CKDGen Chr X Analyses

Analyses of X-chromosomal SNPs and kidney traits

Last Updated: 29/09/2023

Supporting code for the following draft:

• Working title: X-chromosome and kidney function: Evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen-response elements
• Short title: CKDGen ChrX
• Analysis Team / Repository Contributor: Markus Scholz, Katrin Horn, Andreas Kühnapfel and Janne Pott
• Writing Team: Markus Scholz, Katrin Horn, Afshin Parsa, Anna Köttgen, Pascal Schlosser, Cristian Pattaro

We performed a trans-ethnic X-chromosome-wide association study of 7 kidney traits (estimated glomerular filtration rate (eGFR), serum uric acid (UA), urine albumin-creatinine ratio (UACR), blood urea nitrogen (BUN), chronic kidney disease (CKD), gout and microalbuminuria (MA)). Sex-stratified and combined analyses were performed in up to 40 studies including up to 908,697 subjects considering up to 1,032,701 SNPs. Genome-wide significant loci were tested for sex-interactions and were compared between traits. A number of secondary analyses were performed to allocate candidate genes to the discovered loci.

For more information, please contact Markus Scholz (markus.scholz@imise.uni-leipzig.de)

Source File

If you want to reproduce our results, you will need to customize a source file, indicating

• path to R library (please use R Version 4.x, all necessary packages are listed in the source file)
• path to data (summary statistics will be available on zenodo)
• path to PLINK2
• path to GCTA
• path to GTEx v8 data
• path to NEPTUNE
• path to HUNT data
• path to UKBB data

Statistical Analyses included

1. Locus definition: index SNP +/- 500 kb; merging of overlapping loci (union of loci)
2. Interaction analysis: difference test of the stratified associations, correcting for beta-beta correlation between males and females
3. Co-localization analyses of male and female signals: test of the stratfied associations for shared causal signal
4. Relevance to kidney function and CKD: Comparison of eGFR and UA index SNP effects with those in CKD, BUN, and gout (one-sided test)
5. Identification of independent variants: GCTA - conditional joint (COJO) analyses
   • Reference data set: UKBB (best-guessed genotypes)
   • COJO select: step-wise forward selection to indentify independent variants
   • COJO conditional: Estimation of association statistics conditional to previously selected variants (in case of multiple independent variants at a locus)
   • LD estimation: LD between independent variants per locus using PLINK2
6. Credible set analyses: Calculation of Approximate Bayes Factors using (conditional) effect estimates and standard errors
7. Co-localization analyses with eQTLs: test of the associations for shared causal signal with gene-expression of nearby genes using GTEx v8 and NEPTUNE data
   • Extraction of eQTL data of nearby genes in 51 tissues
   • Colocalization analyses per phenotype, tissue and gene
   • Checks and plots
8. Analysis of overlap of eGFR and UA signals:
   • LD estimation: LD between eGFR and UA index SNPs using PLINK2
   • Co-localization analyses of eGFR and UA: test of the associations of eGFR and UA for shared causal signal
9. Replication analysis: replicate findings of eGFR in HUNT study (one-sided)
10. X-chromosomal heritability: GCTA - restricted maximum likelihood (REML) analyses
    • Reference data set: UKBB (genotyped SNPs)
    • REML: estimation of heritability for eGFR and UA
    • REML bivariate: estimation of genetic correlation between eGFR and UA
11. Lookup of reported variants: replicate findings of Graham et al, Kanai et al and Sakaue et al (successful if nominal significance and concordant effect is observed)
12. Trans-ethnic meta-regression analysis: accounting for mixed ethnicities by using MR-Mega
    • Get an overview of MR-Mega results and check results for MetaGWAS loci
    • Generate Forest plots for MR-Mega hits

Statistical Analyses not included

1. Study quality control and harmonization: pipeline from GenStatLeipzig, not yet available on GitHub
2. Meta-analyses: pipeline from GenStatLeipzig, not yet available on GitHub
3. Bioinformatic annotation: pipeline from GenStatLeipzig, not yet available on GitHub
4. Lookup of reported variants: manual evaluation of GWAS catalog data of recent publications from Graham et al, Kanai et al and Sakaue et al
5. Trans-ethnic meta-regression analysis: done by Alexander Teumers lab (Greifswald, Germany), not yet shared
6. Assignment of candidate genes: manual evaluation of all previous applied methods, no code available

Figures

Main Figures

1. Miami-Plot of variants associated with eGFR and UA:
   • color coding: grey, overall; blue, males; red, females; black, not genome-wide significant
   • novelty coding: candidate gene in box novel, candidate gene without box kown
   • sex interaction coding: bold italic gene names indicate loci with sex interactions (--> see script F1_MiamiPlot.R)
2. Regional Association Plot of locus 7 at Xq21.1:
   • panel with all, male, female
3. Beta-Beta-Plot of SNP-by-Sex interaction analysis:
   • panel with results for eGFR and UA (--> see script 02_sex_ia.R)
4. Heatmap of cross-phenotype comparison of eGFR and UA loci:
   • six phenotypes: eGFR, UA, BUN, CKD, UACR and MA
   • 23 SNPs: index SNP per locus + female-specific index SNP of region 7 (--> see script F2_Heatmap_phenotypes.R)
5. Results of eQTL-colocalization analysis:
   • six phenotypes: eGFR ALL, MALE, FEMALE and UA ALL, MALE, FEMALE
   • eight genes: CDKL5, CDK16, USP11, ARMCX2, TCEAL3, MORF4L2, ACSL4, SLC25A5 (--> see scripts starting with 07_)

Supplemental Figures

1. RA-Plots of all loci for ALL, MALE, FEMALE
2. Bar-Plot of X-chromosomal heritability of eGFR and UA (--> see script 10_b_Heritability.R)
3. Beta-Beta-Plot of validation of eGFR hits in HUNT study (--> see script 09_replication_HUNT.R)
4. Forest Plots for SNP rs4328011 (--> see script 12_b_MR-Mega_make_forest_plots.R)
5. Forest Plots of MR-MEGA findings (--> see script 12_b_MR-Mega_make_forest_plots.R)
6. Credible sets and missense mutations (--> see script F4_MainFigure4_PostProbByCredSetSize.R)
7. Study design (not done with a script)
8. Beta-Beta-Plot of index SNPs comparing trans-ethnic metaGWAS and Europeans only metaGWAS
9. QQ-Plots of phenotypes and subgroups

Tables

Main Tables

1. Results of the transethnic X chromosome-wide association analysis of eGFR and UA (--> see script T1_MainTable1.R)
2. Analysis of the overlapping of eGFR and UA loci (--> see script T2_MainTable2.R)

Supplemental Tables

--> see script ST_SupplementTables.R

1. Description of participating studies: study design and phenotype distribution (as received from participating studies)
2. Genotyping and imputation information of participating studies (as received from participating studies)
3. Number of data sets, samples and SNPs contributing to the different association analyses (--> see script ST_SupplementTables.R)
4. Comparisons between sexes, interaction and co-localization (--> see scripts 02_sex_ia.R and 03_coloc_sex_ia.R)
5. Cross-phenotype comparision (--> see script 04_lookup_TopHits_otherTraits.R)
6. Independent variants per locus and analysis group (--> see script 05_b_Cojo_Select.R and 05_c_Cojo_Conditional.R)
7. Annotation of 99% credible sets (done with pipeline from GenStatLeipzig)
8. Co-localization of genetic association signals and eQTLs (--> see scripts starting with 07_)
9. Validation of eGFR associations in HUNT study (--> see script 09_replication_HUNT.R)
10. Look-up of SNPs previously reported for UA, eGFR, creatinine and BUN (reported variants of three GWAS of kidney traits: Graham et al, Kanai et al and Sakaue et al) (--> see script 11_Lookup_Candidates_SNPs.R)
11. Results of meta-regression analysis of the 23 index SNPs (--> see scripts 12_a_MR-Mega_results_check.R)
12. Additional genome-wide significant associations due to meta-regression (--> see script 12_a_MR-Meta_results_check.R)
13. Look-up of sex-biased gene-expressions of candidate genes assigned to genetic sex-interactions (--> see script 13_Lookup_sexbiasedGE_eQTLs_PAR_ARE.R)
14. Co-localization of genetic association signals of CKD traits and testosterone (--> see script 07_d_coloc_testo.R)
15. Look-up of eQTLs in case of positive co-localization (--> see script 13_Lookup_eQTLs_leadSNPs.R)