Case study: CLU xQTL and AD GWAS

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

• Section 0: Sanity check
• Section 1: Fine-mapping for xQTL and GWAS
• Section 2: Multi-context colocalization with Bellenguez 2022
• Section 3: Refinement of colocalized loci with other AD GWAS
• Section 4: Assessment of multi-context xQTL effect sizes
• Section 5: Multi-context causal TWAS (including conventional TWAS and MR)
• Section 6: Context specific multi-gene fine-mapping
• Section 7: Epigenomic QTL and their target regions
• Section 8: Context focused validation in other xQTL data
• Section 9: Non-linear effects of xQTL
• Section 10: in silico functional studies in iPSC model
• Section 11: Functional annotations of selected loci
• Section 12: Candidate loci as trans-xQTL

Overview

FunGen-xQTL resource contains 67 xQTL contexts as well as 9 AD GWAS fine-mapped genome-wide. The overarching goal for case studies is to use these resource to raise questions and learn more about gene targets of interest.

Overall, a case study consists of the following aspects:

• Check the basic information of the gene
• Check the existing xQTL and integrative analysis results, roughly including
  • Summary table for univariate fine-mapping
  • Marginal association results
  • Multi-gene and multi-context fine-mapping
  • Multi-context colocalization with AD GWAS
  • TWAS, MR and causal TWAS
  • Integration with epigenetic QTL
  • Quantile QTL
  • Interaction QTL
  • Validation:
    • Additional xQTL data in FunGen-xQTL
    • Additional AD GWAS data-set already generated by FunGen-xQTL
  • In silico functional studies
    • Additional iPSC data-sets
  • Functional annotations of variants, particularly in relevant cellular contexts
• Creative thinking: generate hypothesis, search in literature, raise questions to discuss

Computing environment setup

Interactive analysis will be done on AWS cloud powered by MemVerge. Please contact Gao Wang to setup accounts for you to start the analysis.

Please follow instructions on https://wanggroup.org/productivity_tips/mmcloud-interactive to configure your computing environment. Here are some additional packages you need to install after the initial configuration, in order to perform these analysis:

micromamba install -n r_libs r-pecotmr
micromamba install -n r_libs r-bedmatrix

How to Use This Notebook

0. Before you start: Load functions from cb_plot.R and utilis.R, located at /data/interactive_analysis/rf2872/codes/. These functions and resources are packaged to streamline the analysis and ensure everything is as clean as possible. And also the codes for ColocBoost under path /data/colocalization/colocboost/R.
1. Inside of this notebook, use sed -i or Ctrl+F to replace the gene CLU 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 (CLU 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, CLU_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.CLU.rds. This can be used as input for Section 12.
• b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, CLU_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

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 = 'CLU'

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>
ENSG00000120885	chr8	25080000	29360000	27614699	chr8:25007602-26225312,chr8:26225312-27515963,chr8:27515963-29469590	8_25007602-26225312,8_26225312-27515963,8_27515963-29469590	8_25007602-26225312,8_26225312_27515963,8_27515963_29469590	TADB_697,TADB_698	chr8_24141394_29490278,chr8_26235323_30360222	27614700	27596917	chr8:18313692-26141968,chr8:19197380-29490278,chr8:21042397-30360222,chr8:24141394-31767006,chr8:26235323-32220984,chr8:28333064-35039104	CLU

$target_LD_ids

A matrix: 1 x 3 of type chr

chr8:25007602-26225312

chr8:26225312-27515963

chr8:27515963-29469590

$target_sums_ids

A matrix: 1 x 3 of type chr

8_25007602-26225312

8_26225312-27515963

8_27515963-29469590

$gene_region

'chr8:25080000-29360000'

$target_TAD_ids

A matrix: 1 x 2 of type chr

chr8_24141394_29490278

chr8_26235323_30360222

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('/data/interactive_analysis/rf2872/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/CLU/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>
Ast; DLPFC; AC; AD_Bellenguez_2022	0.9548921	8	0.2272865	13988; 13987; 13985; 13989; 13981; 13976; 13955; 13990	chr8:27608798:T:C; chr8:27598736:T:C; chr8:27608664:T:C; chr8:27607795:T:C; chr8:27608640:T:C; chr8:27607412:A:G; chr8:27604964:A:G; chr8:27607002:T:C	chr8:27608798:T:C	coloc_sets:Y2_Y7_Y8_Y17:CS1

# effect sign for each coloc sets
get_effect_sign_csets(cb_res)

$`coloc_sets:Y2_Y7_Y8_Y17:CS1` =

A data.frame: 8 x 5

	variants	Ast	DLPFC	AC	AD_Bellenguez_2022
	<chr>	<dbl>	<dbl>	<dbl>	<dbl>
chr8:27608640:T:C	chr8:27608640:T:C	-4.827589	-6.646252	-6.438557	12.08434
chr8:27607795:T:C	chr8:27607795:T:C	-4.827589	-6.646252	-6.438557	12.07229
chr8:27607412:A:G	chr8:27607412:A:G	-4.827589	-6.638479	-6.444127	11.95238
chr8:27608664:T:C	chr8:27608664:T:C	-4.827589	-6.648388	-6.447515	11.91667
chr8:27607002:T:C	chr8:27607002:T:C	-4.783657	-6.612231	-6.438848	11.97590
chr8:27604964:A:G	chr8:27604964:A:G	-4.783657	-6.612231	-6.438848	11.96386
chr8:27598736:T:C	chr8:27598736:T:C	-4.935008	-6.738925	-6.316061	11.53012
chr8:27608798:T:C	chr8:27608798:T:C	-5.048886	-6.865846	-6.422296	0.00000

# 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/CLU/sec3.colocboost_res_allad.pdf', width = 10, height = 5)
replayPlot(cb_ad$p)
dev.off()

pdf: 2

Section 4: Assessment of multi-context xQTL effect sizes

Option 1: ColocBoost + MASH

Use colocboost variants and check for mash posterior contrast to see if the effect size are shared or specific or even opposite. Advantage is that colocboost result is AD GWAS informed; issue is that marginal posterior effects is not always the joint

Option 2: mvSuSiE

Use mvSuSiE multicontext fine-mapping results --- the bubble plot to check posterior effects. Issue is that we don't have this results yet, and this is limited to one cohort at a time, without information from AD.

We should go for option 1 by default and if we want to make claim about opposite effect size we double-check with mvSuSiE multicontext analysis.

Section 5: Multi-context causal TWAS (including conventional TWAS and MR)

The most updated version of cTWAS analysis are under path s3://statfungen/ftp_fgc_xqtl/analysis_result/cTWAS/

TWAS results

We report TWAS from all contexts and methods from the pipeline. Here we will filter it down to the best performing methods and only keep contexts that are significant.

plot_TWAS_res(gene_id = gene_id)

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

A data.frame: 28 x 6

gene_id	#chr	start	end	gene_name	contexts
<chr>	<chr>	<int>	<int>	<chr>	<chr>
ENSG00000012232	chr8	28600468	28600469	EXTL3	ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000015592	chr8	27258419	27258420	STMN4	ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000104228	chr8	27311271	27311272	TRIM35	BM_44_MSBB_eQTL,Exc_DeJager_eQTL,DLPFC_DeJager_eQTL,Exc_mega_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000104290	chr8	28494204	28494205	FZD3	BM_10_MSBB_eQTL,Oli_mega_eQTL
ENSG00000104299	chr8	28890241	28890242	INTS9	ROSMAP_PCC_sQTL
ENSG00000104756	chr8	25458475	25458476	KCTD9	ROSMAP_DLPFC_sQTL,STARNET_eQTL
ENSG00000104765	chr8	26383053	26383054	BNIP3L	MSBB_BM36_pQTL
ENSG00000120875	chr8	29350683	29350684	DUSP4	MiGA_SVZ_eQTL
ENSG00000120899	chr8	27311481	27311482	PTK2B	MiGA_GTS_eQTL,MiGA_SVZ_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL,STARNET_eQTL
ENSG00000120907	chr8	26867277	26867278	ADRA1A	AC_DeJager_eQTL,ROSMAP_DLPFC_sQTL
ENSG00000120915	chr8	27490780	27490781	EPHX2	MSBB_BM36_pQTL,DLPFC_Bennett_pQTL,ROSMAP_PCC_sQTL
ENSG00000134014	chr8	28089672	28089673	ELP3	MiGA_SVZ_eQTL,MiGA_THA_eQTL,PCC_DeJager_eQTL,Exc_mega_eQTL
ENSG00000147419	chr8	27772652	27772653	CCDC25	MiGA_SVZ_eQTL,BM_22_MSBB_eQTL,ROSMAP_AC_sQTL
ENSG00000147421	chr8	28890394	28890395	HMBOX1	Inh_Kellis_eQTL
ENSG00000147437	chr8	25424653	25424654	GNRH1	MiGA_GTS_eQTL,MiGA_THA_eQTL,ROSMAP_AC_sQTL
ENSG00000147459	chr8	25184688	25184689	DOCK5	BM_22_MSBB_eQTL,DLPFC_Bennett_pQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000168077	chr8	27633867	27633868	SCARA3	Exc_DeJager_eQTL,OPC_Kellis_eQTL,Ast_mega_eQTL,Exc_mega_eQTL,ROSMAP_PCC_sQTL
ENSG00000168078	chr8	27838081	27838082	PBK	MiGA_SVZ_eQTL,MiGA_THA_eQTL
ENSG00000168079	chr8	27992672	27992673	SCARA5	ROSMAP_PCC_sQTL
ENSG00000171320	chr8	27771948	27771949	ESCO2	MiGA_GFM_eQTL
ENSG00000184661	chr8	25459198	25459199	CDCA2	STARNET_eQTL
ENSG00000186918	chr8	28402700	28402701	ZNF395	MiGA_THA_eQTL,Oli_Kellis_eQTL
ENSG00000189233	chr8	28083935	28083936	NUGGC	MiGA_SVZ_eQTL
ENSG00000197892	chr8	29263123	29263124	KIF13B	DLPFC_DeJager_eQTL,Oli_Kellis_eQTL,Oli_mega_eQTL,ROSMAP_AC_sQTL,ROSMAP_DLPFC_sQTL,ROSMAP_PCC_sQTL
ENSG00000214050	chr8	28490277	28490278	FBXO16	AC_DeJager_eQTL,Inh_mega_eQTL,ROSMAP_PCC_sQTL,ROSMAP_DLPFC_sQTL
ENSG00000221818	chr8	26045412	26045413	EBF2	DLPFC_DeJager_eQTL,ROSMAP_AC_sQTL
ENSG00000221914	chr8	26291507	26291508	PPP2R2A	MiGA_GTS_eQTL,BM_10_MSBB_eQTL
ENSG00000240694	chr8	26514091	26514092	PNMA2	MiGA_SVZ_eQTL,DLPFC_DeJager_eQTL,Ast_Kellis_eQTL,Exc_Kellis_eQTL,Inh_Kellis_eQTL,Exc_mega_eQTL

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

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on CLU 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. 'chr8:18313692-26141968'
2. 'chr8:19197380-29490278'
3. 'chr8:21042397-30360222'
4. 'chr8:24141394-31767006'
5. 'chr8:26235323-32220984'
6. 'chr8:28333064-35039104'

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 CLU:

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 CLU 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_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.

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(CLU_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for CLU 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/CLU/sec11.interaction_association_CLU_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

func_p

Section 12: Candidate loci as trans-xQTL

see notebook

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/CLU/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.