Shared genomic segment analysis: the power to find rare disease variants

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• High-risk pedigrees are crucial for identifying genetic factors underlying complex diseases.
• Shared genomic segment (SGS) analysis leverages dense genotyping to detect regions of allele sharing among affected individuals.
• Identifying rare, dominant risk variants remains a challenge in genetic studies.

Purpose of the Study:

• To demonstrate the efficacy of SGS analysis in pinpointing dominant rare risk variants.
• To evaluate the power of SGS across diverse genetic models and pedigree structures.
• To assess the performance of SGS in real-world genetic data, such as familial adenomatous polyposis.

Main Methods:

• Simulated 12 disease models varying in prevalence, minor allele frequency, and penetrance.
• Utilized high-risk pedigrees with at least 15 meioses between cases and significant disease excess (P < 0.001 or P < 0.00001).
• Compared SGS power using all cases versus allowing for one sporadic case.
• Applied SGS to a large familial adenomatous polyposis pedigree.

Main Results:

• SGS demonstrated significant power to detect rare variants across various models, often requiring fewer than 10 pedigrees for excellent results.
• Power increased with higher attributable risk, penetrance, and disease excess within pedigrees.
• Excluding sporadic cases improved power compared to including all cases.
• Identified a 1.96 Mb region containing the APC gene in a familial adenomatous polyposis cohort with genome-wide significance.

Conclusions:

• Shared genomic segment analysis is a powerful and efficient method for detecting rare disease-associated variants.
• SGS analysis is a valuable complement to traditional genome-wide association studies and linkage analysis.
• The method shows promise for genetic studies of Mendelian and complex diseases in high-risk families.