PBAT for Family-Based Association Tests, FBAT, FBAT Test

Screening Based on Conditional Mean Model

The key concept of PBAT’s screening technique is the conditional mean model approach [Lange 2002b, Lange 2002c], for which the data space is considered to be partitioned into two independent testing sets. This approach may be described as follows:

First, find which combination of phenotypes as a group and markers have the highest power when tested against, not actual genotypes, but those predicted from the parents’ genotypes.

Second, perform the appropriate FBAT test for the selected combinations of phenotypes and markers on the actual genotypes of the patients, both as a group and individually.

This allows one to control the type I error rates and to overcome one of the most important statistical hurdles when analyzing genome-wide association studies - the multiple comparison problem. PBAT's screening methods are only minimally affected by the non-causal SNPs. In addition, they are robust against effects of population stratification and admixture.

Family-Based Quality Control

The latest version of 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.

Pre-Study Power Calculations

The PBAT capabilities for power calculations are a software implementation of the approaches to analytical power calculations for FBATs by [Lange 2002a, Lange 2002b, Lange 2002c] . They allow you to assess the power of family-based association tests (FBATs) for a large variety of different designs:

Dichotomous/binary and continuous traits.
Missing parental information.
Multiple offspring per family.
Combinations of different family-types.
Different genetic models.
Different ascertainment conditions for the first and second proband.
Marker and disease locus are not identical.
Combination of different family-types and different ascertainment conditions.
Verification of all power calculations by Monte-Carlo simulations.

Power calculations also allow you to assess the power of non-family-based association test designs for both case/control studies and studies based on quantitative traits.

» More about Pre-Study Power Calculations

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 applied to the analysis of CNVs.

» More about Family-Based Analysis

Rapid Extended Pedigree Algorithm

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 analyses.

QUICK LINKS

Golden Helix PBAT

Developed in collaboration with Dr. Christoph Lange of Harvard's School of Public Health, Golden Helix PBAT delivers an exclusive and extensive array of advanced statistical routines for the design and analysis of family-based SNP and CNV association studies.

Tutorial: Family-Based Analysis

Screening Based on Conditional Mean Model

Family-Based Quality Control

Pre-Study Power Calculations

Family-Based SNP and CNV Analysis

Rapid Extended Pedigree Algorithm