Case study: TREM2 xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, TREM2.

  • 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 TREM2 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 (TREM2 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, TREM2_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.TREM2.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, TREM2_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('/data/interactive_analysis/rf2872/codes/xqtl-paper/codes/cb_plot.R')
source('/data/interactive_analysis/rf2872/codes/xqtl-paper/codes/utilis.R')
for(file in list.files("/data/colocalization/colocboost/R", pattern = ".R", full.names = T)){
          source(file)
        }
gene_name = 'TREM2'

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

get basic target gene information

In [3]:
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>
ENSG00000095970chr6387200004216318641163185chr6:38719920-40377803,chr6:40377803-42070711,chr6:42070711-439148756_38719920-40377803,6_40377803-42070711,6_42070711-439148756_38719920-40377803,6_40377803_42070711,6_42070711_43914875TADB_534,TADB_535chr6_37715310_42754109,chr6_40335607_446409414116318641158506chr6:28657001-40030792,chr6:32277122-42754109,chr6:34258981-44640941,chr6:37715310-45450425,chr6:40335607-47080348TREM2
$target_LD_ids
A matrix: 1 x 3 of type chr
chr6:38719920-40377803chr6:40377803-42070711chr6:42070711-43914875
$target_sums_ids
A matrix: 1 x 3 of type chr
6_38719920-403778036_40377803-420707116_42070711-43914875
$gene_region
'chr6:38720000-42163186'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr6_37715310_42754109chr6_40335607_44640941
In [4]:
gene_id = target_gene_info$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 [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 [6]:
cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )
In [7]:
#save colocboost results
cb_res_table <- get_cb_summary(cb_res) 

saveRDS(cb_res_table, paste0(gene_name, "_colocboost_res.rds"))
In [8]:
cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)
No description has been provided for this image
In [9]:
pdf('plots/TREM2/sec2.colocboost_res.pdf', width = 10, height = 5)
replayPlot(cb$p)
dev.off()
pdf: 2
In [10]:
# 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>
DLPFC; AC; PCC1.000000020.622411912431; 12426 chr6:41272084:C:T; chr6:41271095:C:A chr6:41272084:C:Tcoloc_sets:Y2_Y3_Y4:CS2
DLPFC; AC 0.859225850.674734811902; 11925; 11924; 11932; 11934chr6:41190851:GTGTCTGTCTGTC:G; chr6:41192167:A:G; chr6:41192063:A:G; chr6:41194780:T:C; chr6:41194977:G:Achr6:41194780:T:Ccoloc_sets:Y2_Y3:CS1
In [11]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y2_Y3_Y4:CS2`
A data.frame: 2 x 4
variantsDLPFCACPCC
<chr><dbl><dbl><dbl>
chr6:41272084:C:Tchr6:41272084:C:T-6.16115-6.932423-6.331731
chr6:41271095:C:Achr6:41271095:C:A-6.16115-6.876965-6.293139
$`coloc_sets:Y2_Y3:CS1`
A data.frame: 5 x 3
variantsDLPFCAC
<chr><dbl><dbl>
chr6:41190851:GTGTCTGTCTGTC:Gchr6:41190851:GTGTCTGTCTGTC:G6.5332286.048445
chr6:41192167:A:Gchr6:41192167:A:G 6.8179105.685052
chr6:41192063:A:Gchr6:41192063:A:G 6.6808125.685052
chr6:41194780:T:Cchr6:41194780:T:C 7.1332515.301670
chr6:41194977:G:Achr6:41194977:G:A 7.0446875.375782
In [12]:
# 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:Y2_Y3_Y4:CS2coloc_sets:Y2_Y3:CS10.1810797666540760.2218309585750520.210962690645256

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 [13]:
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 [14]:
pdf('plots/TREM2/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 [24]:
plot_TWAS_res(gene_id = gene_id)
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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 [13]:
multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.TREM2.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 33 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000001167chr64107294441072945NFYA MiGA_GTS_eQTL
ENSG00000044090chr64305394243053943CUL7 ROSMAP_PCC_sQTL
ENSG00000096070chr63619674336196744BRPF3 MiGA_GTS_eQTL
ENSG00000112167chr63911518539115186SAYSD1 DLPFC_DeJager_eQTL
ENSG00000112195chr64120114841201149TREML2 monocyte_ROSMAP_eQTL,STARNET_eQTL
ENSG00000112561chr64173625841736259TFEB MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000112576chr64205035642050357CCND3 STARNET_eQTL
ENSG00000112578chr64192149841921499BYSL MiGA_THA_eQTL
ENSG00000112599chr64219495542194956GUCA1B MiGA_THA_eQTL
ENSG00000112619chr64272259642722597PRPH2 BM_44_MSBB_eQTL
ENSG00000112658chr64317126843171269SRF MiGA_THA_eQTL
ENSG00000112659chr64318218443182185CUL9 MiGA_THA_eQTL
ENSG00000124593chr64178034841780349AL365205.1ROSMAP_DLPFC_sQTL
ENSG00000124596chr64109778641097787OARD1 MiGA_GTS_eQTL,ROSMAP_PCC_sQTL
ENSG00000124602chr64103922041039221UNC5CL MiGA_GFM_eQTL
ENSG00000124615chr63993455039934551MOCS1 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000124688chr64362953943629540MAD2L1BP MiGA_SVZ_eQTL
ENSG00000124701chr64105320141053202APOBEC2 MiGA_GFM_eQTL
ENSG00000124702chr64301410243014103KLHDC3 MiGA_GFM_eQTL,MiGA_THA_eQTL
ENSG00000124731chr64128668141286682TREM1 MiGA_GTS_eQTL,MiGA_SVZ_eQTL,STARNET_eQTL
ENSG00000137166chr64154638041546381FOXP4 MiGA_SVZ_eQTL
ENSG00000137171chr64304077643040777KLC4 ROSMAP_PCC_sQTL
ENSG00000137218chr64178654141786542FRS3 ROSMAP_DLPFC_sQTL
ENSG00000146122chr63979229739792298DAAM2 MiGA_GTS_eQTL,Ast_DeJager_eQTL,Oli_DeJager_eQTL,Ast_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000146223chr64287961542879616RPL7L1 ROSMAP_DLPFC_sQTL
ENSG00000156564chr64058736340587364LRFN2 MiGA_GTS_eQTL
ENSG00000164627chr63972540739725408KIF6 Oli_DeJager_eQTL,Oli_mega_eQTL,ROSMAP_PCC_sQTL
ENSG00000183826chr63864014738640148BTBD9 ROSMAP_DLPFC_sQTL
ENSG00000188056chr64122833841228339TREML4 MiGA_GFM_eQTL,MiGA_GTS_eQTL,MiGA_SVZ_eQTL,MiGA_THA_eQTL,BM_22_MSBB_eQTL,BM_44_MSBB_eQTL,STARNET_eQTL
ENSG00000198663chr63687186936871870C6orf89 MiGA_THA_eQTL
ENSG00000204052chr64351068543510686LRRC73 MiGA_SVZ_eQTL
ENSG00000221821chr64289082042890821C6orf226 BM_10_MSBB_eQTL
ENSG00000278224chr64178078141780782PRICKLE4 ROSMAP_DLPFC_sQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with TREM2. Further look into the data-set --- using these genes as targets and repeating what we did above for TREM2 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on TREM2 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 [14]:
sliding_windows <- target_gene_info$gene_info$sliding_windows %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows
  1. 'chr6:28657001-40030792'
  2. 'chr6:32277122-42754109'
  3. 'chr6:34258981-44640941'
  4. 'chr6:37715310-45450425'
  5. 'chr6:40335607-47080348'

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 TREM2:

In [15]:
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
                         L1
ENSG00000065060 6.14539e-07
ENSG00000124772 6.14539e-07
ENSG00000146192 6.14539e-07
ENSG00000137193 6.14539e-07
ENSG00000095970 6.14539e-07

$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            L3         L1
ENSG00000124772 -0.3438072 -4.032918e-08 5.3144e-11
ENSG00000146192 -0.3438072 -4.032918e-08 5.3144e-11
ENSG00000095970 -0.3438072 -4.032918e-08 5.3144e-11
ENSG00000112576 -0.3438072 -4.032918e-08 5.3144e-11
ENSG00000124587 -0.3438072 -4.032918e-08 5.3144e-11
ENSG00000137221 -0.3438072 -4.032918e-08 5.3144e-11

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

$effects
                          L1
ENSG00000095970 0.0002645928
ENSG00000112576 0.0002645928
ENSG00000137413 0.0002645928
ENSG00000124587 0.0002645928
ENSG00000137221 0.0002645928
ENSG00000171453 0.0002645928
No description has been provided for this image

In this case, there is no statistical evidence for TREM2 sharing any of its xQTL with other genes in ROSMAP Microglia data we looked into; although we have not analyzed MiGA this way yet (which showed some potential signals from the quick analysis above).

Section 7: Epigenomic QTL and their target regions¶

fsusie, see notebook

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

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

Only produce the refine plot if we can see either:

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

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

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

Section 8: Context focused validation in other xQTL data¶

see notebook

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

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

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

TODO:

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

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

In [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.
No description has been provided for this image

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(TREM2_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for TREM2 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/TREM2/sec11.interaction_association_TREM2_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 [ ]:
FIXME
In [120]:
vars_p
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In [118]:
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 [ ]:
FIXME
In [120]:
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/TREM2/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.