Biology subjects

Yun S. Song

Publications and source records attributed to Yun S. Song.

8 recordsLinked to original sources

bioRxiv2016

The effects of population size histories on estimates of selection coefficients from time-series genetic data

AO_SCPCAPBSTRACTC_SCPCAPMany approaches have been developed for inferring selection coefficients from time series data while accounting for genetic drift. However, the improvement in inference accuracy that can be attained by modeling drift is unknown. Here, by comparing maximum likelihood estimates of selection coefficients that account for the true population size history with estimates that ignore drift, we address the following questions: how much can modeling the population size history improve estimates of selection coefficients? How much can mis-inferred population sizes hurt inferences of selection coefficients? We conduct our analysis under the discrete Wright-Fisher model by deriving the exact probability of an allele frequency trajectory in a population of time-varying size and we replicate our results under the diffusion model by extending the exact probability of a frequency trajectory derived by Steinrucken et al. (2014) to the case of a piecewise constant population. For both the discrete Wright-Fisher and diffusion models, we find that ignoring drift leads to estimates of selection coefficients that are nearly as accurate as estimates that account for the true population history, even when population sizes are small and drift is high. In populations of time-varying size, estimates of selection coefficients that ignore drift are similar in accuracy to estimates that rely on crude, yet reasonable, estimates of the population history. These results are of interest because inference methods that ignore drift are widely used in evolutionary studies and can be many orders of magnitude faster than methods that account for population sizes.

Evolutionary Biology↗

bioRxiv2016

Genotyping Allelic and Copy Number Variation in the Immunoglobulin Heavy Chain Locus

The study of genomic regions that contain gene copies and structural variation is a major challenge in modern genomics. Unlike variation involving single nucleotide changes, data on the variation of copy number is difficult to collect and few tools exist for analyzing the variation between individuals. The immunoglobulin heavy variable (IGHV) locus, which plays an integral role in the adaptive immune response, is an example of a genomic region that is known to vary in gene copy number. Lack of standard methods to genotype this region prevents it from being included in association studies and is holding back the growing field of antibody repertoire analysis. Here, we establish a convention of representing the locus in terms of a reference panel of operationally distinguishable segments defined by hierarchical clustering. Using this reference set, we develop a pipeline that identifies copy number and allelic variation in the IGHV locus from whole-genome sequencing reads. Tests on simulated reads demonstrate that our approach is feasible and accurate for detecting the presence and absence of gene segments using reads as short as 70 bp. With reads 100 bp and longer, coverage depth can also be used to determine copy number. When applied to a family of European ancestry, our method finds new copy number variants and confirms existing variants. This study paves the way for analyzing population-level patterns of variation in the IGHV locus in larger diverse datasets and for quantitatively handling regions of copy number variation in other structurally varying and complex loci.

Genetics↗

bioRxiv2015

Deep learning for population genetic inference

Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce here a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. In contrast to Approximate Bayesian Computation, another likelihood-free approach widely used in population genetics and other fields, deep learning does not require a distance function on summary statistics or a rejection step, and it is robust to the addition of uninformative statistics. To demonstrate that deep learning can be effectively employed to estimate population genetic parameters and learn informative features of data, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme, likely due in part to the unaccounted impact of selection.

Evolutionary Biology↗

bioRxiv2015

Inference of complex population histories using whole-genome sequences from multiple populations

There has been much interest in analyzing genome-scale DNA sequence data to infer population histories, but inference methods developed hitherto are limited in model complexity and computational scalability. Here we present an efficient, flexible statistical method, diCal2, that can utilize whole-genome sequence data from multiple populations to infer complex demographic models involving population size changes, population splits, admixture, and migration. Applying our method to data from Australian, East Asian, European, and Papuan populations, we find that the population ancestral to Australians and Papuans started separating from East Asians and Europeans about 100,000 years ago, and that the separation of East Asians and Europeans started about 50,000 years ago, with pervasive gene flow between all pairs of populations.

Evolutionary Biology↗

bioRxiv2015

Transition densities and sample frequency spectra of diffusion processes with selection and variable population size

Advances in empirical population genetics have made apparent the need for models that simultaneously account for selection and demography. To address this need, we here study the Wright-Fisher diffusion under selection and variable effective population size. In the case of genic selection and piecewise-constant effective population sizes, we obtain the transition density function by extending a recently developed method for computing an accurate spectral representation for a constant population size. Utilizing this extension, we show how to compute the sample frequency spectrum (SFS) in the presence of genic selection and an arbitrary number of instantaneous changes in the effective population size. We also develop an alternate, efficient algorithm for computing the SFS using a method of moments. We apply these methods to answer the following questions: If neutrality is incorrectly assumed when there is selection, what effects does it have on demographic parameter estimation? Can the impact of negative selection be observed in populations that undergo strong exponential growth?

Genetics↗

bioRxiv2014

Multi-locus analysis of genomic time series data from experimental evolution

Genomic time series data generated by evolve-and-resequence (E&R) experiments offer a powerful window into the mechanisms that drive evolution. However, standard population genetic inference procedures do not account for sampling serially over time, and new methods are needed to make full use of modern experimental evolution data. To address this problem, we develop a Gaussian process approximation to the multi-locus Wright-Fisher process with selection over a time course of tens of generations. The mean and covariance structure of the Gaussian process are obtained by computing the corresponding moments in discrete-time Wright-Fisher models conditioned on the presence of a linked selected site. This enables our method to account for the effects of linkage and selection, both along the genome and across sampled time points, in an approximate but principled manner. Using simulated data, we demonstrate the power of our method to correctly detect, locate and estimate the fitness of a selected allele from among several linked sites. We also study how this power changes for different values of selection strength, initial haplotypic diversity, population size, sampling frequency, experimental duration, number of replicates, and sequencing coverage depth. In addition to providing quantitative estimates of selection parameters from experimental evolution data, our model can be used by practitioners to design E&R experiments with requisite power. Finally, we explore how our likelihood-based approach can be used to infer other model parameters, including effective population size and recombination rate, and discuss extensions to more complex models.

Preprint↗

bioRxiv2014

Efficient inference of population size histories and locus-specific mutation rates from large-sample genomic variation data

With the recent increase in study sample sizes in human genetics, there has been growing interest in inferring historical population demography from genomic variation data. Here, we present an efficient inference method that can scale up to very large samples, with tens or hundreds of thousands of individuals. Specifically, by utilizing analytic results on the expected frequency spectrum under the coalescent and by leveraging the technique of automatic differentiation, which allows us to compute gradients exactly, we develop a very efficient algorithm to infer piecewise-exponential models of the historical effective population size from the distribution of sample allele frequencies. Our method is orders of magnitude faster than previous demographic inference methods based on the frequency spectrum. In addition to inferring demography, our method can also accurately estimate locus-specific mutation rates. We perform extensive validation of our method on simulated data and show that it can accurately infer multiple recent epochs of rapid exponential growth, a signal which is difficult to pick up with small sample sizes. Lastly, we apply our method to analyze data from recent sequencing studies, including a large-sample exome-sequencing dataset of tens of thousands of individuals assayed at a few hundred genic regions.\n\nSoftwareWe have implemented the algorithms described in this paper in an open-source software package called fastNeutrino, which stands for fast Ne (effective population size) and mutation rate inference using analytic optimization. It will be publicly available at https://sourceforge.net/projects/fastneutrino.

Evolutionary Biology↗

bioRxiv2014

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

Summary: We present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.\n\nAvailability: http://sourceforge.net/p/dical-ibd\n\nContact: yss@eecs.berkeley.edu

Bioinformatics↗