SPREG

SPREG performs regression analysis on secondary phenotypes in case-control association studies to produce unbiased estimates of genetic effects while correcting for non-random sampling.

Key Features:

• Addressing Sampling Bias: Incorporates statistical techniques that account for unequal selection probabilities between cases and controls that can invalidate methods such as least-squares regression.
• Unbiased Estimation of Genetic Effects: Produces unbiased estimates of genetic variant effects on secondary phenotypes by reflecting the case-control sampling structure.
• Control of False-Positive Rates: Provides accurate control of false-positive rates in association testing to maintain validity of inferred associations.
• Maximizing Statistical Power: Enhances statistical power for detecting true associations while accounting for sampling bias and controlling error rates.

Scientific Applications:

• Exploration of Biological Pathways: Analysis of secondary phenotypes to infer biological pathways influenced by genetic variants.
• Identification of Genetic Variants: Detection of genetic variants with significant effects on secondary phenotypes relevant to disease progression or treatment response.

Methodology:

SPREG employs advanced statistical methods tailored for case-control association studies that correct for biases introduced by non-random sampling and demonstrates analytically and numerically how standard methods (e.g., least-squares regression) can yield misleading results.