Estimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error.

TitleEstimating population genetic parameters and comparing model goodness-of-fit using DNA sequences with error.
Publication TypeJournal Article
Year of Publication2010
AuthorsLiu, X, Fu, Y-X, Maxwell, TJ, Boerwinkle, E
JournalGenome Res
Volume20
Issue1
Pagination101-9
Date Published2010 Jan
ISSN1549-5469
KeywordsAfrican Americans, African Continental Ancestry Group, Angiopoietin-like 4 Protein, Angiopoietins, Base Sequence, Computational Biology, Computer Simulation, European Continental Ancestry Group, Genetics, Population, Humans, Likelihood Functions, Models, Genetic, Mutation, Polymorphism, Single Nucleotide
Abstract

It is known that sequencing error can bias estimation of evolutionary or population genetic parameters. This problem is more prominent in deep resequencing studies because of their large sample size n, and a higher probability of error at each nucleotide site. We propose a new method based on the composite likelihood of the observed SNP configurations to infer population mutation rate theta = 4N(e)micro, population exponential growth rate R, and error rate epsilon, simultaneously. Using simulation, we show the combined effects of the parameters, theta, n, epsilon, and R on the accuracy of parameter estimation. We compared our maximum composite likelihood estimator (MCLE) of theta with other theta estimators that take into account the error. The results show the MCLE performs well when the sample size is large or the error rate is high. Using parametric bootstrap, composite likelihood can also be used as a statistic for testing the model goodness-of-fit of the observed DNA sequences. The MCLE method is applied to sequence data on the ANGPTL4 gene in 1832 African American and 1045 European American individuals.

DOI10.1101/gr.097543.109
Alternate JournalGenome Res.
PubMed ID19952140
PubMed Central IDPMC2798822
Grant ListP50 GM065509 / GM / NIGMS NIH HHS / United States
5P50GM065509 / GM / NIGMS NIH HHS / United States