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Estimation of Multilocus Linkage Disequilibria in Diploid Populations With Dominant Markers

Yanchun LiDepartment of Computer and Information Science and EngineeringYang LiDepartment of Computer and Information Science and EngineeringSong WuDepartment of Statistics, University of Florida, Gainesville, Florida 32611Kun HanSchool of Forestry and Biotechnology, Zhejiang Forestry University, Lin'an, Zhejiang 311300, People's Republic of China andZhengjia WangSchool of Forestry and Biotechnology, Zhejiang Forestry University, Lin'an, Zhejiang 311300, People's Republic of China andWei HouYanru ZengSchool of Forestry and Biotechnology, Zhejiang Forestry University, Lin'an, Zhejiang 311300, People's Republic of China andRongling WuDepartment of Statistics, University of Florida, Gainesville, Florida 32611

2007en

ABI

Annotatsiya

Analysis of population structure and organization with DNA-based markers can provide important information regarding the history and evolution of a species. Linkage disequilibrium (LD) analysis based on allelic associations between different loci is emerging as a viable tool to unravel the genetic basis of population differentiation. In this article, we derive the EM algorithm to obtain the maximum-likelihood estimates of the linkage disequilibria between dominant markers, to study the patterns of genetic diversity for a diploid species. The algorithm was expanded to estimate and test linkage disequilibria of different orders among three dominant markers and can be technically extended to manipulate an arbitrary number of dominant markers. The feasibility of the proposed algorithm is validated by an example of population genetic studies of hickory trees, native to southeastern China, using dominant random amplified polymorphic DNA markers. Extensive simulation studies were performed to investigate the statistical properties of this algorithm. The precision of the estimates of linkage disequilibrium between dominant markers was compared with that between codominant markers. Results from simulation studies suggest that three-locus LD analysis displays increased power of LD detection relative to two-locus LD analysis. This algorithm is useful for studying the pattern and amount of genetic variation within and among populations.

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Identifikatorlar

DOI: 10.1534/genetics.106.068890

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