PBAT*
PBAT is an advanced statistical package for the design and analysis of family-based SNP and CNV association studies. In addition to an extensive array of analysis methods, PBAT is acclaimed for its ability to overcome the multiple-testing problem using breakthrough screening methods.
PBAT is developed in collaboration with Dr. Christophe Lange of the Harvard School of Public Health.
Data Management
Most programs cannot handle the growing size of today’s whole genome datasets. Golden Helix PBAT solves this by allowing you to easily import your pedigree, genotype, CNV, and phenotype data separately and then joining them once they are in the program. Golden Helix PBAT also supports grid computing and parallel processing to speed up analysis dramatically. Learn more
Screening Based on Conditional Mean Model
PBAT's powerful screening method is based on the conditional mean model which 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 the multiple comparison problem, the most important statistical hurdle in genome-wide association studies. Learn more
Faster Extended Pedigree Analysis
Golden Helix PBAT includes a new option when doing family-based analysis to use an alternative rapid extended pedigree algorithm that can speed up analysis significantly. It can be applied to SNP, haplotype and copy number analysis. Learn more
Pre-Study Power Calculations
Golden Helix PBAT supports the advance planning of family-based association studies by providing calculations of power estimates for virtually any given study design or ascertainment conditions. You can also assess the power of population-based association study designs for both case/control and quantitative traits. Learn more
Family-Based SNP and CNV Analysis
Golden Helix 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. 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.
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