Case study: PLCG2 xQTL and AD GWAS

This notebook documents the analysis of xQTL case study on a targeted gene, PLCG2.

• 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

0. 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.
1. Inside of this notebook, use sed -i or Ctrl+F to replace the gene PLCG2 with the gene you want to analyze.
2. 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.
3. Similarly for the companion notebooks you should also use the sed -i or Ctrl+F replacing gene_name (PLCG2 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, PLCG2_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.PLCG2.rds. This can be used as input for Section 12.
• b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, PLCG2_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

# 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 = 'PLCG2'

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

get basic target gene information

target_gene_info <- get_gene_info(gene_name = gene_name)
target_gene_info

$gene_info

A data.table: 1 x 14

region_id	#chr	start	end	TSS	LD_matrix_id	LD_sumstats_id	LD_sumstats_id_old	TADB_index	TADB_id	gene_start	gene_end	sliding_windows	gene_name
<chr>	<chr>	<dbl>	<dbl>	<int>	<chr>	<chr>	<chr>	<chr>	<chr>	<int>	<int>	<chr>	<chr>
ENSG00000197943	chr16	80739096	85080000	81739096	chr16:80260790-81293081,chr16:81293081-82644764,chr16:82644764-84411478,chr16:84411478-85419908	16_80260790-81293081,16_81293081-82644764,16_82644764-84411478,16_84411478-85419908	16_80260790-81293081,16_81293081_82644764,16_82644764_84411478,16_84411478_85419908	TADB_1178,TADB_1179	chr16_76190834_82020270,chr16_79826074_86094230	81739097	81962685	chr16:69114331-82020270,chr16:72943078-86094230,chr16:76190834-90338345	PLCG2

$target_LD_ids

A matrix: 1 x 4 of type chr

chr16:80260790-81293081

chr16:81293081-82644764

chr16:82644764-84411478

chr16:84411478-85419908

$target_sums_ids

A matrix: 1 x 4 of type chr

16_80260790-81293081

16_81293081-82644764

16_82644764-84411478

16_84411478-85419908

$gene_region

'chr16:80739096-85080000'

$target_TAD_ids

A matrix: 1 x 2 of type chr

chr16_76190834_82020270

chr16_79826074_86094230

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

source('../../codes/utilis.R')
expression_in_rosmap_bulk(target_gene_info)

Section 1: Fine-mapping for xQTL and GWAS

see notebook

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.

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/

cb_res <- readRDS(paste0("/data/analysis_result/ColocBoost/2024_9/",gene_id,"_res.rds") )

#save colocboost results
cb_res_table <- get_cb_summary(cb_res) 

saveRDS(cb_res_table, paste0(gene_name, "_colocboost_res.rds"))

cb <- plot_cb(cb_res = cb_res, cex.pheno = 1.5, x.phen = -0.2)

pdf('plots/PLCG2/sec2.colocboost_res.pdf', width = 10, height = 5)
replayPlot(cb$p)
dev.off()

# colocalized variants
cb_res_table

A data.frame: 3 x 8

colocalized phenotypes	purity	# variants	highest VCP	colocalized index	colocalized variants	max_abs_z_variant	cset_id
<chr>	<dbl>	<dbl>	<dbl>	<chr>	<chr>	<chr>	<chr>
AC; AC_productive; AC_unproductive	1.0000000	1	0.9995644	8051	chr16:81842674:G:T	chr16:81842674:G:T	coloc_sets:Y7_Y11_Y12:CS1
Oli; DLPFC	0.9470132	8	0.3348871	8581; 8588; 8606; 8632; 8615; 8585; 8633; 8617	chr16:81895134:T:A; chr16:81895883:C:T; chr16:81896927:T:C; chr16:81899607:A:G; chr16:81897798:A:G; chr16:81895580:A:G; chr16:81899622:T:C; chr16:81898357:G:C	chr16:81897798:A:G	coloc_sets:Y3_Y6:CS2
DLPFC; PCC	1.0000000	1	0.9999995	7386	chr16:81774628:T:G	chr16:81774628:T:G	coloc_sets:Y6_Y8:MergeCS1

# effect sign for each coloc sets
get_effect_sign_csets(cb_res)

$`coloc_sets:Y7_Y11_Y12:CS1`

A data.frame: 1 x 4

	variants	AC	AC_productive	AC_unproductive
	<chr>	<dbl>	<dbl>	<dbl>
chr16:81842674:G:T	chr16:81842674:G:T	41.48703	-10.92301	26.44815

$`coloc_sets:Y3_Y6:CS2`

A data.frame: 8 x 3

	variants	Oli	DLPFC
	<chr>	<dbl>	<dbl>
chr16:81895134:T:A	chr16:81895134:T:A	6.630979	7.952749
chr16:81895883:C:T	chr16:81895883:C:T	6.720441	7.837459
chr16:81896927:T:C	chr16:81896927:T:C	6.571342	8.006144
chr16:81899607:A:G	chr16:81899607:A:G	6.437596	8.034977
chr16:81897798:A:G	chr16:81897798:A:G	6.396584	8.038195
chr16:81895580:A:G	chr16:81895580:A:G	6.348199	8.009052
chr16:81899622:T:C	chr16:81899622:T:C	6.462049	7.947104
chr16:81898357:G:C	chr16:81898357:G:C	6.396584	7.985373

$`coloc_sets:Y6_Y8:MergeCS1`

A data.frame: 1 x 3

	variants	DLPFC	PCC
	<chr>	<dbl>	<dbl>
chr16:81774628:T:G	chr16:81774628:T:G	7.641081	6.713851

# LD between coloc sets
get_between_purity_simple(cb_res, gene.name = gene_id, path = '/data/colocalization/QTL_data/eQTL/')

A matrix: 3 x 5 of type chr

coloc_csets_1	coloc_csets_2	min_abs_cor	max_abs_cor	median_abs_cor
coloc_sets:Y7_Y11_Y12:CS1	coloc_sets:Y3_Y6:CS2	0.0796129906298416	0.0944176693359274	0.0878608322517201
coloc_sets:Y7_Y11_Y12:CS1	coloc_sets:Y6_Y8:MergeCS1	0.0477331123436312	0.0477331123436312	0.0477331123436312
coloc_sets:Y3_Y6:CS2	coloc_sets:Y6_Y8:MergeCS1	0.000258086520375415	0.0164707029292386	0.00289978341439768

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.

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.Error : File '/data/GWAS/ADGWAS_sumstats/16_81293081-82644764.RSS_QC_RAISS_imputed.AD_Wightman_Full_2021.sumstats.tsv.gz' does not exist or is non-readable. getwd()=='/data/interactive_analysis/hs3163/GIT/xqtl-paper/AD_targets/PLCG2'
No pvalue cutoff. Extract all variants names.

pdf('plots/PLCG2/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

mash_p <- mash_plot(gene_name = 'PLCG2')

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.

plot_TWAS_res(gene_id = gene_id, gene_name = gene_name)

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.

multigene_flat <- get_multigene_multicontext_flatten('Fungen_xQTL_allQTL.overlapped.gwas.export.PLCG2.rds', sQTL = 'no_MSBB')
multigene_flat

A data.frame: 36 x 6

gene_id	#chr	start	end	gene_name	contexts
<chr>	<chr>	<int>	<int>	<chr>	<chr>
ENSG00000064270	chr16	84368526	84368527	ATP2C2	MiGA_SVZ_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000075399	chr16	89720897	89720898	VPS9D1	ROSMAP_AC_sQTL
ENSG00000086696	chr16	82035003	82035004	HSD17B2	MiGA_SVZ_eQTL
ENSG00000103121	chr16	81020269	81020270	CMC2	MiGA_THA_eQTL
ENSG00000103150	chr16	83899114	83899115	MLYCD	MiGA_SVZ_eQTL,MiGA_THA_eQTL,DLPFC_Bennett_pQTL
ENSG00000103154	chr16	83968243	83968244	NECAB2	ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000103160	chr16	84145176	84145177	HSDL1	MiGA_GTS_eQTL,MiGA_SVZ_eQTL,PCC_DeJager_eQTL
ENSG00000103168	chr16	84187069	84187070	TAF1C	ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000103175	chr16	84294845	84294846	WFDC1	MiGA_SVZ_eQTL,OPC_Kellis_eQTL,OPC_mega_eQTL
ENSG00000103187	chr16	84618077	84618078	COTL1	MiGA_SVZ_eQTL
ENSG00000103194	chr16	84699985	84699986	USP10	ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000103196	chr16	84819984	84819985	CRISPLD2	ROSMAP_DLPFC_sQTL
ENSG00000135686	chr16	84648510	84648511	KLHL36	MiGA_SVZ_eQTL,BM_10_MSBB_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000135697	chr16	81238688	81238689	BCO1	BM_44_MSBB_eQTL
ENSG00000135698	chr16	82170223	82170224	MPHOSPH6	MiGA_SVZ_eQTL,MiGA_THA_eQTL,Inh_mega_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000135709	chr16	85027750	85027751	KIAA0513	ROSMAP_PCC_sQTL
ENSG00000140943	chr16	84116941	84116942	MBTPS1	ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000140945	chr16	82626964	82626965	CDH13	MiGA_GTS_eQTL,MiGA_SVZ_eQTL,BM_44_MSBB_eQTL,Oli_DeJager_eQTL,Oli_Kellis_eQTL,DLPFC_Klein_gpQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000140948	chr16	87493023	87493024	ZCCHC14	ROSMAP_PCC_sQTL
ENSG00000140950	chr16	84554032	84554033	MEAK7	MiGA_GFM_eQTL,DLPFC_DeJager_eQTL,AC_DeJager_eQTL,monocyte_ROSMAP_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000140955	chr16	84191137	84191138	ADAD2	Knight_eQTL
ENSG00000140961	chr16	83931310	83931311	OSGIN1	ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000141012	chr16	88856969	88856970	GALNS	ROSMAP_DLPFC_sQTL
ENSG00000153786	chr16	85011534	85011535	ZDHHC7	Ast_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000153815	chr16	81444807	81444808	CMIP	MiGA_GTS_eQTL,MiGA_SVZ_eQTL,monocyte_ROSMAP_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000154099	chr16	84145307	84145308	DNAAF1	ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000166454	chr16	81035841	81035842	ATMIN	MiGA_SVZ_eQTL
ENSG00000166558	chr16	84042794	84042795	SLC38A8	BM_36_MSBB_eQTL
ENSG00000167508	chr16	88663160	88663161	MVD	ROSMAP_AC_sQTL
ENSG00000167523	chr16	89657739	89657740	SPATA33	ROSMAP_DLPFC_sQTL
ENSG00000184860	chr16	82011480	82011481	SDR42E1	MiGA_GFM_eQTL
ENSG00000205078	chr16	77199407	77199408	SYCE1L	MiGA_SVZ_eQTL
ENSG00000230989	chr16	83807977	83807978	HSBP1	MiGA_GTS_eQTL,Exc_mega_eQTL,STARNET_eQTL
ENSG00000260643	chr16	81096295	81096296	AC092718.2	MiGA_SVZ_eQTL
ENSG00000261609	chr16	81314943	81314944	GAN	MiGA_GTS_eQTL,MiGA_SVZ_eQTL,AC_DeJager_eQTL,Exc_mega_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000284512	chr16	81096283	81096284	AC092718.7	ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with PLCG2. Further look into the data-set --- using these genes as targets and repeating what we did above for PLCG2 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on PLCG2 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.

sliding_windows <- target_gene_info$gene_info$sliding_windows %>% strsplit(., ',') %>% unlist %>% as.character
sliding_windows

1. 'chr16:69114331-82020270'
2. 'chr16:72943078-86094230'
3. 'chr16:76190834-90338345'

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 PLCG2:

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

$z_plot
NULL

$effects
                        L1
ENSG00000186153 -0.3497859
ENSG00000153815 -0.3497859
ENSG00000197943 -0.3497859
ENSG00000140945 -0.3497859
ENSG00000140943 -0.3497859
ENSG00000103187 -0.3497859

In this case, there is no statistical evidence for PLCG2 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

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.

finempping_contexts <- readRDS(paste0(gene_name, '_finemapping_contexts.rds')) # from sec1

finempping_contexts <- get_norosmap_contexts(finempping_contexts)

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 pvalue cutoff. Extract all variants names.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

options(repr.plot.width=6, repr.plot.height=6)

ggplot(PLCG2_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for PLCG2 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/PLCG2/sec11.interaction_association_PLCG2_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

vars_p

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

Section 12: Candidate loci as trans-xQTL

see notebook

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/PLCG2/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.