Haseman-Elston regression in ascertained samples: importance of dependent variable and mean correction factor...

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Haseman-Elston regression (HE) is a foundational tool for genetic linkage analysis.
• Numerous HE extensions exist, creating challenges in selecting the appropriate method.
• Highly ascertained samples, often used in genetic studies, may not represent the general population.

Purpose of the Study:

• To evaluate different HE methods, including a novel modification (BLUP-PM), for linkage analysis.
• To propose BLUP-PM as a suitable method for analyzing highly ascertained samples.
• To provide guidance on selecting the best HE dependent variable for specific situations.

Main Methods:

• Simulated data across various genetic models were used for evaluation.
• Type I error rates and statistical power were assessed for different HE dependent variables.
• The performance of the novel BLUP-PM was compared against existing HE approaches.

Main Results:

• For continuous traits in ascertained samples, type I error was stable across HE methods.
• For binary traits in ascertained samples, type I error was elevated and unstable for all methods except BLUP-PM.
• BLUP-PM and optimally weighted HE regression demonstrated the highest statistical power for both trait types.

Conclusions:

• The choice of dependent variable in HE is critical, especially with ascertained samples.
• BLUP-PM demonstrates robust performance across various scenarios, particularly for binary traits in ascertained samples.
• While no single method is universally optimal, BLUP-PM offers a reliable option for many linkage analysis situations.