Determining trait locus position from multipoint analysis: accuracy and power of three different statistics

Area of Science:

• Genetics
• Statistical Genetics
• Bioinformatics

Background:

• Two-point linkage analysis using maximum lod score (MMLS) is powerful for detecting genetic linkage.
• Concerns exist regarding MMLS robustness in multipoint analysis due to potential genetic model misspecification.

Purpose of the Study:

• To evaluate the robustness and power of the MMLS approach in multipoint linkage analysis under various complex inheritance models.
• To compare MMLS performance with other statistics like heterogeneity LOD (HLOD) and nonparametric linkage (NPL).

Main Methods:

• Computer simulations were used to generate data under complex models including heterogeneity, epistasis, and additive effects.
• Multipoint linkage analysis was performed using MMLS, HLOD, and NPL statistics across a 10-marker interval.
• The accuracy of estimated linkage positions and the power to detect linkage were assessed.

Main Results:

• The MMLS approach demonstrated robustness in multipoint analysis, similar to its performance in two-point analysis.
• LOD and HLOD generally showed higher power to detect linkage than NPL across diverse models, even after multiple testing correction.
• HLOD performed best when genetic heterogeneity was present, outperforming LOD and NPL.

Conclusions:

• The MMLS approach is a reliable method for multipoint linkage analysis, even when the exact mode of inheritance is unknown.
• Assuming dominant and recessive models with reduced penetrance provides a good approximation for detecting linkage at a single disease-associated locus.
• LOD and HLOD are generally more powerful than NPL for linkage detection in complex trait studies.