The Whole Genome Analysis Module incorporates several technologies and methods designed to overcome the statistical and computational challenges of large-scale whole genome analysis.  With this module you can mitigate the statistical consequences of multiple testing while conquering data import, management, and analysis challenges.  Further, you can quickly recover missing genotype information and employ novel association methods to identify rare genetic variants that are often missed by standard analytic techniques.

Data Management and Quality Control

• Sparse data storage technology internally compresses SNP data into proprietary storage formats that use a fraction of both system memory and disk space compared to standard genotype file formats. These formats also improve the speed and efficiency of both data import and analysis. With sparse data storage you can perform truly interactive whole genome analysis, and still fit an entire whole genome SNP study on a single 256 MB Flash drive.
• Direct import and integration with all major Affymetrix and Illumina genotyping platforms facilitates efficient import of genotype and annotation information that can be easily joined with phenotype data to begin analysis. (See Illumina and Affymetrix support for more information.)
• Inferring missing genotype algorithm uses the correlation structure of high density arrays to recover missing genotype information with a false assignment probability lower than the experimental error rate.
  ›› More about Inferring Missing Genotypes

Association Tests

• Runs of homozygosity association is a novel analytic method 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. ›› More about Runs of Homozygosity Association

Multiple Testing Mitigation

• SNP tagging routines based on the Carlson Method can be used in addition to the standard multiple testing corrections provided in HelixTree (i.e. Bonferroni adjustment, false discovery rate, Simes method, and full and single scan permutation testing) to minimize the consequences of the multiple testing penalties generally associated with whole genome analysis.
  ›› More about SNP Tagging