GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies

Area of Science:

• Genetics
• Bioinformatics
• Computational Biology

Background:

• Data integrity is critical for cross-dataset genetic studies, including genome-wide association studies (GWAS), meta-analyses, and polygenic risk score development.
• Manual harmonization of variant indexing and allele assignments is labor-intensive, time-consuming, and prone to errors, requiring significant computational expertise.

Purpose of the Study:

• To introduce GRIEVOUS, a novel command-line tool designed for automated cross-dataset variant homogenization.
• To streamline the process of aligning genetic variants and allele assignments across diverse datasets.

Main Methods:

• GRIEVOUS utilizes an internal database and a custom indexing methodology to identify, format, and align biallelic single nucleotide polymorphisms (SNPs).
• The tool processes summary statistic and genotype files to ensure consistency in variant representation.

Main Results:

• GRIEVOUS successfully harmonizes variant indexing and allele assignments across multiple genetic datasets.
• The tool can extract the maximal set of common biallelic SNPs post-harmonization.

Conclusions:

• GRIEVOUS offers an efficient and reliable solution for cross-dataset variant homogenization, crucial for robust genetic research.
• The tool enhances data integrity and facilitates large-scale genetic analyses by simplifying variant alignment.