Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches

[1] § Methods › PRSs ↔ sbayes_prs_quantanx.Rmd, lines 133–165 · score 0.76 · linear regression, variance explained, liability scale, Nagelkerke, components, PRS
[2] § Methods › Participants and measures ↔ extract_anx_factor_score_quantanx.Rmd, lines 27–42 · score 0.68 · factor score, latent factor, maximum likelihood, predict, lavaan, ANX
[3] § Results › Polygenic risk scores ↔ sbayes_prs_quantanx.Rmd, lines 133–165 · score 0.63 · risk score, variance explained, liability scale, PRS, R2, anxiety
[4] § Methods › Meta-analysis ↔ meta-analysis_secondaryanalyses_quantanx.Rmd, lines 7–55 · score 0.63 · AGDS, ALSPAC, TRAILS, Lifelines, MEGA, METAL
[5] § Results › SNP-based heritability and genetic correlations with external traits ↔ meta-analysis_secondaryanalyses_quantanx.Rmd, lines 207–242 · score 0.63 · post traumatic stress, internalizing traits, irritable, neuroticism, depressive, disorder
[6] § Results › SNP-based heritability and genetic correlations with external traits ↔ meta-analysis_secondaryanalyses_quantanx.Rmd, lines 207–242 · score 0.54 · post traumatic stress, major depressive disorder, MDD, traits, SNP

Paper

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The authors' code

R Markdown · 588 lines · 17 KB · MIT · 3 matches

---
title: "Quantitative anxiety symptom genome-wide meta-analysis"
author: "Megan Skelton"
output: html_document
---
Analysis was run on the KCL CREATE HPC: King's College London. (2022). King's Computational Research, Engineering and Technology Environment (CREATE). Retrieved March 2, 2022, from https://doi.org/10.18742/rnvf-m076
# Meta-analysis
```{bash METAL script}
echo '
COLUMNCOUNTING STRICT
SCHEME STDERR
AVERAGEFREQ ON
MINMAXFREQ ON
CUSTOMVARIABLE N
GENOMICCONTROL OFF
OVERLAP OFF
ADDFILTER EAF > 0.01
ADDFILTER INFO > 0.6
MARKERLABEL MARKER
ALLELELABELS EA NEA
EFFECTLABEL BETA
PVALUELABEL P
STDERR SE
FREQLABEL EAF
WEIGHTLABEL N
PROCESSFILE /qcd_cohort_sumstats/AGDS_all.QCed
PROCESSFILE /qcd_cohort_sumstats/ALSPAC_GAD7_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/EstBB_ESTQ2_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/GenScot_GHQ_ANX_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/GLAD_UKB_NBR_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/TEDS_GAD10_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/PsyCoLaus_STAI_STATE_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/MOBA_GAD7_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/MEGA_STAI_TRAIT_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/MVP_GAD2_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/PROTECTAD_140624_GAD7_noX.QCed
PROCESSFILE /qcd_cohort_sumstats/lifelines_GSA_GAD_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/lifelines_CYTO_GAD_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/lifelines_AFFY_GAD_1209_Final.QCed
PROCESSFILE /qcd_cohort_sumstats/TRAILS_allchr.QCed
OUTFILE quantanx_metaanalysis .tbl
ANALYZE HETEROGENEITY
VERBOSE ON
QUIT
' > gadsympt_METAL.txt
module load metal/2020-05-05-gcc-13.2.0
metal gadsympt_METAL.txt
```
## Post-MA QC
Add SNP (rsID) to output by merging with reference panel e.g. HRC, joining on Marker (CHR:POS), and add CHR BP or split Marker for separate columns.
Restrict to SNPs >50% cohorts contributed to and with EAF 0.01 < Freq1 < 0.99.
Select required columns and rename. Order by CHR:POS.
```{bash}
# check where CHR, BP, SNP are in your REF file, and where MARKERID is that you're merging on - here it was $1
awk -F'\t' -v OFS='\t' 'NR==FNR {
CHR[$1] = $18
BP[$1] = $19
SNP[$1] = $17
next
}
FNR==1 {
print $0, "CHR", "BP", "SNP"
next
}
{
print $0, CHR[$1], BP[$1], SNP[$1]
}' \
<(gunzip -c ref_file_with_CHR_BP_SNP) \
<(gunzip -c quantanx_metaanalysis.gz) | gzip \
> quantanx_metaanalysis_rsid.gz
gunzip -c quantanx_metaanalysis_rsid.gz \
| awk '
NR==1 { print; next }
{
chr = $17 #assuming chr is column 17
n_contrib = $14+1 # assuming HetDf (N cohorts - 1) is column 14, add 1 for N contributing cohorts
if ((chr == 23 && n_contrib >= 4) || (chr != 23 && n_contrib >= 8)) { #different thresholds as subset provided x-chr data
print
}
}
' | gzip > quantanx_metaanalysis_filtered.gz
# Select the required cols
zcat quantanx_metaanalysis_filtered.gz | awk 'NR==1 {print "SNP", "MARKERID", "CHR", "BP", "A1", "A2", "FREQ", "BETA", "SE", "P", "N"; next}
{print $19, $1, $17, $18, $2, $3, $4, $8, $9, $10, $16}
' | sort -k3,3n -k4,4n | gzip > pgc-anx2-gadsymptsquant-2026_eur.txt.gz
```
# LDSC SNP h2
```{bash}
/ldsc/munge_sumstats.py \
--chunksize 50000 \
--snp SNP \
--sumstats /metaanalysis_sumstats/quantanx_metaanalysis_filtered.gz \
--out /munged/quantanx_metaanalysis \
--merge-alleles /ldsc/w_hm3.snplist
/ldsc/ldsc.py \
--h2 /munged/quantanx_metaanalysis.sumstats.gz \
--ref-ld-chr /ldsc/eur_w_ld_chr/ \
--w-ld-chr /ldsc/eur_w_ld_chr/ \
--out /snph2/quantanx_metaanalysis_ldsc_h2
```
# Manhattan plot and QQ plot
```{r}
library(data.table)
library(tidyverse)
library(qqman)
palette = c("#3d7667", "#abd4c9")
results <- fread("pgc-anx2-gadsymptsquant-2026_eur.txt.gz", header=T)
bitmap("quantanx_manhattan.png",
type = "png16m", height = 10, width = 30, res = 600)
par(cex.axis = 0.9, cex.lab = 1.3)
manhattan(results,
chr="CHR",
bp="BP",
p="P",
snp="SNP",
ylim = c(0, 16),
col = palette,
cex = 0.5,
suggestiveline = F,
genomewideline = -log10(5e-08))
dev.off()
bitmap("quantanx_qqplot.png", type = "png16m",
height = 10, width = 10, res = 600)
par(mar=c(5,5,1,1))
qq(results$P,
cex.axis = 1.1,
cex.lab = 1.3,
xlim = c(0, 9),
ylim = c(0, 15))
dev.off()
```
# Significant loci
Using the FUMA output (as performed 2 rounds of clumping) from the following parameters:
r2 threshold 0.1
second r2 threshold 0.05
LD blocks on locus 500kb
1000G Phase3 EUR
```{r}
fuma_snps <- fread("../FUMA_job686083/IndSigSNPs.txt")
loci_table <- fuma_snps %>%
select(Locus = GenomicLocus,
SNP = rsID,
CHR = chr,
BP = pos,
p,
NSigSNPs = nGWASSNPs)
sumstats <- fread("pgc-anx2-gadsymptsquant-2026_eur.txt.gz")
sumstats <- sumstats %>%
select(SNP, A1, A2, A1Freq = FREQ, Beta = BETA, SE, N)
loci_table <- loci_table %>%
left_join(., sumstats, by = "SNP") %>%
select(Locus, SNP, CHR, BP, A1, A2, A1Freq, Beta, SE, p, N, NSigSNPs)
all_snps <- fread("../FUMA_job686083/snps.txt")
all_snps_info <- all_snps %>%
select(SNP = rsID, CHR = chr, nearestGene, dist) %>%
left_join(loci_table, ., by = c("SNP", "CHR"))
```
# Novelty and replication
## LDTrait
LDlink results using an r2 of 0.1 and a 500kb basepair window ref panel, saved in a txt file
```{r load ldtrait results}
ldtrait_quantanx <- fread("../ma_results/correction_aug25/ldlink_quantanx_r201_500kb_020925.txt")
```
### Internalising
Identify SNPs identified in previous GWAS of internalising traits
```{r identify replicated snps}
snps_internalising <- ldtrait_quantanx %>% filter(
grepl("anx", `GWAS Trait`, ignore.case = T) |
grepl("depr", `GWAS Trait`, ignore.case = T) &
!grepl("Bipolar disorder vs major depressive disorder \$ordinary least squares \\(OLS\$\\)",
`GWAS Trait`, ignore.case = TRUE) | #ignore this label captured by depr but is a comparison of bipolar + depr
grepl("neuroti", `GWAS Trait`, ignore.case = T) |
grepl("mdd", `GWAS Trait`, ignore.case = T) |
grepl("worry", `GWAS Trait`, ignore.case = T) |
grepl("well-being", `GWAS Trait`, ignore.case = T) |
grepl("satisfaction", `GWAS Trait`, ignore.case = T) |
grepl("feeling", `GWAS Trait`, ignore.case = T) | #captures Feeling miserable, Feeling tense, Feeling nervous, Feeling worry, Feeling fed-up, Feeling guilty - confirmed the few that appear for this latter also have other internalising associations
grepl("Post-traumatic stress", `GWAS Trait`, ignore.case = T) |
`GWAS Trait` == "Positive affect" |
`GWAS Trait` == "Suffering from nerves" |
`GWAS Trait` == "Major mood disorders" | # only one for this and also appeared for other internalising traits e.g. worry, neuroticism, depression (if had only been mood would want to check this wasn't driven by bip/mania)
`GWAS Trait` == "Irritable mood" | # confirmed that all rsIDs for this also appeared for other internalising traits
`GWAS Trait` == "Neurociticism") # capture this trait label which has a typo
nrow(snps_internalising)
# list the snps from our sumstats (query) that had internalising hits
snps_internalising_list <- snps_internalising %>%
distinct(Query) %>%
pull()
# create a binary variable to flag if locus had an internalising hit
loci_table <- loci_table %>%
mutate(
internalising_hit = if_else(SNP %in% snps_internalising_list, 1, 0)
)
```
### Anxiety
```{r}
snps_anx_list <- snps_internalising %>%
filter(
grepl("anx", `GWAS Trait`, ignore.case = T)) %>%
distinct(Query) %>%
pull()
loci_table <- loci_table %>%
mutate(
anx_hit = if_else(SNP %in% snps_anx_list, 1, 0)
)
loci_table_replications <- loci_table %>%
select(Locus, SNP, CHR, BP, internalising_hit, anx_hit)
novel_snps <- setdiff(fuma_snps$rsID, snps_internalising$Query) # Novel = novel for internalising
length(novel_snps)
```
## Bedtools
Use FUMA to clump (same parameters as our sumstats) and bedtools to identify overlap with relevant sumstats not in GWAS Catalog
Li et al. anxiety
Friligkou et al. anxiety
Strom et al. anxiety
Adams et al. MDD
```{bash create BED files for bedtools}
for i in quantanx_GenomicRiskLoci.txt Stromanxiety_GenomicRiskLoci.txt Lianxiety_GenomicRiskLoci.txt Friligkouanxiety_GenomicRiskLoci.txt AdamsMDD23andme_GenomicRiskLoci.txt
do
awk 'NR>1 {print "chr"$4, $7-1, $8, $1}' OFS='\t' "$i" > "${i}.BED"
done
module load bedtools2/2.31.0-gcc-12.3.0-python-3.11.6
for i in Stromanxiety_GenomicRiskLoci.txt.BED Lianxiety_GenomicRiskLoci.txt.BED Friligkouanxiety_GenomicRiskLoci.txt.BED AdamsMDD23andme_GenomicRiskLoci.txt.BED
do
bedtools intersect -a quantanx_GenomicRiskLoci.txt.BED -b "$i" > "intersect_quantanx_${i}.BED"
done
```
Merge individual results in R file
```{r}
library(data.table)
library(tidyverse)
quantanx_loci <- fread("quantanx_GenomicRiskLoci.txt", data.table = F)
strom <- fread("intersect_quantanx_Stromanxiety_GenomicRiskLoci.txt.BED.BED", data.table = F)
li <- fread("intersect_quantanx_Lianxiety_GenomicRiskLoci.txt.BED.BED", data.table = F)
adams <- fread("intersect_quantanx_AdamsMDD23andme_GenomicRiskLoci.txt.BED.BED", data.table = F)
friligkou <- fread("intersect_quantanx_Friligkouanxiety_GenomicRiskLoci.txt.BED.BED", data.table = F)
quantanx_loci <- quantanx_loci %>%
select(GenomicLocus, chr, pos, start, end, rsID, IndSigSNPs, LeadSNPs) %>%
rename(locus = GenomicLocus)
sumstat_names <- c("strom", "li", "friligkou", "adams")
for (name in sumstat_names) {
df <- get(name)
df <- df %>%
rename(chr = V1, start = V2, end = V3, locus = V4) %>%
select(locus) %>%
mutate(!!paste0(name, "_locus") := locus)
quantanx_loci <- left_join(quantanx_loci, df, by = "locus")
}
strom_loci <- fread("Stromanxiety_GenomicRiskLoci.txt", data.table = F)
li_loci <- fread("Lianxiety_GenomicRiskLoci.txt", data.table = F)
friligkou_loci <- fread("Friligkouanxiety_GenomicRiskLoci.txt", data.table = F)
adams_loci <- fread("AdamsMDD23andme_GenomicRiskLoci.txt", data.table = F)
sumstats_list <- list(
strom_rsid = strom_loci,
li_rsid = li_loci,
friligkou_rsid = friligkou_loci,
adams_rsid = adams_loci
)
quantanx_loci <- reduce(names(sumstats_list), function(quantanx_loci, sumstats_name) {
sumstats_df <- sumstats_list[[sumstats_name]]
quantanx_loci %>%
mutate(!!sumstats_name := pmap_chr(list(chr, start, end), ~ {
match_row <- sumstats_df %>%
filter(
chr == ..1 & (
(pos >= ..2 & pos <= ..3) | # SNP within locus
(start <= ..3 & end >= ..2) # reverse-matching where sumstats locus contains the quantanx region including any overlap, not just full containment
)
)
if (nrow(match_row) > 0) match_row$rsID[1] else NA_character_ # takes first match, check for multi matches though for replication flag not critical, or replace match_row$rsID[1] with paste(match_row$rsID, collapse = ";")
}))
},
.init = quantanx_loci)
# Split into 4 pairwise files of our trait rsID and comparison rsID to check R2 in PLINK
rep_cols <- c("strom_rsid", "li_rsid", "friligkou_rsid", "adams_rsid")
snp_pairs_by_study <- quantanx_loci %>%
select(locus, rsID, all_of(rep_cols)) %>%
pivot_longer(cols = all_of(rep_cols), names_to = "study", values_to = "replication_rsid") %>%
separate_rows(replication_rsid, sep = ";", convert = TRUE) %>%
transmute(
study = str_remove(study, "_rsid"),
SNP_A = rsID,
SNP_B = replication_rsid
)
snp_pairs_by_study %>%
group_by(study) %>%
group_split() %>%
walk(~ write_tsv(.x %>% select(SNP_A, SNP_B),
file = paste0("compare_snp_pairs_", unique(.x$study), ".txt"),
col_names = FALSE,
na = ""))
```
```{bash}
for f in compare_snp_pairs_*.txt; do
study=$(basename "$f" .txt | cut -d'_' -f4)
echo "Running LD for $study..."
while read -r a b; do
# skip lines where b is empty (no SNP for comparison study in same locus)
[ -z "$b" ] && continue
plink \
--bfile /users/k1756554/1kg_refpanels/g1000_eur \
--ld $a $b \
--out results_ld/${study}_${a}_${b}
done < "$f"
done
```
```{r novelty flag}
ld_files <- list.files(path = "results_ld", pattern = "\\.log$", full.names = TRUE)
# define function to extract required information from log file
read_ld_log <- function(file){
snps <- str_extract_all(basename(file), "rs[0-9]+")[[1]]
lines <- readLines(file)
r2_line <- lines[grepl("R-sq =", lines)]
if(length(r2_line) == 0) return(NULL)
r2 <- as.numeric(str_match(r2_line, "R-sq = ([0-9\\.eE+-]+)")[,2])
tibble(
SNP_A = snps[1],
SNP_B = snps[2],
R2 = r2
)
}
ld_table <- map_dfr(ld_files, read_ld_log)
ld_table <- ld_table %>%
distinct(SNP_A, SNP_B, .keep_all = TRUE)
replication <- snp_pairs_by_study %>%
left_join(ld_table, by = c("SNP_A", "SNP_B")) %>%
mutate(replication_type = case_when(
SNP_A == SNP_B ~ "Exact replication",
!is.na(R2) & R2 >= 0.1 ~ "LD replication",
TRUE ~ "Novel" #i.e. R2 <0.1 is considered novel
))
```
# MAGMA
## Gene-based association test
```{r}
magma_genes <- fread("/FUMA_job686083/magma.genes.out.txt")
magma_genes_sig <- magma_genes %>%
filter(P < 2.506e-6)
nrow(magma_genes_sig)
magma_genes_sig <- magma_genes_sig %>%
mutate(CHR = as.integer(CHR)) %>%
arrange(CHR, START)
write.table(magma_genes_sig, "magma_genes_sig.txt",
sep = "\t",
row.names = FALSE,
col.names = TRUE,
quote = FALSE)
```
Clump all GWAS variants to p = 1 (i.e. every variant gets put in a clump) with clump-range flag and a gene regions file in the format specified here - https://www.cog-genomics.org/plink/1.9/resources#genelist
Contains one gene per row, with the following four columns:
Chromosome code (example file shows 1, 2 etc. not chr1, chr2)
Start of gene (base-pair units, 1-based)
End of gene (this position is included in the interval)
Gene ID
```{bash}
awk 'NR>1 {print $2, $3, $4, $10}' OFS='\t' magma_genes_sig.txt > magma_genes_sig_plinkformat.txt
plink --bfile /1kg_bed/1KG_Phase3.WG.CLEANED.EUR_MAF001 \
--clump quantanx_metaanalysis_filtered.gz \
--clump-p1 1 \
--clump-p2 1 \
--clump-r2 0.1 \
--clump-kb 500 \
--clump-range magma_genes_sig_plinkformat.txt \
--out quantanx_p1_rangemagma
```
```{r}
gene_loci <- fread("quantanx_p1_rangemagma.clumped.ranges.txt", data.table = FALSE)
# Genes appear in multiple clumps and need to identify where they multiple clumps share any member and merge together into one cluster. Can also look at POS to calculate this.
library(igraph)
gene_loci_sig <- gene_loci %>%
mutate(RANGES = if_else(RANGES == "[]", NA_character_, RANGES)) %>% #PLINK represents no overlapping range as "[]" rather than NA - convert to NA
filter(!is.na(RANGES)) %>%
mutate(RANGES = str_remove_all(RANGES, "[\\[\\]]") %>% str_trim()) %>%
filter(RANGES != "") %>%
separate_rows(RANGES, sep = ",") %>%
mutate(RANGES = str_trim(RANGES)) %>%
filter(RANGES %in% magma_genes_sig$SYMBOL)
gene_edges <- gene_loci_sig %>%
group_by(SNP) %>%
filter(n() > 1) %>% # only clumps with multiple sig genes
summarise(genes = list(RANGES)) %>%
mutate(edges = map(genes, ~ as.data.frame(t(combn(.x, 2))))) %>%
unnest(edges) %>%
rename(from = V1, to = V2)
gene_edges <- gene_edges %>% distinct(from, to)
g <- graph_from_data_frame(gene_edges, directed = FALSE)
# Add isolated nodes (sig genes that never co-occur with another sig gene)
isolated <- setdiff(magma_genes_sig$SYMBOL, V(g)$name)
g <- add_vertices(g, length(isolated), name = isolated)
components <- components(g)
sig_genes_clustered <- tibble(GENE = names(components$membership),
cluster = components$membership) %>%
group_by(cluster) %>%
summarise(genes = list(sort(GENE)), n_genes = n())
sig_genes_clustered <- sig_genes_clustered %>%
mutate(genes_str = map_chr(genes, ~ paste(.x, collapse = ", ")))
sig_genes_clustered
```
## Gene sets and gene tissue
```{r}
gene_sets <- fread("FUMA_job686083/magma.gsa.out.txt")
gene_sets <- gene_sets %>%
filter(P < 0.0001) %>%
arrange(P) %>%
mutate(bonf_sig = if_else(P < 2.94e-06, "Yes", "No"))
gene_tissue_dev <- fread("FUMA_job686083/magma_exp_bs_dev_avg_log2RPKM.gsa.out.txt", skip = 4)
gene_tissue_dev <- gene_tissue_dev %>%
filter(P < 0.05/nrow(gene_tissue_dev)) %>%
arrange(P)
gene_tissue_body <- fread("/FUMA_job686083/magma_exp_gtex_v8_ts_general_avg_log2TPM.gsa.out.txt", skip = 4)
gene_tissue_body <- gene_tissue_body %>%
filter(P < 0.05/nrow(gene_tissue_body)) %>%
arrange(P)
gene_tissue_body
```
# LDSC rgs
Genetic correlations with external traits
```{bash}
output_dir="/rg_ldsc"
# My focal GWAS
focal="/munged/quantanx_metaanalysis.sumstats.gz"
# Sumstats location
dir1="/externalrg/external_sumstats/munged"
dir1_files=( #list trait names, example subset shown here
ADHD06
SCHI07
SUBD01
PPDP01
SMOK07
EXTE01
AGAB02
EXTR02
CONS02
OPEN02
IBOS01
ASTH02
BODY16
BIPO03
AUTI09
ANOR02
EDUC03
INCO01
COAD03
DIAB05
)
# Create a single comma-separated list of all external sumstats starting with focal phenotype
all_sumstats="$focal"
for f in "${dir1_files[@]}"; do
all_sumstats="${all_sumstats},${dir1}/${f}.sumstats.gz"
done
# check with echo "$all_sumstats"
# If sumstats are in more than one location, list as above and add to all_sumstats
# for f in "${dir2_files[@]}"; do
# all_sumstats="${all_sumstats},${dir2}/${f}.sumstats.gz"
# done
python /ldsc/ldsc.py \
--rg "$all_sumstats" \
--ref-ld-chr /ldsc/eur_w_ld_chr/ \
--w-ld-chr /ldsc/eur_w_ld_chr/ \
--out "${output_dir}/quant_anx_rg"
```
Extract results from log file
```{bash}
awk '/^p1/{found=1} found' quant_anx_rg.log | awk '{print $2, $3, $4, $6}' > rg_extracted.txt
```

meta-analysis_secondaryanalyses_quantanx.Rmd at commit dc47e29, under MIT · at the source

Overview

Authors: Megan Skelton¹, Brittany L. Mitchell^2,3,4, Elham Assary¹, Danyang Li¹, Genevieve Morneau-Vaillancourt⁵, Alan E. Murphy^6,7, Abigail R. ter Kuile⁸, Rujia Wang^1,9, Mark J. Adams¹⁰, Enda M. Byrne¹¹, Elizabeth C. Corfield^12,13,14,15, Poppy Z. Grimes¹⁰, Laurie J. Hannigan^12,13,14, Jihua Hu^2,3, Kadri Kõiv¹⁶, Alex S. F. Kwong^10,14, Sergi Papiol^17,18, Johanne H. Pettersen¹², Giorgio Pistis¹⁹, Enrique Castelao¹⁹

and 38 other authors

Nora I. Strom^20,21,22,23, Peter J. van der Most²⁴, Anxiety Disorders Working Group of the Psychiatric Genomics Consortium, GLAD+ authors, Lifelines Cohort Study, NIHR BioResource, PROTECT-AD Consortium, Ole A. Andreassen^25,26, Angelika Erhardt-Lehmann^17,27, Alexandra Havdahl^12,13,14,28, Nathan Skene^6,7, Brad Verhulst²⁹, Heike Weber²⁷, Chérie Armour³⁰, Helga Ask¹², William E. Copeland³¹, Udo Dannlowski^32,33,34,35, Jürgen Deckert^27,36, Katharina Domschke^37,38, Ian B. Hickie³⁹, Kelli Lehto¹⁶, Tina B. Lonsdorf^40,41, Ulrike Lueken^20,27,38, Michelle K. Lupton^2,3,4, Sarah E. Medland^2,42,43, Andrew M. McIntosh¹⁰, Albertine J. Oldehinkel⁴⁴, Martin Preisig¹⁹, Andreas Reif^45,46, Harold Snieder²⁴, James T. R. Walters⁴⁷, Naomi R. Wray^48,49, Catharina A. Hartman⁴⁴, Nicholas G. Martin^2,3, John M. Hettema²⁹, Gerome Breen^1,9, Jonathan R. I. Coleman^1,9, Thalia C. Eley^1,9

49 affiliations

Institutions: King's College London (United Kingdom); QIMR Berghofer Medical Research Institute (Australia); The University of Queensland (Australia); Queensland University of Technology (Australia); Université de Montréal (Canada); Imperial College London (United Kingdom); University College London (United Kingdom); National Institute for Health and Care Research (United Kingdom); University of Edinburgh (United Kingdom); Norwegian Institute of Public Health (Norway); Lovisenberg Diakonale Sykehus (Norway); University of Bristol (United Kingdom); University of Tartu (Estonia); Max Planck Institute of Psychiatry (Germany); Centro de Investigación Biomédica en Red de Salud Mental (Spain); University of Lausanne (Switzerland); Humboldt-Universität zu Berlin (Germany); Institut für Psychiatrische Phänomik und Genomik (Germany); Stockholm Health Care Services (Sweden); Aarhus University (Denmark); University Medical Center Groningen (Netherlands); University of Groningen (Netherlands); University of Oslo (Norway); Oslo University Hospital (Norway); Universitätsklinikum Würzburg (Germany); Texas A&M University (United States); Queen's University Belfast (United Kingdom); University of Vermont (United States); University of Münster (Germany); Bielefeld University (Germany); Deutsches Zentrum für Psychische Gesundheit (Germany); University of Würzburg (Germany); University of Freiburg (Germany); The University of Sydney (Australia); University Medical Center Hamburg-Eppendorf (Germany); University Medical Center of the Johannes Gutenberg University Mainz (Germany); Fraunhofer Institute for Translational Medicine and Pharmacology (Germany); Cardiff University (United Kingdom); University of Oxford (United Kingdom)

Journal: Nature human behaviour, volume 10, issue 8, pages 1594-1608

Dates: received 8 July 2025; accepted 13 April 2026; published online 9 June 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1038/s41562-026-02476-7 · PMID 42265330 · PMCID PMC13485698 · OpenAlex W7164002364

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: genetics / omics (modality), human (organism), other condition (population), clinical / translational (subfield)

Methods: Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing

Keywords: Anxiety, Quantitative trait

MeSH: Anxiety*, Generalized Anxiety Disorder*, Genome-Wide Association Study*, White People*, Black People, Genetic Risk Score, Humans, Polymorphism, Single Nucleotide, Severity of Illness Index, South Asian People (* major topic)

Topic: Genetic Associations and Epidemiology (Genetics, Biochemistry, Genetics and Molecular Biology), according to OpenAlex

Funding: UK Medical Research Council (MR/V012878/1)

Citations: not cited yet (Europe PMC); 95 references in the paper

Abstract

Anxiety is heritable and exists on a continuum, with symptoms ranging from adaptive threat response to clinical disorder. Here we performed a genome-wide association meta-analysis of generalized anxiety symptom severity in 693,869 individuals of European ancestry from 14 cohorts. We identified 80 independent genome-wide significant variants within 74 loci, 39 of which were newly associated with anxiety. SNP-based heritability was 5.9% (posterior s.d. = 0.15%). Polygenic scores were significantly associated with anxiety symptom severity and disorder in European, African and South Asian ancestry samples (R2 = 1.2–2.9%). Significant genetic correlations (rg) were estimated with mental and physical health traits, including case–control anxiety, neuroticism and depression (rg = 0.71–0.85), irritable bowel syndrome (rg = 0.57), coronary artery disease, endometriosis and migraine (rg = 0.20–0.27). Gene-based and pathway analyses implicated synaptic and axonal processes, with enriched expression in the brain. These findings highlight the discovery power gained from analysing a quantitative trait rather than a case–control phenotype in anxiety genetics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

megskelton/gad-sympt-metagwas

License: MIT

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: dc47e296441f46bb1ce1882d5d48a9913d3c5d54, 2 September 2026

Languages: R (5)

Size: 7 files, 5 scripts

Software Heritage: not archived

Found in: “Code availability”

Holds: README, license file, 5 notebooks

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: tidyverse (4 files), data.table (3 files), BCFtools (1 file), BEDTools (1 file), igraph (1 file), lavaan (1 file), psych (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

7 files

extract_anx_factor_score_quantanx.Rmd, R, 85 lines, 1 match
gwas_regenie_quantanx.Rmd, R, 192 lines
meta-analysis_secondaryanalyses_quantanx.Rmd, R, 588 lines, 3 matches
mtcojo_quantanx.Rmd, R, 134 lines
sbayes_prs_quantanx.Rmd, R, 199 lines, 2 matches
LICENSE, License, 21 lines
README.md, Text, 10 lines

At the source: github.com/megskelton/gad-sympt-metagwas

Code availability

Analytical code is available via GitHub at: https://github.com/megskelton/gad-sympt-metagwas.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
5 scripts, each with its path and the digest of its content;
6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

Summary statistics are available on the PGC download page (https://pgc.unc.edu/for-researchers/download-result). Individual study data can be accessed following review and approval by the individual study cohorts; see https://pgc.unc.edu/for-researchers/individual-level-data-access/ for more information. Summary statistics for the genetic correlations are available following the procedure detailed within the relevant publications (searchable using the PMID in Supplementary Table 9) or via GWAS Catalog (https://www.ebi.ac.uk/gwas/home).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 58 authors, 2 keywords, 10 MeSH terms, 1 funder, 95 references.

Cite

This paper

Skelton, M., Mitchell, B. L., Assary, E., Li, D., Morneau-Vaillancourt, G., Murphy, A. E., ter Kuile, A. R., Wang, R., Adams, M. J., Byrne, E. M., Corfield, E. C., Grimes, P. Z., Hannigan, L. J., Hu, J., Kõiv, K., Kwong, A. S. F., Papiol, S., Pettersen, J. H., Pistis, G., . . . Eley, T. C. (2026). Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry. Nature human behaviour, 10(8), 1594-1608. https://doi.org/10.1038/s41562-026-02476-7

BibTeX

@article{skelton2026genome, author = {Skelton, Megan and Mitchell, Brittany L. and Assary, Elham and Li, Danyang and Morneau-Vaillancourt, Genevieve and Murphy, Alan E. and ter Kuile, Abigail R. and Wang, Rujia and Adams, Mark J. and Byrne, Enda M. and Corfield, Elizabeth C. and Grimes, Poppy Z. and Hannigan, Laurie J. and Hu, Jihua and Kõiv, Kadri and Kwong, Alex S. F. and Papiol, Sergi and Pettersen, Johanne H. and Pistis, Giorgio and Castelao, Enrique and Strom, Nora I. and van der Most, Peter J. and {Anxiety Disorders Working Group of the Psychiatric Genomics Consortium} and {GLAD+ authors} and {Lifelines Cohort Study} and {NIHR BioResource} and {PROTECT-AD Consortium} and Andreassen, Ole A. and Erhardt-Lehmann, Angelika and Havdahl, Alexandra and Skene, Nathan and Verhulst, Brad and Weber, Heike and Armour, Chérie and Ask, Helga and Copeland, William E. and Dannlowski, Udo and Deckert, Jürgen and Domschke, Katharina and Hickie, Ian B. and Lehto, Kelli and Lonsdorf, Tina B. and Lueken, Ulrike and Lupton, Michelle K. and Medland, Sarah E. and McIntosh, Andrew M. and Oldehinkel, Albertine J. and Preisig, Martin and Reif, Andreas and Snieder, Harold and Walters, James T. R. and Wray, Naomi R. and Hartman, Catharina A. and Martin, Nicholas G. and Hettema, John M. and Breen, Gerome and Coleman, Jonathan R. I. and Eley, Thalia C.}, title = {{Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry}}, journal = {Nature human behaviour}, year = {2026}, month = jun, volume = {10}, number = {8}, pages = {1594--1608}, publisher = {Nature Portfolio}, issn = {2397-3374}, doi = {10.1038/s41562-026-02476-7}, url = {https://doi.org/10.1038/s41562-026-02476-7}, pmid = {42265330}, pmcid = {PMC13485698} }

RIS

TY - JOUR AU - Skelton, Megan AU - Mitchell, Brittany L. AU - Assary, Elham AU - Li, Danyang AU - Morneau-Vaillancourt, Genevieve AU - Murphy, Alan E. AU - ter Kuile, Abigail R. AU - Wang, Rujia AU - Adams, Mark J. AU - Byrne, Enda M. AU - Corfield, Elizabeth C. AU - Grimes, Poppy Z. AU - Hannigan, Laurie J. AU - Hu, Jihua AU - Kõiv, Kadri AU - Kwong, Alex S. F. AU - Papiol, Sergi AU - Pettersen, Johanne H. AU - Pistis, Giorgio AU - Castelao, Enrique AU - Strom, Nora I. AU - van der Most, Peter J. AU - Anxiety Disorders Working Group of the Psychiatric Genomics Consortium AU - GLAD+ authors AU - Lifelines Cohort Study AU - NIHR BioResource AU - PROTECT-AD Consortium AU - Andreassen, Ole A. AU - Erhardt-Lehmann, Angelika AU - Havdahl, Alexandra AU - Skene, Nathan AU - Verhulst, Brad AU - Weber, Heike AU - Armour, Chérie AU - Ask, Helga AU - Copeland, William E. AU - Dannlowski, Udo AU - Deckert, Jürgen AU - Domschke, Katharina AU - Hickie, Ian B. AU - Lehto, Kelli AU - Lonsdorf, Tina B. AU - Lueken, Ulrike AU - Lupton, Michelle K. AU - Medland, Sarah E. AU - McIntosh, Andrew M. AU - Oldehinkel, Albertine J. AU - Preisig, Martin AU - Reif, Andreas AU - Snieder, Harold AU - Walters, James T. R. AU - Wray, Naomi R. AU - Hartman, Catharina A. AU - Martin, Nicholas G. AU - Hettema, John M. AU - Breen, Gerome AU - Coleman, Jonathan R. I. AU - Eley, Thalia C. TI - Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry T2 - Nature human behaviour J2 - Nat Hum Behav PY - 2026 DA - 2026/06/09 VL - 10 IS - 8 SP - 1594 EP - 1608 SN - 2397-3374 PB - Nature Portfolio DO - 10.1038/s41562-026-02476-7 UR - https://doi.org/10.1038/s41562-026-02476-7 LA - en ER -

CSL-JSON

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Genome-wide meta-analysis of quantitatively measured generalized anxiety symptoms in individuals of European ancestry.

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