SMILE

Stochastic Models for the Inference of Life Evolution

Bibtex

@article{achaz_testing_2008,
Author = {Achaz, Guillaume},
Title = {Testing for neutrality in samples with sequencing
errors},
Journal = {Genetics},
Volume = {179},
Number = {3},
Pages = {1409--1424},
abstract = {Many data sets one could use for population genetics
contain artifactual sites, i.e., sequencing errors.
Here, we first explore the impact of such errors on
several common summary statistics, assuming that
sequencing errors are mostly singletons. We thus show
that in the presence of those errors, estimators of can
be strongly biased. We further show that even with a
moderate number of sequencing errors, neutrality tests
based on the frequency spectrum reject neutrality. This
implies that analyses of data sets with such errors
will systematically lead to wrong inferences of
evolutionary scenarios. To avoid to these errors, we
propose two new estimators of theta that ignore
singletons as well as two new tests Y and Y* that can
be used to test neutrality despite sequencing errors.
All in all, we show that even though singletons are
ignored, these new tests show some power to detect
deviations from a standard neutral model. We therefore
advise the use of these new tests to strengthen
conclusions in suspicious data sets.},
doi = {10.1534/genetics.107.082198},
issn = {0016-6731},
language = {eng},
month = jul,
pmcid = {PMC2475743},
pmid = {18562660},
year = 2008
}