Case study: NUP88 xQTL and AD GWAS¶

This notebook documents the analysis of xQTL case study on a targeted gene, NUP88.

  • 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 = 'NUP88'

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>
ENSG00000108559chr17436096384000005419675chr17:3524642-4611485,chr17:4611485-5782462,chr17:5782462-7411566,chr17:7411566-920191317_3524642-4611485,17_4611485-5782462,17_5782462-7411566,17_7411566-920191317_3524642-4611485,17_4611485_5782462,17_5782462_7411566,17_7411566_9201913TADB_1182,TADB_1183chr17_1059843_6175034,chr17_3710595_939782654196765360963chr17:0-9397826,chr17:1059843-10729687,chr17:3710595-13143830,chr17:6187187-17353469,chr17:8250471-20190245NUP88
$target_LD_ids
A matrix: 1 x 4 of type chr
chr17:3524642-4611485chr17:4611485-5782462chr17:5782462-7411566chr17:7411566-9201913
$target_sums_ids
A matrix: 1 x 4 of type chr
17_3524642-461148517_4611485-578246217_5782462-741156617_7411566-9201913
$gene_region
'chr17:4360963-8400000'
$target_TAD_ids
A matrix: 1 x 2 of type chr
chr17_1059843_6175034chr17_3710595_9397826
In [3]:
gene_id = target_gene_info$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)
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In [ ]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(NUP88_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for NUP88 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/NUP88/sec11.interaction_association_NUP88_lessPIP25.pdf', height = 5, width = 8)
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; AC; PCC; AC_unproductive; DLPFC_productive; PCC_productive0.76484891210.049082085858; 5862; 5867; 5859; 5860; 5885; 5887; 5886; 5843; 5869; 5855; 5875; 5878; 5880; 5833; 5836; 5865; 5866; 5839; 5840; 5876; 5882; 5883; 5884; 5879; 5889; 5899; 5913; 5874; 5837; 5844; 5892; 5873; 5870; 5842; 5957; 5802; 5894; 5800; 5958; 5906; 6096; 5936; 6118; 5826; 5827; 6016; 6020; 5961; 6012; 6017; 6019; 6026; 6028; 6039; 6056; 6069; 6075; 6084; 6092; 6093; 6095; 6097; 6114; 6117; 6121; 6123; 6125; 6126; 5945; 5944; 5963; 5964; 5948; 6120; 5910; 5917; 5920; 5921; 5927; 5928; 5939; 5941; 5947; 5949; 5965; 5967; 5968; 5976; 5977; 5990; 5993; 5998; 5999; 6004; 6007; 6009; 6038; 6042; 6044; 6045; 6055; 6060; 6067; 6068; 6072; 6073; 6078; 6032; 6088; 6074; 6035; 5997; 5934; 6087; 5985; 6085; 6005; 5943; 5978; 5788chr17:5374434:A:T; chr17:5372759:ATT:AT; chr17:5385474:G:C; chr17:5384753:A:G; chr17:5384698:G:A; chr17:5384883:C:T; chr17:5385440:A:G; chr17:5385449:CA:C; chr17:5384762:ATTATT:A; chr17:5381403:A:G; chr17:5381424:T:C; chr17:5380322:A:T; chr17:5381185:T:C; chr17:5385708:G:A; chr17:5383977:AG:A; chr17:5386713:T:C; chr17:5387361:A:C; chr17:5387504:C:A; chr17:5387279:G:A; chr17:5388268:T:C; chr17:5388390:G:A; chr17:5381927:C:T; chr17:5388319:A:ACT; chr17:5386545:CAT:C; chr17:5387956:AAAAT:A; chr17:5388057:T:A; chr17:5388234:T:C; chr17:5387383:G:A; chr17:5381248:TAA:T; chr17:5389442:C:T; chr17:5388542:T:C; chr17:5389888:T:C; chr17:5391656:T:A; chr17:5375012:G:A; chr17:5386356:C:T; chr17:5385903:ATAAC:A; chr17:5382083:ATT:AT; chr17:5389760:A:C; chr17:5381691:TTTTC:T; chr17:5379466:C:T; chr17:5379468:C:T; chr17:5390307:T:G; chr17:5393829:T:C; chr17:5396036:C:T; chr17:5396039:C:T; chr17:5400173:A:G; chr17:5416997:GT:G; chr17:5394446:C:G; chr17:5398608:T:C; chr17:5403658:TCAACAA:TCAACAACAACAA; chr17:5415894:C:T; chr17:5414802:C:CCA; chr17:5394519:T:G; chr17:5394441:A:G; chr17:5403144:G:A; chr17:5390857:G:A; chr17:5391982:G:GT; chr17:5392051:T:C; chr17:5392079:C:T; chr17:5393119:AT:A; chr17:5393230:C:T; chr17:5394137:T:C; chr17:5394231:A:G; chr17:5395000:C:T; chr17:5395170:T:C; chr17:5397350:ATT:AT; chr17:5397750:C:G; chr17:5397844:A:AT; chr17:5398391:C:T; chr17:5398486:G:A; chr17:5399325:T:C; chr17:5399895:G:A; chr17:5400352:T:C; chr17:5400427:T:C; chr17:5401255:T:C; chr17:5401707:G:C; chr17:5402050:T:C; chr17:5406801:A:C; chr17:5407823:T:C; chr17:5408422:C:T; chr17:5408951:A:G; chr17:5409832:T:A; chr17:5411037:A:C; chr17:5411769:G:A; chr17:5411979:C:T; chr17:5413139:A:G; chr17:5413177:GC:G; chr17:5406370:G:C; chr17:5414848:T:C; chr17:5396863:T:C; chr17:5396885:A:G; chr17:5413619:C:T; chr17:5395076:G:A; chr17:5413360:G:A; chr17:5416762:C:CT; chr17:5396354:C:G; chr17:5402567:C:G; chr17:5403216:G:T; chr17:5403351:C:T; chr17:5404517:C:T; chr17:5404959:G:A; chr17:5406988:G:C; chr17:5410202:C:T; chr17:5412182:C:T; chr17:5413422:T:C; chr17:5414349:C:T; chr17:5415683:G:A; chr17:5415733:C:G; chr17:5415780:G:A; chr17:5415974:G:A; chr17:5416300:C:A; chr17:5416724:T:A; chr17:5417118:A:G; chr17:5417365:C:G; chr17:5417488:G:A; chr17:5417908:A:G; chr17:5406713:C:G; chr17:5393689:A:G; chr17:5414486:C:T; chr17:5401391:AC:A; chr17:5399134:A:Gchr17:5414486:C:Tcoloc_sets:Y7_Y8_Y9_Y13_Y14_Y16:CS1
In [12]:
# effect sign for each coloc sets
get_effect_sign_csets(cb_res)
$`coloc_sets:Y7_Y8_Y9_Y13_Y14_Y16:CS1` =
A data.frame: 121 x 7
variantsDLPFCACPCCAC_unproductiveDLPFC_productivePCC_productive
<chr><dbl><dbl><dbl><dbl><dbl><dbl>
chr17:5384698:G:Achr17:5384698:G:A -9.990027-19.41196-16.408095.847291-8.207013-8.950090
chr17:5384883:C:Tchr17:5384883:C:T -9.990027-19.41196-16.408095.847291-8.207013-8.950090
chr17:5385474:G:Cchr17:5385474:G:C -9.994139-19.33208-16.321505.857001-8.136883-9.054680
chr17:5384753:A:Gchr17:5384753:A:G -10.018403-19.36755-16.364875.846287-8.180391-8.994469
chr17:5384762:ATTATT:Achr17:5384762:ATTATT:A -9.857265-19.19898-16.364875.849489-8.158989-8.994469
chr17:5388268:T:Cchr17:5388268:T:C -10.130243-19.48274-16.155645.742927-8.104786-8.894590
chr17:5388390:G:Achr17:5388390:G:A -10.130243-19.48274-16.155645.742927-8.104786-8.894590
chr17:5388319:A:ACTchr17:5388319:A:ACT -10.166935-19.48274-16.155645.742927-8.093255-8.850047
chr17:5381927:C:Tchr17:5381927:C:T -9.934629-19.30497-16.344835.790286-8.210361-8.858039
chr17:5385708:G:Achr17:5385708:G:A -9.939312-19.22413-16.258495.799611-8.140043-8.961609
chr17:5383977:AG:Achr17:5383977:AG:A -9.963120-19.25913-16.301655.789200-8.183360-8.902418
chr17:5386713:T:Cchr17:5386713:T:C -9.963120-19.25913-16.301655.789200-8.183360-8.902418
chr17:5387361:A:Cchr17:5387361:A:C -9.963120-19.25913-16.301655.789200-8.183360-8.902418
chr17:5387504:C:Achr17:5387504:C:A -9.963120-19.25913-16.301655.789200-8.183360-8.902418
chr17:5380322:A:Tchr17:5380322:A:T -9.934629-19.29927-16.344835.681010-8.249181-8.858039
chr17:5381185:T:Cchr17:5381185:T:C -9.934629-19.29927-16.344835.681010-8.249181-8.858039
chr17:5385440:A:Gchr17:5385440:A:G -9.881689-19.20715-16.243135.674594-8.072151-8.963759
chr17:5385449:CA:Cchr17:5385449:CA:C -9.881689-19.20715-16.243135.674594-8.072151-8.963759
chr17:5381403:A:Gchr17:5381403:A:G -9.942614-19.17675-16.344835.722647-8.282299-8.858039
chr17:5381424:T:Cchr17:5381424:T:C -9.942614-19.17675-16.344835.722647-8.282299-8.858039
chr17:5387279:G:Achr17:5387279:G:A -9.953797-19.11655-16.301655.866626-8.182178-8.902418
chr17:5387956:AAAAT:Achr17:5387956:AAAAT:A -9.985161-19.25913-16.155645.789200-8.152201-8.894590
chr17:5388057:T:Achr17:5388057:T:A -9.985161-19.25913-16.155645.789200-8.152201-8.894590
chr17:5388234:T:Cchr17:5388234:T:C -9.985161-19.25913-16.155645.789200-8.152201-8.894590
chr17:5387383:G:Achr17:5387383:G:A -9.972429-19.13233-16.187375.803167-8.182316-8.833996
chr17:5388542:T:Cchr17:5388542:T:C -10.096634-19.25913-16.065195.789200-8.026108-8.873450
chr17:5389888:T:Cchr17:5389888:T:C -10.096634-19.25913-16.065195.789200-8.026108-8.873450
chr17:5391656:T:Achr17:5391656:T:A -10.096634-19.25913-16.065195.789200-8.026108-8.873450
chr17:5386545:CAT:Cchr17:5386545:CAT:C -9.963967-19.07657-16.280465.767841-8.158706-8.843790
chr17:5381248:TAA:Tchr17:5381248:TAA:T -9.935544-19.29927-16.034455.681010-8.236836-8.867935
........................
chr17:5399895:G:Achr17:5399895:G:A -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5400352:T:Cchr17:5400352:T:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5400427:T:Cchr17:5400427:T:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5401255:T:Cchr17:5401255:T:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5401707:G:Cchr17:5401707:G:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5402050:T:Cchr17:5402050:T:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5406801:A:Cchr17:5406801:A:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5407823:T:Cchr17:5407823:T:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5408422:C:Tchr17:5408422:C:T -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5408951:A:Gchr17:5408951:A:G -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5409832:T:Achr17:5409832:T:A -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5411037:A:Cchr17:5411037:A:C -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5411769:G:Achr17:5411769:G:A -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5411979:C:Tchr17:5411979:C:T -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5413139:A:Gchr17:5413139:A:G -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5413177:GC:Gchr17:5413177:GC:G -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5413619:C:Tchr17:5413619:C:T -9.949973-19.60614-15.663345.865654-7.723840-8.845608
chr17:5406370:G:Cchr17:5406370:G:C -9.911110-19.50487-15.713655.944633-7.748405-8.804243
chr17:5414848:T:Cchr17:5414848:T:C -9.911110-19.50487-15.713655.944633-7.748405-8.804243
chr17:5413360:G:Achr17:5413360:G:A -9.957099-19.60614-15.663345.865654-7.709250-8.845608
chr17:5406713:C:Gchr17:5406713:C:G -9.884755-19.64475-15.634225.900603-7.750938-8.777853
chr17:5400173:A:Gchr17:5400173:A:G -10.156792-19.02695-15.273616.034172-7.604261-8.996323
chr17:5393689:A:Gchr17:5393689:A:G -9.921675-19.60614-15.663345.865654-7.726621-8.845608
chr17:5414802:C:CCAchr17:5414802:C:CCA -9.950672-19.32078-15.842765.945738-7.721120-8.687460
chr17:5399134:A:Gchr17:5399134:A:G -9.892881-19.55276-15.514375.882697-7.744588-8.929223
chr17:5414486:C:Tchr17:5414486:C:T -9.813636-19.71597-15.713655.782801-7.791221-8.804243
chr17:5401391:AC:Achr17:5401391:AC:A -9.921389-19.56976-15.713655.800867-7.751278-8.804243
chr17:5394441:A:Gchr17:5394441:A:G -10.146994-19.44009-15.634505.807491-7.636506-8.706614
chr17:5398608:T:Cchr17:5398608:T:C -9.926077-19.41541-15.784075.699783-7.735857-8.810421
chr17:5372759:ATT:ATchr17:5372759:ATT:AT-10.238930-18.46133-14.330164.609433-5.677211-7.429360
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.
No description has been provided for this image
In [15]:
pdf('plots/NUP88/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 = 'NUP88')
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)
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.NUP88.rds', sQTL = 'no_MSBB')
multigene_flat
A data.frame: 162 x 6
gene_id#chrstartendgene_namecontexts
<chr><chr><int><int><chr><chr>
ENSG00000004975chr1772345167234517DVL2 MiGA_SVZ_eQTL,MiGA_THA_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000005100chr1754689815468982DHX33 MiGA_GTS_eQTL
ENSG00000006047chr1772946387294639YBX2 ROSMAP_PCC_sQTL
ENSG00000007168chr1725932092593210PAFAH1B1MiGA_SVZ_eQTL
ENSG00000029725chr1752822645282265RABEP1 Knight_eQTL,MiGA_GTS_eQTL,MiGA_SVZ_eQTL,BM_10_MSBB_eQTL,BM_36_MSBB_eQTL,BM_44_MSBB_eQTL,PCC_DeJager_eQTL,AC_DeJager_eQTL,DLPFC_Bennett_pQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000040531chr1736364583636459CTNS MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000040633chr1772397217239722PHF23 MiGA_SVZ_eQTL
ENSG00000070366chr1723037842303785SMG6 MiGA_THA_eQTL
ENSG00000070444chr1724011032401104MNT MiGA_SVZ_eQTL
ENSG00000072778chr1772171247217125ACADVL MiGA_SVZ_eQTL,MiGA_THA_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000072818chr1773365287336529ACAP1 MiGA_SVZ_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000072849chr1754868105486811DERL2 MiGA_SVZ_eQTL
ENSG00000074356chr1738462453846246NCBP3 MiGA_GTS_eQTL,MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000074370chr1739644633964464ATP2A3 MiGA_SVZ_eQTL
ENSG00000074755chr1741430294143030ZZEF1 MiGA_GTS_eQTL,ROSMAP_PCC_sQTL
ENSG00000091592chr1756194235619424NLRP1 Knight_eQTL,Exc_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000091622chr1765565546556555PITPNM3 BM_10_MSBB_eQTL,BM_44_MSBB_eQTL,Inh_mega_eQTL,monocyte_ROSMAP_eQTL,ROSMAP_AC_sQTL
ENSG00000091640chr1749678164967817SPAG7 Knight_eQTL,MiGA_GFM_eQTL,MiGA_SVZ_eQTL,MiGA_THA_eQTL,BM_10_MSBB_eQTL,Exc_DeJager_eQTL,Inh_mega_eQTL,ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000108405chr1739164753916476P2RX1 MiGA_SVZ_eQTL
ENSG00000108509chr1749876744987675CAMTA2 MiGA_GTS_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000108515chr1749480914948092ENO3 MiGA_SVZ_eQTL
ENSG00000108518chr1749490604949061PFN1 MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000108523chr1749400074940008RNF167 MiGA_THA_eQTL,ROSMAP_AC_sQTL,STARNET_eQTL
ENSG00000108528chr1749400524940053SLC25A11MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL
ENSG00000108556chr1749344374934438CHRNE AC_DeJager_eQTL
ENSG00000108561chr1754488295448830C1QBP Oli_Kellis_eQTL,Inh_mega_eQTL,Oli_mega_eQTL,ROSMAP_AC_sQTL,STARNET_eQTL
ENSG00000108590chr1766516336651634MED31 MiGA_THA_eQTL,BM_10_MSBB_eQTL
ENSG00000108839chr1769960486996049ALOX12 Knight_eQTL,MiGA_GFM_eQTL,DLPFC_DeJager_eQTL
ENSG00000108947chr1777052017705202EFNB3 ROSMAP_PCC_sQTL
ENSG00000108961chr1782886538288654RANGRF MiGA_SVZ_eQTL,Inh_Kellis_eQTL
..................
ENSG00000196388chr1749976094997610INCA1 MiGA_SVZ_eQTL,MiGA_THA_eQTL,AC_DeJager_eQTL,ROSMAP_PCC_sQTL
ENSG00000196544chr1781901798190180BORCS6 MiGA_SVZ_eQTL
ENSG00000196689chr1736094103609411TRPV1 ROSMAP_PCC_sQTL
ENSG00000198844chr1783102408310241ARHGEF15 ROSMAP_AC_sQTL
ENSG00000198920chr1766407106640711KIAA0753 MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000205710chr1748994174899418C17orf107 DLPFC_DeJager_eQTL,AC_DeJager_eQTL
ENSG00000213977chr1736686783668679TAX1BP3 MiGA_GTS_eQTL
ENSG00000215041chr1773293927329393NEURL4 DLPFC_Bennett_pQTL,ROSMAP_PCC_sQTL
ENSG00000219200chr1770124167012417RNASEK MiGA_GTS_eQTL,DLPFC_DeJager_eQTL,STARNET_eQTL
ENSG00000220205chr1781635458163546VAMP2 Knight_eQTL,MiGA_GFM_eQTL,MiGA_GTS_eQTL,Oli_DeJager_eQTL,Inh_DeJager_eQTL
ENSG00000221882chr1733863163386317OR3A2 MiGA_THA_eQTL
ENSG00000239697chr1775490577549058TNFSF12 MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000248871chr1775490987549099TNFSF12-TNFSF13ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000256806chr1766517616651762C17orf100 Exc_DeJager_eQTL,Exc_Kellis_eQTL,Exc_mega_eQTL
ENSG00000257950chr1736961933696194P2RX5-TAX1BP3 ROSMAP_PCC_sQTL
ENSG00000258315chr1770144947014495C17orf49 MiGA_SVZ_eQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000259224chr1774813317481332SLC35G6 MiGA_GTS_eQTL,BM_44_MSBB_eQTL
ENSG00000261915chr1773191737319174AC026954.2 MiGA_SVZ_eQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000262165chr1748070124807013AC233723.1 MiGA_THA_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000262302chr1772620887262089AC003688.1 STARNET_eQTL
ENSG00000262304chr1736362483636249AC027796.3 ROSMAP_PCC_sQTL
ENSG00000262526chr1772446347244635AC120057.2 ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000262628chr1730636063063607OR1D5 MiGA_THA_eQTL
ENSG00000262730chr1779306217930622AC104581.2 ROSMAP_PCC_sQTL
ENSG00000263620chr1781629748162975AC129492.3 DLPFC_DeJager_eQTL
ENSG00000263809chr1783831868383187AC135178.3 ROSMAP_PCC_sQTL
ENSG00000276231chr1788676768867677PIK3R6 MiGA_GTS_eQTL,MiGA_THA_eQTL
ENSG00000277957chr1775632867563287SENP3-EIF4A1 ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000282936chr1766404556640456AC004706.3 MiGA_THA_eQTL
ENSG00000286190chr1755010055501006AC055839.2 Knight_eQTL,MiGA_GTS_eQTL,MiGA_THA_eQTL,BM_36_MSBB_eQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with NUP88. Further look into the data-set --- using these genes as targets and repeating what we did above for NUP88 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on NUP88 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. 'chr17:0-9397826'
  2. 'chr17:1059843-10729687'
  3. 'chr17:3710595-13143830'
  4. 'chr17:6187187-17353469'
  5. 'chr17:8250471-20190245'

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 NUP88:

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 NUP88 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.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.No pvalue cutoff. Extract all variants names.
No 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 [25]:
options(repr.plot.width=6, repr.plot.height=6)

ggplot(NUP88_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for NUP88 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/NUP88/sec11.interaction_association_NUP88_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/NUP88/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.