Epigenetic inheritance in plants.

The function of plant genomes depends on chromatin marks such as the methylation of DNA and the post-translational modification of histones. Techniques for studying model plants such as Arabidopsis thaliana have enabled researchers to begin to uncover the pathways that establish and maintain chromatin modifications, and genomic studies are allowing the mapping of modifications such as DNA methylation on a genome-wide scale. Small RNAs seem to be important in determining the distribution of chromatin modifications, and RNA might also underlie the complex epigenetic interactions that occur between homologous sequences. Plants use these epigenetic silencing mechanisms extensively to control development and parent-of-origin imprinted gene expression.

Robust estimation of local genetic ancestry in admixed populations using a nonparametric Bayesian approach.

We present a new haplotype-based approach for inferring local genetic ancestry of individuals in an admixed population. Most existing approaches for local ancestry estimation ignore the latent genetic relatedness between ancestral populations and treat them as independent. In this article, we exploit such information by building an inheritance model that describes both the ancestral populations and the admixed population jointly in a unified framework. Based on an assumption that the common hypothetical founder haplotypes give rise to both the ancestral and the admixed population haplotypes, we employ an infinite hidden Markov model to characterize each ancestral population and further extend it to generate the admixed population. Through an effective utilization of the population structural information under a principled nonparametric Bayesian framework, the resulting model is significantly less sensitive to the choice and the amount of training data for ancestral populations than state-of-the-art algorithms. We also improve the robustness under deviation from common modeling assumptions by incorporating population-specific scale parameters that allow variable recombination rates in different populations. Our method is applicable to an admixed population from an arbitrary number of ancestral populations and also performs competitively in terms of spurious ancestry proportions under a general multiway admixture assumption. We validate the proposed method by simulation under various admixing scenarios and present empirical analysis results from a worldwide-distributed dataset from the Human Genome Diversity Project.