A sequentially Markov conditional sampling distribution for structured populations with migration and recombination.
نویسندگان
چکیده
Conditional sampling distributions (CSDs), sometimes referred to as copying models, underlie numerous practical tools in population genomic analyses. Though an important application that has received much attention is the inference of population structure, the explicit exchange of migrants at specified rates has not hitherto been incorporated into the CSD in a principled framework. Recently, in the case of a single panmictic population, a sequentially Markov CSD has been developed as an accurate, efficient approximation to a principled CSD derived from the diffusion process dual to the coalescent with recombination. In this paper, the sequentially Markov CSD framework is extended to incorporate subdivided population structure, thus providing an efficiently computable CSD that admits a genealogical interpretation related to the structured coalescent with migration and recombination. As a concrete application, it is demonstrated empirically that the CSD developed here can be employed to yield accurate estimation of a wide range of migration rates.
منابع مشابه
An accurate sequentially Markov conditional sampling distribution for the coalescent with recombination.
The sequentially Markov coalescent is a simplified genealogical process that aims to capture the essential features of the full coalescent model with recombination, while being scalable in the number of loci. In this article, the sequentially Markov framework is applied to the conditional sampling distribution (CSD), which is at the core of many statistical tools for population genetic analyses...
متن کاملScalable Statistical Methods for Ancestral Inference from Genomic Variation Data
Scalable Statistical Methods for Ancestral Inference from Genomic Variation Data by Andrew Hans Chan Doctor of Philosophy in Computer Science University of California, Berkeley Professor Yun S. Song, Chair Developments in DNA sequencing technology over the last few years have yielded unprecedented volumes of genetic data. The resulting datasets are indispensable for a variety of purposes, from ...
متن کاملConditional Sampling Distributions for Coalescent Models Incorporating Recombination
Conditional Sampling Distributions for Coalescent Models Incorporating Recombination by Joshua Samuel Paul Doctor of Philosophy in Computer Science and the Designated Emphasis in Computational and Genomic Biology University of California, Berkeley Professor Yun S. Song, Chair With the volume of available genomic data increasing at an exponential rate, we have unprecedented ability to address ke...
متن کاملFinancial Risk Modeling with Markova Chain
Investors use different approaches to select optimal portfolio. so, Optimal investment choices according to return can be interpreted in different models. The traditional approach to allocate portfolio selection called a mean - variance explains. Another approach is Markov chain. Markov chain is a random process without memory. This means that the conditional probability distribution of the nex...
متن کاملAn Improved Merge-split Sampler for Conjugate Dirichlet Process Mixture Models
The Gibbs sampler is the standard Markov chain Monte Carlo sampler for drawing samples from the posterior distribution of conjugate Dirichlet process mixture models. Researchers have noticed the Gibbs sampler’s tendency to get stuck in local modes and, thus, poorly explore the posterior distribution. Jain and Neal (2004) proposed a merge-split sampler in which a naive random split is sweetened ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Theoretical population biology
دوره 87 شماره
صفحات -
تاریخ انتشار 2013