Case study: NCK2 xQTL and AD GWAS

This notebook documents the analysis of xQTL case study on a targeted gene, NCK2.

• 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 /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 NCK2 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 (NCK2 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, NCK2_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.NCK2.rds. This can be used as input for Section 12.
• b. Section 2: A variant list showing colocalization in cohorts we analyzed with ColocBoost, NCK2_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('../../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 = 'NCK2'

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>
ENSG00000071051	chr2	104744911	106894274	105744911	chr2:103145764-105444433,chr2:105444433-106385118,chr2:106385118-107317691	2_103145764-105444433,2_105444433-106385118,2_106385118-107317691	2_103145764-105444433,2_105444433_106385118,2_106385118_107317691	TADB_166,TADB_167,TADB_168	chr2_101418688_106438513,chr2_104264391_107544297,chr2_105093311_109885477	105744912	105894274	chr2:96046587-106438513,chr2:99271875-107544297,chr2:101418688-109885477,chr2:104264391-111577222,chr2:105093311-114601261	NCK2

$target_LD_ids

A matrix: 1 x 3 of type chr

chr2:103145764-105444433

chr2:105444433-106385118

chr2:106385118-107317691

$target_sums_ids

A matrix: 1 x 3 of type chr

2_103145764-105444433

2_105444433-106385118

2_106385118-107317691

$gene_region

'chr2:104744911-106894274'

$target_TAD_ids

A matrix: 1 x 3 of type chr

chr2_101418688_106438513

chr2_104264391_107544297

chr2_105093311_109885477

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/NCK2/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>
Mic; PCC_unproductive; AD_Bellenguez_2022	1	1	0.9999995	5068	chr2:105749599:T:C	chr2:105749599:T:C	coloc_sets:Y1_Y17_Y18:CS1

# effect sign for each coloc sets
get_effect_sign_csets(cb_res)

$`coloc_sets:Y1_Y17_Y18:CS1` =

A data.frame: 1 x 4

	variants	Mic	PCC_unproductive	AD_Bellenguez_2022
	<chr>	<dbl>	<dbl>	<dbl>
chr2:105749599:T:C	chr2:105749599:T:C	-6.172269	3.575186	6.503322

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

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/NCK2/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 = 'NCK2')
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.

message("Multi context in ROSMAP data")

multi_context_rosmap_tmp <- tryCatch(
    readRDS(paste0('/data/analysis_result/multi_context/ROSMAP/mnm/ROSMAP_DeJager.',
                   target_gene_info$gene_info$`#chr`, '_', gene_id, '.multicontext_bvsr.rds')),
    error = function(e) message('Error in loading ROSMAP multi context data')
)
if (!is.null(multi_context_rosmap_tmp[[1]]$mvsusie_fitted)) {
    plot_and_save(multi_context_rosmap_tmp[[1]], 'plots/NCK2/sec4.multi_context_ROSMAP')
} else {
    message('Multi Context results are empty in ROSMAP data')
}

# Load and process MSBB data
message("Multi context in MSBB data")

multi_context_msbb_tmp <- tryCatch(
    readRDS(paste0('/data/analysis_result/multi_context/MSBB/mnm/MSBB_eQTL.',
                   target_gene_info$gene_info$`#chr`, '_', gene_id, '.multicontext_bvsr.rds')),
    error = function(e)  message('Error in loading MSBB multi context data')
)
if (!is.null(multi_context_msbb_tmp[[1]]$mvsusie_fitted)) {
    plot_and_save(multi_context_msbb_tmp[[1]], 'plots/NCK2/sec4.multi_context_MSBB')
} else {
    message('Multi Context results are empty in MSBB data')
}

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.NCK2.rds', sQTL = 'no_MSBB')
multigene_flat

A data.frame: 7 x 6

gene_id	#chr	start	end	gene_name	contexts
<chr>	<chr>	<int>	<int>	<chr>	<chr>
ENSG00000115652	chr2	106194300	106194301	UXS1	MiGA_GFM_eQTL,MiGA_SVZ_eQTL,ROSMAP_PCC_sQTL
ENSG00000119147	chr2	106063245	106063246	ECRG4	MiGA_SVZ_eQTL,AC_DeJager_eQTL
ENSG00000135972	chr2	105038068	105038069	MRPS9	AC_DeJager_eQTL
ENSG00000135973	chr2	105241742	105241743	GPR45	MiGA_SVZ_eQTL
ENSG00000135974	chr2	105337531	105337532	C2orf49	MiGA_SVZ_eQTL
ENSG00000144057	chr2	106887107	106887108	ST6GAL2	AC_DeJager_eQTL,Ast_mega_eQTL,OPC_mega_eQTL
ENSG00000153165	chr2	106468412	106468413	RGPD3	MiGA_SVZ_eQTL

Other genes implicated are PROC and HS6ST1 in MiGA cohort which may share causal eQTL with NCK2. Further look into the data-set --- using these genes as targets and repeating what we did above for NCK2 --- might be needed to establish a more certain conclusion.

Alternatively, we may be able to apply a multi-gene statistical fine-mapping test on NCK2 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. 'chr2:96046587-106438513'
2. 'chr2:99271875-107544297'
3. 'chr2:101418688-109885477'
4. 'chr2:104264391-111577222'
5. 'chr2:105093311-114601261'

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 NCK2:

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
ENSG00000204634 -0.174516
ENSG00000163162 -0.174516
ENSG00000135966 -0.174516
ENSG00000071051 -0.174516

$pip_plot

$effect_plot

$z_plot
NULL

$effects
                         L1          L2
ENSG00000071051 -0.01280692 -0.02664638
ENSG00000135968 -0.01280692 -0.02664638
ENSG00000169756 -0.01280692 -0.02664638
ENSG00000172985 -0.01280692 -0.02664638

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

1. 'chr2_101418688_106438513'
2. 'chr2_104264391_107544297'
3. 'chr2_105093311_109885477'

gene_id

'ENSG00000071051'

 context_files <- list.files('/data/analysis_result/multi_gene/ROSMAP/mnm_genes/', 'chr2_105093311_109885477', full.names = T) %>% .[str_detect(., 'ROSMAP_Mic_DeJager_eQTL')]
mnm_gene_tmp <- readRDS(context_files[2])
mnm_gene_tmp[[1]]$mvsusie_fitted$condition_names

1. 'ENSG00000153201'
2. 'ENSG00000169756'
3. 'ENSG00000172985'
4. 'ENSG00000186522'

options(repr.plot.width=16, repr.plot.height=6)

plot_and_save(mnm_gene_tmp[[1]], 'plots/NCK2/sec6.multigene')

# Main loop to process sliding windows
mnm_gene <- list()
for (window in sliding_windows) {
    context_files <- list.files('/data/analysis_result/multi_gene/ROSMAP/mnm_genes/', window, full.names = T) %>% .[str_detect(., '.multigene_bvrs.rds')]
    for(context_file in context_files){
        context_mnm = context_file %>% basename %>% str_split(., '[.]', simplify = T) %>% .[,1]
        # Load multi-gene data
        mnm_gene_tmp <- tryCatch(
            readRDS(context_file),
            error = function(e) NULL
        )
        
        if (!is.null(mnm_gene_tmp)) {
            # Check if target gene is in the condition names
            if (target_gene_info$gene_info$region_id %in% mnm_gene_tmp[[1]]$mvsusie_fitted$condition_names) {
                # Use a common prefix format for multi-gene plots
                plot_and_save(mnm_gene_tmp[[1]], 'plots/NCK2/sec6.multigene')
            } else {
                message('There is mnm result for TAD window ', window, ' in ', context_mnm,
                        ', but it does not include target gene ', gene_name, ' in CS.')
            }
            # Append to the results list
            mnm_gene <- append(mnm_gene, list(mnm_gene_tmp))
        } 
    }
}

In this case, there is no statistical evidence for NCK2 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 = 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.

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(NCK2_int_res, aes(x = variant_id, y = qvalue_interaction)) +
  geom_point(alpha = 0.7, size = 6) +
  labs(title = "qvalue for NCK2 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/NCK2/sec11.interaction_association_NCK2_lessPIP25.pdf', height = 5, width = 8) 

In conclusion, there is no interaction QTL with APOE identified.

Quantile QTL analysis

Anjing will soon provide the vignette to assess that.

quant_coef_colocvar

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/NCK2/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.