Estimating the effective population size from temporal allele frequency changes in experimental evolution

A.J. and T.T. are members of the Vienna Graduate School of Population Genetics, which is funded by the Austrian Science Fund (FWF, W1225). C.S. is also supported by the European Research Council grant ”ArchAdapt,” and T.T. is a recipient of a Doctoral Fellowship (DOC) of the Austrian Academy of Sciences.The effective population size (Ne) is a major factor determining allele frequency changes in natural and experimental populations. Temporal methods provide a powerful and simple approach to estimate short-term Ne. They use allele frequency shifts between temporal samples to calculate the standardized variance, which is directly related to Ne. Here we focus on experimental evolution studies that often rely on repeated sequencing of samples in pools (Pool-seq). Pool-seq is cost-effective and often outperforms individual-based sequencing in estimating allele frequencies, but it is associated with atypical sampling properties: Additional to sampling individuals, sequencing DNA in pools leads to a second round of sampling, which increases the variance of allele frequency estimates. We propose a new estimator of Ne, which relies on allele frequency changes in temporal data and corrects for the variance in both sampling steps. In simulations, we obtain accurate Ne estimates, as long as the drift variance is not too small compared to the sampling and sequencing variance. In addition to genome-wide Ne estimates, we extend our method using a recursive partitioning approach to estimate Ne locally along the chromosome. Since the type I error is controlled, our method permits the identification of genomic regions that differ significantly in their Ne estimates. We present an application to Pool-seq data from experimental evolution with Drosophila and provide recommendations for whole-genome data. The estimator is computationally efficient and available as an R package at https://github.com/ThomasTaus/Nest.Publisher PDFPeer reviewe

Choosing summary statistics by least angle regression for approximate Bayesian computation

YesBayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.Higher Education Commission (HEC), College Deanship of Scientific Research, King Saud University, Riyadh Saudi Arabia - research group project RGP-VPP-280

PoPoolation: A Toolbox for Population Genetic Analysis of Next Generation Sequencing Data from Pooled Individuals

Recent statistical analyses suggest that sequencing of pooled samples provides a cost effective approach to determine genome-wide population genetic parameters. Here we introduce PoPoolation, a toolbox specifically designed for the population genetic analysis of sequence data from pooled individuals. PoPoolation calculates estimates of θWatterson, θπ, and Tajima's D that account for the bias introduced by pooling and sequencing errors, as well as divergence between species. Results of genome-wide analyses can be graphically displayed in a sliding window plot. PoPoolation is written in Perl and R and it builds on commonly used data formats. Its source code can be downloaded from http://code.google.com/p/popoolation/. Furthermore, we evaluate the influence of mapping algorithms, sequencing errors, and read coverage on the accuracy of population genetic parameter estimates from pooled data

Analysis of the Basidiomycete Coprinopsis cinerea Reveals Conservation of the Core Meiotic Expression Program over Half a Billion Years of Evolution

Coprinopsis cinerea (also known as Coprinus cinereus) is a multicellular basidiomycete mushroom particularly suited to the study of meiosis due to its synchronous meiotic development and prolonged prophase. We examined the 15-hour meiotic transcriptional program of C. cinerea, encompassing time points prior to haploid nuclear fusion though tetrad formation, using a 70-mer oligonucleotide microarray. As with other organisms, a large proportion (∼20%) of genes are differentially regulated during this developmental process, with successive waves of transcription apparent in nine transcriptional clusters, including one enriched for meiotic functions. C. cinerea and the fungi Saccharomyces cerevisiae and Schizosaccharomyces pombe diverged ∼500–900 million years ago, permitting a comparison of transcriptional programs across a broad evolutionary time scale. Previous studies of S. cerevisiae and S. pombe compared genes that were induced upon entry into meiosis; inclusion of C. cinerea data indicates that meiotic genes are more conserved in their patterns of induction across species than genes not known to be meiotic. In addition, we found that meiotic genes are significantly more conserved in their transcript profiles than genes not known to be meiotic, which indicates a remarkable conservation of the meiotic process across evolutionarily distant organisms. Overall, meiotic function genes are more conserved in both induction and transcript profile than genes not known to be meiotic. However, of 50 meiotic function genes that were co-induced in all three species, 41 transcript profiles were well-correlated in at least two of the three species, but only a single gene (rad50) exhibited coordinated induction and well-correlated transcript profiles in all three species, indicating that co-induction does not necessarily predict correlated expression or vice versa. Differences may reflect differences in meiotic mechanisms or new roles for paralogs. Similarities in induction, transcript profiles, or both, should contribute to gene discovery for orthologs without currently characterized meiotic roles

Interval Estimates for the Mode of Intensity and Density Functions

[[abstract]]We propose interval estimates for the point(s) of maximum of the intensity function of an inhomogeneous Poisson process. Possible applications include the identification of times when customer arrivals or accident occurrences are typically maximal. In particular we present an application where the goal has been to identify time periods of maximal ship arrival intensity at Keelung harbor. Asymptotic results c~ncerning the coverage probability are given under minimal smoothness assumptions and without assuming a unique point of global maximum. The proposed confidence sets may also be used in the context of mode estimation for probability densities. The finite sample performance is investigated in a small simulation study.[[sponsorship]]Tamkang University[[conferencetype]]國內[[conferencetkucampus]]淡水校園[[conferencedate]]20001208~20001210[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺北縣, 臺