Biology subjects

Liuyang Wang

Publications and source records attributed to Liuyang Wang.

2 recordsLinked to original sources

bioRxiv2016

MINOTAUR: A platform for the analysis and visualization of multivariate results from genome scans with R Shiny

Genome scans are widely used to identify 'outliers' in genomic data: loci with different patterns compared with the rest of the genome due to the action of selection or other non-adaptive forces of evolution. These genomic datasets are often high-dimensional, with complex correlation structures among variables, making it a challenge to identify outliers in a robust way. The Mahalanobis distance has been widely used for this purpose, but has the major limitation of assuming that data follow a simple parametric distribution. Here we develop three new metrics that can be used to identify outliers in multivariate space, while making no strong assumptions about the distribution of the data. These metrics are implemented in the R package MINOTAUR, which also includes an interactive web-based application for visualizing outliers in high-dimensional datasets. We illustrate how these metrics can be used to identify outliers from simulated genetic data, and discuss some of the limitations they may face in application.

Genomics↗

bioRxiv2015

Bayesian co-estimation of selfing rate and locus-specific mutation rates for a partially selfing population

We present a Bayesian method for characterizing the mating system of populations reproducing through a mixture of self-fertilization and random outcrossing. Our method uses patterns of genetic variation across the genome as a basis for inference about pure hermaphroditism, androdioecy, and gynodioecy. We extend the standard coalescence model to accommodate these mating systems, accounting explicitly for multilocus identity disequilibrium, inbreeding depression, and variation in fertility among mating types. We incorporate the Ewens Sampling Formula (ESF) under the infinite-alleles model of mutation to obtain a novel expression for the likelihood of mating system parameters. Our Markov chain Monte Carlo (MCMC) algorithm assigns locus-specific mutation rates, drawn from a common mutation rate distribution that is itself estimated from the data using a Dirichlet Process Prior model. Among the parameters jointly inferred are the population-wide rate of self-fertilization, locus-specific mutation rates, and the number of generations since the most recent outcrossing event for each sampled individual.

Evolutionary Biology↗