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Open Access Highly Accessed Methodology article

Genome-wide Two-marker linkage disequilibrium mapping of quantitative trait loci

Jie Yang1, Wei Zhu2, Jiansong Chen2, Qiao Zhang2 and Song Wu2*

Author Affiliations

1 Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11790, USA

2 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11790, USA

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BMC Genetics 2014, 15:20  doi:10.1186/1471-2156-15-20

Published: 8 February 2014

Abstract

Background

In a natural population, the alleles of multiple tightly linked loci on the same chromosome co-segregate and are passed non-randomly from generation to generation. Capitalizing on this phenomenon, a group of mapping methods, commonly referred to as the linkage disequilibrium-based mapping (LD mapping), have been developed recently for detecting genetic associations. However, most current LD mapping methods mainly employed single-marker analysis, overlooking the rich information contained within adjacent linked loci.

Results

We extend the single-marker LD mapping to include two linked loci and explicitly incorporate their LD information into genetic mapping models (tmLD). We establish the theoretical foundations for the tmLD mapping method and also provide a thorough examination of its statistical properties. Our simulation studies demonstrate that the tmLD mapping method significantly improves the detection power of association compared to the single-marker based and also haplotype based mapping methods. The practical usage and properties of the tmLD mapping method were further elucidated through the analysis of a large-scale dental caries GWAS data set. It shows that the tmLD mapping method can identify significant SNPs that are missed by the traditional single-marker association analysis and haplotype based mapping method. An R package for our proposed method has been developed and is freely available.

Conclusions

The proposed tmLD mapping method is more powerful than single marker mapping generally used in GWAS data analysis. We recommend the usage of this improved method over the traditional single marker association analysis.

Keywords:
Genetic mapping; Linkage disequilibrium mapping; Linked loci; Genome wide association study