Case study: CD33 xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, CD33.

  • 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 CD33 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 (CD33 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, CD33_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.CD33.rds. This can be used as input for Section 12.
  • b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, CD33_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 = 'CD33'

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>
ENSG00000105383chr19472800005448000051225063chr19:46842901-48590136,chr19:48590136-51139804,chr19:51139804-52482108,chr19:52482108-53788025,chr19:53788025-5521582119_46842901-48590136,19_48590136-51139804,19_51139804-52482108,19_52482108-53788025,19_53788025-5521582119_46842901-48590136,19_48590136_51139804,19_51139804_52482108,19_52482108_53788025,19_53788025_55215821TADB_1263chr19_46290022_554732965122506451240016chr19:34641744-48886315,chr19:40837074-55473296,chr19:43631573-57160893,chr19:46290022-58617616CD33
$target_LD_ids
A matrix: 1 x 5 of type chr
chr19:46842901-48590136chr19:48590136-51139804chr19:51139804-52482108chr19:52482108-53788025chr19:53788025-55215821
$target_sums_ids
A matrix: 1 x 5 of type chr
19_46842901-4859013619_48590136-5113980419_51139804-5248210819_52482108-5378802519_53788025-55215821
$gene_region
'chr19:47280000-54480000'
$target_TAD_ids
A matrix: 1 x 1 of type chr
chr19_46290022_55473296
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>
ENSG00000105383chr19472800005448000051225063chr19:46842901-48590136,chr19:48590136-51139804,chr19:51139804-52482108,chr19:52482108-53788025,chr19:53788025-5521582119_46842901-48590136,19_48590136-51139804,19_51139804-52482108,19_52482108-53788025,19_53788025-5521582119_46842901-48590136,19_48590136_51139804,19_51139804_52482108,19_52482108_53788025,19_53788025_55215821TADB_1263chr19_46290022_554732965122506451240016chr19:34641744-48886315,chr19:40837074-55473296,chr19:43631573-57160893,chr19:46290022-58617616CD33
$target_LD_ids
A matrix: 1 x 5 of type chr
chr19:46842901-48590136chr19:48590136-51139804chr19:51139804-52482108chr19:52482108-53788025chr19:53788025-55215821
$target_sums_ids
A matrix: 1 x 5 of type chr
19_46842901-4859013619_48590136-5113980419_51139804-5248210819_52482108-5378802519_53788025-55215821
$gene_region
'chr19:47280000-54480000'
$target_TAD_ids
A matrix: 1 x 1 of type chr
chr19_46290022_55473296
In [3]:
gene_id = target_gene_info$gene_info$region_id
chrom = target_gene_info$gene_info$`#chr`
In [4]:
source('../../codes/utilis.R')
expression_in_rosmap_bulk(target_gene_info)
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 [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)
No description has been provided for this image
In [10]:
pdf('plots/CD33/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: 2 x 8
colocalized phenotypespurity# variantshighest VCPcolocalized indexcolocalized variantsmax_abs_z_variantcset_id
<chr><dbl><dbl><dbl><chr><chr><chr><chr>
DLPFC; AC; PCC; Monocyte 1.0000000 10.987844423220 chr19:51225847:CCCGG:C chr19:51225847:CCCGG:Ccoloc_sets:Y2_Y3_Y4_Y5:CS2
DLPFC; AC; PCC; AC_productive0.9621583140.122255423219; 23188; 23205; 23202; 23201; 23211; 23213; 23216; 23174; 23189; 23208; 23175; 23196; 23204chr19:51225385:A:G; chr19:51216668:GAA:GA; chr19:51222568:G:T; chr19:51220906:A:G; chr19:51220856:A:G; chr19:51223655:A:C; chr19:51224066:A:G; chr19:51224937:T:C; chr19:51215061:T:A; chr19:51217369:A:T; chr19:51223099:G:C; chr19:51215581:C:T; chr19:51219326:A:G; chr19:51221277:A:ACACAATATTTTACchr19:51215061:T:A coloc_sets:Y2_Y3_Y4_Y6:CS1
In [12]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y2_Y3_Y4_Y5:CS2`
A data.frame: 1 x 5
variantsDLPFCACPCCMonocyte
<chr><dbl><dbl><dbl><dbl>
chr19:51225847:CCCGG:Cchr19:51225847:CCCGG:C-5.164188-8.441708-4.759994-6.327889
$`coloc_sets:Y2_Y3_Y4_Y6:CS1`
A data.frame: 14 x 5
variantsDLPFCACPCCAC_productive
<chr><dbl><dbl><dbl><dbl>
chr19:51225385:A:Gchr19:51225385:A:G 5.2298116.6776416.162289-9.200024
chr19:51216668:GAA:GAchr19:51216668:GAA:GA 5.6069126.4690606.127078-9.118039
chr19:51222568:G:Tchr19:51222568:G:T 5.5813906.4494156.103214-9.203931
chr19:51220906:A:Gchr19:51220906:A:G 5.6459396.4703716.096658-9.107024
chr19:51220856:A:Gchr19:51220856:A:G 5.6208366.4703716.096658-9.107024
chr19:51223655:A:Cchr19:51223655:A:C 5.6208366.4703716.096658-9.107024
chr19:51224066:A:Gchr19:51224066:A:G 5.6208366.4703716.096658-9.107024
chr19:51224937:T:Cchr19:51224937:T:C 5.6208366.4703716.096658-9.107024
chr19:51215061:T:Achr19:51215061:T:A 5.4292316.3914646.152618-9.236405
chr19:51217369:A:Tchr19:51217369:A:T 5.5375676.3749926.127078-9.125115
chr19:51223099:G:Cchr19:51223099:G:C 5.5533376.4427276.096658-9.048954
chr19:51215581:C:Tchr19:51215581:C:T 5.5770466.3958516.120482-9.028350
chr19:51219326:A:Gchr19:51219326:A:G 5.5770466.3958516.120482-9.028350
chr19:51221277:A:ACACAATATTTTACchr19:51221277:A:ACACAATATTTTAC5.7966276.4900765.800398-8.964028
In [13]:
# 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_Y5:CS2coloc_sets:Y2_Y3_Y4_Y6:CS10.1780075805917830.1834245036872310.181641866081508

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.
No description has been provided for this image
In [15]:
pdf('plots/CD33/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 = 'CD33')

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.

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)
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 [18]:
multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.CD33.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 95 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000010361chr194981737549817376FUZ Exc_Kellis_eQTL,ROSMAP_AC_sQTL
ENSG00000039650chr194986790849867909PNKP ROSMAP_DLPFC_sQTL
ENSG00000063127chr194932521449325215SLC6A16 ROSMAP_DLPFC_sQTL
ENSG00000063241chr195546234255462343ISOC2 ROSMAP_PCC_sQTL
ENSG00000063245chr195567522555675226EPN1 MiGA_SVZ_eQTL
ENSG00000074219chr194936245649362457TEAD2 BM_44_MSBB_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000087076chr194883650948836510HSD17B14MiGA_THA_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000088002chr194855217148552172SULT2B1 MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000104848chr194907294049072941KCNA7 MiGA_SVZ_eQTL
ENSG00000104852chr194908541849085419SNRNP70 MiGA_THA_eQTL,MSBB_BM36_pQTL
ENSG00000104870chr194951237449512375FCGRT MiGA_THA_eQTL
ENSG00000104888chr194944235949442360SLC17A7 MiGA_GTS_eQTL,ROSMAP_AC_sQTL
ENSG00000104894chr194933517049335171CD37 ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000104951chr194992953849929539IL4I1 ROSMAP_AC_sQTL
ENSG00000104960chr194985073449850735PTOV1 MiGA_GTS_eQTL
ENSG00000104972chr195461715754617158LILRB1 ROSMAP_DLPFC_sQTL
ENSG00000104973chr194981828849818289MED25 BM_44_MSBB_eQTL,ROSMAP_AC_sQTL
ENSG00000105357chr195018818550188186MYH14 MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL
ENSG00000105366chr195145845551458456SIGLEC8 ROSMAP_AC_sQTL
ENSG00000105373chr194774554547745546NOP53 MSBB_BM36_pQTL
ENSG00000105374chr195137270051372701NKG7 MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000105419chr194741952647419527MEIS3 ROSMAP_PCC_sQTL
ENSG00000105443chr194846920748469208CYTH2 ROSMAP_DLPFC_sQTL
ENSG00000105464chr194839366748393668GRIN2D STARNET_eQTL
ENSG00000105472chr195072336350723364CLEC11A AC_DeJager_eQTL,Oli_Kellis_eQTL,ROSMAP_AC_sQTL
ENSG00000105479chr194832197048321971CCDC114 MiGA_GTS_eQTL
ENSG00000105501chr195164554451645545SIGLEC5 MiGA_SVZ_eQTL
ENSG00000105516chr194863737848637379DBP MiGA_GFM_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000105559chr194886861648868617PLEKHA4 ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000105605chr195390927753909278CACNG7 MiGA_GFM_eQTL
..................
ENSG00000176920chr194869597048695971FUT2 DLPFC_DeJager_eQTL
ENSG00000177051chr194573089545730896FBXO46 MiGA_SVZ_eQTL
ENSG00000178150chr194827008048270081ZNF114 MiGA_SVZ_eQTL
ENSG00000178980chr194777858447778585SELENOW ROSMAP_DLPFC_sQTL
ENSG00000179213chr195124634751246348SIGLECL1 MiGA_GTS_eQTL
ENSG00000182310chr195168912751689128SPACA6 ROSMAP_AC_sQTL
ENSG00000183207chr194899356148993562RUVBL2 MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000185453chr194817067948170680ZSWIM9 ROSMAP_PCC_sQTL
ENSG00000189068chr195406395254063953VSTM1 MiGA_THA_eQTL
ENSG00000196417chr195338979253389793ZNF765 Exc_mega_eQTL
ENSG00000197928chr195325489753254898ZNF677 MiGA_GFM_eQTL,MiGA_SVZ_eQTL
ENSG00000198093chr195190503951905040ZNF649 DLPFC_DeJager_eQTL,AC_DeJager_eQTL,ROSMAP_AC_sQTL
ENSG00000198482chr195252765152527652ZNF808 ROSMAP_AC_sQTL
ENSG00000198538chr195285759952857600ZNF28 MiGA_GFM_eQTL,ROSMAP_AC_sQTL,STARNET_eQTL
ENSG00000198633chr195242918652429187ZNF534 ROSMAP_AC_sQTL
ENSG00000203326chr195336570353365704ZNF525 ROSMAP_PCC_sQTL
ENSG00000204653chr195051468950514690ASPDH ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000213799chr195333374853333749ZNF845 MiGA_SVZ_eQTL
ENSG00000218891chr195558084755580848ZNF579 Exc_Kellis_eQTL
ENSG00000221923chr195236991652369917ZNF880 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000239961chr195433916154339162LILRA4 BM_10_MSBB_eQTL,BM_22_MSBB_eQTL,BM_36_MSBB_eQTL,BM_44_MSBB_eQTL,DLPFC_DeJager_eQTL,PCC_DeJager_eQTL,AC_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000244482chr195424279054242791LILRA6 MiGA_THA_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000254415chr195164680051646801SIGLEC14 MiGA_GFM_eQTL
ENSG00000256087chr195209573752095738ZNF432 MiGA_SVZ_eQTL
ENSG00000256683chr195198685551986856ZNF350 MiGA_SVZ_eQTL
ENSG00000261341chr195079314150793142AC010325.1MiGA_SVZ_eQTL
ENSG00000269179chr194995839049958391AC011452.1ROSMAP_DLPFC_sQTL
ENSG00000269404chr195041893750418938SPIB MiGA_SVZ_eQTL
ENSG00000269825chr195269053252690533AC022150.4ROSMAP_AC_sQTL
ENSG00000277531chr194642895046428951PNMA8C MiGA_SVZ_eQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with CD33. Further look into the data-set --- using these genes as targets and repeating what we did above for CD33 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on CD33 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. 'chr19:34641744-48886315'
  2. 'chr19:40837074-55473296'
  3. 'chr19:43631573-57160893'
  4. 'chr19:46290022-58617616'

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 CD33:

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 CD33 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) )

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.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

Section 9: Non-linear effects of xQTL¶

see notebook

APOE interaction¶

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

ggplot(CD33_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for CD33 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/CD33/sec11.interaction_association_CD33_lessPIP25.pdf', height = 5, width = 8)
No description has been provided for this image

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/CD33/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.