Linkage Analysis with Dense SNP Maps in Isolated Populations

Abstract

Objective: SNP maps are becoming the gold standard for genetic markers, even for linkage analyses. However, because of the density of SNPs on most high throughput platforms, the resulting significant linkage disequilibrium (LD) can bias classical nonparametric multipoint linkage analyses. This problem may be even stronger in population isolates where LD can extend over larger distances and with a more stochastic pattern. We investigate the issue of linkage analysis with SNPs from the Affymetrix 500K GeneChip array in extended families from the isolated Hutterite population. Methods: We minimized LD between SNPs by two methods based on a LD block pattern (Merlin and SNPLINK) and by MASEL, a new algorithm that we proposed to select SNP subsets with minimum LD and with no prior hypothesis about the LD pattern. Results: Simulations, performed using the real LD pattern observed in the Hutterite population, show that sizeable inflation of linkage statistics persist when LD between SNPs is minimized by Merlin and SNPLINK. Inflation of linkage statistics is better controlled with MASEL. Conclusion: In this population, it may be difficult to extract from standard GeneChip arrays a SNP map without LD-driven bias that is more informative than a dense microsatellite map.