gTDT

gTDT performs gene-based or group-wise transmission/disequilibrium tests (TDT) to detect associations of rare genetic variants in family-based sequencing studies.


Key Features:

  • Robustness Against Population Stratification: Uses family-based transmission tests to control for population stratification and admixture, reducing spurious associations.
  • Haplotype-Based Framework: Constructs haplotypes by transmission when possible and aggregates signals at the haplotype level, accounting for linkage disequilibrium among variants.
  • Flexibility Across Genetic Models: Supports six genetic models (M1–M6), including additive, dominant, and compound heterozygous (recessive) models.
  • Increased Analytical Power and Type I Error Control: Simulation studies demonstrate correct type I error control under investigated models, including with population stratification, and higher power than single-marker TDT.
  • Application to Trio Exome Data: Applied to autism exome sequencing data from 118 trios, identifying candidate genes with compound heterozygous rare variants.
  • Implementation: Implemented in C++.

Scientific Applications:

  • Rare Variant Association Studies in Family-Based Sequencing: Detects gene- or group-level associations of rare variants using family-based designs.
  • Detection of Compound Heterozygous Effects: Identifies compound heterozygous rare variant associations, as demonstrated in autism trio exome analysis.

Methodology:

Performs gene- or group-wise TDT using a haplotype-based framework that constructs haplotypes by transmission when possible, evaluates six genetic models (M1–M6), and was assessed by simulation studies for type I error control and power; implemented in C++.

Topics

Details

Tool Type:
command-line tool
Operating Systems:
Linux
Added:
8/3/2017
Last Updated:
11/25/2024

Operations

Publications

Chen R, Wei Q, Zhan X, Zhong X, Sutcliffe JS, Cox NJ, Cook EH, Li C, Chen W, Li B. A haplotype-based framework for group-wise transmission/disequilibrium tests for rare variant association analysis. Bioinformatics. 2015;31(9):1452-1459. doi:10.1093/bioinformatics/btu860. PMID:25568282. PMCID:PMC4410665.

Documentation

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