Hypometric genetics: Improved power in genetic discovery by incorporating quality control flags

Area of Science:

• Human Genetics
• Metabolomics
• Bioinformatics

Background:

• Balancing data quantity and quality is crucial in omics studies.
• Measurements below the limit of quantification (BLQ) are often underutilized in human genetics.
• Extreme phenotype sampling aids in mapping rare variant effects.

Purpose of the Study:

• To introduce and validate "hypometric genetics" (hMG) analysis.
• To investigate the genetic basis of BLQ flags in metabolomic data.
• To demonstrate the utility of hMG for genetic discovery, especially in extreme phenotypes.

Main Methods:

• Applied hMG analysis to 227,469 UK Biobank individuals with metabolomic profiles.
• Assessed heritability of BLQ flags.
• Utilized polygenic scores for common variants and analyzed rare coding variants.

Main Results:

• Identified over 5% heritability for BLQ flags, with significant genetic associations.
• Polygenic scores for BLQ flags accurately predicted quantitative traits (91% accuracy).
• Found higher enrichment of metabolite-lowering associations for BLQ flags with rare variants.
• Joint analysis of BLQ flags and quantitative data improved gene prioritization by 181%.

Conclusions:

• BLQ flags possess a genetic basis, offering a novel signal for genetic discovery.
• Integrating quality control flags with quantitative measurements enhances omics analysis power.
• hMG analysis is broadly applicable and particularly beneficial for extreme phenotype studies.