.05 (fixed)"
df3=df3[,c(1,6,7,2,3,4,5)]
options(digits=4)
# prv=readRDS("/storage11_7T/fuy/TADA-A/cell_WES/DNM/2021-05-21_selected14_fix_pi_estim_pi_joint_sep_rr.rds")[,3:4]
df3$annota=c(
"coding constraint >= 90",
"hnRNPL binding regions",
"dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure",
"deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus",
"deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA9",
"RNA modifications, including m6a, m1A, m5C, and etc.(filter_low_RMVar_hb)",
"1<= mpc score < 2",
"mpc score >= 2",
"interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2",
"pathogenic missense: primateAI_MVP_union",
"union of spidex_low3% and merged spliceai, without PTVs",
"ptv in [0,0.5)",
"ptv in [0.5,0.995)",
"ptv in [0.995,1)",
paste0("RBP dysreg eff on psych risk---Deepsea union of ",c("ASD","SCZ","ADHD","BD","MDD")),
"top psych RBPs dysreg eff aft con QTLs---Deepsea union of ASD",
paste0("top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD"),
"stop gained","prior"
)
df3
A data.frame: 23 × 7
annotajoint_pi=0.168 (estim)joint_pi=0.05 (fixed)separate_pi=0.05 (fixed)upper.lstlower.lstidx
<chr><dbl><dbl><dbl><dbl><dbl><int>
1coding constraint >= 90 0.25684 0.28431.8972.1031.6915 1
3hnRNPL binding regions 0.59636 1.13361.5993.0840.1152 3
6dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure 0.74332 0.94621.8442.2791.4099 6
8deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus 0.35584 0.35052.4842.9042.0640 8
9deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA9 0.47674-0.16032.5933.0062.1794 9
10RNA modifications, including m6a, m1A, m5C, and etc.(filter_low_RMVar_hb) 0.29249 0.98541.6242.9170.331310
111<= mpc score < 2 0.17573 0.21231.6221.8361.407311
12mpc score >= 2 0.64919 0.79102.3212.5482.094912
13interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2 0.70745 0.95782.6002.8282.372913
14pathogenic missense: primateAI_MVP_union 0.67166 1.01171.7571.8921.622014
15union of spidex_low3% and merged spliceai, without PTVs 0.42991 0.85371.3512.0510.650415
17ptv in [0,0.5) 0.32408 1.01742.0822.5581.605317
18ptv in [0.5,0.995) 0.34586 0.83782.2102.9651.454918
19ptv in [0.995,1) 1.66833 2.21813.4943.7593.228119
20RBP dysreg eff on psych risk---Deepsea union of ASD 0.66915 0.33322.1012.3481.854520
21RBP dysreg eff on psych risk---Deepsea union of SCZ 0.07431 0.39441.6731.8491.497121
22RBP dysreg eff on psych risk---Deepsea union of ADHD -0.28589-0.43581.6681.8541.481722
23RBP dysreg eff on psych risk---Deepsea union of BD -0.08843-0.23851.5891.8021.376023
24RBP dysreg eff on psych risk---Deepsea union of MDD -0.11368 0.60621.8372.0571.616024
25top psych RBPs dysreg eff aft con QTLs---Deepsea union of ASD -0.42078-0.39041.7672.1031.430225
26top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD 0.52580 0.27861.7982.0331.564126
27stop gained 0.73179 0.75821.9532.1221.782627
16prior 0.16769 0.05000.050 NA NANA

DD

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setwd("/storage11_7T/fuy/TADA-A/nature/rds")
df=readRDS("all_27annota_sep_rr.rds")
df2 = df[df$rr.lst > 0,]
df2=df2[!(df2$idx %in% c(5)),]
df2=df2[,c(5,1:4)]
new=data.frame(annota="prior",rr.lst=0.05,upper.lst=NA,lower.lst=NA,idx=NA)
df3=rbind(df2,new)
e=readRDS("2021-08-30_selected_DD_sf_uni_prior_joint_rr.rds")
f=readRDS("2021-09-03_selected_DD_fix_pi_sf_uni_prior_joint_rr.rds")
df3$`joint_pi=0.10 (estim)` = e$par_estimate
df3$`joint_pi=0.05 (fixed)` = c(f,.05)
colnames(df3)[2]="separate_pi=0.05 (fixed)"

options(digits=4)
df3$annota=c(
"coding constraint >= 90",
"RADAR_RBP_top5%",
"dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure",
"deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus",
"deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA9",
"RNA modifications, including m6a, m1A, m5C, and etc.(filter_low_RMVar_hb)",
"1<= mpc score < 2",
"mpc score >= 2",
"interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2",
"pathogenic missense: primateAI_MVP_union",
"union of spidex_low3% and merged spliceai, without PTVs",
"ptv in [0,0.5)",
"ptv in [0.5,0.995)",
"ptv in [0.995,1)",
paste0("RBP dysreg eff on psych risk---Deepsea union of ",c("ASD","SCZ","ADHD","BD","MDD")),
"top psych RBPs dysreg eff aft con QTLs---Deepsea union of ASD",
paste0("top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD"),
"stop gained","prior"
)
df3
A data.frame: 23 × 7
annotaseparate_pi=0.05 (fixed)upper.lstlower.lstidxjoint_pi=0.10 (estim)joint_pi=0.05 (fixed)
<chr><dbl><dbl><dbl><int><dbl><dbl>
1coding constraint >= 90 2.5852.6372.532 1 0.56318 0.545262
4RADAR_RBP_top5% 1.9512.0581.844 4-0.01944-0.005925
6dbSNP.RBP-Var: likely to affcted RNA-bind, RNA 2nd structure 2.2782.4522.104 6 0.69379 0.794727
8deepsea: mix ago_adult_brain.BA4 & ago_adult_brain.Cingulate.gyrus 2.8232.9562.689 8 0.45451 0.472001
9deepsea: mix elavl_Adult_brain.all_human_samples & elavl_Adult_brain.BA9_Alzheimer & elavl_Adult_brain.BA92.6802.8302.529 9 0.36834 0.331987
10RNA modifications, including m6a, m1A, m5C, and etc.(filter_low_RMVar_hb) 1.8672.2341.50110 0.46034 0.528346
111<= mpc score < 2 1.9412.0171.86511 0.51663 0.551862
12mpc score >= 2 2.9372.9902.88412 1.44156 1.437347
13interaction-disrupting: mutations annotated as interface residues and probably damaging by PolyPhen-2 3.0053.0822.92813 0.51609 0.522523
14pathogenic missense: primateAI_MVP_union 2.2822.3262.23714 0.77448 0.860829
15union of spidex_low3% and merged spliceai, without PTVs 2.0352.1851.88615 0.34511 0.395993
17ptv in [0,0.5) 2.0352.2131.85717 0.96710 1.246204
18ptv in [0.5,0.995) 3.1173.3142.92018 1.86489 2.076186
19ptv in [0.995,1) 4.1644.2534.07419 3.20614 3.289513
20RBP dysreg eff on psych risk---Deepsea union of ASD 2.2582.3502.16620 0.16783 0.099368
21RBP dysreg eff on psych risk---Deepsea union of SCZ 2.0262.0901.96321 0.57322 0.395515
22RBP dysreg eff on psych risk---Deepsea union of ADHD 1.9842.0471.92222-0.11254 0.147144
23RBP dysreg eff on psych risk---Deepsea union of BD 1.9692.0351.90323-0.13238-0.115899
24RBP dysreg eff on psych risk---Deepsea union of MDD 2.0862.1662.00624-0.26847-0.115534
25top psych RBPs dysreg eff aft con QTLs---Deepsea union of ASD 2.2192.3302.10925-0.11496-0.072656
26top psych RBPs dysreg eff aft con QTLs---Deepsea union of SCZ=MDD=BD=ADHD 2.1932.2732.11326 0.16582 0.091867
27stop gained 3.4423.5093.37427 0.16582 0.091867
16prior 0.050 NA NANA 0.10152 0.050000