HTR

HTR associates inferred haplotype frequencies with discrete and continuous traits to detect genetic associations in studies of unrelated individuals.

Key Features:

• Statistical Testing: Implements statistical tests that assess correlations between inferred haplotype frequencies and both discrete and continuous traits.
• Haplotype Frequency Estimation: Estimates haplotype frequencies from genotype data using the expectation-maximization (EM) algorithm, probabilistically expanding individuals into possible haplotype configurations conditional on observed genotypes.
• Regression-Based Analysis: Uses a regression-based approach to relate inferred haplotype probabilities to phenotypic responses.
• Comparison and Validation: Facilitates comparisons between haplotype-based tests and single-marker tests using simulations, reporting increased statistical power under alternative hypotheses.
• Application to Real Data: Applied to dense maps of single nucleotide polymorphisms (SNPs) spanning a 12-cM chromosomal region to analyze multiple correlated markers.

Scientific Applications:

• Genetic Association Studies: Links haplotype configurations to phenotypic traits to identify genetic variants associated with disease or trait variation.
• Pharmacogenomics: Explores how haplotype variation influences individual responses to drugs.
• Disease Predisposition Research: Investigates genetic predispositions to conditions via haplotype–trait associations.

Methodology:

Haplotype frequencies are estimated by the expectation-maximization (EM) algorithm; statistical tests assess correlations with discrete and continuous traits; regression models relate inferred haplotype probabilities to phenotypic responses; simulations are used to compare haplotype-based tests with single-marker tests.