Detecting rare variants for complex traits using family and unrelated data

Area of Science:

• Genetics
• Genomics
• Disease Association Studies

Background:

• Genome-wide association studies (GWAS) primarily identify common variants with small effect sizes for common diseases.
• Candidate gene resequencing suggests rare variants significantly contribute to disease variance.
• Existing GWAS methods have limited power to detect rare genetic variants.

Purpose of the Study:

• To develop and validate novel designs for detecting rare genetic variants in association studies.
• To enhance the power of detecting rare risk haplotypes using sibpair and unrelated-case designs.
• To apply these methods to real-world data for identifying novel disease-associated genes.

Main Methods:

• Proposed sibpair and unrelated-case designs for detecting rare genetic variants.
• Utilized a two-stage approach: initial detection in small samples, followed by association testing in larger case-control cohorts.
• Applied the methodology to Wellcome Trust Case Control Consortium (WTCCC) data for coronary artery disease (CAD) and hypertension (HT).

Main Results:

• Successfully detected and classified rare risk haplotypes with increased power in larger samples.
• Identified one gene associated with hypertension (HT) and four genes associated with coronary artery disease (CAD) at genome-wide significance.
• Demonstrated the feasibility of detecting rare variants in existing GWAS and resequencing data.

Conclusions:

• Searching for rare genetic variants is a feasible and potentially fruitful strategy in current genetic studies.
• The proposed designs offer a powerful approach to uncover the contribution of rare variants to common diseases.
• This work expands the utility of GWAS, candidate gene studies, and resequencing efforts.