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

Ann Hum Genet. 2012 Nov;76(6):500-9. doi: 10.1111/j.1469-1809.2012.00728.x. Epub 2012 Sep 19.


Shared genomic segment (SGS) analysis uses dense single nucleotide polymorphism genotyping in high-risk (HR) pedigrees to identify regions of sharing between cases. Here, we illustrate the power of SGS to identify dominant rare risk variants. Using simulated pedigrees, we consider 12 disease models based on disease prevalence, minor allele frequency and penetrance to represent disease loci that explain 0.2-99.8% of total disease risk. Pedigrees were required to contain ≥ 15 meioses between all cases and to be HR based on significant excess of disease (P < 0.001 or P < 0.00001). Across these scenarios, the power for a single pedigree ranged widely. Nonetheless, fewer than 10 pedigrees were sufficient for excellent power in the majority of models. Power increased with the risk attributable to the disease locus, penetrance and the excess of disease in the pedigree. Sharing allowing for one sporadic case was uniformly more powerful than sharing using all cases. Furthermore, an SGS analysis using a large attenuated familial adenomatous polyposis pedigree identified a 1.96 Mb region containing the known causal APC gene with genome-wide significance. SGS is a powerful method for detecting rare variants and a valuable complement to genome-wide association studies and linkage analysis.

MeSH terms

  • Adenomatous Polyposis Coli / genetics
  • Computer Simulation
  • Genes, APC
  • Genes, Dominant
  • Genetic Predisposition to Disease
  • Genome-Wide Association Study
  • Genomics*
  • Humans
  • Models, Genetic
  • Pedigree
  • Polymorphism, Single Nucleotide*
  • Rare Diseases / genetics*