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

BMC Genetics

Song Wu¹  Qiao Zhang¹  Jiansong Chen¹  Wei Zhu¹  Jie Yang² 

[1] Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11790, USA;Department of Preventive Medicine, Stony Brook University, Stony Brook, NY 11790, USA

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

Others  :  1085842
DOI  :  10.1186/1471-2156-15-20

 received in 2013-08-28, accepted in 2014-01-31,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Yang et al.; licensee BioMed Central Ltd.

【 图 表 】

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