A comprehensive survey of models for dissecting local ancestry deconvolution in human genome.

Over the past decade, studies of admixed populations have increasingly gained interest in both medical and population genetics. These studies have so far shed light on the patterns of genetic variation throughout modern human evolution and have improved our understanding of the demographics and adaptive processes of human populations. To date, there exist about 20 methods or tools to deconvolve local ancestry. These methods have merits and drawbacks in estimating local ancestry in multiway admixed populations. In this article, we survey existing ancestry deconvolution methods, with special emphasis on multiway admixture, and compare these methods based on simulation results reported by different studies, computational approaches used, including mathematical and statistical models, and biological challenges related to each method. This should orient users on the choice of an appropriate method or tool for given population admixture characteristics and update researchers on current advances, challenges and opportunities behind existing ancestry deconvolution methods.

A post-gene silencing bioinformatics protocol for plant-defence gene validation and underlying process identification: case study of the Arabidopsis thaliana NPR1.

BACKGROUND: Advances in forward and reverse genetic techniques have enabled the discovery and identification of several plant defence genes based on quantifiable disease phenotypes in mutant populations. Existing models for testing the effect of gene inactivation or genes causing these phenotypes do not take into account eventual uncertainty of these datasets and potential noise inherent in the biological experiment used, which may mask downstream analysis and limit the use of these datasets. Moreover, elucidating biological mechanisms driving the induced disease resistance and influencing these observable disease phenotypes has never been systematically tackled, eliciting the need for an efficient model to characterize completely the gene target under consideration. RESULTS: We developed a post-gene silencing bioinformatics (post-GSB) protocol which accounts for potential biases related to the disease phenotype datasets in assessing the contribution of the gene target to the plant defence response. The post-GSB protocol uses Gene Ontology semantic similarity and pathway dataset to generate enriched process regulatory network based on the functional degeneracy of the plant proteome to help understand the induced plant defence response. We applied this protocol to investigate the effect of the NPR1 gene silencing to changes in Arabidopsis thaliana plants following Pseudomonas syringae pathovar tomato strain DC3000 infection. Results indicated that the presence of a functionally active NPR1 reduced the plant's susceptibility to the infection, with about 99% of variability in Pseudomonas spore growth between npr1 mutant and wild-type samples. Moreover, the post-GSB protocol has revealed the coordinate action of target-associated genes and pathways through an enriched process regulatory network, summarizing the potential target-based induced disease resistance mechanism. CONCLUSIONS: This protocol can improve the characterization of the gene target and, potentially, elucidate induced defence response by more effectively utilizing available phenotype information and plant proteome functional knowledge.

A multi-scenario genome-wide medical population genetics simulation framework.

MOTIVATION: Recent technological advances in high-throughput sequencing and genotyping have facilitated an improved understanding of genomic structure and disease-associated genetic factors. In this context, simulation models can play a critical role in revealing various evolutionary and demographic effects on genomic variation, enabling researchers to assess existing and design novel analytical approaches. Although various simulation frameworks have been suggested, they do not account for natural selection in admixture processes. Most are tailored to a single chromosome or a genomic region, very few capture large-scale genomic data, and most are not accessible for genomic communities. RESULTS: Here we develop a multi-scenario genome-wide medical population genetics simulation framework called 'FractalSIM'. FractalSIM has the capability to accurately mimic and generate genome-wide data under various genetic models on genetic diversity, genomic variation affecting diseases and DNA sequence patterns of admixed and/or homogeneous populations. Moreover, the framework accounts for natural selection in both homogeneous and admixture processes. The outputs of FractalSIM have been assessed using popular tools, and the results demonstrated its capability to accurately mimic real scenarios. They can be used to evaluate the performance of a range of genomic tools from ancestry inference to genome-wide association studies. AVAILABILITY AND IMPLEMENTATION: The FractalSIM package is available at http://www.cbio.uct.ac.za/FractalSIM. CONTACT: emile.chimusa@uct.ac.za. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.