Development and validation of 'AutoRIF': Software for the automated analysis of radiation-induced foci

Copyright @ 2012 McVean et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.Background: The quantification of radiation-induced foci (RIF) to investigate the induction and subsequent repair of DNA double strands breaks is now commonplace. Over the last decade systems specific for the automatic quantification of RIF have been developed for this purpose, however to ask more mechanistic questions on the spatio-temporal aspects of RIF, an automated RIF analysis platform that also quantifies RIF size/volume and relative three-dimensional (3D) distribution of RIF within individual nuclei, is required. Results: A java-based image analysis system has been developed (AutoRIF) that quantifies the number, size/volume and relative nuclear locations of RIF within 3D nuclear volumes. Our approach identifies nuclei using the dynamic Otsu threshold and RIF by enhanced Laplacian filtering and maximum entropy thresholding steps and, has an application ‘batch optimisation’ process to ensure reproducible quantification of RIF. AutoRIF was validated by comparing output against manual quantification of the same 2D and 3D image stacks with results showing excellent concordance over a whole range of sample time points (and therefore range of total RIF/nucleus) after low-LET radiation exposure. Conclusions: This high-throughput automated RIF analysis system generates data with greater depth of information and reproducibility than that which can be achieved manually and may contribute toward the standardisation of RIF analysis. In particular, AutoRIF is a powerful tool for studying spatio-temporal relationships of RIF using a range of DNA damage response markers and can be run independently of other software, enabling most personal computers to perform image analysis. Future considerations for AutoRIF will likely include more complex algorithms that enable multiplex analysis for increasing combinations of cellular markers.This article is made available through the Brunel Open Access Publishing Fund

Gene-history correlation and population structure

Correlation of gene histories in the human genome determines the patterns of genetic variation (haplotype structure) and is crucial to understanding genetic factors in common diseases. We derive closed analytical expressions for the correlation of gene histories in established demographic models for genetic evolution and show how to extend the analysis to more realistic (but more complicated) models of demographic structure. We identify two contributions to the correlation of gene histories in divergent populations: linkage disequilibrium, and differences in the demographic history of individuals in the sample. These two factors contribute to correlations at different length scales: the former at small, and the latter at large scales. We show that recent mixing events in divergent populations limit the range of correlations and compare our findings to empirical results on the correlation of gene histories in the human genome.Comment: Revised and extended version: 26 pages, 5 figures, 1 tabl

Positive Selection and Increased Antiviral Activity Associated with the PARP-Containing Isoform of Human Zinc-Finger Antiviral Protein

Intrinsic immunity relies on specific recognition of viral epitopes to mount a cell-autonomous defense against viral infections. Viral recognition determinants in intrinsic immunity genes are expected to evolve rapidly as host genes adapt to changing viruses, resulting in a signature of adaptive evolution. Zinc-finger antiviral protein (ZAP) from rats was discovered to be an intrinsic immunity gene that can restrict murine leukemia virus, and certain alphaviruses and filoviruses. Here, we used an approach combining molecular evolution and cellular infectivity assays to address whether ZAP also acts as a restriction factor in primates, and to pinpoint which protein domains may directly interact with the virus. We find that ZAP has evolved under positive selection throughout primate evolution. Recurrent positive selection is only found in the poly(ADP-ribose) polymerase (PARP)–like domain present in a longer human ZAP isoform. This PARP-like domain was not present in the previously identified and tested rat ZAP gene. Using infectivity assays, we found that the longer isoform of ZAP that contains the PARP-like domain is a stronger suppressor of murine leukemia virus expression and Semliki forest virus infection. Our study thus finds that human ZAP encodes a potent antiviral activity against alphaviruses. The striking congruence between our evolutionary predictions and cellular infectivity assays strongly validates such a combined approach to study intrinsic immunity genes

Perspectives on Human Genetic Variation from the HapMap Project

The completion of the International HapMap Project marks the start of a new phase in human genetics. The aim of the project was to provide a resource that facilitates the design of efficient genome-wide association studies, through characterising patterns of genetic variation and linkage disequilibrium in a sample of 270 individuals across four geographical populations. In total, over one million SNPs have been typed across these genomes, providing an unprecedented view of human genetic diversity. In this review we focus on what the HapMap project has taught us about the structure of human genetic variation and the fundamental molecular and evolutionary processes that shape it

Bayesian meta-analysis across genome-wide association studies of diverse phenotypes

Genome-wide association studies (GWAS) are a powerful tool for understanding the genetic basis of diseases and traits, but most studies have been conducted in isolation, with a focus on either a single or a set of closely related phenotypes. We describe MetABF, a simple Bayesian framework for performing integrative meta-analysis across multiple GWAS using summary statistics. The approach is applicable across a wide range of study designs and can increase the power by 50% compared with standard frequentist tests when only a subset of studies have a true effect. We demonstrate its utility in a meta-analysis of 20 diverse GWAS which were part of the Wellcome Trust Case Control Consortium 2. The novelty of the approach is its ability to explore, and assess the evidence for a range of possible true patterns of association across studies in a computationally efficient framework.Peer reviewe

How Many Subpopulations is Too Many? Exponential Lower Bounds for Inferring Population Histories

Reconstruction of population histories is a central problem in population genetics. Existing coalescent-based methods, like the seminal work of Li and Durbin (Nature, 2011), attempt to solve this problem using sequence data but have no rigorous guarantees. Determining the amount of data needed to correctly reconstruct population histories is a major challenge. Using a variety of tools from information theory, the theory of extremal polynomials, and approximation theory, we prove new sharp information-theoretic lower bounds on the problem of reconstructing population structure -- the history of multiple subpopulations that merge, split and change sizes over time. Our lower bounds are exponential in the number of subpopulations, even when reconstructing recent histories. We demonstrate the sharpness of our lower bounds by providing algorithms for distinguishing and learning population histories with matching dependence on the number of subpopulations. Along the way and of independent interest, we essentially determine the optimal number of samples needed to learn an exponential mixture distribution information-theoretically, proving the upper bound by analyzing natural (and efficient) algorithms for this problem.Comment: 38 pages, Appeared in RECOMB 201

Population Stratification of a Common APOBEC Gene Deletion Polymorphism

The APOBEC3 gene family plays a role in innate cellular immunity inhibiting retroviral infection, hepatitis B virus propagation, and the retrotransposition of endogenous elements. We present a detailed sequence and population genetic analysis of a 29.5-kb common human deletion polymorphism that removes the APOBEC3B gene. We developed a PCR-based genotyping assay, characterized 1,277 human diversity samples, and found that the frequency of the deletion allele varies significantly among major continental groups (global F (ST) = 0.2843). The deletion is rare in Africans and Europeans (frequency of 0.9% and 6%), more common in East Asians and Amerindians (36.9% and 57.7%), and almost fixed in Oceanic populations (92.9%). Despite a worldwide frequency of 22.5%, analysis of data from the International HapMap Project reveals that no single existing tag single nucleotide polymorphism may serve as a surrogate for the deletion variant, emphasizing that without careful analysis its phenotypic impact may be overlooked in association studies. Application of haplotype-based tests for selection revealed potential pitfalls in the direct application of existing methods to the analysis of genomic structural variation. These data emphasize the importance of directly genotyping structural variation in association studies and of accurately resolving variant breakpoints before proceeding with more detailed population-genetic analysis