Meta-analysis of genome-wide association data from UK Biobank and FinnGen highlights risk loci for pregnancy complications

Citation

When using data or code from this repository, please cite:

@Article{genes13122255,
AUTHOR = {Changalidis, Anton I. and Maksiutenko, Evgeniia M. and Barbitoff, Yury A. and Tkachenko, Alexander A. and Vashukova, Elena S. and Pachuliia, Olga V. and Nasykhova, Yulia A. and Glotov, Andrey S.},
TITLE = {Aggregation of Genome-Wide Association Data from FinnGen and UK Biobank Replicates Multiple Risk Loci for Pregnancy Complications},
JOURNAL = {Genes},
VOLUME = {13},
YEAR = {2022},
NUMBER = {12},
ARTICLE-NUMBER = {2255},
URL = {https://www.mdpi.com/2073-4425/13/12/2255},
PubMedID = {36553520},
ISSN = {2073-4425},
ABSTRACT = {Complications endangering mother or fetus affect around one in seven pregnant women. Investigation of the genetic susceptibility to such diseases is of high importance for better understanding of the disease biology as well as for prediction of individual risk. In this study, we collected and analyzed GWAS summary statistics from the FinnGen cohort and UK Biobank for 24 pregnancy complications. In FinnGen, we identified 11 loci associated with pregnancy hypertension, excessive vomiting, and gestational diabetes. When UK Biobank and FinnGen data were combined, we discovered six loci reaching genome-wide significance in the meta-analysis. These include rs35954793 in FGF5 (p=6.1×10?9), rs10882398 in PLCE1 (p=8.9×10?9), and rs167479 in RGL3 (p=5.2×10?9) for pregnancy hypertension, rs10830963 in MTNR1B (p=4.5×10?41) and rs36090025 in TCF7L2 (p=3.4×10?15) for gestational diabetes, and rs2963457 in the EBF1 locus (p=6.5×10?9) for preterm birth. In addition to the identified genome-wide associations, we also replicated 14 out of 40 previously reported GWAS markers for pregnancy complications, including four more preeclampsia-related variants. Finally, annotation of the GWAS results identified a causal relationship between gene expression in the cervix and gestational hypertension, as well as both known and previously uncharacterized genetic correlations between pregnancy complications and other traits. These results suggest new prospects for research into the etiology and pathogenesis of pregnancy complications, as well as early risk prediction for these disorders.},
DOI = {10.3390/genes13122255}
}

Code

Launch meta-analysis

To tun meta-analysis using METAL: ./1.1_parallel.sh data/file_mapping.csv analysis_n And then using MTAG: ./1.1_parallel_mtag.sh data/file_mapping.csv analysis_n (should be launched in the specific MATG's environment)

(Also, to postprocess all files, you can use: ./1.3_process_mtag_output.py)

For this script you also need tsv-mapping file with 4 columns:

• name_of_trait
• path_to_ukb_file
• path_to_finngen_file
• n_samples_in_finngen_file (from manifest)

To draw Manhattan and Q-Q plots for all FinnGen traits: 1.2_parallel_draw_fg.sh

Auxilary scripts:

• 1.0.0_pipe_go_R6.py - script for meta-analysis using metal tool for specific trait
• 1.0.0_pipe_go_R6_mtag.py - the same analysis, but with MTAG tool (this script uses output of previous one).
• 1.0.1_DRAW.R - script for drawing sketches of Manhattan and QQ plots

Genetic correlations

1. Run the same meta-analysis on lots of pairs of traits.
2. The considered traits should be located in one directory (PREG_DATA in our case) and other in the directories, starting with analysis_n_*.

In case you re-launch calculations of correlations, first, clean-up old correlations:

find . -type f -name '*.cors' -delete
find . -type f -name '*.cors.full' -delete
find . -type f -name '*.labels' -delete
find . -type f -name '*.overlap' -delete
find . -type f -name '*.progress' -delete

Launch in parallel for lots of pairs of traits: 2.1.0_parallel_prepare_traits_for_ldak.sh (here 2.0.0_prepare_traits_for_ldak.py is aucilary script).

Launch for considered traits in PREG_DATA directory: ./2.1.1_prepare_pregnancy_for_ldak.py

3. Launch ldak itself:

./2.2_parallel_launch_ldak.sh

4. Assemble all correlations:

TEMP_T=("GEST_DIABETES1" "I9_HYPTENSPREG1" "O15_PRETERM1")

for t in "${TEMP_T[@]}" ; do for i in analysis_n* ; do ls ${i}/data/${t}*.cors 2> /dev/null ; cat ${i}/data/${t}*.cors 2> /dev/null | grep Cor_All | awk '{  if ($2 > 2.57*$3)  print }' | grep -v nan ; done | grep -B 1 Cor_All > data/cor_${t}.txt ; done

for t in "${TEMP_T[@]}" ; do for i in analysis_n* ; do ls ${i}/data/${t}*.cors 2> /dev/null ; cat ${i}/data/${t}*.cors 2> /dev/null | grep Cor_All | awk '{ print }' | grep -v nan ; done | grep -B 1 Cor_All > data/cor_full_${t}.txt ; done

As a result we will have cor_full_*.txt with all not-na genetic correlations for specific trait and cor_*.txt files with filtered by significance genetic correlations.

5. Draw genetic correlation plot:

• Launch 2.3.1_make_table_for_r.ipynb and 2.3.1_make_table_for_r_FG.ipynb to prepare tables for meta-analysis GWAS / FG GWAS respectively
• Launch 2.3.2_draw_gen_cor.R - to draw the forrest plots and heatmaps of genetic correlations.

Select and annotate top snps

3.1_making_top_snp_table_FG.ipynb and 3.2_making_top_snp_table_META.ipynb -- selecting and annotation of top SNPs for FinnGen-only and meta-analysis respectively.

Final Manhattan and Q-Q plots

4_final_mh_qq.R - for drawing final versions of Q-Q and Manhattan plots.

Images

All images are located in img directory.

• img/*_gen_cor.pdf and img/*_gen_cor_heatmap.pdf - forrest plots and heatmaps (respectively) with genetic correlations:
  • meta_* - for meta-analysis GWAS;
  • fg_supp_ - for FG GWAS and supported by researches traits;
  • fg_not_supp_ - for FG GWAS and not supported by researches traits.
• img/QQplot.pval__*.pdf - Q-Q plots of significant traits:
  • img/QQplot.pval__FG_*.pdf - for FinnGen data only;
  • img/QQplot.pval__MET_*.pdf - for meta-analysis data only.
• img/Rectangular-Manhattan..pval__*.pdf - Manhattan plots of significant traits:
  • img/Rectangular-Manhattan..pval__FG_*.pdf - for FinnGen data only;
  • img/Rectangular-Manhattan..pval__MET_*.pdf - for meta-analysis data only.

Data

All data is located in data directory:

• Selected summary statistics:
  • data/f_special/ - directory with filtered FinnGen GWAS summary statistics (only selected as significant).
  • data/f_special/ - directory with meta-analysis summary statistics (only selected as significant).
• Genetic correlations:
  • data/cor_full_<trait>.txt - file with all genetic correlations for selected traits
  • data/cor_<trait>.txt - file with significant genetic correlations for selected traits
  • data/meta_feature.csv - annotated table with significant genetic correlations for meta-analysis
  • data/fg_feature.csv - annotated table with significant genetic correlations for FG GWAS:
    • fg_feature_supp.csv - selected only supported by researches traits
    • fg_feature_not_supp.csv - selected only not supported by researches traits
• Annotated SNPs:
  • data/finn_top.csv - significant annotated summstats from FinnGen GWAS.
  • data/finn_top_short.csv - significant and filtered (selected 1 per loci) annotated summstats from FinnGen GWAS.
  • data/meta_top.csv- significant annotated summstats from meta-analysis.
  • data/meta_top_short.csv - significant and filtered (selected 1 per loci) annotated summstats from meta-analysis.
• Other:
  • data/file_mapping - mapping of selected 24 traits files and N_samples for finngen
  • All summary statistics can be found here:
    • maf_fg_*.tsv - FinnGen summary statistics filtered by MAF.
    • extended_*.TBL - summary statistics from meta-analysis by METAL.