FFBSKAT
FFBSKAT performs region-based sequence kernel association testing to detect associations between rare genetic variants and continuous phenotypes in family-based samples.
Key Features:
- Region-based association analysis: Performs region-based tests to identify associations involving rare genetic variants.
- Score-based variance component test: Implements a score-based variance component test within a kernel machine framework for association testing.
- SNPs and continuous phenotypes: Evaluates associations between single nucleotide polymorphisms (SNPs) and continuous phenotypes in family-based samples.
- Whole-exome kernel machine regression: Supports whole-exome kernel machine-based regression association analysis.
- Genetic substructure handling: Addresses computational complexity arising from genetic substructures in related samples.
- Computational performance: Enhances computational efficiency and speed, reported to outperform ASKAT and famSKAT in runtime.
- Accuracy: Maintains accuracy comparable to other family-based sequence kernel association testing software.
- Integration of methods: Combines features from ASKAT and famSKAT to support multiple analysis modes.
Scientific Applications:
- Quantitative trait analysis in families: Tests associations of rare variants with quantitative (continuous) traits in related individuals.
- Rare variant discovery in exomes: Applies region-based SKAT methods to whole-exome sequencing data for rare variant association studies.
Methodology:
Implements a score-based variance component test within kernel machine-based regression for region-based association analysis of SNPs and continuous phenotypes in family-based samples.
Topics
Details
- License:
- GPL-3.0
- Tool Type:
- library
- Operating Systems:
- Linux, Windows, Mac
- Programming Languages:
- R
- Added:
- 5/8/2018
- Last Updated:
- 12/10/2018
Operations
Publications
Svishcheva GR, Belonogova NM, Axenovich TI. FFBSKAT: Fast Family-Based Sequence Kernel Association Test. PLoS ONE. 2014;9(6):e99407. doi:10.1371/journal.pone.0099407. PMID:24905468. PMCID:PMC4048315.