Linkage analysis with dense SNP maps in isolated populations

Area of Science:

• Human Genetics
• Population Genetics
• Bioinformatics

Background:

• Single nucleotide polymorphism (SNP) maps are increasingly used as genetic markers.
• High SNP density can cause significant linkage disequilibrium (LD), potentially biasing multipoint linkage analyses, especially in isolated populations with extensive LD.

Purpose of the Study:

• To investigate the impact of SNP density and LD on linkage analysis in an isolated population (Hutterites).
• To evaluate methods for minimizing LD bias in SNP-based linkage analysis using the Affymetrix 500K GeneChip array.

Main Methods:

• Compared LD minimization strategies: Merlin, SNPLINK (LD block-based), and MASEL (a novel algorithm selecting minimum LD SNP subsets).
• Utilized simulations based on the Hutterite population's LD pattern.

Main Results:

• Standard LD minimization methods (Merlin, SNPLINK) showed persistent inflation of linkage statistics.
• The proposed MASEL algorithm demonstrated better control over linkage statistic inflation.

Conclusions:

• Extracting an unbiased SNP map from standard GeneChip arrays for informative linkage analysis in this population remains challenging.
• MASEL offers improved performance in managing LD-driven bias in SNP-based linkage analysis.