Design considerations for massively parallel sequencing studies of complex human disease

Area of Science:

• Genomics
• Complex Disease Genetics
• Bioinformatics

Background:

• Massively Parallel Sequencing (MPS) is cost-effective for whole exome/genome sequencing.
• Complex diseases present challenges like heterogeneity and genotype-phenotype complexity.
• Identifying true disease susceptibility genes requires robust analytical strategies.

Purpose of the Study:

• To compare various Massively Parallel Sequencing (MPS) study designs for complex diseases.
• To evaluate the impact of different filtering strategies on gene discovery.
• To provide guidelines for optimizing MPS study designs in complex disease research.

Main Methods:

• Simulation-based analyses were conducted to assess different MPS sequencing strategies.
• Factors examined included genetic architecture, sample size, and individual selection within pedigrees.
• A two-stage design with cost-function evaluation was employed.

Main Results:

• Filtering variants found in multiple pedigrees or individuals reduces false positives but can lead to over-filtering.
• Sequencing more than two individuals per pedigree generally reduces power without cost benefits.
• No single strategy is universally optimal, but simulations offer valuable design insights.

Conclusions:

• Careful selection of filtering strategies is essential for Massively Parallel Sequencing in complex disease genetics.
• Optimizing sample size and pedigree structure is critical for maximizing discovery power.
• Simulation studies are vital for guiding the design of effective MPS studies for complex traits.