Biostatistical Aspects of Whole Genome Sequencing Studies: Preprocessing and Quality Control

Area of Science:

• Genomics and Bioinformatics
• High-Throughput Sequencing Data Analysis

Background:

• Large-scale whole genome sequencing (WGS) studies are increasingly common due to advances in DNA sequencing technology.
• Preprocessing and quality control (QC) are critical steps before association analysis between phenotypes and genotypes.
• Many biostatisticians are new to handling WGS data, necessitating clear guidelines and methodologies.

Purpose of the Study:

• To provide a comprehensive overview of preprocessing and QC for large-scale WGS studies.
• To detail essential QC metrics applicable at various stages of WGS data analysis.
• To illustrate QC procedures using real-world data from a large human WGS study.

Main Methods:

• Description of Illumina's short-read sequencing technology and a general WGS preprocessing pipeline.
• Overview of key QC metrics: raw data, post-mapping/alignment, post-variant calling, and multisample variant calling.
• Empirical data on raw data compression using DRAGEN Original Read Archive (ORA) and QC validation with GENEtic SequencIng Study Hamburg-Davos (GENESIS-HD) data.

Main Results:

• Key QC metrics identified include genetic similarity, sample cross-contamination, Het/Hom ratio deviations, relatedness, and coverage.
• The DRAGEN ORA achieved a compression ratio of 5.6:1 for raw WGS files, with compression time linearly related to genome coverage.
• Demonstrated feasibility of preprocessing, joint calling, and QC for over 9000 human genomes within a reasonable timeframe.

Conclusions:

• Preprocessing, joint calling, and QC of large-scale WGS studies are achievable and efficient.
• Established QC procedures are readily available and effective for ensuring the quality of WGS data.
• The findings support the reliable application of WGS data in large genetic studies.