Case study: ACE xQTL and AD GWAS

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

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

# 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 = 'ACE'

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

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>
ENSG00000159640	chr17	62240000	64498380	63477060	chr17:60570445-65149278	17_60570445-65149278	17_60570445-65149278	TADB_1204,TADB_1205,TADB_1206	chr17_59677452_63615481,chr17_61231470_64931385,chr17_62832081_65919480	63477061	63498380	chr17:57192725-62392838,chr17:57982638-63615481,chr17:58565557-64931385,chr17:59677452-65919480,chr17:61231470-67256525,chr17:62832081-69046854,chr17:63747264-70327244	ACE

$target_LD_ids

A matrix: 1 x 1 of type chr

chr17:60570445-65149278

$target_sums_ids

A matrix: 1 x 1 of type chr

17_60570445-65149278

$gene_region

'chr17:62240000-64498380'

$target_TAD_ids

A matrix: 1 x 3 of type chr

chr17_59677452_63615481

chr17_61231470_64931385

chr17_62832081_65919480

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>
ENSG00000159640	chr17	62240000	64498380	63477060	chr17:60570445-65149278	17_60570445-65149278	17_60570445-65149278	TADB_1204,TADB_1205,TADB_1206	chr17_59677452_63615481,chr17_61231470_64931385,chr17_62832081_65919480	63477061	63498380	chr17:57192725-62392838,chr17:57982638-63615481,chr17:58565557-64931385,chr17:59677452-65919480,chr17:61231470-67256525,chr17:62832081-69046854,chr17:63747264-70327244	ACE

$target_LD_ids

A matrix: 1 x 1 of type chr

chr17:60570445-65149278

$target_sums_ids

A matrix: 1 x 1 of type chr

17_60570445-65149278

$gene_region

'chr17:62240000-64498380'

$target_TAD_ids

A matrix: 1 x 3 of type chr

chr17_59677452_63615481

chr17_61231470_64931385

chr17_62832081_65919480

gene_id = target_gene_info$gene_info$region_id
chrom = target_gene_info$gene_info$`#chr`

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

pdf: 2

# colocalized variants
cb_res_table

A data.frame: 1 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>
Exc; DLPFC; PCC; pQTL; AD_Bellenguez_2022	0.9928046	2	0.8495075	4218; 4223	chr17:63476980:C:T; chr17:63478937:C:G	chr17:63476980:C:T	coloc_sets:Y1_Y2_Y4_Y6_Y13:CS1

# effect sign for each coloc sets
get_effect_sign_csets(cb_res)

$`coloc_sets:Y1_Y2_Y4_Y6_Y13:CS1` =

A data.frame: 2 x 6

	variants	Exc	DLPFC	PCC	pQTL	AD_Bellenguez_2022
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
chr17:63476980:C:T	chr17:63476980:C:T	-5.617463	-12.08937	-15.52633	-6.902631	8.190476
chr17:63478937:C:G	chr17:63478937:C:G	-5.359208	-11.90218	-15.41718	-7.008341	8.107143

# 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.

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.

pdf('plots/ACE/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

mash_p <- mash_plot(gene_name = 'ACE')

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.ACE.rds', sQTL = 'no_MSBB')
multigene_flat

A data.frame: 32 x 6

gene_id	#chr	start	end	gene_name	contexts
<chr>	<chr>	<int>	<int>	<chr>	<chr>
ENSG00000008283	chr17	63446353	63446354	CYB561	BM_22_MSBB_eQTL,PCC_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000011028	chr17	62627669	62627670	MRC2	ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000087191	chr17	63827151	63827152	PSMC5	MiGA_GTS_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000087995	chr17	62423890	62423891	METTL2A	BM_10_MSBB_eQTL,ROSMAP_PCC_sQTL
ENSG00000108370	chr17	65100811	65100812	RGS9	ROSMAP_DLPFC_sQTL
ENSG00000108510	chr17	62065277	62065278	MED13	MiGA_GTS_eQTL
ENSG00000108588	chr17	63776350	63776351	CCDC47	MiGA_SVZ_eQTL
ENSG00000108592	chr17	63830011	63830012	FTSJ3	MiGA_GFM_eQTL,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
ENSG00000108604	chr17	63843064	63843065	SMARCD2	MiGA_SVZ_eQTL,BM_22_MSBB_eQTL,PCC_DeJager_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000108622	chr17	64020633	64020634	ICAM2	MiGA_GTS_eQTL
ENSG00000108654	chr17	64508198	64508199	DDX5	Mic_13_Kellis_eQTL,Inh_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000108854	chr17	64662306	64662307	SMURF2	ROSMAP_PCC_sQTL
ENSG00000125695	chr17	63752096	63752097	AC046185.1	BM_10_MSBB_eQTL
ENSG00000136463	chr17	63600894	63600895	TACO1	MiGA_SVZ_eQTL
ENSG00000136478	chr17	64263259	64263260	TEX2	Inh_Kellis_eQTL,Oli_mega_eQTL,ROSMAP_AC_sQTL
ENSG00000136485	chr17	63550476	63550477	DCAF7	Inh_DeJager_eQTL
ENSG00000136490	chr17	63701171	63701172	LIMD2	MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000136492	chr17	61863527	61863528	BRIP1	MiGA_GTS_eQTL,MiGA_SVZ_eQTL
ENSG00000141376	chr17	60677452	60677453	BCAS3	ROSMAP_DLPFC_sQTL
ENSG00000146872	chr17	62458657	62458658	TLK2	MiGA_SVZ_eQTL
ENSG00000170921	chr17	63009555	63009556	TANC2	OPC_Kellis_eQTL,Ast_10_Kellis_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000173826	chr17	63523333	63523334	KCNH6	ROSMAP_DLPFC_sQTL
ENSG00000176809	chr17	64919479	64919480	LRRC37A3	MiGA_SVZ_eQTL,Inh_Kellis_eQTL
ENSG00000178607	chr17	64130818	64130819	ERN1	Exc_DeJager_eQTL,ROSMAP_PCC_sQTL
ENSG00000198231	chr17	63773602	63773603	DDX42	MiGA_GFM_eQTL,MiGA_THA_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL
ENSG00000198909	chr17	63622414	63622415	MAP3K3	Oli_DeJager_eQTL
ENSG00000224383	chr17	63998350	63998351	PRR29	DLPFC_DeJager_eQTL,AC_DeJager_eQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000253506	chr17	61591218	61591219	NACA2	MiGA_GTS_eQTL
ENSG00000261371	chr17	64413775	64413776	PECAM1	MiGA_SVZ_eQTL
ENSG00000264813	chr17	63484822	63484823	AC113554.1	BM_44_MSBB_eQTL,DLPFC_DeJager_eQTL,PCC_DeJager_eQTL,ROSMAP_AC_sQTL
ENSG00000266173	chr17	63741985	63741986	STRADA	MiGA_GTS_eQTL,ROSMAP_AC_sQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000271605	chr17	64449036	64449037	MILR1	monocyte_ROSMAP_eQTL

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

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on ACE 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. 'chr17:57192725-62392838'
2. 'chr17:57982638-63615481'
3. 'chr17:58565557-64931385'
4. 'chr17:59677452-65919480'
5. 'chr17:61231470-67256525'
6. 'chr17:62832081-69046854'
7. 'chr17:63747264-70327244'

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 ACE:

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 ACE 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 <- 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.

Section 9: Non-linear effects of xQTL

see notebook

APOE interaction

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

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