BMI - Brain Cortex

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Last updated: 2022-02-20

Checks: 6 1

Knit directory: cTWAS_analysis/

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absolute	relative
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/	data
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R	code/ctwas_config.R

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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11768

#number of imputed weights by chromosome
table(qclist_all$chr)

   1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16 
1184  820  686  454  547  667  562  437  448  483  708  635  232  389  392  543 
  17   18   19   20   21   22 
 711  182  893  367  129  299 

#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)

[1] 9186

#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)

[1] 0.7806

Check convergence of parameters

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
376c5ad	sq-96	2022-02-14
e6bc169	sq-96	2022-02-13

#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior)

     gene       snp 
0.0081589 0.0002882 

#estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var)

 gene   snp 
24.27 17.33 

#report sample size
print(sample_size)

[1] 336107

#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)

[1]   11768 7535010

#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve)

    gene      snp 
0.006933 0.111980 

#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))

[1]  0.1376 17.8636

Genes with highest PIPs

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
376c5ad	sq-96	2022-02-14

       genename region_tag susie_pip      mu2       PVE      z num_eqtl
7845      PPM1M       3_36    1.0000   515.52 1.534e-03  4.386        2
766       MAPK6      15_21    0.9899 27658.60 8.146e-02 -4.662        1
3469      CCND2       12_4    0.9597    28.83 8.231e-05 -5.088        2
6507    TMEM219      16_24    0.9251   602.84 1.659e-03 12.063        1
9172     EFEMP2      11_36    0.9141   105.05 2.857e-04 -8.201        1
10518    MAPK11      22_24    0.9042    26.69 7.180e-05 -4.904        1
13972     NOL12      22_15    0.9030    62.83 1.688e-04 -4.505        2
646       NTHL1       16_2    0.8587    31.15 7.958e-05  5.296        1
6673      TADA1       1_82    0.7945    23.83 5.633e-05 -4.112        3
3936       XPO5       6_33    0.7575    36.46 8.218e-05  5.843        1
6116       ECE2      3_113    0.7479    30.25 6.730e-05 -5.315        1
10911     SKOR1      15_31    0.7418    54.44 1.202e-04 -9.754        1
4803      YWHAQ        2_6    0.7373    26.28 5.766e-05  4.911        1
13989 HIST1H2BE       6_20    0.7371    29.08 6.378e-05 -6.515        1
1413       CBX5      12_33    0.7103    25.61 5.411e-05 -4.691        1
8996     MRPL36        5_2    0.7098    22.91 4.838e-05 -4.294        2
8986      KCNK3       2_16    0.7052    48.31 1.014e-04  6.753        1
4701    CSNK1G2       19_2    0.6893    31.16 6.390e-05 -5.493        1
7344      NLRX1      11_71    0.6785    27.73 5.598e-05  5.171        2
8133     METTL3       14_2    0.6713    24.53 4.899e-05 -4.435        1

Genes with largest effect sizes

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
376c5ad	sq-96	2022-02-14

      genename region_tag susie_pip   mu2       PVE       z num_eqtl
11      SEMA3F       3_35 0.000e+00 74412 0.000e+00   7.582        1
11131 C6orf106       6_28 0.000e+00 66708 0.000e+00  -9.175        2
7839     CAMKV       3_35 0.000e+00 53036 0.000e+00  -9.848        1
3949     SPDEF       6_28 0.000e+00 52296 0.000e+00  -9.270        1
642      TAF11       6_28 0.000e+00 50843 0.000e+00  -4.738        1
8020   CCDC171       9_13 0.000e+00 50628 0.000e+00   7.997        1
2226    PIK3R2      19_16 0.000e+00 47199 0.000e+00  -7.140        1
30        RBM5       3_35 0.000e+00 42354 0.000e+00  12.473        1
40        RBM6       3_35 0.000e+00 40962 0.000e+00  12.536        1
143       NADK        1_2 0.000e+00 40719 0.000e+00   5.478        2
7012    ZNF689      16_24 1.958e-13 39221 2.285e-14   6.014        1
7841     MST1R       3_35 0.000e+00 34975 0.000e+00 -12.626        1
2238   TMEM59L      19_16 0.000e+00 29070 0.000e+00   6.060        2
766      MAPK6      15_21 9.899e-01 27659 8.146e-02  -4.662        1
5570    LYSMD2      15_21 0.000e+00 26175 0.000e+00  -4.403        1
5565     MFAP1      15_16 5.408e-05 23671 3.809e-06   4.303        1
4963      HEY2       6_84 0.000e+00 23331 0.000e+00   3.066        1
7835    RNF123       3_35 0.000e+00 23172 0.000e+00 -10.957        1
1475     MAST3      19_16 0.000e+00 22329 0.000e+00   5.994        1
12095   CKMT1A      15_16 0.000e+00 21648 0.000e+00   4.119        2

Genes with highest PVE

          genename region_tag susie_pip      mu2       PVE      z num_eqtl
766          MAPK6      15_21   0.98993 27658.60 8.146e-02 -4.662        1
7837         MFSD8       4_84   0.50000  7628.12 1.135e-02  2.512        1
7838        ABHD18       4_84   0.50000  7628.12 1.135e-02 -2.512        1
3146        LANCL1      2_124   0.47751  4728.54 6.718e-03 -3.535        1
291           CPS1      2_124   0.47751  4728.54 6.718e-03 -3.535        1
6507       TMEM219      16_24   0.92505   602.84 1.659e-03 12.063        1
7845         PPM1M       3_36   1.00000   515.52 1.534e-03  4.386        2
11102       TTC30B      2_107   0.38049   762.78 8.635e-04 -3.137        1
9172        EFEMP2      11_36   0.91413   105.05 2.857e-04 -8.201        1
13972        NOL12      22_15   0.90301    62.83 1.688e-04 -4.505        2
6960         GPR61       1_67   0.62221    79.86 1.478e-04  8.755        1
10911        SKOR1      15_31   0.74183    54.44 1.202e-04 -9.754        1
5705       C18orf8      18_12   0.66061    58.70 1.154e-04  7.575        2
14182       DHRS11      17_22   0.61553    62.56 1.146e-04 -8.142        1
8986         KCNK3       2_16   0.70520    48.31 1.014e-04  6.753        1
7478          TAL1       1_29   0.56798    49.14 8.304e-05 -6.866        1
3469         CCND2       12_4   0.95969    28.83 8.231e-05 -5.088        2
3936          XPO5       6_33   0.75750    36.46 8.218e-05  5.843        1
646          NTHL1       16_2   0.85866    31.15 7.958e-05  5.296        1
14186 CTC-543D15.8       19_9   0.02817   937.42 7.858e-05  3.963        1

Genes with largest z scores

         genename region_tag susie_pip      mu2       PVE       z num_eqtl
5315        ADCY3       2_15 1.485e-04   273.97 1.211e-07  13.649        1
7841        MST1R       3_35 0.000e+00 34975.11 0.000e+00 -12.626        1
40           RBM6       3_35 0.000e+00 40962.06 0.000e+00  12.536        1
30           RBM5       3_35 0.000e+00 42353.53 0.000e+00  12.473        1
6507      TMEM219      16_24 9.251e-01   602.84 1.659e-03  12.063        1
9439       KCTD13      16_24 1.152e-03   491.21 1.684e-06  11.491        1
7835       RNF123       3_35 0.000e+00 23172.22 0.000e+00 -10.957        1
9556        NUPR1      16_23 2.730e-01    68.38 5.553e-05 -10.540        1
10881        CLN3      16_23 8.431e-02    67.59 1.695e-05  10.453        1
1888        MAPK3      16_24 3.635e-09   951.95 1.029e-11  10.247        2
8399       ZNF646      16_24 1.054e-08  7159.87 2.246e-10 -10.000        1
8398       ZNF668      16_24 1.054e-08  7159.87 2.246e-10  10.000        1
9120      C1QTNF4      11_29 1.746e-03   104.43 5.425e-07   9.950        2
8750       INO80E      16_24 1.977e-10  1116.43 6.568e-13   9.923        2
7839        CAMKV       3_35 0.000e+00 53036.18 0.000e+00  -9.848        1
486         PRSS8      16_24 7.979e-10  6796.28 1.613e-11  -9.765        1
10911       SKOR1      15_31 7.418e-01    54.44 1.202e-04  -9.754        1
11900         LAT      16_23 2.780e-01    55.49 4.590e-05  -9.553        1
2634        MTCH2      11_29 4.161e-05    90.75 1.123e-08  -9.551        1
12917 CTC-467M3.3       5_52 0.000e+00   460.00 0.000e+00   9.482        1

Comparing z scores and PIPs

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
376c5ad	sq-96	2022-02-14

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
376c5ad	sq-96	2022-02-14

[1] 0.02405

         genename region_tag susie_pip      mu2       PVE       z num_eqtl
5315        ADCY3       2_15 1.485e-04   273.97 1.211e-07  13.649        1
7841        MST1R       3_35 0.000e+00 34975.11 0.000e+00 -12.626        1
40           RBM6       3_35 0.000e+00 40962.06 0.000e+00  12.536        1
30           RBM5       3_35 0.000e+00 42353.53 0.000e+00  12.473        1
6507      TMEM219      16_24 9.251e-01   602.84 1.659e-03  12.063        1
9439       KCTD13      16_24 1.152e-03   491.21 1.684e-06  11.491        1
7835       RNF123       3_35 0.000e+00 23172.22 0.000e+00 -10.957        1
9556        NUPR1      16_23 2.730e-01    68.38 5.553e-05 -10.540        1
10881        CLN3      16_23 8.431e-02    67.59 1.695e-05  10.453        1
1888        MAPK3      16_24 3.635e-09   951.95 1.029e-11  10.247        2
8399       ZNF646      16_24 1.054e-08  7159.87 2.246e-10 -10.000        1
8398       ZNF668      16_24 1.054e-08  7159.87 2.246e-10  10.000        1
9120      C1QTNF4      11_29 1.746e-03   104.43 5.425e-07   9.950        2
8750       INO80E      16_24 1.977e-10  1116.43 6.568e-13   9.923        2
7839        CAMKV       3_35 0.000e+00 53036.18 0.000e+00  -9.848        1
486         PRSS8      16_24 7.979e-10  6796.28 1.613e-11  -9.765        1
10911       SKOR1      15_31 7.418e-01    54.44 1.202e-04  -9.754        1
11900         LAT      16_23 2.780e-01    55.49 4.590e-05  -9.553        1
2634        MTCH2      11_29 4.161e-05    90.75 1.123e-08  -9.551        1
12917 CTC-467M3.3       5_52 0.000e+00   460.00 0.000e+00   9.482        1

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)

[1] 50

Uploading data to Enrichr... Done.
  Querying GO_Biological_Process_2021... Done.
  Querying GO_Cellular_Component_2021... Done.
  Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"

                              Term Overlap Adjusted.P.value        Genes
1 MAP kinase activity (GO:0004707)    2/14          0.04648 MAPK11;MAPK6

DisGeNET enrichment analysis for genes with PIP>0.5

                                                           Description     FDR
105                                 Interfrontal craniofaciosynostosis 0.03608
106                                            Osteoglophonic dwarfism 0.03608
144                                      Disproportionate tall stature 0.03608
146                                 Ceroid Lipofuscinosis, Neuronal, 7 0.03608
147    Holoprosencephaly, Ectrodactyly, and Bilateral Cleft Lip-Palate 0.03608
176                        CHROMOSOME 8p11 MYELOPROLIFERATIVE SYNDROME 0.03608
179                           CUTIS LAXA, AUTOSOMAL RECESSIVE, TYPE IB 0.03608
184                                 PULMONARY HYPERTENSION, PRIMARY, 4 0.03608
190 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 3 0.03608
191                                              CONE-ROD DYSTROPHY 20 0.03608
    Ratio BgRatio
105  1/21  1/9703
106  1/21  1/9703
144  1/21  1/9703
146  1/21  1/9703
147  1/21  1/9703
176  1/21  1/9703
179  1/21  1/9703
184  1/21  1/9703
190  1/21  1/9703
191  1/21  1/9703

WebGestalt enrichment analysis for genes with PIP>0.5

Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...

Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet, minNum =
minNum, : No significant gene set is identified based on FDR 0.05!

NULL

PIP Manhattan Plot

Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Sensitivity, specificity and precision for silver standard genes

#number of genes in known annotations
print(length(known_annotations))

[1] 41

#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))

[1] 25

#significance threshold for TWAS
print(sig_thresh)

[1] 4.599

#number of ctwas genes
length(ctwas_genes)

[1] 8

#number of TWAS genes
length(twas_genes)

[1] 283

#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols]

      genename region_tag susie_pip    mu2       PVE      z num_eqtl
7845     PPM1M       3_36     1.000 515.52 0.0015338  4.386        2
13972    NOL12      22_15     0.903  62.83 0.0001688 -4.505        2

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.00000 0.09756 

#specificity
print(specificity)

 ctwas   TWAS 
0.9993 0.9762 

#precision / PPV
print(precision)

  ctwas    TWAS 
0.00000 0.01413 

Locus Plots - 1_46

Locus Plots - 1_60

Locus Plots - 7_49

Locus Plots - 15_21

Locus Plots - 3_36

Session information

sessionInfo()

R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.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] readxl_1.3.1      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
 [5] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tidyverse_1.3.1  
 [9] tibble_3.1.6      WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0      
[13] cowplot_1.0.0     ggplot2_3.3.5     workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] fs_1.5.2          lubridate_1.8.0   bit64_4.0.5       doParallel_1.0.16
 [5] httr_1.4.2        rprojroot_2.0.2   tools_3.6.1       backports_1.4.1  
 [9] doRNG_1.8.2       utf8_1.2.2        R6_2.5.1          vipor_0.4.5      
[13] DBI_1.1.1         colorspace_2.0-2  withr_2.4.3       ggrastr_1.0.1    
[17] tidyselect_1.1.1  bit_4.0.4         curl_4.3.2        compiler_3.6.1   
[21] git2r_0.26.1      cli_3.1.0         rvest_1.0.2       Cairo_1.5-12.2   
[25] xml2_1.3.3        labeling_0.4.2    scales_1.1.1      apcluster_1.4.8  
[29] digest_0.6.29     rmarkdown_2.11    svglite_1.2.2     pkgconfig_2.0.3  
[33] htmltools_0.5.2   dbplyr_2.1.1      fastmap_1.1.0     highr_0.9        
[37] rlang_0.4.12      rstudioapi_0.13   RSQLite_2.2.8     jquerylib_0.1.4  
[41] farver_2.1.0      generics_0.1.1    jsonlite_1.7.2    vroom_1.5.7      
[45] magrittr_2.0.1    Matrix_1.2-18     ggbeeswarm_0.6.0  Rcpp_1.0.7       
[49] munsell_0.5.0     fansi_0.5.0       gdtools_0.1.9     lifecycle_1.0.1  
[53] stringi_1.7.6     whisker_0.3-2     yaml_2.2.1        plyr_1.8.6       
[57] grid_3.6.1        blob_1.2.2        ggrepel_0.9.1     parallel_3.6.1   
[61] promises_1.0.1    crayon_1.4.2      lattice_0.20-38   haven_2.4.3      
[65] hms_1.1.1         knitr_1.36        pillar_1.6.4      igraph_1.2.10    
[69] rjson_0.2.20      rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16 
[73] reprex_2.0.1      glue_1.5.1        evaluate_0.14     data.table_1.14.2
[77] modelr_0.1.8      vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[81] foreach_1.5.1     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[89] iterators_1.0.13  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2