Case study: MS4A4E xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, MS4A4E.

  • 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 MS4A4E 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 (MS4A4E 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, MS4A4E_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.MS4A4E.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, MS4A4E_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 = 'MS4A4E'

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>
ENSG00000214787chr11572800006144000060243136chr11:56858541-60339997,chr11:60339997-6381833211_56858541-60339997,11_60339997-6381833211_56858541-60339997,11_60339997_63818332TADB_903,TADB_904chr11_56299638_62485822,chr11_60203514_653218116024313760200270chr11:44804071-58324952,chr11:46980557-62485822,chr11:50840000-65321811,chr11:56299638-66671716,chr11:60203514-68955802MS4A4E
$target_LD_ids
A matrix: 1 x 2 of type chr
chr11:56858541-60339997chr11:60339997-63818332
$target_sums_ids
A matrix: 1 x 2 of type chr
11_56858541-6033999711_60339997-63818332
$gene_region
'chr11:57280000-61440000'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr11_56299638_62485822chr11_60203514_65321811
In [4]:
gene_id = target_gene_info$gene_info$region_id
chrom = target_gene_info$gene_info$`#chr`
In [4]:
source('../../codes/utilis.R')
expression_in_rosmap_bulk(target_gene_info)
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Section 1: Fine-mapping for xQTL and GWAS¶

see notebook

In [15]:
region_p
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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
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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)
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In [10]:
pdf('plots/MS4A4E/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>
DLPFC; AD_Bellenguez_20220.825047240.155879211569; 11568; 11573; 11574; 11562; 11551; 11581; 11540; 11518; 11900; 11914; 11589; 11632; 11625; 11640; 11641; 11633; 11624; 11615; 11613; 11606; 11659; 11661; 11665chr11:60175636:A:T; chr11:60175342:A:G; chr11:60177107:C:T; chr11:60177337:T:C; chr11:60173126:T:A; chr11:60169453:A:C; chr11:60178272:T:C; chr11:60165106:G:A; chr11:60161199:G:C; chr11:60251788:G:GTA; chr11:60254475:G:A; chr11:60180901:C:G; chr11:60193954:G:A; chr11:60192496:G:A; chr11:60194693:G:A; chr11:60194716:G:A; chr11:60194013:G:T; chr11:60192370:T:C; chr11:60190040:C:T; chr11:60189465:C:T; chr11:60187884:G:A; chr11:60198316:G:A; chr11:60198822:T:C; chr11:60200053:A:Gchr11:60175342:A:Gcoloc_sets:Y2_Y6:CS1
In [12]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y2_Y6:CS1` =
A data.frame: 24 x 3
variantsDLPFCAD_Bellenguez_2022
<chr><dbl><dbl>
chr11:60175636:A:Tchr11:60175636:A:T -4.374553 -9.530120
chr11:60175342:A:Gchr11:60175342:A:G -4.387689 -9.457831
chr11:60177107:C:Tchr11:60177107:C:T -4.367298 -9.469880
chr11:60177337:T:Cchr11:60177337:T:C -4.374553 -9.445783
chr11:60173126:T:Achr11:60173126:T:A -4.285175 -9.882353
chr11:60169453:A:Cchr11:60169453:A:C -4.329628 -9.518072
chr11:60178272:T:Cchr11:60178272:T:C -4.374553 -9.361446
chr11:60165106:G:Achr11:60165106:G:A -4.264497 -9.506024
chr11:60161199:G:Cchr11:60161199:G:C -4.171721 -9.554217
chr11:60251788:G:GTAchr11:60251788:G:GTA-3.725960-10.283989
chr11:60254475:G:Achr11:60254475:G:A -3.703405-10.238095
chr11:60180901:C:Gchr11:60180901:C:G -4.236167 -9.325301
chr11:60193954:G:Achr11:60193954:G:A -4.243795 -9.228916
chr11:60192496:G:Achr11:60192496:G:A -4.221645 -9.253012
chr11:60194693:G:Achr11:60194693:G:A -4.255751 -9.204819
chr11:60194716:G:Achr11:60194716:G:A -4.224732 -9.192771
chr11:60194013:G:Tchr11:60194013:G:T -4.224514 -9.180723
chr11:60192370:T:Cchr11:60192370:T:C -4.108100 -9.253012
chr11:60190040:C:Tchr11:60190040:C:T -4.120288 -9.228916
chr11:60189465:C:Tchr11:60189465:C:T -4.120288 -9.228916
chr11:60187884:G:Achr11:60187884:G:A -4.120288 -9.228916
chr11:60198316:G:Achr11:60198316:G:A -4.092060 -9.228916
chr11:60198822:T:Cchr11:60198822:T:C -4.086291 -9.216867
chr11:60200053:A:Gchr11:60200053:A:G -4.092060 -9.204819
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.
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In [15]:
pdf('plots/MS4A4E/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 [16]:
mash_p <- mash_plot(gene_name = 'MS4A4E')

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.

In [8]:
message("Multi context in ROSMAP data")

multi_context_rosmap_tmp <- tryCatch(
    readRDS(paste0('/data/analysis_result/multi_context/ROSMAP/mnm/ROSMAP_DeJager.',
                   target_gene_info$gene_info$`#chr`, '_', gene_id, '.multicontext_bvsr.rds')),
    error = function(e) message('Error in loading ROSMAP multi context data')
)
if (!is.null(multi_context_rosmap_tmp[[1]]$mvsusie_fitted)) {
    plot_and_save(multi_context_rosmap_tmp[[1]], 'plots/MS4A4E/sec4.multi_context_ROSMAP')
} else {
    message('Multi Context results are empty in ROSMAP data')
}
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In [9]:
# Load and process MSBB data
message("Multi context in MSBB data")

multi_context_msbb_tmp <- tryCatch(
    readRDS(paste0('/data/analysis_result/multi_context/MSBB/mnm/MSBB_eQTL.',
                   target_gene_info$gene_info$`#chr`, '_', gene_id, '.multicontext_bvsr.rds')),
    error = function(e)  message('Error in loading MSBB multi context data')
)
if (!is.null(multi_context_msbb_tmp[[1]]$mvsusie_fitted)) {
    plot_and_save(multi_context_msbb_tmp[[1]], 'plots/MS4A4E/sec4.multi_context_MSBB')
} else {
    message('Multi Context results are empty in MSBB data')
}

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)
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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.MS4A4E.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 13 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000006118chr116092445960924460TMEM132AAC_DeJager_eQTL,STARNET_eQTL
ENSG00000011347chr116158114761581148SYT7 MiGA_SVZ_eQTL
ENSG00000013725chr116097167960971680CD6 BM_22_MSBB_eQTL
ENSG00000071203chr116049277760492778MS4A12 MiGA_GTS_eQTL
ENSG00000110079chr116018565660185657MS4A4A ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000110446chr116095265260952653SLC15A3 MiGA_SVZ_eQTL
ENSG00000134809chr115753080257530803TIMM10 BM_10_MSBB_eQTL
ENSG00000149124chr115873197358731974GLYAT MiGA_GTS_eQTL
ENSG00000149131chr115759738657597387SERPING1MiGA_SVZ_eQTL
ENSG00000149150chr115751577957515780SLC43A1 ROSMAP_DLPFC_sQTL
ENSG00000149503chr116212399762123998INCENP MiGA_THA_eQTL
ENSG00000149506chr116086756160867562ZP1 MiGA_THA_eQTL
ENSG00000156603chr115771232257712323MED19 MiGA_GFM_eQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with MS4A4E. Further look into the data-set --- using these genes as targets and repeating what we did above for MS4A4E --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on MS4A4E 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 [6]:
sliding_windows <- target_gene_info$gene_info$TADB_id %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows
  1. 'chr11_56299638_62485822'
  2. 'chr11_60203514_65321811'

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 NCK2:

In [ ]:
mnm_gene_tmp[[1]] %>%
In [11]:
gene_ref <- fread('/data/resource/References/Homo_sapiens.GRCh38.103.chr.reformatted.collapse_only.gene.region_list')
In [15]:
ms4a_genes <- gene_ref %>% filter(str_detect(gene_name, 'MS4A')) %>% pull(gene_id)
In [19]:
 gene_ref %>% filter(str_detect(gene_name, 'MS4A'))
A data.table: 17 x 5
#chrstartendgene_idgene_name
<chr><int><int><chr><chr>
chr116005658660056587ENSG00000149516MS4A3
chr116008826060088261ENSG00000149534MS4A2
chr116018466560184666ENSG00000110077MS4A6A
chr116018565660185657ENSG00000110079MS4A4A
chr116024313660243137ENSG00000214787MS4A4E
chr116033483060334831ENSG00000166926MS4A6E
chr116037848460378485ENSG00000166927MS4A7
chr116037852960378530ENSG00000166928MS4A14
chr116042957160429572ENSG00000166930MS4A5
chr116045584560455846ENSG00000156738MS4A1
chr116049277760492778ENSG00000071203MS4A12
chr116051539160515392ENSG00000204979MS4A13
chr116057785560577856ENSG00000283601MS4A19P
chr116069961160699612ENSG00000166959MS4A8
chr116072930360729304ENSG00000214782MS4A18
chr116075686660756867ENSG00000166961MS4A15
chr116078533260785333ENSG00000172689MS4A10
In [18]:
# Main loop to process sliding windows
mnm_gene <- list()
for (window in sliding_windows) {
    context_files <- list.files('/data/analysis_result/multi_gene/ROSMAP/mnm_genes/', window, full.names = T) %>% .[str_detect(., '.multigene_bvrs.rds')]
    for(context_file in context_files){
        context_mnm = context_file %>% basename %>% str_split(., '[.]', simplify = T) %>% .[,1]
        # Load multi-gene data
        mnm_gene_tmp <- tryCatch(
            readRDS(context_file),
            error = function(e) NULL
        )
        
        if (!is.null(mnm_gene_tmp)) {
            # Check if target gene is in the condition names
            if (any(ms4a_genes %in% mnm_gene_tmp[[1]]$mvsusie_fitted$condition_names)) {
                # Use a common prefix format for multi-gene plots
                plot_and_save(mnm_gene_tmp[[1]], 'plots/MS4A4E/sec6.multigene')
            } else {
                message('There is mnm result for TAD window ', window, ' in ', context_mnm,
                        ', but it does not include MS4A family in CS.')
            }
            # Append to the results list
            mnm_gene <- append(mnm_gene, list(mnm_gene_tmp))
        } 
    }
}
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In this case, there is no statistical evidence for MS4A4E 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 [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.
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Section 9: Non-linear effects of xQTL¶

see notebook

APOE interaction¶

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

ggplot(MS4A4E_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for MS4A4E 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/MS4A4E/sec11.interaction_association_MS4A4E_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 [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/MS4A4E/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.