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bioRxiv2017

Bayesian Inference Of Phylogenetic Networks From Bi-allelic Genetic Markers

Phylogenetic networks are rooted, directed, acyclic graphs that model reticulate evolutionary histories. Recently, statistical methods were devised for inferring such networks from either gene tree estimates or the sequence alignments of multiple unlinked loci. Bi-allelic markers, most notably single nucleotide polymorphisms (SNPs) and amplified fragment length polymorphisms (AFLPs), provide a powerful source of genome-wide data. In a recent paper, a method called SNAPP was introduced for statistical inference of species trees from unlinked bi-allelic markers. The generative process assumed by the method combined both a model of evolution for the bi-allelic markers, as well as the multispecies coalescent. A novel component of the method was a polynomial-time algorithm for exact computation of the likelihood of a fixed species tree via integration over all possible gene trees for a given marker. Here we report on a method for Bayesian inference of phylogenetic networks from bi-allelic markers. Our method significantly extends the algorithm for exact computation of phylogenetic network likelihood via integration over all possible gene trees. Unlike the case of species trees, the algorithm is no longer polynomial-time on all instances of phylogenetic networks. Furthermore, the method utilizes a reversible-jump MCMC technique to sample the posterior of phylogenetic networks given bi-allelic marker data. Our method has a very good performance in terms of accuracy and robustness as we demonstrate on simulated data, as well as a data set of multiple New Zealand species of the plant genus Ourisia(Plantaginaceae). We implemented the method in the publicly available, open-source PhyloNet software package.\n\nAuthor summaryThe availability of genomic data has revolutionized the study of evolutionary histories and phylogeny inference. Inferring evolutionary histories from genomic data requires, in most cases, accounting for the fact that different genomic regions could have evolutionary histories that differ from each other as well as from that of the species from which the genomes were sampled. In this paper, we introduce a method for inferring evolutionary histories while accounting for two processes that could give rise to such differences across the genomes, namely incomplete lineage sorting and hybridization. We introduce a novel algorithm for computing the likelihood of phylogenetic networks from bi-allelic genetic markers and use it in a Bayesian inference method. Analyses of synthetic and empirical data sets show a very good performance of the method in terms of the estimates it obtains.

evolutionary biology↗

bioRxiv2017

Orexin/Hypocretin Immunoreactivity In The Lateral Hypothalamus Is Reduced In Genetically Obese But Not In Diet-Induced Obese Mice

The mechanisms that link diet and body weight are not fully understood. A diet high in fat often leads to obesity, and this in part is the consequence of diet-induced injury to specific hypothalamic nuclei. It has been suggested that a diet high in fat leads to cell loss in the lateral hypothalamus, which contains specific populations of neurones that are essential for regulating energy homoeostasis; however, we do not know which cell types are affected by the diet. We studied the possibility that high-fat diet leads to a reduction in orexin/hypocretin (O/H) and/or melanin-concentrating hormone (MCH) immunoreactivity in the lateral hypothalamus. We quantified immuno-labelled O/H and MCH cells in brain sections of mice fed a diet high in fat for up to 12 weeks starting at 4 weeks of age and found that this diet did not modify the number of O/H- or MCH-immunoreactive neurones. By contrast, there were fewer O/H- (but not MCH-) immunoreactive cells in geneticallyobese db/db mice compared to wild-type mice. Non-obese, heterozygous db/+ mice also had fewer O/H-immunoreactive cells. Differences in the number of O/H-immunoreactive cells were only a function of the db genotype but not of diet or body weight. Our findings show that the lateral hypothalamus is affected differently in genetic and in diet-induced obesity, and support the idea that hypothalamic neurones involved in energy balance regulation are not equally sensitive to the effects of diet.

neuroscience↗

bioRxiv2017

Genetic architecture of early childhood growth phenotypes gives insights into their link with later obesity

Early childhood growth patterns are associated with adult metabolic health, but the underlying mechanisms are unclear. We performed genome-wide meta-analyses and follow-up in up to 22,769 European children for six early growth phenotypes derived from longitudinal data: peak height and weight velocities, age and body mass index (BMI) at adiposity peak (AP ~9 months) and rebound (AR ~5-6 years). We identified four associated loci (P< 5x10-8): LEPR/LEPROT with BMI at AP, FTO and TFAP2B with Age at AR and GNPDA2 with BMI at AR. The observed AR-associated SNPs at FTO, TFAP2B and GNPDA2 represent known adult BMI-associated variants. The common BMI at AP associated variant at LEPR/LEPROT was not associated with adult BMI but was associated with LEPROT gene expression levels, especially in subcutaneous fat (P<2x10-51). We identify strong positive genetic correlations between early growth and later adiposity traits, and analysis of the full discovery stage results for Age at AR revealed enrichment for insulin-like growth factor 1 (IGF-1) signaling and apolipoprotein pathways. This genome-wide association study suggests mechanistic links between early childhood growth and adiposity in later childhood and adulthood, highlighting these early growth phenotypes as potential targets for the prevention of obesity.

genomics↗

bioRxiv2017

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large scale genetic studies

BackgroundGenetic analyses of plant root system development require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming).\n\nFindingsWe trained a Random Forest algorithm to infer architectural traits from automatically-extracted image descriptors. The training was performed on a subset of the dataset, then applied to its entirety. This strategy allowed us to (i) decrease the image analysis time by 73% and (ii) extract meaningful architectural traits based on image descriptors. We also show that these traits are sufficient to identify Quantitative Trait Loci that had previously been discovered using a semi-automated method.\n\nConclusionsWe have shown that combining semi-automated image analysis with machine learning algorithms has the power to increase the throughput in large scale root studies. We expect that such an approach will enable the quantification of more complex root systems for genetic studies. We also believe that our approach could be extended to other area of plant phenotyping.

plant biology↗

bioRxiv2017

Determining controllability of sepsis using genetic algorithms on a proxy agent-based model of systemic inflammation

Sepsis, a manifestation of the bodys inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process in the clinical setting. We propose that this is in great part due to the considerable heterogeneity of the clinical trajectories that constitute clinical \"sepsis,\" and that determining how this system can be controlled back into a state of health requires the application of concepts drawn from the field of dynamical systems. In this work, we consider the human immune system to be a random dynamical system, and investigate its potential controllability using an agent-based model of the innate immune response (the Innate Immune Response ABM or IIRABM) as a surrogate, proxy system. Simulation experiments with the IIRABM provide an explanation as to why single/limited cytokine perturbations at a single, or small number of, time points is unlikely to significantly improve the mortality rate of sepsis. We then use genetic algorithms (GA) to explore and characterize multi-targeted control strategies for the random dynamical immune system that guide it from a persistent, non-recovering inflammatory state (functionally equivalent to the clinical states of systemic inflammatory response syndrome (SIRS) or sepsis) to a state of health. We train the GA on a single parameter set with multiple stochastic replicates, and show that while the calculated results show good generalizability, more advanced strategies are needed to achieve the goal of adaptive personalized medicine. This work evaluating the extent of interventions needed to control a simplified surrogate model of sepsis provides insight into the scope of the clinical challenge, and can serve as a guide on the path towards true \"precision control\" of sepsis.\n\nAuthor summarySepsis, characterized by the bodys inflammatory response to injury and infection, has a mortality rate of between 28%-50% and affects approximately 1 million patients annually in the United States. Currently, there are no therapies targeting the cellular/molecular processes driving sepsis that have demonstrated the ability to control this disease process. In this work, we utilize a computational model of the human immune response to infectious injury to offer an explanation as to why previously attempted treatment strategies are inadequate and why the current approach to drug/therapy-development is inadequate. We then use evolutionary computation algorithms to explore drug-intervention space using this same computational model. This allows us to characterize the scale and scope of interventions needed to successfully control sepsis, as well as the types of data needed to derive these interventions. We demonstrate that multi-point and time-dependent varying controls are necessary and able to control the cytokine network dynamics of the immune system.

systems biology↗

bioRxiv2017

A computational panel of pathological RAS mutants with implications for personalized medicine and genetic medicine

The RAS proteins (KRAS, NRAS, and HRAS) play important roles in multiple diseases. This includes many types of cancer and the developmental syndromes collectively referred to as the RASopathies. There are many different RAS mutants that are found to drive these diseases. Mutant-to-mutant differences pose a challenge for personalized medicine. To investigate this problem, we extend our previously developed model of oncogenic RAS mutants to a total of 16 oncogenic mutants. We also extend our model to RASopathy associated mutants using data for 14 such RAS mutants. The model finds that the known biochemical defects of these mutants are typically sufficient to explain their elevated levels of RAS signaling. In general, our analysis finds that the oncogenic mutants are stronger than the RASopathy mutants. However, the model suggests that RAS signal intensities are spanned by the pathological variants; there does not appear to be a perfect separation between cancer promoting and developmental syndrome promoting mutants. Analysis of the panel also finds that the relative strengths of pathological RAS mutants is not absolute, but rather can vary depending on context. We discuss implications of this finding for personalized cancer medicine and for medical genetics. As genomics permeates clinical medicine, computational models that can resolve mutant specific differences, like the one presented here, may be useful for augmenting clinical thinking with their ability to logically translate biochemical knowledge into system level outputs of perceived clinical relevance.

pathology↗

bioRxiv2017

Bacteria facilitate viral co-infection of mammalian cells and promote genetic recombination

Intestinal bacteria promote infection of several mammalian enteric viruses, but the mechanisms and consequences are unclear. We screened a panel of 41 bacterial strains as a platform to determine how different bacteria impact enteric viruses. We found that most bacterial strains bound poliovirus, a model enteric virus. Given that each bacterium bound multiple virions, we hypothesized that bacteria may deliver multiple viral genomes to a mammalian cell even when very few virions are present, such as during the first replication cycle after inter-host transmission. We found that exposure to certain bacterial strains increased viral co-infection even when the ratio of virus to host cells was low. Bacteria-mediated viral co-infection correlated with bacterial adherence to cells. Importantly, bacterial strains that induced viral co-infection facilitated viral fitness restoration through genetic recombination. Thus, bacteria-virus interactions may increase viral fitness through viral recombination at initial sites of infection, potentially limiting abortive infections.

microbiology↗

bioRxiv2017

Natural Genetic Variation Shapes Root System Responses to Phytohormones in Arabidopsis

Plants adjust their architecture by modulating organ growth. This ability is largely dependent on phytohormones. While many genes involved in phytohormone pathways have been identified, it remains unclear to which extent and how these pathways are modulated in non-reference strains and whether this is relevant for local adaptation. Here we assess variation of root traits in response to perturbations of the auxin, cytokinin, and abscisic acid pathways in 192 Arabidopsis accessions. We identify common response patterns, uncover the extent of their modulation by specific genotypes, and find that the Col-0 reference accession is not a good representative of the species in this regard. We conduct GWAS and identify 114 significant associations, most of them relating to ABA treatment. The numerous ABA candidate genes are not enriched for known ABA associated genes indicating that we largely uncovered unknown players. We then study two associated regions in detail and identify the CRF3 gene as a modulator of multiple hormone pathways. Finally, we show that natural variation in root traits is significantly associated with climate parameters relevant for local adaption in Arabidopsis thaliana and that, in particular, ABA regulated lateral root traits are likely to be relevant for adaptation to soil moisture.\n\nAuthor SummaryThe root system is a key component for plant survival and productivity. Apart from anchoring the plant, its architecture determines which parts of the soil are foraged for water and nutrients, and it serves as an interface for interaction with microbes and other soil organisms. Plant hormones play crucial roles in the development of root system architecture and its plasticity. However, while there is substantial natural variation of root architectures, it is not clear to which extent genetic variation in hormone related pathways contribute. Using the model species Arabidopsis thaliana we quantitatively explore the breadth of natural variation in plant hormone responses to three major plant hormones: auxin, cytokinin, and abscisic acid. The Col-0 reference strain can be quite different from a large proportion of the natural accessions of the species, illustrating a severe caveat of relying on a single reference strain in model species and drawing generalizations from it. Using GWAS, we further identify a large number of loci underlying the variation of responses to plant hormones, in particular to abscisic acid, find links between local adaptation and root responses to hormones, and finally using mutants for GWAS candidate genes, identify novel players involved in regulating hormone responses.

plant biology↗

bioRxiv2017

The genetic basis and evolution of red blood cell sickling in deer

Crescent-shaped red blood cells, the hallmark of sickle cell disease, present a striking departure from the biconcave disc shape normally found in mammals. Characterized by increased mechanical fragility, sickled cells promote haemolytic anaemia and vaso-occlusions and contribute directly to disease in humans. Remarkably, a similar sickle-shaped morphology has been observed in erythrocytes from several deer species, without pathological consequences. The genetic basis of erythrocyte sickling in deer, however, remains unknown, limiting the utility of deer as comparative models for sickling. Here, we determine the sequences of human {beta}-globin orthologs in 15 deer species and identify a set of co-evolving, structurally related residues that distinguish sickling from non-sickling deer. Protein structural modelling indicates a sickling mechanism distinct from human sickle cell disease, coordinated by a derived valine (E22V) in the second alpha helix of the {beta}-globin protein. The evolutionary history of deer {beta}-globins is characterized by incomplete lineage sorting, episodes of gene conversion between adult and foetal {beta}-globin paralogs, and the presence of a trans-species polymorphism that is best explained by long-term balancing selection, suggesting that sickling in deer is adaptive. Our results reveal structural and evolutionary parallels and differences in erythrocyte sickling between human and deer, with implications for understanding the ecological regimes and molecular architectures that favour the evolution of this dramatic change in erythrocyte shape.

evolutionary biology↗

bioRxiv2017

Genetic-metabolic coupling for targeted metabolic engineering

To produce chemicals, microbes typically employ potent biosynthetic enzymes that interact with native metabolism to affect cell fitness as well as chemical production. However, production optimization largely relies on data collected from wild type strains in the absence of metabolic perturbations, thus limiting their relevance to specific process scenarios. Here, we address this issue by coupling cell fitness to the production of thiamine diphosphate in Escherichia coli using a synthetic RNA biosensor.\n\nWe apply this system to interrogate a library of transposon mutants to elucidate the native gene network influencing both cell fitness and thiamine production. Specifically, we identify uncharacterized effectors of the OxyR-SoxR stress response that limit thiamine biosynthesis via alternative regulation of iron storage and Fe-S-cluster inclusion in enzymes.\n\nOur study represents a new generalizable approach for the reliable high-throughput identification of genetic targets of both known and unknown function that are directly relevant to a specific biosynthetic process.

bioengineering↗

bioRxiv2017

PHYLOSCANNER: Analysing Within- and Between-Host Pathogen Genetic Diversity to Identify Transmission, Multiple Infection, Recombination and Contamination

A central feature of pathogen genomics is that different infectious particles (virions, bacterial cells, etc.) within an infected individual may be genetically distinct, with patterns of relatedness amongst infectious particles being the result of both within-host evolution and transmission from one host to the next. Here we present a new software tool, phyloscanner, which analyses pathogen diversity from multiple infected hosts. phyloscanner provides unprecedented resolution into the transmission process, allowing inference of the direction of transmission from sequence data alone. Multiply infected individuals are also identified, as they harbour subpopulations of infectious particles that are not connected by within-host evolution, except where recombinant types emerge. Low-level contamination is flagged and removed. We illustrate phyloscanner on both viral and bacterial pathogens, namely HIV-1 sequenced on Illumina and Roche 454 platforms, HCV sequenced with the Oxford Nanopore MinION platform, and Streptococcus pneumoniae with sequences from multiple colonies per individual. phyloscanner is available from https://github.com/BDI-pathogens/phyloscanner.

evolutionary biology↗

bioRxiv2017

Reducing Pyrrolysine tRNA Copy Number Improves the Performance of Genetic Code Expansion in Live Cell Imaging of Bioorthogonally Labeled Proteins

Genetic code expansion enables the incorporation of non-canonical amino acids (ncAAs) into expressed proteins. ncAAs are usually encoded by a stop codon that is decoded by an exogenous orthogonal aminoacyl tRNA synthetase and its cognate suppressor tRNA, such as the pyrrolysine synthetase/[Formula] pair. In such systems, stop codon suppression is dependent on the intracellular levels of the exogenous tRNA. Therefore, multiple copies of the tRNAPyl gene (PylT) are encoded to improve ncAA incorporation. However, certain applications in mammalian cells, such as live-cell imaging applications, where labelled tRNA contributes to background fluorescence, can benefit from the use of less invasive minimal expression systems. Accordingly, we studied the effect of tRNAPyl on live-cell fluorescence imaging of bioorthogonally-labelled intracellular proteins. We found that in COS7 cells, a decrease in PylT copy numbers had no measurable effect on protein expression levels. Importantly, reducing PylT copy numbers improved the quality of live-cells images by enhancing the signal-to-noise ratio and reducing an immobile tRNAPyl population. This enabled us to improve live cell imaging of bioorthogonally labelled intracellular proteins, and to simultaneously label two different proteins in a cell. Our results indicate that the number of introduced PylT genes can be minimized according to the transfected cell line, incorporated ncAA, and application.

synthetic biology↗

bioRxiv2017

Innovative assembly strategy contributes to the understanding of evolution and conservation genetics of the critically endangered Solenodon paradoxus from the island of Hispaniola

Solenodons are insectivores living on the Caribbean islands, with few surviving related taxa. The genus occupies one of the most ancient branches among the placental mammals. The history, unique biology and adaptations of these enigmatic venomous species, can be greatly advanced given the availability of genome data, but the whole genome assembly for solenodons has never been previously performed, partially due to the difficulty in obtaining samples from the field. Island isolation has likely resulted in extreme homozygosity within the Hispaniolan solenodon (Solenodon paradoxus), thus we tested the performance of several assembly strategies for performance with genetically impoverished species genomes. The string-graph based assembly strategy seems a better choice compared to the conventional de Brujn graph approach, due to the high levels of homozygosity, which is often a hallmark of endemic or endangered species. A consensus reference genome was assembled from sequences of five individuals from the southern subspecies (S. p. woodi). In addition, we obtained one additional sequence of the northern subspecies (S. p. paradoxus). The resulting genome assemblies were compared to each other, and annotated for genes, with a specific emphasis on the venomous genes, repeats, variable microsatellite loci and other genomic variants. Phylogenetic positioning and selection signatures were inferred based on 4,416 single copy orthologs from 10 other mammals. Patterns of SNP variation allowed us to infer population demography, which indicated a subspecies split within the Hispaniolan solenodon at least 300 Kya.

evolutionary biology↗

bioRxiv2017

Limited genetic variation for male mating success reveals low evolutionary potential for thermal plasticity in Drosophila melanogaster

Populations respond to novel environmental challenges either through genetic changes, through adaptive phenotypic plasticity for the traits in question, or by a combination of these factors. Here, we investigated the evolutionary potential of phenotypic plasticity for male mating success, locomotory ability, and heating rate (a physiological performance trait) in the fruitfly Drosophila melanogaster, using isogenic male lines from the Drosophila Reference Genome Panel (DGRP) and hemi-clonal males. We quantified thermal reaction norms of how male mating success changed in relation to a temperate gradient, ranging from cold (18 {degrees}C) via optimal (24 {degrees}C) to hot and stressful environments (either 30 {degrees}C or 36 {degrees}C). We found significant differences in male mating success and locomotory performance between different lines, as well as significant main effects of temperature, but no significant genotype-by-environment interactions (GEI:s). A statistical power analysis revealed that the variance explained by GEI:s for thermal plasticity using this sample size is likely to be modest or very small, and represent only 4% of the total variation in male mating success. The lack of strong GEI:s for these two behavioral traits contrast with the presence of significant GEI:s for male heating rate, as measured by thermal imaging (infrared camera technology). These results suggest that sexual selection through male mating success is not likely to be efficient in mediating evolutionary rescue through changed plasticity in response to changing temperatures.

evolutionary biology↗

bioRxiv2017

The contribution of genetic variation of Streptococcus pneumoniae to the clinical manifestation of invasive pneumococcal disease

BackgroundDifferent clinical manifestations of invasive pneumococcal disease (IPD) have thus far mainly been explained by patient characteristics. Here we studied the contribution of pneumococcal genetic variation to IPD phenotype.\n\nMethodsThe index cohort consisted of 349 patients admitted to two Dutch hospitals between 2000-2011 with pneumococcal bacteraemia. We performed genome-wide association studies to identify pneumococcal lineages, genes and allelic variants associated with 23 clinical IPD phenotypes. The identified associations were validated in a nationwide (n=482) and a post-pneumococcal vaccination cohort (n=121). The contribution of confirmed pneumococcal genotypes to the clinical IPD phenotype, relative to known clinical predictors, was tested by regression analysis.\n\nFindingsThe presence of pneumococcal gene slaA was a nationwide confirmed independent predictor of meningitis (OR=10.5, p=0.001), as was sequence cluster 9 (OR=3.68, p=0.057). A set of 4 pneumococcal genes co-located on a prophage was a confirmed independent predictor of 30-day mortality (OR=3.4, p=0.003). We could detect the pneumococcal variants of concern in these patients blood samples by molecular amplification. In the post-vaccination cohort where the distribution of both patient characteristics and pneumococcal serotypes had changed, the relative importance of the prophage was no longer supported.\n\nInterpretationKnowledge of pneumococcal genotypic variants improved our clinical risk assessment for detrimental manifestations of IPD. This provides us with novel opportunities to target, anticipate or avert the pathogenic effects that are related to particular pneumococcal variants. Therefore, future diagnostics should facilitate prompt appreciation of pathogen diversity in clinical sepsis management. Ongoing surveillance is warranted to monitor the clinical value of information on pathogen variants in dynamic microbial and susceptible host populations.\n\nFundingNone.

microbiology↗

bioRxiv2017

Expanding primary cells from mucoepidermoid and other salivary gland neoplasms for genetic and chemosensitivity testing

Restricted availability of cell and animal models is a rate-limiting step for investigation of salivary gland neoplasm pathophysiology and therapeutic response. Conditionally reprogrammed cell (CRC) technology enables establishment of primary epithelial cell cultures from patient material. This study tested a translational workflow for acquisition, expansion and testing of CRC-derived primary cultures of salivary gland neoplasms from patients presenting to an academic surgical practice. Results showed cultured cells were sufficient for epithelial cell-specific transcriptome characterization to detect candidate therapeutic pathways and fusion genes in addition to screening for cancer-risk-associated single nucleotide polymorphisms (SNPs) and driver gene mutations through exome sequencing. Focused study of primary cultures of a low-grade mucoepidermoid carcinoma demonstrated Amphiregulin-Mechanistic Target of Rapamycin-AKT/Protein kinase B (AKT) pathway activation, identified through bioinformatics and subsequently confirmed as present in primary tissue and preserved through different secondary 2D and 3D culture media and xenografts. Candidate therapeutic testing showed that the allosteric AKT inhibitor MK2206 reproducibly inhibited cell survival across different culture formats. In contrast, the cells appeared resistant to the adenosine triphosphate competitive AKT inhibitor GSK690693. Procedures employed here illustrate an approach for reproducibly obtaining material for pathophysiological studies of salivary gland neoplasms, and other less common epithelial cancer types, that can be executed without compromising pathological examination of patient specimens. The approach permits combined genetic and cell-based physiological and therapeutic investigations in addition to more traditional pathologic studies and can be used to build sustainable bio-banks for future inquiries.

cancer biology↗

bioRxiv2017

A quantitative chemotherapy genetic interaction map reveals new factors associated with PARP inhibitor resistance

Nearly every cancer patient is treated with chemotherapy yet our understanding of factors that dictate response and resistance to such agents remains limited. We report the generation of a quantitative chemical-genetic interaction map in human mammary epithelial cells that charts the impact of knockdown of 625 cancer and DNA repair related genes on sensitivity to 29 drugs, covering all classes of cancer chemotherapeutics. This quantitative map is predictive of interactions maintained in cancer cell lines and can be used to identify new cancer-associated DNA repair factors, predict cancer cell line responses to therapy and prioritize drug combinations. We identify that GPBP1 loss in breast and ovarian cancer confers resistance to cisplatin and PARP inhibitors through the regulation of genes involved in homologous recombination. This map may help navigate patient genomic data and optimize chemotherapeutic regimens by delineating factors involved in the response to specific types of DNA damage.

cancer biology↗

bioRxiv2017

Does genetic risk help to predict amyloid burden in a non-demented population? A Bayesian approach.

INTRODUCTIONIn this study we investigate the association between A{beta} levels in cerebrospinal fluid (CSF) and genetic risk in a non-demented population. This paper presents the first analysis to use a Bayesian methodology in this area.\n\nMETHODSData from the Alzheimers Disease Neuroimaging Initiative (ADNI) and the EDAR* and DESCRIPA** studies was used in a Bayesian logistic regression analysis. We modeled CSF A{beta} burden using age, diagnosis (healthy control or mild cognitive impairment), APOE and a polygenic risk score (PGRS) associated with Alzheimers Disease (AD). We compared models built using informative priors on age, diagnosis and APOE with non-informative priors on all variables.\n\nRESULTSThe use of informative priors did not improve model performance in the majority of cases. Models using only age, diagnosis and APOE genotype showed the best predictive ability.\n\nDISCUSSIONA previous study indicated that a PGRS of AD case/control status was associated with CSF A{beta} burden in healthy controls. The current study suggests that this association does not lead to models that are more predictive of amyloid positivity than already known factors such as age and APOE.\n\n* Beta amyloid oligomers in the early diagnosis of AD and as marker for treatment response\n\n** Development of screening guidelines and criteria for pre-dementia Alzheimers disease

bioinformatics↗