Case study: BLNK xQTL and AD GWAS¶

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

  • 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:

micromamba install -n r_libs r-pecotmr
micromamba install -n r_libs r-bedmatrix

How to Use This Notebook¶

  1. Before you start: Load functions from cb_plot.R and utilis.R, located at /data/interactive_analysis/rf2872/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 BLNK 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 (BLNK 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, BLNK_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.BLNK.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, BLNK_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¶

In [1]:
source('/data/interactive_analysis/rf2872/codes/cb_plot.R')
source('/data/interactive_analysis/rf2872/codes/utilis.R')
for(file in list.files("/data/colocalization/colocboost/R", pattern = ".R", full.names = T)){
          source(file)
        }
gene_name = 'BLNK'

dir.create(paste0('plots/', gene_name), recursive = T)

get basic target gene information

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>
ENSG00000095585chr10945200009776000096271586chr10:93602293-95280986,chr10:95280986-9832087410_93602293-95280986,10_95280986-9832087410_93602293-95280986,10_95280986_98320874TADB_854,TADB_855chr10_91451684_96291012,chr10_93402540_987609079627158796189171chr10:83138420-94806272,chr10:85935540-96291012,chr10:89740823-98760907,chr10:91451684-101286680,chr10:93402540-103120368,chr10:96435909-104855543BLNK
$target_LD_ids
A matrix: 1 x 2 of type chr
chr10:93602293-95280986chr10:95280986-98320874
$target_sums_ids
A matrix: 1 x 2 of type chr
10_93602293-9528098610_95280986-98320874
$gene_region
'chr10:94520000-97760000'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr10_91451684_96291012chr10_93402540_98760907
In [3]:
gene_id = target_gene_info$gene_info$region_id
chrom = target_gene_info$gene_info$`#chr`

Take a quick look for the expression of target gene in ROSMAP bulk data, we don't want them to be too low

In [4]:
source('/data/interactive_analysis/rf2872/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 [16]:
region_p
No description has been provided for this image
In [27]:
pip_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.

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 [16]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [17]:
#save colocboost results
cb_res_table <- get_cb_summary(cb_res) 

saveRDS(cb_res_table, paste0(gene_name, "_colocboost_res.rds"))
In [18]:
cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)
No description has been provided for this image
In [19]:
pdf('plots/BLNK/sec2.colocboost_res.pdf', width = 10, height = 5)
replayPlot(cb$p)
dev.off()
pdf: 2
In [20]:
# colocalized variants
cb_res_table
A data.frame: 2 x 8
colocalized phenotypespurity# variantshighest VCPcolocalized indexcolocalized variantsmax_abs_z_variantcset_id
<chr><dbl><dbl><dbl><chr><chr><chr><chr>
Mic; DLPFC; AC; PCC; AD_Bellenguez_20220.9867176150.11244348364; 8324; 8293; 8291; 8323; 8295; 8315; 8321; 8286; 8282; 8259; 8247; 8277; 8214; 8217chr10:96281986:CA:C; chr10:96266650:G:A; chr10:96259400:A:G; chr10:96259356:A:G; chr10:96266554:A:G; chr10:96259827:T:C; chr10:96265101:G:A; chr10:96266318:A:G; chr10:96258109:A:G; chr10:96255961:T:C; chr10:96253690:T:A; chr10:96251179:T:A; chr10:96255397:A:T; chr10:96244776:C:T; chr10:96245257:A:Cchr10:96281986:CA:Ccoloc_sets:Y1_Y2_Y3_Y4_Y12:CS2
AC_unproductive; PCC_unproductive 0.9244346 20.85471357681; 7730 chr10:96142826:G:A; chr10:96153763:G:A chr10:96142826:G:A coloc_sets:Y7_Y11:CS1
In [21]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y1_Y2_Y3_Y4_Y12:CS2`
A data.frame: 15 x 6
variantsMicDLPFCACPCCAD_Bellenguez_2022
<chr><dbl><dbl><dbl><dbl><dbl>
chr10:96281986:CA:Cchr10:96281986:CA:C-8.874665-8.030545-8.430595-8.247098-5.238739
chr10:96266650:G:Achr10:96266650:G:A 8.654664 8.034742 8.644846 8.247098 5.224299
chr10:96259400:A:Gchr10:96259400:A:G 8.654664 8.034742 8.644846 8.247098 5.210280
chr10:96259356:A:Gchr10:96259356:A:G 8.654664 8.034742 8.644846 8.247098 5.210280
chr10:96266554:A:Gchr10:96266554:A:G 8.654664 8.034742 8.644846 8.247098 5.205607
chr10:96259827:T:Cchr10:96259827:T:C 8.654664 8.034742 8.644846 8.247098 5.200935
chr10:96265101:G:Achr10:96265101:G:A -8.654664-8.034742-8.644846-8.247098-5.186047
chr10:96266318:A:Gchr10:96266318:A:G 8.654664 8.034742 8.644846 8.247098 5.182243
chr10:96258109:A:Gchr10:96258109:A:G -8.654664-8.034742-8.644846-8.247098-5.162791
chr10:96255961:T:Cchr10:96255961:T:C -8.713714-8.028471-8.348343-8.311296-5.148837
chr10:96253690:T:Achr10:96253690:T:A -8.713714-8.028471-8.348343-8.311296-5.139535
chr10:96251179:T:Achr10:96251179:T:A -8.713714-8.028471-8.348343-8.311296-5.097222
chr10:96255397:A:Tchr10:96255397:A:T -8.654664-8.028471-8.348343-8.311296-5.162791
chr10:96244776:C:Tchr10:96244776:C:T 8.654664 8.028471 8.348343 8.311296 5.100917
chr10:96245257:A:Cchr10:96245257:A:C -8.654664-8.028471-8.348343-8.311296-4.954545
$`coloc_sets:Y7_Y11:CS1`
A data.frame: 2 x 3
variantsAC_unproductivePCC_unproductive
<chr><dbl><dbl>
chr10:96142826:G:Achr10:96142826:G:A16.8300314.78811
chr10:96153763:G:Achr10:96153763:G:A16.1812513.06718
In [22]:
# LD between coloc sets
get_between_purity_simple(cb_res, gene.name = gene_id, path = '/data/colocalization/QTL_data/eQTL/')
A matrix: 1 x 5 of type chr
coloc_csets_1coloc_csets_2min_abs_cormax_abs_cormedian_abs_cor
coloc_sets:Y1_Y2_Y3_Y4_Y12:CS2coloc_sets:Y7_Y11:CS10.02535846355347630.03099266237951670.027911853601941

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 [24]:
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.Error : File '/data/GWAS/ADGWAS_sumstats/10_95280986-98320874.RSS_QC_RAISS_imputed.AD_Kunkle_Stage1_2019.sumstats.tsv.gz' does not exist or is non-readable. getwd()=='/data/interactive_analysis/lz2838/xqtl-paper/AD_targets/BLNK'
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 [25]:
pdf('plots/BLNK/sec3.colocboost_res_allad.pdf', width = 10, height = 5)
replayPlot(cb_ad$p)
dev.off()
pdf: 2

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

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 [26]:
plot_TWAS_res(gene_id = gene_id)

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 [30]:
multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.BLNK.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 36 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000052749chr109742607597426076RRP12 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000059573chr109565671095656711ALDH18A1 ROSMAP_DLPFC_sQTL
ENSG00000077147chr109658745196587452TM9SF3 ROSMAP_AC_sQTL
ENSG00000095587chr109651392596513926TLL2 Oli_DeJager_eQTL,Inh_Kellis_eQTL,Inh_mega_eQTL,ROSMAP_PCC_sQTL
ENSG00000095637chr109556141395561414SORBS1 Exc_mega_eQTL,DLPFC_Bennett_pQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000108231chr109375783993757840LGI1 MiGA_GTS_eQTL,DLPFC_Klein_gpQTL
ENSG00000108239chr109440254094402541TBC1D12 MiGA_THA_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000119943chr109841518198415182PYROXD2 ROSMAP_DLPFC_sQTL
ENSG00000119969chr109450143394501434HELLS ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000119977chr109569414295694143TCTN3 Exc_DeJager_eQTL,Exc_Kellis_eQTL,Exc_mega_eQTL,Inh_mega_eQTL
ENSG00000119986chr109768724097687241AVPI1 ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000138119chr109348233393482334MYOF ROSMAP_PCC_sQTL
ENSG00000138160chr109257410492574105KIF11 ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000138185chr109571177895711779ENTPD1 MiGA_SVZ_eQTL,AC_DeJager_eQTL
ENSG00000138193chr109399393093993931PLCE1 ROSMAP_DLPFC_sQTL
ENSG00000155229chr109749879397498794MMS19 Inh_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000155252chr109764068597640686PI4K2A MiGA_SVZ_eQTL
ENSG00000155254chr109771317297713173MARVELD1 MiGA_GTS_eQTL
ENSG00000155256chr109773712097737121ZFYVE27 ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000165879chr109731927097319271FRAT1 MiGA_THA_eQTL
ENSG00000165886chr109749892397498924UBTD1 ROSMAP_DLPFC_sQTL
ENSG00000171160chr109763349997633500MORN4 MiGA_SVZ_eQTL,BM_10_MSBB_eQTL,BM_36_MSBB_eQTL,BM_44_MSBB_eQTL,PCC_DeJager_eQTL
ENSG00000171307chr109744616997446170ZDHHC16 MiGA_THA_eQTL
ENSG00000171311chr109744601697446017EXOSC1 ROSMAP_PCC_sQTL
ENSG00000171314chr109742619097426191PGAM1 MiGA_SVZ_eQTL
ENSG00000172987chr109923586199235862HPSE2 ROSMAP_DLPFC_sQTL
ENSG00000173124chr109519419995194200ACSM6 MiGA_GFM_eQTL
ENSG00000177853chr109612971496129715ZNF518A MiGA_GTS_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000181274chr109733472897334729FRAT2 MiGA_THA_eQTL
ENSG00000187122chr109718595897185959SLIT1 MiGA_GFM_eQTL,MiGA_THA_eQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000188649chr109590760295907603CC2D2B MiGA_SVZ_eQTL,AC_DeJager_eQTL
ENSG00000196233chr109683225396832254LCOR BM_36_MSBB_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000197430chr109635936496359365OPALIN MiGA_SVZ_eQTL,MiGA_THA_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000269891chr109729263697292637ARHGAP19-SLIT1ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000276490chr109468815394688154AL583836.1 ROSMAP_AC_sQTL
ENSG00000285932chr109973211199732112AL133353.2 ROSMAP_DLPFC_sQTL

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

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on BLNK 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 [31]:
sliding_windows <- target_gene_info$gene_info$sliding_windows %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows
  1. 'chr10:83138420-94806272'
  2. 'chr10:85935540-96291012'
  3. 'chr10:89740823-98760907'
  4. 'chr10:91451684-101286680'
  5. 'chr10:93402540-103120368'
  6. 'chr10:96435909-104855543'

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 BLNK:

In [32]:
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))
    }
}

$pip_plot

$effect_plot
No description has been provided for this image 
$z_plot
NULL

$effects
                           L2            L1
ENSG00000165338 -5.436711e-08 -1.223854e-05
ENSG00000148690 -5.436711e-08 -1.223854e-05
ENSG00000173145 -5.436711e-08 -1.223854e-05
ENSG00000108239 -5.436711e-08 -1.223854e-05
ENSG00000188649 -5.436711e-08 -1.223854e-05
ENSG00000095585 -5.436711e-08 -1.223854e-05

$pip_plot
No description has been provided for this image 
$effect_plot
No description has been provided for this image 
$z_plot
NULL

$effects
                       L4           L2         L3      L1
ENSG00000173145 0.4128248 3.291789e-06 0.02611942 0.02831
ENSG00000108239 0.4128248 3.291789e-06 0.02611942 0.02831
ENSG00000188649 0.4128248 3.291789e-06 0.02611942 0.02831
ENSG00000095585 0.4128248 3.291789e-06 0.02611942 0.02831
ENSG00000166024 0.4128248 3.291789e-06 0.02611942 0.02831

$pip_plot
No description has been provided for this image 
$effect_plot
No description has been provided for this image 
$z_plot
NULL

$effects
                         L2         L1
ENSG00000188649 0.004220688 0.00280786
ENSG00000095585 0.004220688 0.00280786
ENSG00000166024 0.004220688 0.00280786
No description has been provided for this image

In this case, there is no statistical evidence for BLNK 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 [12]:
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) )
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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 [19]:
finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1
In [20]:
finempping_contexts <- get_norosmap_contexts(finempping_contexts)
In [21]:
cb_ad <- 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.
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In conclusion from what's shown above, when we check the association signals in STARNET and MiGA on colocalization established from ROSMAP and AD GWAS, we see additional evidences.

Section 9: Non-linear effects of xQTL¶

see notebook

APOE interaction¶

In [9]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(BLNK_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for BLNK 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/BLNK/sec11.interaction_association_BLNK_lessPIP25.pdf', height = 5, width = 8)
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In conclusion, there is no interaction QTL with APOE identified.

Section 10: in silico functional studies in iPSC model¶

see notebook

In [16]:
vars_p
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In [18]:
apoe_p
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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 [17]:
func_p
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Section 12: Candidate loci as trans-xQTL¶

see notebook

In [16]:
options(repr.plot.width=12, repr.plot.height=6)
if(!is.null(flat_var)){
    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/BLNK/sec12.trans_fine_mapping_',gene_name,'.pdf'), height = 5, width = 8)
} 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.