mikelove/Liang_2021_inpeak_caQTL_ASCA_eQTL_neurons.csv

## Liang_2021_inpeak_caQTL_ASCA_eQTL_neurons.csv

          
            snp_hg38
            rsid
            refAllele
            altAllele
            lfcASCA
            lfcQTL
            lfcEQTL

            
              chr2:208557023:C:G
              rs1036014
              C
              G
              0.679909666011761
              0.2499407877
              0.2194325508

            
              chr4:9924989:T:C
              rs10939614
              T
              C
              -2.21500762508123
              -0.355952769
              -0.2118339332

            
              chr7:138187033:C:T
              rs17603855
              C
              T
              -1.73696246880911
              -0.4433277822
              -0.4808215973

            
              chr7:139495451:C:G
              rs2530731
              C
              G
              -1.77729493517346
              -0.6283644361
              -0.3881908065

            
              chr11:86515916:G:C
              rs2125358
              G
              C
              1.36463250810021
              0.4783681025
              0.4154173162

            
              chr14:22848616:A:G
              rs17882077
              A
              G
              -1.34268551797455
              -0.2681226196
              -0.2210454793

            
              chr18:25019730:G:A
              rs10502466
              G
              A
              0.716207533070227
              0.2821604048
              0.2185292588

            
              chr21:43270976:A:G
              rs2838227
              A
              G
              0.8181622133663
              0.304950793
              0.4107778657

## liang_supp_tables.R
library(readr)
library(dplyr)
library(tibble)
library(tidyr)

# variants are from Liang 2021
# https://pubmed.ncbi.nlm.nih.gov/34017130/
# doi: 10.1038/s41593-021-00858-w

# Dan's file are hg38

qtl <- read_csv("Supplementary Table 3-Table 1.csv") %>%
  rename(snp_hg38 = SNP, chr = seqnames) %>%
  select(-A1, -A2)
asca <- read.csv("Supplementary Table 6-Table 1.csv") %>%
  tibble() %>%
  filter(CellType == "Neuron") %>%
  mutate(sameCoding = refAllele == caQTL_A1) %>% # A1 is denominator in QTL
  mutate(caQTL_Beta = caQTL_Beta * ifelse(sameCoding, 1, -1)) %>%
  select(rsid,
         refAllele, altAllele,
         lfcASCA=log2FoldChange, lfcQTL=caQTL_Beta,
         fdrASCA=padj, pvalQTL=caQTL_P)

tab <- qtl %>% filter(InPeak, CellType == "Neuron") %>%
  drop_na(rsid) %>%
  inner_join(asca, by="rsid") %>%
  filter(pvalQTL < 1e-3) %>%
  filter(!duplicated(peakid))
# 68 rows...

library(ggplot2)
ggplot(tab, aes(lfcQTL, lfcASCA)) + geom_point()

eqtl <- read_csv("Supplementary Table 4-Table 1.csv") %>%
  filter(Type == "NeuroncaQTLandeQTL") %>%
  rename(snp_hg38 = SNP) %>%
  select(-seqnames, -peakstart, -peakend, -peakid)

tab <- tab %>% inner_join(eqtl, by="snp_hg38") %>%
  mutate(lfcEQTL = eBeta * ifelse(sign(lfcQTL) == sign(caBeta), 1, -1)) %>%
  group_by(rsid) %>%
  filter(eP == min(eP))

# 8 rows
ggplot(tab, aes(lfcQTL, lfcASCA, color=lfcEQTL)) + geom_point(size=2) +
  scale_color_gradient2(low="red",mid="white",high="blue") +
  geom_hline(yintercept=0) + geom_vline(xintercept=0) +
  ggtitle("Neuronal caQTL + ASCA + eQTL, in own peak")

out <- tab %>% select(snp_hg38, rsid, refAllele, altAllele, lfcASCA, lfcQTL, lfcEQTL)
write.csv(out, file="Liang_2021_inpeak_caQTL_ASCA_eQTL_neurons.csv", row.names=FALSE, quote=FALSE)
snp_hg38	rsid	refAllele	altAllele	lfcASCA	lfcQTL	lfcEQTL
chr2:208557023:C:G	rs1036014	C	G	0.679909666011761	0.2499407877	0.2194325508
chr4:9924989:T:C	rs10939614	T	C	-2.21500762508123	-0.355952769	-0.2118339332
chr7:138187033:C:T	rs17603855	C	T	-1.73696246880911	-0.4433277822	-0.4808215973
chr7:139495451:C:G	rs2530731	C	G	-1.77729493517346	-0.6283644361	-0.3881908065
chr11:86515916:G:C	rs2125358	G	C	1.36463250810021	0.4783681025	0.4154173162
chr14:22848616:A:G	rs17882077	A	G	-1.34268551797455	-0.2681226196	-0.2210454793
chr18:25019730:G:A	rs10502466	G	A	0.716207533070227	0.2821604048	0.2185292588
chr21:43270976:A:G	rs2838227	A	G	0.8181622133663	0.304950793	0.4107778657
	library(readr)
	library(dplyr)
	library(tibble)
	library(tidyr)

	# variants are from Liang 2021
	# https://pubmed.ncbi.nlm.nih.gov/34017130/
	# doi: 10.1038/s41593-021-00858-w

	# Dan's file are hg38

	qtl <- read_csv("Supplementary Table 3-Table 1.csv") %>%
	rename(snp_hg38 = SNP, chr = seqnames) %>%
	select(-A1, -A2)
	asca <- read.csv("Supplementary Table 6-Table 1.csv") %>%
	tibble() %>%
	filter(CellType == "Neuron") %>%
	mutate(sameCoding = refAllele == caQTL_A1) %>% # A1 is denominator in QTL
	mutate(caQTL_Beta = caQTL_Beta * ifelse(sameCoding, 1, -1)) %>%
	select(rsid,
	refAllele, altAllele,
	lfcASCA=log2FoldChange, lfcQTL=caQTL_Beta,
	fdrASCA=padj, pvalQTL=caQTL_P)

	tab <- qtl %>% filter(InPeak, CellType == "Neuron") %>%
	drop_na(rsid) %>%
	inner_join(asca, by="rsid") %>%
	filter(pvalQTL < 1e-3) %>%
	filter(!duplicated(peakid))
	# 68 rows...

	library(ggplot2)
	ggplot(tab, aes(lfcQTL, lfcASCA)) + geom_point()

	eqtl <- read_csv("Supplementary Table 4-Table 1.csv") %>%
	filter(Type == "NeuroncaQTLandeQTL") %>%
	rename(snp_hg38 = SNP) %>%
	select(-seqnames, -peakstart, -peakend, -peakid)

	tab <- tab %>% inner_join(eqtl, by="snp_hg38") %>%
	mutate(lfcEQTL = eBeta * ifelse(sign(lfcQTL) == sign(caBeta), 1, -1)) %>%
	group_by(rsid) %>%
	filter(eP == min(eP))

	# 8 rows
	ggplot(tab, aes(lfcQTL, lfcASCA, color=lfcEQTL)) + geom_point(size=2) +
	scale_color_gradient2(low="red",mid="white",high="blue") +
	geom_hline(yintercept=0) + geom_vline(xintercept=0) +
	ggtitle("Neuronal caQTL + ASCA + eQTL, in own peak")

	out <- tab %>% select(snp_hg38, rsid, refAllele, altAllele, lfcASCA, lfcQTL, lfcEQTL)
	write.csv(out, file="Liang_2021_inpeak_caQTL_ASCA_eQTL_neurons.csv", row.names=FALSE, quote=FALSE)