Case study: MS4A6E xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, MS4A6E.

  • 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¶

In [109]:
region_p
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In [121]:
pip_p
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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 = 'MS4A6E'

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>
ENSG00000166926chr11572800006144000060334830chr11:56858541-60339997,chr11:60339997-6381833211_56858541-60339997,11_60339997-6381833211_56858541-60339997,11_60339997_63818332TADB_903,TADB_904chr11_56299638_62485822,chr11_60203514_653218116033483160396596chr11:44804071-58324952,chr11:46980557-62485822,chr11:50840000-65321811,chr11:56299638-66671716,chr11:60203514-68955802MS4A6E
$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'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr11_56299638_62485822chr11_60203514_65321811
In [3]:
gene_id = target_gene_info$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)
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Section 1: Fine-mapping for xQTL and GWAS¶

see notebook

In [5]:
region_p

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 [6]:
pip_p

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 [7]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [8]:
#save colocboost results
cb_res_table <- get_cb_summary(cb_res) 

saveRDS(cb_res_table, paste0(gene_name, "_colocboost_res.rds"))
In [9]:
cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)
In [ ]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(MS4A6E_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for MS4A6E 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/MS4A6E/sec11.interaction_association_MS4A6E_lessPIP25.pdf', height = 5, width = 8)
In [11]:
# colocalized variants
cb_res_table
$max_abs_z_variant =
In [12]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
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.
In [15]:
pdf('plots/MS4A6E/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 = 'MS4A6E')
for (plot in mash_p) {
    print(plot)
}

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)
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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.MS4A6E.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 44 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000006118chr116092445960924460TMEM132A MiGA_SVZ_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000071203chr116049277760492778MS4A12 MiGA_THA_eQTL
ENSG00000110031chr115857821958578220LPXN BM_10_MSBB_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000110077chr116018466560184666MS4A6A MiGA_SVZ_eQTL
ENSG00000110079chr116018565660185657MS4A4A MiGA_SVZ_eQTL
ENSG00000110104chr116084211260842113CCDC86 MiGA_THA_eQTL
ENSG00000110107chr116090658460906585PRPF19 MiGA_GTS_eQTL
ENSG00000110448chr116110248861102489CD5 MiGA_THA_eQTL
ENSG00000134802chr115742757957427580SLC43A3 MiGA_GTS_eQTL,MiGA_THA_eQTL,ROSMAP_PCC_sQTL
ENSG00000134824chr116179297961792980FADS2 ROSMAP_DLPFC_sQTL
ENSG00000134825chr116179280161792802TMEM258 ROSMAP_DLPFC_sQTL
ENSG00000149131chr115759738657597387SERPING1 MiGA_SVZ_eQTL
ENSG00000149136chr115733589157335892SSRP1 Exc_mega_eQTL
ENSG00000149476chr116133322061333221TKFC MiGA_SVZ_eQTL
ENSG00000149483chr116136236061362361TMEM138 MiGA_THA_eQTL
ENSG00000149499chr116261277462612775EML3 ROSMAP_PCC_sQTL
ENSG00000149506chr116086756160867562ZP1 MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000149516chr116005658660056587MS4A3 MiGA_GTS_eQTL
ENSG00000156587chr115756828357568284UBE2L6 MiGA_SVZ_eQTL,ROSMAP_AC_sQTL,STARNET_eQTL
ENSG00000156603chr115771232257712323MED19 ROSMAP_PCC_sQTL
ENSG00000166927chr116037848460378485MS4A7 BM_22_MSBB_eQTL,AC_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000166928chr116037852960378530MS4A14 Knight_eQTL,BM_10_MSBB_eQTL,BM_22_MSBB_eQTL,BM_36_MSBB_eQTL,BM_44_MSBB_eQTL,Mic_DeJager_eQTL,DLPFC_DeJager_eQTL,PCC_DeJager_eQTL,AC_DeJager_eQTL,Mic_Kellis_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000167986chr116134259561342596DDB1 ROSMAP_DLPFC_sQTL
ENSG00000168003chr116285600362856004SLC3A2 ROSMAP_DLPFC_sQTL
ENSG00000172320chr115943646859436469OR5A1 MiGA_GTS_eQTL
ENSG00000172324chr115942641259426413OR5A2 MiGA_GFM_eQTL
ENSG00000172365chr115842812058428121OR5B2 MiGA_SVZ_eQTL
ENSG00000172409chr115764818757648188CLP1 BM_36_MSBB_eQTL
ENSG00000172689chr116078533260785333MS4A10 MiGA_GTS_eQTL
ENSG00000172742chr115951136759511368OR4D9 MiGA_SVZ_eQTL
ENSG00000176495chr115935889459358895OR5AN1 MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000183908chr115718174657181747LRRC55 MiGA_SVZ_eQTL
ENSG00000186652chr115739064957390650PRG2 MiGA_GFM_eQTL
ENSG00000186660chr115857906258579063ZFP91 MiGA_THA_eQTL
ENSG00000186907chr115746052757460528RTN4RL2 MiGA_GFM_eQTL
ENSG00000187049chr116139235961392360TMEM216 ROSMAP_AC_sQTL
ENSG00000198561chr115775324257753243CTNND1 monocyte_ROSMAP_eQTL
ENSG00000233436chr115775178057751781BTBD18 MiGA_GTS_eQTL
ENSG00000254466chr115947331459473315OR4D10 MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000256591chr116142921961429220AP003108.2MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000279051chr115803092958030930OR6Q1 MiGA_GTS_eQTL
ENSG00000286264chr116424109464241095AP001453.3ROSMAP_DLPFC_sQTL
ENSG00000286264chr116424109464241095FKBP2 ROSMAP_DLPFC_sQTL
ENSG00000288534chr115771258157712582AP001931.2MiGA_GTS_eQTL,MiGA_SVZ_eQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with MS4A6E. Further look into the data-set --- using these genes as targets and repeating what we did above for MS4A6E --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on MS4A6E 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. 'chr11:44804071-58324952'
  2. 'chr11:46980557-62485822'
  3. 'chr11:50840000-65321811'
  4. 'chr11:56299638-66671716'
  5. 'chr11:60203514-68955802'

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 MS4A6E:

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 MS4A6E 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 [21]:
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 = 4) +  
    geom_point(data = effect_of_interest ,
                aes_string(y = "pip", x = "pos", col = "cs_coverage_0.95"), size = 4) +
    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) )

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 [22]:
finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1
In [23]:
finempping_contexts <- get_norosmap_contexts(finempping_contexts)
In [24]:
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.

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 [25]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(MS4A6E_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for MS4A6E 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/MS4A6E/sec11.interaction_association_MS4A6E_lessPIP25.pdf', height = 5, width = 8)

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

Section 10: in silico functional studies in iPSC model¶

see notebook

In [26]:
vars_p
In [27]:
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 [28]:
func_p

Section 12: Candidate loci as trans-xQTL¶

see notebook

In [29]:
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/MS4A6E/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.