Inferring population parameters from single-feature polymorphism data.
This article is concerned with a statistical modeling procedure to call single-feature polymorphisms from microarray experiments. We use this new type of polymorphism data to estimate the mutation and recombination parameters in a population. The mutation parameter can be estimated via the number of single-feature polymorphisms called in the sample. For the recombination parameter, a two-feature sampling distribution is derived in a way analogous to that for the two-locus sampling distribution with SNP data. The approximate-likelihood approach using the two-feature sampling distribution is examined and found to work well. A coalescent simulation study is used to investigate the accuracy and robustness of our method. Our approach allows the utilization of single-feature polymorphism data for inference in population genetics.
Jiang
Rong
R
Molecular and Computational Biology Program, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles 90089, USA.
Marjoram
Paul
P
Borevitz
Justin O
JO
Tavaré
Simon
S
D017360:Arabidopsis / Q000235:genetics*; D003198:Computer Simulation; D018744:DNA, Plant / Q000235:genetics*; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci / Q000235:genetics*; D011995:Recombination, Genetic
D018744:DNA, Plant