Polygenic coronary artery disease association with brain atrophy in the cognitively impaired.

While a number of low-frequency genetic variants of large effect size have been shown to underlie both cardiovascular disease and dementia, recent studies have highlighted the importance of common genetic variants of small effect size, which, in aggregate, are embodied by a polygenic risk score. We investigate the effect of polygenic risk for coronary artery disease on brain atrophy in Alzheimer's disease using whole-brain volume and put our findings in context with the polygenic risk for Alzheimer's disease and presumed small vessel disease as quantified by white-matter hyperintensities. We use 730 subjects from the Alzheimer's disease neuroimaging initiative database to investigate polygenic risk score effects (beyond APOE) on whole-brain volumes, total and regional white-matter hyperintensities and amyloid beta across diagnostic groups. In a subset of these subjects (N = 602), we utilized longitudinal changes in whole-brain volume over 24 months using the boundary shift integral approach. Linear regression and linear mixed-effects models were used to investigate the effect of white-matter hyperintensities at baseline as well as Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score on whole-brain atrophy and whole-brain atrophy acceleration, respectively. All genetic associations were examined under the oligogenic (P = 1e-5) and the more variant-inclusive polygenic (P = 0.5) scenarios. Results suggest no evidence for a link between the polygenic risk score and markers of Alzheimer's disease pathology at baseline (when stratified by diagnostic group). However, both Alzheimer's disease-polygenic risk score and coronary artery disease-polygenic risk score were associated with longitudinal decline in whole-brain volume (Alzheimer's disease-polygenic risk score t = 3.3, P FDR = 0.007 over 24 months in healthy controls) and surprisingly, under certain conditions, whole-brain volume atrophy is statistically more correlated with cardiac polygenic risk score than Alzheimer's disease-polygenic risk score (coronary artery disease-polygenic risk score t = 2.1, P FDR = 0.04 over 24 months in the mild cognitive impairment group). Further, in our regional analysis of white-matter hyperintensities, Alzheimer's disease-polygenic risk score beyond APOE is predictive of white-matter volume in the occipital lobe in Alzheimer's disease subjects in the polygenic regime. Finally, the rate of change of brain volume (or atrophy acceleration) may be sensitive to Alzheimer's disease-polygenic risk beyond APOE in healthy individuals (t = 2, P = 0.04). For subjects with mild cognitive impairment, beyond APOE, a more inclusive polygenic risk score including more variants, shows coronary artery disease-polygenic risk score to be more predictive of whole-brain volume atrophy, than an oligogenic approach including fewer larger effect size variants.

A comprehensive analysis of methods for assessing polygenic burden on Alzheimer's disease pathology and risk beyond APOE.

Genome-wide association studies have identified dozens of loci that alter the risk to develop Alzheimer's disease. However, with the exception of the APOE-ε4 allele, most variants bear only little individual effect and have, therefore, limited diagnostic and prognostic value. Polygenic risk scores aim to collate the disease risk distributed across the genome in a single score. Recent works have demonstrated that polygenic risk scores designed for Alzheimer's disease are predictive of clinical diagnosis, pathology confirmed diagnosis and changes in imaging biomarkers. Methodological innovations in polygenic risk modelling include the polygenic hazard score, which derives effect estimates for individual single nucleotide polymorphisms from survival analysis, and methods that account for linkage disequilibrium between genomic loci. In this work, using data from the Alzheimer's disease neuroimaging initiative, we compared different approaches to quantify polygenic disease burden for Alzheimer's disease and their association (beyond the APOE locus) with a broad range of Alzheimer's disease-related traits: cross-sectional CSF biomarker levels, cross-sectional cortical amyloid burden, clinical diagnosis, clinical progression, longitudinal loss of grey matter and longitudinal decline in cognitive function. We found that polygenic scores were associated beyond APOE with clinical diagnosis, CSF-tau levels and, to a minor degree, with progressive atrophy. However, for many other tested traits such as clinical disease progression, CSF amyloid, cognitive decline and cortical amyloid load, the additional effects of polygenic burden beyond APOE were of minor nature. Overall, polygenic risk scores and the polygenic hazard score performed equally and given the ease with which polygenic risk scores can be derived; they constitute the more practical choice in comparison with polygenic hazard scores. Furthermore, our results demonstrate that incomplete adjustment for the APOE locus, i.e. only adjusting for APOE-ε4 carrier status, can lead to overestimated effects of polygenic scores due to APOE-ε4 homozygous participants. Lastly, on many of the tested traits, the major driving factor remained the APOE locus, with the exception of quantitative CSF-tau and p-tau measures.

Regulation of release factor expression using a translational negative feedback loop: a systems analysis.

The essential eukaryote release factor eRF1, encoded by the yeast SUP45 gene, recognizes stop codons during ribosomal translation. SUP45 nonsense alleles are, however, viable due to the establishment of feedback-regulated readthrough of the premature termination codon; reductions in full-length eRF1 promote tRNA-mediated stop codon readthrough, which, in turn, drives partial production of full-length eRF1. A deterministic mathematical model of this eRF1 feedback loop was developed using a staged increase in model complexity. Model predictions matched the experimental observation that strains carrying the mutant SUQ5 tRNA (a weak UAA suppressor) in combination with any of the tested sup45(UAA) nonsense alleles exhibit threefold more stop codon readthrough than that of an SUQ5 yeast strain. The model also successfully predicted that eRF1 feedback control in an SUQ5 sup45(UAA) mutant would resist, but not completely prevent, imposed changes in eRF1 expression. In these experiments, the introduction of a plasmid-borne SUQ5 copy into a sup45(UAA) SUQ5 mutant directed additional readthrough and full-length eRF1 expression, despite feedback. Secondly, induction of additional sup45(UAA) mRNA expression in a sup45(UAA) SUQ5 strain also directed increased full-length eRF1 expression. The autogenous sup45 control mechanism therefore acts not to precisely control eRF1 expression, but rather as a damping mechanism that only partially resists changes in release factor expression level. The validated model predicts that the degree of feedback damping (i.e., control precision) is proportional to eRF1 affinity for the premature stop codon. The validated model represents an important tool to analyze this and other translational negative feedback loops.

Inferring pandemic growth rates from sequence data.

Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic.

Transmission characteristics of the 2009 H1N1 influenza pandemic: comparison of 8 Southern hemisphere countries.

While in Northern hemisphere countries, the pandemic H1N1 virus (H1N1pdm) was introduced outside of the typical influenza season, Southern hemisphere countries experienced a single wave of transmission during their 2009 winter season. This provides a unique opportunity to compare the spread of a single virus in different countries and study the factors influencing its transmission. Here, we estimate and compare transmission characteristics of H1N1pdm for eight Southern hemisphere countries/states: Argentina, Australia, Bolivia, Brazil, Chile, New Zealand, South Africa and Victoria (Australia). Weekly incidence of cases and age-distribution of cumulative cases were extracted from public reports of countries' surveillance systems. Estimates of the reproduction numbers, R(0), empirically derived from the country-epidemics' early exponential phase, were positively associated with the proportion of children in the populations (p = 0.004). To explore the role of demography in explaining differences in transmission intensity, we then fitted a dynamic age-structured model of influenza transmission to available incidence data for each country independently, and for all the countries simultaneously. Posterior median estimates of R0 ranged 1.2-1.8 for the country-specific fits, and 1.29-1.47 for the global fits. Corresponding estimates for overall attack-rate were in the range 20-50%. All model fits indicated a significant decrease in susceptibility to infection with age. These results confirm the transmissibility of the 2009 H1N1 pandemic virus was relatively low compared with past pandemics. The pattern of age-dependent susceptibility found confirms that older populations had substantial--though partial--pre-existing immunity, presumably due to exposure to heterologous influenza strains. Our analysis indicates that between-country-differences in transmission were at least partly due to differences in population demography.

A mathematical modelling framework for elucidating the role of feedback control in translation termination.

Translation is the final stage of gene expression where messenger RNA is used as a template for protein polymerization from appropriate amino acids. Release of the completed protein requires a release factor protein acting at the termination/stop codon to liberate it. In this paper we focus on a complex feedback control mechanism involved in the translation and synthesis of release factor proteins, which has been observed in different systems. These release factor proteins are involved in the termination stage of their own translation. Further, mutations in the release factor gene can result in a premature stop codon. In this case translation can result either in early termination and the production of a truncated protein or readthrough of the premature stop codon and production of the complete release factor protein. Thus during translation of the release factor mRNA containing a premature stop codon, the full length protein negatively regulates its production by its action on a premature stop codon, while positively regulating its production by its action on the regular stop codon. This paper develops a mathematical modelling framework to investigate this complex feedback control system involved in translation. A series of models is established to carefully investigate the role of individual mechanisms and how they work together. The steady state and dynamic behaviour of the resulting models are examined both analytically and numerically.

Autoregulatory systems controlling translation factor expression: thermostat-like control of translational accuracy.

In both prokaryotes and eukaryotes, the expression of a large number of genes is controlled by negative feedback, in some cases operating at the level of translation of the mRNA transcript. Of particular interest are those cases where the proteins concerned have cell-wide function in recognizing a particular codon or RNA sequence. Examples include the bacterial translation termination release factor RF2, initiation factor IF3, and eukaryote poly(A) binding protein. The regulatory loops that control their synthesis establish a negative feedback control mechanism based upon that protein's RNA sequence recognition function in translation (for example, stop codon recognition) without compromising the accurate recognition of that codon, or sequence during general, cell-wide translation. Here, the bacterial release factor RF2 and initiation factor IF3 negative feedback loops are reviewed and compared with similar negative feedback loops that regulate the levels of the eukaryote release factor, eRF1, established artificially by mutation. The control properties of such negative feedback loops are discussed as well as their evolution. The role of negative feedback to control translation factor expression is considered in the context of a growing body of evidence that both IF3 and RF2 can play a role in stimulating stalled ribosomes to abandon translation in response to amino acid starvation. Here, we make the case that negative feedback control serves primarily to limit the overexpression of these translation factors, preventing the loss of fitness resulting from an unregulated increase in the frequency of ribosome drop-off.

Estimating the size of the human interactome.

After the completion of the human and other genome projects it emerged that the number of genes in organisms as diverse as fruit flies, nematodes, and humans does not reflect our perception of their relative complexity. Here, we provide reliable evidence that the size of protein interaction networks in different organisms appears to correlate much better with their apparent biological complexity. We develop a stable and powerful, yet simple, statistical procedure to estimate the size of the whole network from subnet data. This approach is then applied to a range of eukaryotic organisms for which extensive protein interaction data have been collected and we estimate the number of interactions in humans to be approximately 650,000. We find that the human interaction network is one order of magnitude bigger than the Drosophila melanogaster interactome and approximately 3 times bigger than in Caenorhabditis elegans.

The effects of incomplete protein interaction data on structural and evolutionary inferences.

BACKGROUND: Present protein interaction network data sets include only interactions among subsets of the proteins in an organism. Previously this has been ignored, but in principle any global network analysis that only looks at partial data may be biased. Here we demonstrate the need to consider network sampling properties explicitly and from the outset in any analysis. RESULTS: Here we study how properties of the yeast protein interaction network are affected by random and non-random sampling schemes using a range of different network statistics. Effects are shown to be independent of the inherent noise in protein interaction data. The effects of the incomplete nature of network data become very noticeable, especially for so-called network motifs. We also consider the effect of incomplete network data on functional and evolutionary inferences. CONCLUSION: Crucially, when only small, partial network data sets are considered, bias is virtually inevitable. Given the scope of effects considered here, previous analyses may have to be carefully reassessed: ignoring the fact that present network data are incomplete will severely affect our ability to understand biological systems.

Recombination hotspots as a point process.

The variation of the recombination rate along chromosomal DNA is one of the important determinants of the patterns of linkage disequilibrium. A number of inferential methods have been developed which estimate the recombination rate and its variation from population genetic data. The majority of these methods are based on modelling the genealogical process underlying a sample of DNA sequences and thus explicitly include a model of the demographic process. Here we propose a different inferential procedure based on a previously introduced framework where recombination is modelled as a point process along a DNA sequence. The approach infers regions containing putative hotspots based on the inferred minimum number of recombination events; it thus depends only indirectly on the underlying population demography. A Poisson point process model with local rates is then used to infer patterns of recombination rate estimation in a fully Bayesian framework. We illustrate this new approach by applying it to several population genetic datasets, including a region with an experimentally confirmed recombination hotspot.

Complex networks and simple models in biology.

The analysis of molecular networks, such as transcriptional, metabolic and protein interaction networks, has progressed substantially because of the power of models from statistical physics. Increasingly, the data are becoming so detailed--though not always complete or correct--that the simple models are reaching the limits of their usefulness. Here, we will discuss how network information can be described and to some extent quantified. In particular statistics offers a range of tools, such as model selection, which have not yet been widely applied in the analysis of biological networks. We will also outline a number of present challenges posed by biological network data in systems biology, and the extent to which these can be addressed by new developments in statistics, physics and applied mathematics.

The extent and importance of intragenic recombination.

We have studied the recombination rate behaviour of a set of 140 genes which were investigated for their potential importance in inflammatory disease. Each gene was extensively sequenced in 24 individuals of African descent and 23 individuals of European descent, and the recombination process was studied separately in the two population samples. The results obtained from the two populations were highly correlated, suggesting that demographic bias does not affect our population genetic estimation procedure. We found evidence that levels of recombination correlate with levels of nucleotide diversity. High marker density allowed us to study recombination rate variation on a very fine spatial scale. We found that about 40 per cent of genes showed evidence of uniform recombination, while approximately 12 per cent of genes carried distinct signatures of recombination hotspots. On studying the locations of these hotspots, we found that they are not always confined to introns but can also stretch across exons. An investigation of the protein products of these genes suggested that recombination hotspots can sometimes separate exons belonging to different protein domains; however, this occurs much less frequently than might be expected based on evolutionary studies into the origins of recombination. This suggests that evolutionary analysis of the recombination process is greatly aided by considering nucleotide sequences and protein products jointly.

HIV and the CCR5-Delta32 resistance allele.

The combination of molecular biology, epidemiology, virology, evolutionary and population genetics has enabled us to understand the delicate interplay between HIV and the CCR5-Delta32 HIV resistance allele. We here review and collect from the different approaches to show how they can be combined to elucidate the interaction between host and pathogen genetics in this system. We will present an overview of the normal role of CCR5, its involvement in HIV, the molecular biology of the CCR5-Delta32 allele and its probable origins. By focusing on this well-documented and important system we hope to demonstrate the power that such a "holistic" approach might offer in the study of infectious diseases.