Case study: PPP5C xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, PPP5C.

  • Section 0: Sanity check
  • Section 1: Fine-mapping for xQTL and GWAS
  • Section 2: Multi-context colocalization with Bellenguez 2022
  • Section 3: Refinement of colocalized loci with other AD GWAS
  • Section 4: Assessment of multi-context xQTL effect sizes
  • Section 5: Multi-context causal TWAS (including conventional TWAS and MR)
  • Section 6: Context specific multi-gene fine-mapping
  • Section 7: Epigenomic QTL and their target regions
  • Section 8: Context focused validation in other xQTL data
  • Section 9: Non-linear effects of xQTL
  • Section 10: in silico functional studies in iPSC model
  • Section 11: Functional annotations of selected loci
  • Section 12: Candidate loci as trans-xQTL

Overview¶

FunGen-xQTL resource contains 67 xQTL contexts as well as 9 AD GWAS fine-mapped genome-wide. The overarching goal for case studies is to use these resource to raise questions and learn more about gene targets of interest.

Overall, a case study consists of the following aspects:

  • Check the basic information of the gene
  • Check the existing xQTL and integrative analysis results, roughly including
    • Summary table for univariate fine-mapping
    • Marginal association results
    • Multi-gene and multi-context fine-mapping
    • Multi-context colocalization with AD GWAS
    • TWAS, MR and causal TWAS
    • Integration with epigenetic QTL
    • Quantile QTL
    • Interaction QTL
    • Validation:
      • Additional xQTL data in FunGen-xQTL
      • Additional AD GWAS data-set already generated by FunGen-xQTL
    • In silico functional studies
      • Additional iPSC data-sets
    • Functional annotations of variants, particularly in relevant cellular contexts
  • Creative thinking: generate hypothesis, search in literature, raise questions to discuss

Computing environment setup¶

Interactive analysis will be done on AWS cloud powered by MemVerge. Please contact Gao Wang to setup accounts for you to start the analysis.

Please follow instructions on https://wanggroup.org/productivity_tips/mmcloud-interactive to configure your computing environment. Here are some additional packages you need to install after the initial configuration, in order to perform these analysis:

in terminal with bash:

micromamba install -n r_libs r-pecotmr

in R:

install.packages('BEDMatrix')

How to Use This Notebook¶

  1. Before you start: Load functions from cb_plot.R and utilis.R, located at <xqtl-paper>/codes/. These functions and resources are packaged to streamline the analysis and ensure everything is as clean as possible. And also the codes for ColocBoost under path /data/colocalization/colocboost/R.
  2. Inside of this notebook, use sed -i or Ctrl+F to replace the gene PPP5C with the gene you want to analyze.
  3. For detailed analysis in some sections, please refer to the corresponding analysis notebooks as indicated. These companion notebooks are available under this same folder. The rest of the tasks can be completed with a few lines of code, as demonstrated in this notebook.
  4. Similarly for the companion notebooks you should also use the sed -i or Ctrl+F replacing gene_name (PPP5C in this case) with the gene you want to investigate.

While using this notebook, you may need to generate three intermediate files from Sections 1 and 2, which will be useful for downstream analysis:

  • a. Section 1:
    • Fine-mapping contexts that indicate shared signals with AD, PPP5C_finemapping_contexts.rds. This can be used as input for Section 8 the multi-cohort validation step
    • A subset of the xQTL-AD table, Fungen_xQTL_allQTL.overlapped.gwas.export.PPP5C.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, PPP5C_colocboost_res.rds. this can be used as input for Sections 7, 9, 10, and 12.

Section 0: Sanity check¶

Check the basic information of the gene¶

  • To gain a preliminary understanding of this gene’s expression—specifically, whether it is cell-specific—can help us quickly determine if our results are consistent with expectations.

Useful websites:

  1. check gene function, (immune) cell type specificity, tissue specifity, protein location: https://www.proteinatlas.org
  2. check gene position and structure: https://www.ncbi.nlm.nih.gov/gene/
  3. other collective information: https://www.genecards.org

Check the existing results which are inputs to this analysis¶

Check the existing results which are inputs to this analysis¶

In [1]:
# If an error occurs while sourcing scripts, it might be because your get() returned NULL. 
#Please restart the kernel or click the R kernel in the upper right corner to resolve the issue.
source('../../codes/cb_plot.R')
source('../../codes/utilis.R')

for(file in list.files("/data/colocalization/colocboost/R", pattern = ".R", full.names = T)){
          source(file)
        }
gene_name = 'PPP5C'

dir.create(paste0('plots/', gene_name), recursive = T)
In [2]:
target_gene_info <- get_gene_info(gene_name = gene_name)
target_gene_info
$gene_info
A data.table: 1 x 14
region_id#chrstartendTSSLD_matrix_idLD_sumstats_idLD_sumstats_id_oldTADB_indexTADB_idgene_startgene_endsliding_windowsgene_name
<chr><chr><dbl><dbl><int><chr><chr><chr><chr><chr><int><int><chr><chr>
ENSG00000011485chr19446800004796000046347086chr19:42346101-44935906,chr19:44935906-46842901,chr19:46842901-4859013619_42346101-44935906,19_44935906-46842901,19_46842901-4859013619_42346101-44935906,19_44935906_46842901,19_46842901_48590136TADB_1261,TADB_1262,TADB_1263chr19_40837074_46645602,chr19_43631573_48886315,chr19_46290022_554732964634708746392981chr19:31719752-46645602,chr19:34641744-48886315,chr19:40837074-55473296,chr19:43631573-57160893,chr19:46290022-58617616PPP5C
$target_LD_ids
A matrix: 1 x 3 of type chr
chr19:42346101-44935906chr19:44935906-46842901chr19:46842901-48590136
$target_sums_ids
A matrix: 1 x 3 of type chr
19_42346101-4493590619_44935906-4684290119_46842901-48590136
$gene_region
'chr19:44680000-47960000'
$target_TAD_ids
A matrix: 1 x 3 of type chr
chr19_40837074_46645602chr19_43631573_48886315chr19_46290022_55473296
In [2]:
target_gene_info <- get_gene_info(gene_name = gene_name)
target_gene_info
$gene_info
A data.table: 1 x 14
region_id#chrstartendTSSLD_matrix_idLD_sumstats_idLD_sumstats_id_oldTADB_indexTADB_idgene_startgene_endsliding_windowsgene_name
<chr><chr><dbl><dbl><int><chr><chr><chr><chr><chr><int><int><chr><chr>
ENSG00000011485chr19446800004796000046347086chr19:42346101-44935906,chr19:44935906-46842901,chr19:46842901-4859013619_42346101-44935906,19_44935906-46842901,19_46842901-4859013619_42346101-44935906,19_44935906_46842901,19_46842901_48590136TADB_1261,TADB_1262,TADB_1263chr19_40837074_46645602,chr19_43631573_48886315,chr19_46290022_554732964634708746392981chr19:31719752-46645602,chr19:34641744-48886315,chr19:40837074-55473296,chr19:43631573-57160893,chr19:46290022-58617616PPP5C
$target_LD_ids
A matrix: 1 x 3 of type chr
chr19:42346101-44935906chr19:44935906-46842901chr19:46842901-48590136
$target_sums_ids
A matrix: 1 x 3 of type chr
19_42346101-4493590619_44935906-4684290119_46842901-48590136
$gene_region
'chr19:44680000-47960000'
$target_TAD_ids
A matrix: 1 x 3 of type chr
chr19_40837074_46645602chr19_43631573_48886315chr19_46290022_55473296
In [3]:
gene_id = target_gene_info$gene_info$region_id
chrom = target_gene_info$gene_info$`#chr`
In [4]:
source('../../codes/utilis.R')
expression_in_rosmap_bulk(target_gene_info)
No description has been provided for this image

Section 1: Fine-mapping for xQTL and GWAS¶

see notebook

In [15]:
region_p
No description has been provided for this image

Bellenguez et al GWAS signals has many overlap with CS from other xQTL sources. This motivates us to look further. The figure above shows the ranges of CS to give us a loci level idea. Below, we show the variants in those CS, color-coding the variants that are shared between them in the same color. In particular, AD GWAS signals are also captured by a few xQTL data, although at this point we don't have formal statistical (colocalization) evidences for these observations yet.

In [17]:
pip_p
No description has been provided for this image

Section 2: Multi-context colocalization with Bellenguez 2022¶

This is done using ColocBoost. The most updated version of ColocBoost results are under path s3://statfungen/ftp_fgc_xqtl/analysis_result/ColocBoost/2024_9/

In [4]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [4]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [9]:
cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)
No description has been provided for this image
In [10]:
pdf('plots/PPP5C/sec2.colocboost_res.pdf', width = 10, height = 5)
replayPlot(cb$p)
dev.off()
pdf: 2
In [11]:
# colocalized variants
cb_res_table
A data.frame: 1 x 8
colocalized phenotypespurity# variantshighest VCPcolocalized indexcolocalized variantsmax_abs_z_variantcset_id
<chr><dbl><dbl><dbl><chr><chr><chr><chr>
Ast; DLPFC; AC; PCC1130.076317619161; 9168; 9169; 9176; 9187; 9195; 9217; 9221; 9227; 9231; 9268; 9085; 9104chr19:46363525:T:G; chr19:46365473:C:G; chr19:46365703:A:G; chr19:46368010:G:T; chr19:46370708:A:G; chr19:46372136:T:C; chr19:46379203:A:G; chr19:46380837:C:T; chr19:46382675:A:G; chr19:46383750:G:C; chr19:46390208:A:G; chr19:46346219:A:G; chr19:46351675:A:Gchr19:46346219:A:Gcoloc_sets:Y2_Y7_Y8_Y9:CS1
In [12]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y2_Y7_Y8_Y9:CS1` =
A data.frame: 13 x 5
variantsAstDLPFCACPCC
<chr><dbl><dbl><dbl><dbl>
chr19:46363525:T:Gchr19:46363525:T:G-6.142655-4.618099-4.884360-4.216317
chr19:46365473:C:Gchr19:46365473:C:G-6.142655-4.618099-4.884360-4.216317
chr19:46365703:A:Gchr19:46365703:A:G-6.142655-4.618099-4.884360-4.216317
chr19:46368010:G:Tchr19:46368010:G:T-6.142655-4.618099-4.884360-4.216317
chr19:46370708:A:Gchr19:46370708:A:G-6.142655-4.618099-4.884360-4.216317
chr19:46372136:T:Cchr19:46372136:T:C-6.142655-4.618099-4.884360-4.216317
chr19:46379203:A:Gchr19:46379203:A:G-6.142655-4.618099-4.884360-4.216317
chr19:46380837:C:Tchr19:46380837:C:T-6.142655-4.618099-4.884360-4.216317
chr19:46382675:A:Gchr19:46382675:A:G-6.142655-4.618099-4.884360-4.216317
chr19:46383750:G:Cchr19:46383750:G:C-6.142655-4.618099-4.884360-4.216317
chr19:46390208:A:Gchr19:46390208:A:G-6.142655-4.618099-4.884360-4.216317
chr19:46346219:A:Gchr19:46346219:A:G-6.142655-4.702220-4.792844-4.216317
chr19:46351675:A:Gchr19:46351675:A:G-6.142655-4.620000-4.690080-4.216317
In [13]:
# LD between coloc sets
get_between_purity_simple(cb_res, gene.name = gene_id, path = '/data/colocalization/QTL_data/eQTL/')

Here, different colors refer to different 95% Colocalization Sets (CoS, a metric developed in ColocBoost indicating that there is 95% probabilty that this CoS captures a colocalization event). We only included ROSMAP data for this particular ColocBoost analysis. In this case, we observe cell specific eQTL, bulk sQTL colocalization on ROSMAP data with AD as two separate CoS, suggesting two putative causal signals. We did not detect colocalization with pQTL of statistical significance although from Section 1 there are some overlap with pQTL signals in fine-mapping CS, the overlapped variants in CS have small PIP.

Section 3: Refinement of colocalized loci with other AD GWAS¶

Here we refine the colocalization with other AD GWAS to iron out any heterogeniety between studies (heterogeniety can come from many sources), to get additional candidate loci from these more heterogenous sources as candidates to study.

In [14]:
AD_cohorts <- c('AD_Jansen_2021', 'AD_Bellenguez_EADB_2022', 'AD_Bellenguez_EADI_2022',
             'AD_Kunkle_Stage1_2019', 'AD_Wightman_Excluding23andMe_2021',
             'AD_Wightman_ExcludingUKBand23andME_2021', 'AD_Wightman_Full_2021')
cb_ad <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2, add_gwas = TRUE, gene_id = gene_id, cohorts = AD_cohorts)

No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.
No description has been provided for this image
In [15]:
pdf('plots/PPP5C/sec3.colocboost_res_allad.pdf', width = 10, height = 5)
replayPlot(cb_ad$p)
dev.off()

Section 4: Assessment of multi-context xQTL effect sizes¶

Option 1: ColocBoost + MASH¶

Use colocboost variants and check for mash posterior contrast to see if the effect size are shared or specific or even opposite. Advantage is that colocboost result is AD GWAS informed; issue is that marginal posterior effects is not always the joint

In [16]:
mash_p <- mash_plot(gene_name = 'PPP5C')

options(repr.plot.width = 10, repr.plot.height = 10)

for (mash_p_tmp in mash_p) {
    print(mash_p_tmp)
}

Option 2: mvSuSiE¶

Use mvSuSiE multicontext fine-mapping results --- the bubble plot to check posterior effects. Issue is that we don't have this results yet, and this is limited to one cohort at a time, without information from AD.

We should go for option 1 by default and if we want to make claim about opposite effect size we double-check with mvSuSiE multicontext analysis.

Section 5: Multi-context causal TWAS (including conventional TWAS and MR)¶

The most updated version of cTWAS analysis are under path s3://statfungen/ftp_fgc_xqtl/analysis_result/cTWAS/

TWAS results¶

We report TWAS from all contexts and methods from the pipeline. Here we will filter it down to the best performing methods and only keep contexts that are significant.

In [17]:
plot_TWAS_res(gene_id = gene_id, gene_name = gene_name)
No description has been provided for this image

MR results¶

This is only available for genes that are deemed significant in TWAS and have summary statistics available for effect size and standard errors in GWAS, in addition to z-scores --- current version does not support z-scores although we will soon also support z-scores in MR using MAF from reference panel.

cTWAS results¶

To be updated soon.

Section 6: Context specific multi-gene fine-mapping¶

A quick analysis: using the xQTL-AD summary table (flatten table)¶

We extract from xQTL-AD summary table the variants to get other genes also have CS with the variants shared by target gene and AD.

In [18]:
multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.PPP5C.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 117 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000007047chr194507928745079288MARK4 MiGA_GTS_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000010310chr194566822045668221GIPR MiGA_SVZ_eQTL,MiGA_THA_eQTL,BM_10_MSBB_eQTL,DLPFC_DeJager_eQTL,Oli_Kellis_eQTL,Oli_mega_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000011478chr194569240245692403QPCTL MiGA_SVZ_eQTL
ENSG00000012061chr194547882745478828ERCC1 MiGA_THA_eQTL,DLPFC_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000039650chr194986790849867909PNKP ROSMAP_DLPFC_sQTL
ENSG00000062822chr195038420350384204POLD1 ROSMAP_PCC_sQTL
ENSG00000063127chr194932521449325215SLC6A16 ROSMAP_DLPFC_sQTL
ENSG00000069399chr194474783544747836BCL3 ROSMAP_AC_sQTL,STARNET_eQTL
ENSG00000073050chr194358047243580473XRCC1 ROSMAP_PCC_sQTL
ENSG00000074219chr194936245649362457TEAD2 ROSMAP_DLPFC_sQTL
ENSG00000079432chr194226853642268537CIC ROSMAP_AC_sQTL
ENSG00000090372chr194674699346746994STRN4 monocyte_ROSMAP_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000090554chr194947420649474207FLT3LG ROSMAP_AC_sQTL
ENSG00000104783chr194378125643781257KCNN4 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000104812chr194899330948993310GYS1 MiGA_SVZ_eQTL
ENSG00000104853chr194495459044954591CLPTM1 Knight_eQTL,PCC_DeJager_eQTL,DLPFC_Klein_gpQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000104859chr194503904445039045CLASRP ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000104866chr194509139545091396PPP1R37 STARNET_eQTL
ENSG00000104879chr194532287445322875CKM BM_10_MSBB_eQTL
ENSG00000104881chr194540634845406349PPP1R13LMiGA_SVZ_eQTL,ROSMAP_PCC_sQTL
ENSG00000104884chr194537091745370918ERCC2 MiGA_GTS_eQTL,MiGA_THA_eQTL
ENSG00000104936chr194578255145782552DMPK MiGA_GFM_eQTL,MiGA_THA_eQTL,Ast_DeJager_eQTL,Exc_Kellis_eQTL,Inh_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000104941chr194581530745815308RSPH6A DLPFC_DeJager_eQTL,PCC_DeJager_eQTL,AC_DeJager_eQTL
ENSG00000104951chr194992953849929539IL4I1 ROSMAP_AC_sQTL
ENSG00000104967chr194597404345974044NOVA2 MiGA_SVZ_eQTL
ENSG00000104983chr194599546045995461CCDC61 ROSMAP_DLPFC_sQTL
ENSG00000105281chr194678859346788594SLC1A5 MiGA_GTS_eQTL,MiGA_THA_eQTL,monocyte_ROSMAP_eQTL,STARNET_eQTL
ENSG00000105287chr194671712646717127PRKD2 MiGA_SVZ_eQTL,MiGA_THA_eQTL,Exc_DeJager_eQTL,OPC_Kellis_eQTL,Exc_Kellis_eQTL,Exc_mega_eQTL,ROSMAP_AC_sQTL
ENSG00000105321chr194725597947255980CCDC9 MiGA_GTS_eQTL,MiGA_THA_eQTL,MSBB_BM36_pQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000105357chr195018818550188186MYH14 ROSMAP_PCC_sQTL
..................
ENSG00000177464chr194560221145602212OPA3 DLPFC_Bennett_pQTL
ENSG00000177464chr194560221145602212GPR4 DLPFC_Bennett_pQTL
ENSG00000178980chr194777858447778585SELENOW ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000181027chr194674604546746046FKRP MiGA_SVZ_eQTL,MiGA_THA_eQTL,BM_44_MSBB_eQTL,Ast_DeJager_eQTL,AC_DeJager_eQTL,Oli_Kellis_eQTL,Ast_mega_eQTL,Exc_mega_eQTL,Oli_mega_eQTL,STARNET_eQTL
ENSG00000182013chr194647156246471563PNMA8A Oli_DeJager_eQTL,Inh_DeJager_eQTL,AC_DeJager_eQTL
ENSG00000182264chr194874690848746909IZUMO1 MiGA_SVZ_eQTL
ENSG00000186567chr194466227744662278CEACAM19 MiGA_SVZ_eQTL
ENSG00000187244chr194480907044809071BCAM ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000188624chr194612468746124688IGFL3 MiGA_THA_eQTL
ENSG00000189114chr194517878345178784BLOC1S3 PCC_DeJager_eQTL,AC_DeJager_eQTL
ENSG00000189190chr195278680652786807ZNF600 MiGA_THA_eQTL
ENSG00000197380chr194666118146661182DACT3 ROSMAP_PCC_sQTL
ENSG00000197405chr194729002247290023C5AR1 MiGA_SVZ_eQTL
ENSG00000203326chr195336570353365704ZNF525 Knight_eQTL,BM_36_MSBB_eQTL
ENSG00000204673chr194987845849878459AKT1S1 DLPFC_Bennett_pQTL
ENSG00000204869chr194607709346077094IGFL4 Inh_DeJager_eQTL,Exc_Kellis_eQTL,Inh_Kellis_eQTL,Exc_mega_eQTL,Inh_mega_eQTL
ENSG00000204941chr194318653543186536PSG5 ROSMAP_DLPFC_sQTL
ENSG00000213889chr194548877645488777PPM1N MiGA_THA_eQTL
ENSG00000216588chr194461356244613563IGSF23 MiGA_THA_eQTL
ENSG00000221923chr195236991652369917ZNF880 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000224916chr194494223744942238APOC4-APOC2MiGA_GFM_eQTL,STARNET_eQTL
ENSG00000234906chr194494603444946035APOC2 STARNET_eQTL
ENSG00000256683chr195198685551986856ZNF350 ROSMAP_PCC_sQTL
ENSG00000267467chr194494223644942237APOC4 STARNET_eQTL
ENSG00000267680chr194409433844094339ZNF224 ROSMAP_AC_sQTL
ENSG00000268500chr195164688851646889AC018755.2 STARNET_eQTL
ENSG00000269403chr195136809851368099AC008750.7 ROSMAP_DLPFC_sQTL
ENSG00000269469chr194946275149462752AC010619.1 ROSMAP_AC_sQTL
ENSG00000277531chr194642895046428951PNMA8C BM_36_MSBB_eQTL
ENSG00000285505chr194199423141994232AC010616.1 ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with PPP5C. Further look into the data-set --- using these genes as targets and repeating what we did above for PPP5C --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on PPP5C region to find these genes, as you will see in the section below.

A statistically solid approach: mvSuSiE multi-gene analysis¶

This multi-gene fine-mapping analysis was done for each xQTL context separately. We will need to check the results for all contexts where this gene has an xQTL, in order to identify if there are other genes also sharing the same xQTL with this target gene. We included other genes in the same TAD window along with this gene, and extended it into a sliding window approach to include multiple TADs just in case. You need to check the sliding windows belongs to that gene (TSS) on analysis repo.

In [19]:
sliding_windows <- target_gene_info$gene_info$sliding_windows %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows
  1. 'chr19:31719752-46645602'
  2. 'chr19:34641744-48886315'
  3. 'chr19:40837074-55473296'
  4. 'chr19:43631573-57160893'
  5. 'chr19:46290022-58617616'

The most updated version of mvSuSiE multi-gene results are under path s3://statfungen/ftp_fgc_xqtl/analysis_result/mvsusie_multi_gene_test/multi_gene/ Currently it is still WIP. You can revisit this later when we prompt you to. Here is an example for PPP5C:

In [20]:
mnm_gene <- list()
for (window in sliding_windows) {
    mnm_gene_tmp <- NULL
    mnm_gene_tmp <- tryCatch(
        readRDS(paste0('/data/analysis_result/mvsusie_multi_gene/multi_gene/ROSMAP_multi_gene.', window, '.mnm.rds')),
        error = function(e) NULL
    )
    
    if (!is.null(mnm_gene_tmp)) {
        if(target_gene_info$gene_info$region_id %in% mnm_gene_tmp$mvsusie_fitted$condition_names){
        tryCatch({
            p <- mvsusieR::mvsusie_plot(mnm_gene_tmp$mvsusie_fitted, sentinel_only = F, add_cs = T)
            print(p)  # This ensures the plot is displayed in JupyterLab
        }, error = function(e) NULL)
        } else {
            message('There is mnm result for sliding window ',window,', but not include target gene ', gene_name, ' in CS')
        }
        mnm_gene <- append(mnm_gene, list(mnm_gene_tmp))
    }
}

In this case, there is no statistical evidence for PPP5C sharing any of its xQTL with other genes in ROSMAP Microglia data we looked into; although we have not analyzed MiGA this way yet (which showed some potential signals from the quick analysis above).

Section 7: Epigenomic QTL and their target regions¶

fsusie, see notebook

Generate a crude plot to determined whether the story is interesting¶

This is a crude version of the case study plot which shows the fsusie Effect (colored line), the gene body (black arrow), the epi-QTL (large dots with the same color as the effects) and ADGWAS cs position (small red dots).

Only produce the refine plot if we can see either:

  1. There are sharing snp between epi-QTL and AD CS
  2. There are the AD CS located within one of the effect range
  3. The crude plot suggest something interesting
In [9]:
options(repr.plot.width = 40, repr.plot.height = 40)

 ggplot() + theme_bw() + facet_grid(cs_coverage_0.95 + study + region ~ ., labeller = labeller(.rows = function(x) gsub("([_:,-])", "\n", x)), scale = "free_y") +

      theme(text = element_text(size = 20), strip.text.y = element_text(size = 25, angle = 0.5)) +
     # xlim(view_win) +
      ylab("Estimated effect") +
   #   geom_line(data = haQTL_df %>% mutate(study = "haQTL effect") %>% filter(CS == 5),
    #            aes_string(y = "fun_plot", x = "x", col = "CS"), size = 4, col = "#00AEEF") +
  geom_line(data = effect_of_interest ,
                aes_string(y = "fun_plot", x = "x", col = "cs_coverage_0.95"), size = 2) +  
    geom_point(data = effect_of_interest ,
                aes_string(y = "pip", x = "pos", col = "cs_coverage_0.95"), size = 10) +
    theme(text = element_text(size = 40), strip.text.y = element_text(size = 15, angle = 0.5), 
            axis.text.x = element_text(size = 40), axis.title.x = element_text(size = 40)) +
      xlab("Position") +
      ylab("Estimated\neffect") +
      geom_segment(arrow = arrow(length = unit(1, "cm")), aes(x = gene_start, xend = gene_end, y = 1, yend = 1), size = 6,
                  data = tar_gene_info$gene_info, alpha = 0.3) +
      geom_text(aes(x = (gene_start + gene_end) / 2, y = 1 , label = gene_name), size = 10, 
              data = tar_gene_info$gene_info)+
        geom_point(aes(x = pos, y = pip  ) ,color = "red", data = flatten_table%>%filter( str_detect(study,"AD_") , cs_coverage_0.95 != 0  )%>%mutate(AD_study = study%>%str_replace_all("_","\n" ))%>%select(-study,-region,-cs_coverage_0.95) )
No description has been provided for this image

Section 8: Context focused validation in other xQTL data¶

see notebook

add fake version for now, so you don't have to refer to above link

Background: our "discovery set" is ROSMAP but we have additional "validation" sets including:

  • STARNET
  • MiGA
  • KnightADRC
  • MSBB
  • metaBrain
  • UKB pQTL

TODO:

  • Get from Carlos WashU CSF based resource (pQTL and metabolomic QTL)

This section shows verification of findings from these data-sets. In principle we should check them through sections 1-6 more formally. In practice we will start with colocalization via colocboost --- since our study is genetics (variant and loci level) focused. We can selectively follow them up for potentially intereting validations. We therefore only demonstrate validation via colocboost as a starting point.

In [5]:
finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1
In [5]:
finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1
In [6]:
finempping_contexts <- get_norosmap_contexts(finempping_contexts)
In [7]:
cb_contexts <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2, add_QTL = TRUE, cohorts = finempping_contexts, gene_id = gene_id)

No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.
No description has been provided for this image

Section 9: Non-linear effects of xQTL¶

see notebook

APOE interaction¶

In [10]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(PPP5C_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for PPP5C csets in interaction association nalysis",
       x = "Gene Name",
       y = "qvalue_interaction",
       size = "qvalue_interaction") +
  theme_minimal(base_size = 14) +
  theme(panel.background = element_blank(),
        panel.grid.major = element_line(color = "grey80"),
        legend.position = NULL,
        axis.text.x = element_text(angle = 45, hjust = 1))  + ylim(0,1)
  # scale_color_manual(values = colorRampPalette(brewer.pal(8, "Set1"))(length(unique(flat_var$gene_name))))
ggsave('plots/PPP5C/sec11.interaction_association_PPP5C_lessPIP25.pdf', height = 5, width = 8)
No description has been provided for this image

In conclusion, there is no interaction QTL with APOE identified.

Section 10: in silico functional studies in iPSC model¶

see notebook

In [11]:
vars_p
In [13]:
apoe_p

Section 11: Functional annotations of selected loci¶

see notebook

TODO

  • Touch base with Ryan on the snATAC annotations
  • Run this by Pavel to see if there are additional comments on how we do this
In [ ]:

Section 12: Candidate loci as trans-xQTL¶

see notebook

In [9]:
options(repr.plot.width=12, repr.plot.height=6)
if(!is.null(flat_var)){
   p =  ggplot(flat_var, aes(x = gene_name, y = pip, size = pip)) +
      geom_point(alpha = 0.7) +
      labs(title = paste0("PIP values for trans fine mapped Genes in ", gene_name ," csets with AD"),
           x = "Gene Name",
           y = "PIP",
           size = "PIP",
           color = "CS Coverage 0.95 Min Corr") +
      theme_minimal(base_size = 14) +
      theme(panel.background = element_blank(),
            panel.grid.major = element_line(color = "grey80"),
            legend.position = NULL,
            axis.text.x = element_text(angle = 45, hjust = 1))  
      # scale_color_manual(values = colorRampPalette(brewer.pal(8, "Set1"))(length(unique(flat_var$gene_name))))
    ggsave(paste0('plots/PPP5C/sec12.trans_fine_mapping_',gene_name,'.pdf'),p, height = 5, width = 8)
    p
    } else{
    message('There are no detectable trans signals for ', gene_name)
}

Creative thinking: generate hypothesis, search in literature, raise questions to discuss¶

You can now generate some preliminary hypotheses based on the above results. Next, you should search for evidence in the literature to support or refine these hypotheses and identify additional analyses needed to confirm them.