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DOI

Developing brain mega xQTL

Data sharing

1: Munging data

2: Genotype

2-1: Pre-imputation

  • Run plinkQC on data as a sanity check
  • First apply PLINK filters, then split by chromosome and sort
    • Walker data is already filtered; split by chromosome and impute
    • For all the other datasets, we applied the same filters that the Walker data used --hwe 1e-6 --maf 0.01 --mind 0.10 --geno 0.05
    • Note: for HDBR, we used --mind 0.3; for LIBD, we fixed strand flips by running an extra step of conform-gt, which automatically splits the data by chromosome

2-2: Post-imputation

  • Scripts in prelim/: inputs are imputed genotype files downloaded from Michigan Imputation Server; concatenate by chromosomes, index, filter by R2, and take the intersection of high impute quality variants across datasets
    • Note: except for Walker data, we applied R2>.3 filter during imputation; so here we only applied R2>.3 on Walker imputed data and intersected with the other datasets
  • ancestry.ipynb: infer data ancestry, make plots
  • IBD.ipynb: relatedness check
  • Snakefile

3: RNA-seq

# In picard/
# -d -dd 1: to keep identical sample ID from different folders
python3 -m multiqc -d -dd 1 Walker/ Obrien Werling_final/ hdbr libd -o all_multiqc
txi <- tximport(files, type="salmon", tx2gene=tx2gene, dropInfReps=TRUE, countsFromAbundance="lengthScaledTPM")
write.table(txi$counts,file="gene.noVersion.scaled.counts.tsv",quote=FALSE, sep='\t')
write.table(txi$abundance,file="gene.noVersion.TPM.tsv",quote=FALSE, sep='\t')

txi.tx <- tximport(files, type="salmon", txOut=TRUE, dropInfReps=TRUE, countsFromAbundance="lengthScaledTPM")
write.table(txi.tx$counts,file="tx.counts.scaled.tsv",quote=FALSE, sep='\t')
write.table(txi.tx$abundance,file="tx.TPM.tsv",quote=FALSE, sep='\t')
  • Sample swap check:
    • VerifyBamID (slow. Use --smID to add subject ID to BAM sequence file)
    • check.ipynb: called SNP from BAM, merged with imputed genotype (Mike)

4: xQTL

4-1: cis-eQTL

  • ancestry.ipynb
  • combat-seq.ipynb
  • decon.ipynb: cell type specific and interacting analysis
  • eqtl_analysis.ipynb: identify optimal #HCP in covariates, gene expression PCA, dTSS, etc.
  • fetal_adult.ipynb
  • func_enrich.ipynb: functional enrichment analysis of QTL
  • metadata.ipynb: plot data age, sex, infer NA sex, etc.
  • module_eigengene.ipynb
  • paintor.ipynb: PAINTOR multi-ethnic fine-mapping
  • pLI.ipynb
  • sex_specific.ipynb
  • susie.ipynb: susie finemapping results
  • tri_egene_biotype.ipynb
  • tri_h2_supp.ipynb
  • tri_specific.ipynb
  • walker_fetal.ipynb
  • Snakefile
  • decon.smk
  • paintor.smk

4-2: cis-isoQTL

  • isoqtl_analysis.ipynb
  • prep.ipynb: sex and trimester specific QTL
  • Snakefile: follows a similar pipeline as cis-eQTL, except that run grouped permutation as GTEx did

4-3: cis-sQTL

  • sqtl_analysis.ipynb
  • e_iso_s.ipynb
  • qvalue_pi0.ipynb
  • check.ipynb: check chunk size
  • Snakefile

4-4: trans

  • gbat.ipynb
  • Snakefile

4-5: APEX

  • apex_analysis.ipynb
  • Snakefile

5: Integrative analysis

See coloc_ecaviar_May_2024/ for colocalization analysis

5-1: sLDSC

  • ldsc_analysis.ipynb
  • Snakefile
  • pec.smk

5-2: TWAS-FUSION

  • TWAS.ipynb
  • LDREF.ipynb
  • run_focus.sh
  • Snakefile

5-3: MESC

  • MESC.ipynb
  • Snakefile
  • test.smk

5-4: Colocalization (eCAVIAR)

  • eCAVIAR.ipynb
  • GRIN2A.ipynb
  • SP4_gviz.ipynb
  • sqtlviztools.ipynb
  • Visualizing_Loci_working.ipynb
  • celltype.smk
  • eqtl.smk
  • isoqtl.smk
  • mod_ieqtl.smk
  • sex_tri.smk
  • sqtl.smk
  • sashimi plot related code

6: Further Analyses

6-1: eGene/sGene Enrichment

  • fetal_only_egenes.ipynb: biotype and cell type analysis for fetal-specific eGenes
  • trimester_egenes_sgenes.ipynb: biotype and cell type analysis for trimester-specific e/sGenes

6-2: WGCNA

  • compare_module_enrichment.ipynb: compare enrichment across networks and across correlated cell types
  • dashboard_generator.ipynb: generate dashboards using ST6.xlsx
  • dashboards: folder containing dashboards for each module