Optimal sequencing strategies for identifying disease-associated singletons

Area of Science:

• Genetics
• Genomic sequencing
• Statistical genetics

Background:

• Rare variants are increasingly important in genetic association studies.
• Sequencing strategies must balance variant detection with sample size and cost-effectiveness.
• Singleton variants, found in only one individual, are challenging to detect at low sequencing depths.

Purpose of the Study:

• To investigate the impact of sequencing depth on the detection and association of rare variants, specifically singletons.
• To determine the optimal sequencing coverage for maximizing the power of rare variant association studies.

Main Methods:

• Sensitivity analysis of singleton variant detection using simulated and down-sampled deep sequencing data.
• Power calculations for case-control association studies comparing singleton variant burden.
• Evaluation across various genetic and epidemiological parameters.

Main Results:

• Singleton detection sensitivity increases with sequencing coverage, plateauing around 25x.
• Maximum association study power for singletons is achieved at 15-20x coverage when total sequencing capacity is fixed.
• Optimal coverage is independent of relative risk, disease prevalence, singleton burden, and case-control ratio.

Conclusions:

• A sequencing depth of 15-20x offers a practical compromise for rare variant studies.
• This coverage level optimizes the balance between detecting rare variants and maintaining adequate sample size.
• Informs cost-effective study design for identifying genetic associations with complex diseases.