A Data Harmonization Pipeline To Leverage External Controls AND BOOST POWER IN GWAS

We propose a unified pipeline to harmonize control samples from different cohort studies that may have been genotyped using multiple different array platforms. The pipeline contains four modules: (i) Stratification Within-array processing, (ii) Imputation, (iii) Cross-array comparison, and (iv) Re-imputation.

Module 1: Stratification.

• Cohort-level QC: perform cohort-level quality control cohort_qc.py
• Ancestry matching: infer the ancestry of each sample match_ancestry.py
• Merging: merge samples sharing the same genotyping array and ancestry group merge_cohorts.py
• Array-level QC: perform array-level quality control array_qc.py

Module 2: Imputation.

We use the Michigan Imputation server, with 1000 Genomes data as the reference panel. It requires vcf-formatted input, which could be obtained with the script convert_to_vcf.py .

Module 3: Cross-array comparison.

• Merging: convert and merge imputed VCFs from Michigan imputation server to MatrixTables merge_vcfs_and_convert_to_mt.py
• Post-imputation QC.: removes variants with low minor allele frequencies, or small Hardy-Weinberg Equilibrium p-values, or low imputation info scores postimp_qc.py
• Cross-array pseudo GWAS: perform cross-array type pseudo-GWAS cross_array_comparison.py

Module 4: Re-imputation.

• Generating Blacklist of SNPs: generate a blacklist of SNPs based on ER2 filter get_blacklist.py
• Re-run analysis. remove the blacklist of SNPs from the data set produced by module 1, and then re-run module 2, 3, 4.

Blacklist

• We applied this pipeline to aggregate 27,517 European samples from 16 collections within dbGaP. Here we provide the list of problematic SNPs identified and removed in different steps of the pipeline.

• The blacklist of SNPs based on ER2 filter for array types HumanHap300, HumanHap550, HumanHap610, HumanHap660, Affymetrix 6.0 and Axiom_KP_UCSF_EUR

• The blacklist of SNPs that exhibit significant p-values in cross-array-type comparison: cross-comparison blacklist

Reference:

Chen, Tashman, Palmer, Neale, Roeder, Bloemendal, Churchhouse and Ke (2020). A data harmonization pipeline to leverage external controls and boost power in GWAS.