A quantitative linkage score for an association study following a linkage analysis

Area of Science:

• Genetics
• Statistical Genetics

Background:

• Genome-wide linkage analysis is used to identify broad chromosomal regions for complex traits.
• Association analysis then fine-maps genetic variations within these linked regions.
• Key questions involve optimizing association study design and determining if associations explain linkage signals.

Purpose of the Study:

• To develop a quantitative linkage score (QLS) for improving sequential linkage and association studies.
• To address how to design efficient association studies using prior linkage information.
• To determine if identified genetic associations can explain observed linkage signals.

Main Methods:

• Derivation of a quantitative linkage score (QLS) based on Haseman-Elston regression.
• Utilizing the QLS to guide subsample selection for association studies, particularly in the presence of heterogeneity.
• Developing a paired t-statistic to test if a marker allele explains part of a linkage signal.

Main Results:

• QLS-guided sample selection can increase the proportion of affected individuals, significantly boosting association study power.
• A significant difference in QLS with/without marker association indicates the marker partly explains the linkage.
• The proposed statistic can also detect spurious associations.

Conclusions:

• Examination of QLSs is crucial for interpreting results from both linkage and association studies.
• The QLS provides a unified approach to optimize study design and inference in genetic mapping.