Case study: GALNT6 xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, GALNT6.

  • 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 /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 GALNT6 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 (GALNT6 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, GALNT6_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.GALNT6.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, GALNT6_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 [2]:
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 = 'GALNT6'

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

get basic target gene information

In [5]:
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>
ENSG00000139629chr12486800005239286751392866chr12:47790772-48762437,chr12:48762437-51189586,chr12:51189586-5264528912_47790772-48762437,12_48762437-51189586,12_51189586-5264528912_47790772-48762437,12_48762437_51189586,12_51189586_52645289TADB_968,TADB_969chr12_47653211_53108261,chr12_50815042_546774085139286751351247chr12:41238446-49957487,chr12:42353866-53108261,chr12:44164185-54677408,chr12:47653211-57041806,chr12:50815042-58959148,chr12:52006281-61419293GALNT6
$target_LD_ids
A matrix: 1 x 3 of type chr
chr12:47790772-48762437chr12:48762437-51189586chr12:51189586-52645289
$target_sums_ids
A matrix: 1 x 3 of type chr
12_47790772-4876243712_48762437-5118958612_51189586-52645289
$gene_region
'chr12:48680000-52392867'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr12_47653211_53108261chr12_50815042_54677408
In [6]:
gene_id = target_gene_info$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 [7]:
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 [8]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [9]:
#save colocboost results
cb_res_table <- get_cb_summary(cb_res) 

saveRDS(cb_res_table, paste0(gene_name, "_colocboost_res.rds"))
In [10]:
cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)
No description has been provided for this image
In [11]:
pdf('plots/GALNT6/sec2.colocboost_res.pdf', width = 10, height = 5)
replayPlot(cb$p)
dev.off()
pdf: 2
In [12]:
# 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>
Oli; DLPFC; AC; PCC; Monocyte; AD_Bellenguez_20221.00000010.999681210602 chr12:51362485:T:C chr12:51362485:T:Ccoloc_sets:Y1_Y2_Y3_Y4_Y5_Y12:CS1
Oli; DLPFC; AC; PCC; Monocyte 0.98380220.896436810715; 10708chr12:51391617:A:AAGCCGC; chr12:51389636:T:Achr12:51389636:T:Acoloc_sets:Y1_Y2_Y3_Y4_Y5:CS2
In [13]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y1_Y2_Y3_Y4_Y5_Y12:CS1`
A data.frame: 1 x 7
variantsOliDLPFCACPCCMonocyteAD_Bellenguez_2022
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
chr12:51362485:T:Cchr12:51362485:T:C-19.06122-22.94871-24.94123-16.17026-4.2088664.43617
$`coloc_sets:Y1_Y2_Y3_Y4_Y5:CS2`
A data.frame: 2 x 6
variantsOliDLPFCACPCCMonocyte
<chr><dbl><dbl><dbl><dbl><dbl>
chr12:51391617:A:AAGCCGCchr12:51391617:A:AAGCCGC5.2311288.4103797.7248884.4543803.850570
chr12:51389636:T:Achr12:51389636:T:A 5.3929848.6198797.3792384.2614493.650899
In [14]:
# 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_Y5_Y12:CS1coloc_sets:Y1_Y2_Y3_Y4_Y5:CS20.05114226923961210.05235726294346270.0517497660915374

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 [12]:
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 [13]:
pdf('plots/GALNT6/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

In [14]:
mash_p <- mash_plot(gene_name = 'GALNT6')
for (plot in mash_p) {
    print(plot)
}
No description has been provided for this image

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 [15]:
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 [16]:
multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.GALNT6.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 70 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000050405chr125028354550283546LIMA1 MiGA_THA_eQTL
ENSG00000050426chr125104796151047962LETMD1 MiGA_GFM_eQTL,MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL
ENSG00000050438chr125139131651391317SLC4A8 MiGA_SVZ_eQTL,Oli_DeJager_eQTL,Oli_Kellis_eQTL,Oli_mega_eQTL,DLPFC_Bennett_pQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000061273chr124783313147833132HDAC7 MiGA_SVZ_eQTL
ENSG00000066084chr125050498450504985DIP2B MiGA_SVZ_eQTL,MiGA_THA_eQTL,BM_36_MSBB_eQTL,MSBB_BM36_pQTL
ENSG00000066117chr125008519950085200SMARCD1MiGA_GTS_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000079387chr124810607848106079SENP1 ROSMAP_DLPFC_sQTL
ENSG00000086159chr124996723849967239AQP6 MiGA_GTS_eQTL
ENSG00000110844chr124956821749568218PRPF40BAst_DeJager_eQTL,Oli_DeJager_eQTL,Exc_DeJager_eQTL,Inh_DeJager_eQTL,Oli_Kellis_eQTL,OPC_Kellis_eQTL,Exc_Kellis_eQTL,Inh_Kellis_eQTL,Ast_mega_eQTL,Exc_mega_eQTL,Inh_mega_eQTL,OPC_mega_eQTL,Oli_mega_eQTL
ENSG00000110881chr125005754750057548ASIC1 MiGA_GFM_eQTL,AC_DeJager_eQTL,Oli_mega_eQTL
ENSG00000110911chr125102856551028566SLC11A2MiGA_GTS_eQTL,Ast_DeJager_eQTL,Exc_Kellis_eQTL,Exc_mega_eQTL
ENSG00000110934chr125132466751324668BIN2 MiGA_GFM_eQTL,Mic_DeJager_eQTL
ENSG00000111057chr125294887052948871KRT18 MiGA_SVZ_eQTL
ENSG00000111371chr124627001646270017SLC38A1ROSMAP_PCC_sQTL
ENSG00000123268chr125076370950763710ATF1 MiGA_GTS_eQTL,ROSMAP_AC_sQTL
ENSG00000123349chr125329529053295291PFDN5 MiGA_SVZ_eQTL
ENSG00000123352chr124936658349366584SPATS2 MiGA_GTS_eQTL,BM_44_MSBB_eQTL,Oli_Kellis_eQTL,Exc_mega_eQTL,Oli_mega_eQTL
ENSG00000123358chr125202283152022832NR4A1 ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000123395chr125206924552069246ATG101 STARNET_eQTL
ENSG00000123416chr124913139649131397TUBA1B STARNET_eQTL
ENSG00000125084chr124897832148978322WNT1 MiGA_THA_eQTL
ENSG00000129315chr124871699748716998CCNT1 MiGA_GFM_eQTL,MiGA_SVZ_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000135390chr125367740753677408ATP5MC2ROSMAP_DLPFC_sQTL
ENSG00000135451chr124932323549323236TROAP MiGA_GTS_eQTL
ENSG00000135457chr125117313451173135TFCP2 MiGA_THA_eQTL
ENSG00000135472chr124990421649904217FAIM2 MiGA_GFM_eQTL,MiGA_GTS_eQTL,Oli_DeJager_eQTL,Oli_Kellis_eQTL,Inh_Kellis_eQTL,Oli_mega_eQTL,ROSMAP_AC_sQTL
ENSG00000135503chr125195169851951699ACVR1B MiGA_GTS_eQTL,MiGA_THA_eQTL,Exc_DeJager_eQTL,Inh_DeJager_eQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000135519chr124953902949539030KCNH3 ROSMAP_PCC_sQTL
ENSG00000139537chr124890410948904110CCDC65 MiGA_SVZ_eQTL,Ast_DeJager_eQTL,Exc_DeJager_eQTL,Inh_DeJager_eQTL,Oli_Kellis_eQTL,Exc_mega_eQTL
ENSG00000139549chr124909480049094801DHH MiGA_SVZ_eQTL
..................
ENSG00000161800chr125003313550033136RACGAP1 MiGA_SVZ_eQTL,Oli_Kellis_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000161813chr125039238250392383LARP4 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000161835chr125200694552006946TAMALIN MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000167528chr124835141348351414ZNF641 MiGA_SVZ_eQTL
ENSG00000167548chr124906079349060794KMT2D ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000167550chr124907002449070025RHEBL1 MiGA_THA_eQTL
ENSG00000167553chr124918873549188736TUBA1C Knight_eQTL
ENSG00000167612chr125188800851888009ANKRD33 MiGA_GTS_eQTL
ENSG00000167767chr125219201352192014KRT80 BM_22_MSBB_eQTL
ENSG00000169884chr124897173448971735WNT10B MiGA_GTS_eQTL,BM_10_MSBB_eQTL,BM_22_MSBB_eQTL
ENSG00000170421chr125294991952949920KRT8 MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000170523chr125232139752321398KRT83 BM_10_MSBB_eQTL,BM_22_MSBB_eQTL,DLPFC_DeJager_eQTL,PCC_DeJager_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000170545chr125127041451270415SMAGP DLPFC_DeJager_eQTL
ENSG00000170653chr125362640953626410ATF7 ROSMAP_DLPFC_sQTL
ENSG00000174233chr124878908848789089ADCY6 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000174243chr124885284148852842DDX23 Ast_DeJager_eQTL,ROSMAP_PCC_sQTL
ENSG00000177627chr124848249748482498C12orf54 MiGA_SVZ_eQTL
ENSG00000178401chr124934688749346888DNAJC22 BM_10_MSBB_eQTL,DLPFC_DeJager_eQTL
ENSG00000178449chr125011208150112082COX14 MiGA_THA_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000181418chr124899937448999375DDN ROSMAP_PCC_sQTL
ENSG00000182544chr125325125053251251MFSD5 MiGA_THA_eQTL
ENSG00000183283chr125123872351238724DAZAP2 ROSMAP_DLPFC_sQTL
ENSG00000184271chr125121770751217708POU6F1 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000185432chr125092347150923472METTL7A MiGA_SVZ_eQTL,MiGA_THA_eQTL,Oli_DeJager_eQTL,Oli_mega_eQTL
ENSG00000186666chr124984310549843106BCDIN3D MiGA_GFM_eQTL,DLPFC_DeJager_eQTL
ENSG00000187778chr124956814449568145MCRS1 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000196876chr125159026551590266SCN8A ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000205352chr125344167753441678PRR13 MiGA_SVZ_eQTL,BM_44_MSBB_eQTL
ENSG00000205426chr125229153352291534KRT81 BM_36_MSBB_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000272822chr124895736448957365AC073610.1ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with GALNT6. Further look into the data-set --- using these genes as targets and repeating what we did above for GALNT6 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on GALNT6 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 [17]:
sliding_windows <- target_gene_info$gene_info$sliding_windows %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows
  1. 'chr12:41238446-49957487'
  2. 'chr12:42353866-53108261'
  3. 'chr12:44164185-54677408'
  4. 'chr12:47653211-57041806'
  5. 'chr12:50815042-58959148'
  6. 'chr12:52006281-61419293'

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 GALNT6:

In [18]:
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 GALNT6 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) )
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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 [15]:
finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1
In [16]:
finempping_contexts <- get_norosmap_contexts(finempping_contexts)
In [17]:
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.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.
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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 [10]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(GALNT6_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for GALNT6 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/GALNT6/sec11.interaction_association_GALNT6_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 [13]:
vars_p
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In [15]:
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 [16]:
func_p
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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/CR1/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.