An algorithmic model for constructing a linkage and linkage disequilibrium map in outcrossing plant populations

Jiahan Li, Qin Li, Wei Hou, Kun Han, Yao Li, Song Wu, Yanchun Li, Rongling Wu

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

A linkage-linkage disequilibrium map that describes the pattern and extent of linkage dis-equilibrium (LD) decay with genomic distance has now emerged as a viable tool to unravel the genetic structure of population differentiation and fine-map genes for complex traits. The prerequisite for constructing such a map is the simultaneous estimation of the linkage and LD between different loci. Here, we develop a computational algorithm for simultaneously estimating the recombination fraction and LD in a natural outcrossing population with multilocus marker data, which are often estimated separately in most molecular genetic studies. The algorithm is founded on a commonly used progeny test with open-pollinated offspring sampled from a natural population. The information about LD is reflected in the co-segregation of alleles at different loci among parents in the population. Open mating of parents will reveal the genetic linkage of alleles during meiosis. The algorithm was constructed within the polynomial-based mixture framework and implemented with the Expectation-Maximization (EM) algorithm. The by-product of the derivation of this algorithm is the estimation of outcrossing rate, a parameter useful to explore the genetic diversity of the population. We performed computer simulation to investigate the influences of different sampling strategies and different values of parameters on parameter estimation. By providing a number of testable hypotheses about population genetic parameters, this algorithmic model will open a broad gateway to understand the genetic structure and dynamics of an outcrossing population under natural selection.

Original languageEnglish (US)
Pages (from-to)9-21
Number of pages13
JournalGenetics Research
Volume91
Issue number1
DOIs
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • Genetics

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