BMI - Brain Frontal Cortex BA9

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Last updated: 2022-02-21

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] 11472

#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 
1144  832  640  455  556  662  528  439  419  459  689  634  234  371  365  512 
  17   18   19   20   21   22 
 717  176  886  353  119  282 

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

[1] 8962

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

[1] 0.7812

Check convergence of parameters

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Version	Author	Date
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#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 
0.0067252 0.0002911 

#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 
25.11 17.26 

#report sample size
print(sample_size)

[1] 336107

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

[1]   11472 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 
0.005764 0.112682 

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

[1]  0.3711 15.5333

Genes with highest PIPs

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Version	Author	Date
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          genename region_tag susie_pip      mu2       PVE       z num_eqtl
8994         NEGR1       1_46    1.0000 17779.39 5.290e-02 -10.657        2
13051 RP11-490G2.2       1_60    1.0000 33092.91 9.846e-02   5.044        1
10446         GSAP       7_49    1.0000 32937.39 9.800e-02   5.260        1
751          MAPK6      15_21    1.0000 35089.11 1.044e-01  -4.600        1
7741         PPM1M       3_36    1.0000   247.30 7.358e-04   4.675        3
3673          FLT3       13_7    0.9523    33.57 9.512e-05  -5.360        1
979         PIK3C3      18_23    0.9326    52.82 1.466e-04   6.864        2
8027         CASP7      10_71    0.8054    25.18 6.035e-05   4.610        2
9305        ASPHD1      16_24    0.7995   119.96 2.854e-04 -11.938        1
3823         PREX1      20_30    0.7871    34.78 8.145e-05   5.634        1
1486        RASSF7       11_1    0.7687    22.53 5.153e-05   3.477        1
5622       C18orf8      18_12    0.7675    60.95 1.392e-04   7.723        3
5021         DCAF7      17_37    0.7484    28.87 6.429e-05   5.437        1
10039        KCNB2       8_53    0.7452    66.52 1.475e-04  -8.226        1
12703  CTC-467M3.3       5_52    0.7200    90.78 1.945e-04   9.482        1
2989         IFT57       3_67    0.7135    44.79 9.508e-05  -5.822        2
13296       AARSD1      17_25    0.7088    33.54 7.073e-05   5.542        1
4728         YWHAQ        2_6    0.6955    26.36 5.455e-05   4.911        1
6022          ECE2      3_113    0.6950    29.99 6.201e-05  -5.305        1
10781        SF3B3      16_37    0.6811    35.86 7.266e-05  -6.852        1

Genes with largest effect sizes

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Version	Author	Date
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          genename region_tag susie_pip   mu2       PVE       z num_eqtl
9           SEMA3F       3_35 0.000e+00 72070 0.000e+00   7.681        1
10678      SLC38A3       3_35 0.000e+00 67358 0.000e+00   6.726        1
7734         CAMKV       3_35 0.000e+00 52493 0.000e+00 -10.226        2
7917       CCDC171       9_13 0.000e+00 50279 0.000e+00   8.023        2
1462         MAST3      19_14 0.000e+00 42245 0.000e+00   6.953        2
31            RBM5       3_35 0.000e+00 42133 0.000e+00  12.473        1
41            RBM6       3_35 0.000e+00 40749 0.000e+00  12.536        1
751          MAPK6      15_21 1.000e+00 35089 1.044e-01  -4.600        1
8150          LEO1      15_21 2.603e-03 34888 2.702e-04   4.603        1
13051 RP11-490G2.2       1_60 1.000e+00 33093 9.846e-02   5.044        1
10446         GSAP       7_49 1.000e+00 32937 9.800e-02   5.260        1
6317         CNNM2      10_66 0.000e+00 31589 0.000e+00  -5.981        2
5488         MFAP1      15_16 7.668e-04 23547 5.372e-05   4.303        1
12490         HYPK      15_16 6.638e-06 23445 4.631e-07   4.322        1
7732        RNF123       3_35 0.000e+00 23053 0.000e+00 -10.957        1
11029       MRPL21      11_38 0.000e+00 22517 0.000e+00   4.215        2
1379         WDR76      15_16 0.000e+00 21367 0.000e+00   4.687        2
11910       CKMT1A      15_16 0.000e+00 21087 0.000e+00   4.130        1
9680         DHFR2       3_59 0.000e+00 17796 0.000e+00   3.266        3
8994         NEGR1       1_46 1.000e+00 17779 5.290e-02 -10.657        2

Genes with highest PVE

          genename region_tag susie_pip      mu2       PVE       z num_eqtl
751          MAPK6      15_21  1.000000 35089.11 1.044e-01  -4.600        1
13051 RP11-490G2.2       1_60  1.000000 33092.91 9.846e-02   5.044        1
10446         GSAP       7_49  1.000000 32937.39 9.800e-02   5.260        1
8994         NEGR1       1_46  1.000000 17779.39 5.290e-02 -10.657        2
10925       TTC30B      2_107  0.349013   762.39 7.917e-04  -3.137        1
7741         PPM1M       3_36  1.000000   247.30 7.358e-04   4.675        3
3029         SPCS1       3_36  0.516681   361.49 5.557e-04  -5.067        1
9305        ASPHD1      16_24  0.799534   119.96 2.854e-04 -11.938        1
8150          LEO1      15_21  0.002603 34887.56 2.702e-04   4.603        1
12703  CTC-467M3.3       5_52  0.720017    90.78 1.945e-04   9.482        1
10039        KCNB2       8_53  0.745172    66.52 1.475e-04  -8.226        1
979         PIK3C3      18_23  0.932638    52.82 1.466e-04   6.864        2
5622       C18orf8      18_12  0.767469    60.95 1.392e-04   7.723        3
6868         GPR61       1_67  0.582832    80.07 1.389e-04   8.755        1
8172          MC4R      18_34  0.344096   130.50 1.336e-04  13.312        1
5343         G3BP2       4_51  0.360464   121.38 1.302e-04  -2.134        1
7387          TAL1       1_29  0.666872    48.92 9.705e-05  -6.745        2
3673          FLT3       13_7  0.952277    33.57 9.512e-05  -5.360        1
2989         IFT57       3_67  0.713549    44.79 9.508e-05  -5.822        2
5424          SUOX      12_35  0.519005    58.30 9.003e-05  -5.807        1

Genes with largest z scores

           genename region_tag susie_pip      mu2       PVE       z num_eqtl
8172           MC4R      18_34  0.344096   130.50 1.336e-04  13.312        1
41             RBM6       3_35  0.000000 40748.94 0.000e+00  12.536        1
31             RBM5       3_35  0.000000 42132.93 0.000e+00  12.473        1
9305         ASPHD1      16_24  0.799534   119.96 2.854e-04 -11.938        1
6407          TAOK2      16_24  0.051558   125.44 1.924e-05  11.849        1
7736          MST1R       3_35  0.000000  6793.57 0.000e+00 -11.803        2
9306         KCTD13      16_24  0.033411   112.45 1.118e-05  11.491        1
9304         SEZ6L2      16_24  0.020287   110.70 6.682e-06 -11.407        1
7732         RNF123       3_35  0.000000 23052.76 0.000e+00 -10.957        1
8634         INO80E      16_24  0.023304    98.36 6.820e-06  10.794        2
8994          NEGR1       1_46  1.000000 17779.39 5.290e-02 -10.657        2
10707          CLN3      16_23  0.089642    68.19 1.819e-05  10.453        1
7734          CAMKV       3_35  0.000000 52493.18 0.000e+00 -10.226        2
12221 RP11-196G11.6      16_24  0.336710    81.09 8.123e-05  10.128        1
11002       SULT1A2      16_23  0.040750    64.26 7.791e-06 -10.075        2
12241        NPIPB7      16_23  0.029322    65.41 5.706e-06  10.038        1
8290         ZNF646      16_24  0.081105    77.81 1.878e-05 -10.000        1
2891       COL4A3BP       5_44  0.022154    72.01 4.747e-06  -9.828        1
10737         SKOR1      15_31  0.542610    52.50 8.476e-05  -9.635        1
8993        C1QTNF4      11_29  0.008762    87.67 2.286e-06   9.564        1

Comparing z scores and PIPs

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[1] 0.02223

           genename region_tag susie_pip      mu2       PVE       z num_eqtl
8172           MC4R      18_34  0.344096   130.50 1.336e-04  13.312        1
41             RBM6       3_35  0.000000 40748.94 0.000e+00  12.536        1
31             RBM5       3_35  0.000000 42132.93 0.000e+00  12.473        1
9305         ASPHD1      16_24  0.799534   119.96 2.854e-04 -11.938        1
6407          TAOK2      16_24  0.051558   125.44 1.924e-05  11.849        1
7736          MST1R       3_35  0.000000  6793.57 0.000e+00 -11.803        2
9306         KCTD13      16_24  0.033411   112.45 1.118e-05  11.491        1
9304         SEZ6L2      16_24  0.020287   110.70 6.682e-06 -11.407        1
7732         RNF123       3_35  0.000000 23052.76 0.000e+00 -10.957        1
8634         INO80E      16_24  0.023304    98.36 6.820e-06  10.794        2
8994          NEGR1       1_46  1.000000 17779.39 5.290e-02 -10.657        2
10707          CLN3      16_23  0.089642    68.19 1.819e-05  10.453        1
7734          CAMKV       3_35  0.000000 52493.18 0.000e+00 -10.226        2
12221 RP11-196G11.6      16_24  0.336710    81.09 8.123e-05  10.128        1
11002       SULT1A2      16_23  0.040750    64.26 7.791e-06 -10.075        2
12241        NPIPB7      16_23  0.029322    65.41 5.706e-06  10.038        1
8290         ZNF646      16_24  0.081105    77.81 1.878e-05 -10.000        1
2891       COL4A3BP       5_44  0.022154    72.01 4.747e-06  -9.828        1
10737         SKOR1      15_31  0.542610    52.50 8.476e-05  -9.635        1
8993        C1QTNF4      11_29  0.008762    87.67 2.286e-06   9.564        1

GO enrichment analysis for genes with PIP>0.5

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

[1] 40

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"

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

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

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

DisGeNET enrichment analysis for genes with PIP>0.5

                                         Description     FDR Ratio  BgRatio
39                        Sulfite oxidase deficiency 0.02288  1/18   1/9703
42   Acute myeloid leukemia, minimal differentiation 0.02288  1/18   1/9703
63                                  Sulfocysteinuria 0.02288  1/18   1/9703
65           FANCONI ANEMIA, COMPLEMENTATION GROUP P 0.02288  1/18   1/9703
72                    OROFACIODIGITAL SYNDROME XVIII 0.02288  1/18   1/9703
74 PROTEASOME-ASSOCIATED AUTOINFLAMMATORY SYNDROME 1 0.02288  1/18   1/9703
15            Childhood Acute Lymphoblastic Leukemia 0.02613  2/18  52/9703
16                   L2 Acute Lymphoblastic Leukemia 0.02613  2/18  50/9703
18                       Leukemia, Myelocytic, Acute 0.02613  3/18 173/9703
50                                   Nakajo syndrome 0.02613  1/18   2/9703

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: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
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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] 26

#significance threshold for TWAS
print(sig_thresh)

[1] 4.594

#number of ctwas genes
length(ctwas_genes)

[1] 8

#number of TWAS genes
length(twas_genes)

[1] 255

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

[1] genename   region_tag susie_pip  mu2        PVE        z          num_eqtl  
<0 rows> (or 0-length row.names)

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02439 0.12195 

#specificity
print(specificity)

 ctwas   TWAS 
0.9994 0.9782 

#precision / PPV
print(precision)

  ctwas    TWAS 
0.12500 0.01961 

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Locus Plots - 1_46

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Locus Plots - 1_60

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Locus Plots - 7_49

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Locus Plots - 15_21

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Locus Plots - 3_36

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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