WHOLE GENOME HOMOZYGOSITY ASSOCIATION: USING RUNS OF HOMOZYGOSITY TO IDENTIFY RECESSIVE LOCI

The recent development of microarray platforms capable of genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs) has provided an opportunity to rapidly identify novel susceptibility genes for complex phenotypes. Studies employing genotyping microarrays have typically utilized a whole genome association approach, in which each SNP is examined individually for association with disease. While this approach has resulted in several important breakthroughs in recent years, it is biased toward detecting common alleles with additive effects. At the same time, structural properties of whole genome association datasets, including patterns of linkage disequilibrium (LD), have not yet been exploited in these analyses.

Consequently, Dr. Todd Lencz, Associate Director of Research at The Zucker Hillside Hospital, working in collaboration with Dr. Christophe Lambert of Golden Helix, has developed a novel analytic approach that first identifies patterned clusters of SNPs demonstrating extended homozygosity (runs of homozygosity or “ROHs”) and then employs both genome-wide and regionally-specific statistical tests for association to disease. This approach can identify chromosomal segments that may harbor rare, penetrant recessive loci.

Using a simulated dataset, this tutorial will lead you step-by-step through the workflow for finding runs of homozygosity outlined in Dr. Lencz’s paper.

NOTE: This tutorial will not cover the data importing and quality control procedures Dr. Lencz employed in his study, most of which can be done with HelixTree. To learn more about these procedures, please refer to the manual.

Overview:

1. 1. Import genotype and phenotype data
2. 2. Import and apply genetic marker map
3. 3. Identify runs of homozygosity
4. 4. Perform association testing with runs of homozygosity covariates
   

REQUIREMENTS

To complete this tutorial, you will need the following:

• HelixTree
• Whole Genome Association Module
• 500kCC.zip (Sample dataset containing 48 samples with 500K SNPs and case/control status)

Prerequisite Knowledge:
Intermediate SVS functionality