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.

Documentation