QUICK LINKS
Free Trial
Request Pricing & Information
Get Technical Support
Email or Call 1-888-589-4629
Never Let the Important Become Urgent: A reflection on the genetics supply chain and our need to increase value to the end patient
» Read blog post
Check out the following tutorial for an introduction to family-based association analysis using the PBAT statistical package incorporated into SNP & Variation Suite 7. Covered workflows include data preparation, quality assurance testing, association analysis, and basic visualization of results.
Data Management
SVS 7 streamlines the import of most family-based data formats including: PED, TPED, BED, FBAT Pedigree, and FBAT Phenotype files. It also makes it easy to join and merge this data once imported to ensure all genotype, phenotype, and pedigree data is formatted properly for analysis. Further, SVS 7 supports grid computing and parallel processing for to significantly speed up various computationally intense processes.
Screening Based on Conditional Mean Model
SVS 7 employs PBAT's powerful screening methods, based on the conditional mean mode [Lange 2002b, Lange 2002c]l, that first identifies the combination of markers and phenotypes with the highest power and then performs appropriate FBAT tests on only those combinations. This helps control type I error rates and overcome comparison problems, the most important statistical hurdle in genome-wide association studies.
» More about Conditional Tests for Family-Based Association Studies
Family-Based SNP Association
SVS 7 incorporates PBAT's comprehensive and powerful tool set for family-based SNP association. PBAT offers a unified approach to the FBAT statistic, a generalization of the transmission disequilibrium test (TDT), to cover different genetic models, tests of different sampling designs, tests involving different disease phenotypes, tests with missing parents and tests of different null hypotheses, all in the same framework.
Family-Based CNV Association
PBAT also supports the testing for copy-number variation (CNV) in a family-based setting. All robustness properties of the FBAT approach are maintained as in PBAT for SNP analysis. In addition, all previously-developed FBAT extensions, including FBATs for time-to-onset, multivariate FBATs, and FBAT-testing strategies, can be directly transferred to the analysis of CNVs.
Family-Based QC
The latest version of Golden Helix PBAT incorporates a novel test that assesses the genotyping quality of individual probands in family-based association studies. Published in PLoS Genetics [Fardo, 2009] these tests are “ideally suited as the final layer of quality control filters in the cleaning process of genome-wide association studies." You can also assess Mendelian errors, Hardy-Weinberg Equilibrium and call rates per marker.
Enhanced Extended Pedigree Analysis
Breaking up extended pedigrees into trios, which is a computationally fast strategy, does not take full advantage of the structure of the known extended pedigree. On the other hand, analyzing extended pedigrees as such, which does take full advantage of all the information and is the most powerful option, can be computationally slow when many of the genotypes in a pedigree are missing.
Golden Helix PBAT includes a new hybrid option that identifies clusters of nuclear families in extended pedigrees which are directly linked (i.e. that share a family member) and analyzes such clusters as extended pedigrees. At the same time, clusters that are linked only through two or more family members without genotypic information are broken up into separate extended-pedigree clusters. These clusters are analyzed in the same way that extended pedigrees would be under the original algorithm, but independently of each other.
The extra information provided to the computation of the genetic distribution under the original algorithm by linking together the extended pedigree clusters is minimal, while the effort required for taking advantage of this information is disproportionally enormous. This puts the original algorithm at a severe disadvantage
Under the new hybrid approach, however, such links between family clusters within extended pedigrees are dropped. The increased statistical power of the original extended pedigree algorithm is, therefore, maintained while almost having the computational speed of a pure nuclear-family analysis.
