The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling

Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator

CSF neurofilament light chain profiling and quantitation in neurological diseases.

Neurofilament light chain is an established marker of neuroaxonal injury that is elevated in CSF and blood across various neurological diseases. It is increasingly used in clinical practice to aid diagnosis and monitor progression and as an outcome measure to assess safety and efficacy of disease-modifying therapies across the clinical translational neuroscience field. Quantitative methods for neurofilament light chain in human biofluids have relied on immunoassays, which have limited capacity to describe the structure of the protein in CSF and how this might vary in different neurodegenerative diseases. In this study, we characterized and quantified neurofilament light chain species in CSF across neurodegenerative and neuroinflammatory diseases and healthy controls using targeted mass spectrometry. We show that the quantitative immunoprecipitation-tandem mass spectrometry method developed in this study strongly correlates to single-molecule array measurements in CSF across the broad spectrum of neurodegenerative diseases and was replicable across mass spectrometry methods and centres. In summary, we have created an accurate and cost-effective assay for measuring a key biomarker in translational neuroscience research and clinical practice, which can be easily multiplexed and translated into clinical laboratories for the screening and monitoring of neurodegenerative disease or acute brain injury

Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process

iPSC-derived neuronal models of PANK2-associated neurodegeneration reveal mitochondrial dysfunction contributing to early disease

Mutations in PANK2 lead to neurodegeneration with brain iron accumulation. PANK2 has a role in the biosynthesis of coenzyme A (CoA) from dietary vitamin B5, but the neuropathological mechanism and reasons for iron accumulation remain unknown. In this study, atypical patient-derived fibroblasts were reprogrammed into induced pluripotent stem cells (iPSCs) and subsequently differentiated into cortical neuronal cells for studying disease mechanisms in human neurons. We observed no changes in PANK2 expression between control and patient cells, but a reduction in protein levels was apparent in patient cells. CoA homeostasis and cellular iron handling were normal, mitochondrial function was affected; displaying activated NADH-related and inhibited FADH-related respiration, resulting in increased mitochondrial membrane potential. This led to increased reactive oxygen species generation and lipid peroxidation in patient-derived neurons. These data suggest that mitochondrial deficiency is an early feature of the disease process and can be explained by altered NADH/FADH substrate supply to oxidative phosphorylation. Intriguingly, iron chelation appeared to exacerbate the mitochondrial phenotype in both control and patient neuronal cells. This raises caution for the use iron chelation therapy in general when iron accumulation is absent

Improved cardiovascular diagnostic accuracy by pocket size imaging device in non-cardiologic outpatients: the NaUSiCa (Naples Ultrasound Stethoscope in Cardiology) study

Miniaturization has evolved in the creation of a pocket-size imaging device which can be utilized as an ultrasound stethoscope. This study assessed the additional diagnostic power of pocket size device by both experts operators and trainees in comparison with physical examination and its appropriateness of use in comparison with standard echo machine in a non-cardiologic population

High-throughput sequencing of Astrammina rara: Sampling the giant genome of a giant foraminiferan protist

<p>Abstract</p> <p>Background</p> <p>Foraminiferan protists, which are significant players in most marine ecosystems, are also genetic innovators, harboring unique modifications to proteins that make up the basic eukaryotic cell machinery. Despite their ecological and evolutionary importance, foraminiferan genomes are poorly understood due to the extreme sequence divergence of many genes and the difficulty of obtaining pure samples: exogenous DNA from ingested food or ecto/endo symbionts often vastly exceed the amount of "native" DNA, and foraminiferans cannot be cultured axenically. Few foraminiferal genes have been sequenced from genomic material, although partial sequences of coding regions have been determined by EST studies and mass spectroscopy. The lack of genomic data has impeded evolutionary and cell-biology studies and has also hindered our ability to test ecological hypotheses using genetic tools.</p> <p>Results</p> <p>454 sequence analysis was performed on a library derived from whole genome amplification of microdissected nuclei of the Antarctic foraminiferan <it>Astrammina rara</it>. Xenogenomic sequence, which was shown not to be of eukaryotic origin, represented only 12% of the sample. The first foraminiferal examples of important classes of genes, such as tRNA genes, are reported, and we present evidence that sequences of mitochondrial origin have been translocated to the nucleus. The recovery of a 3' UTR and downstream sequence from an actin gene suggests that foraminiferal mRNA processing may have some unusual features. Finally, the presence of a co-purified bacterial genome in the library also permitted the first calculation of the size of a foraminiferal genome by molecular methods, and statistical analysis of sequence from different genomic sources indicates that low-complexity tracts of the genome may be endoreplicated in some stages of the foraminiferal life cycle.</p> <p>Conclusions</p> <p>These data provide the first window into genomic organization and genetic control in these organisms, and also complement and expands upon information about foraminiferal genes based on EST projects. The genomic data obtained are informative for environmental and cell-biological studies, and will also be useful for efforts to understand relationships between foraminiferans and other protists.</p

Characterization of a rat osteotomy model with impaired healing

<p>Abstract</p> <p>Background</p> <p>Delayed union or nonunion are frequent and feared complications in fracture treatment. Animal models of impaired bone healing are rare. Moreover, specific descriptions are limited although understanding of the biological course of pathogenesis of fracture nonunion is essential for therapeutic approaches.</p> <p>Methods</p> <p>A rat tibial osteotomy model with subsequent intramedullary stabilization was performed. The healing progress of the osteotomy model was compared to a previously described closed fracture model. Histological analyses, biomechanical testing and radiological screening were undertaken during the observation period of 84 days (d) to verify the status of the healing process. In this context, particular attention was paid to a comparison of bone slices by histological and immunohistological (IHC) methods at early points in time, <it>i.e</it>. at 5 and 10 d post bone defect.</p> <p>Results</p> <p>In contrast to the closed fracture technique osteotomy led to delayed union or nonunion until 84 d post intervention. The dimensions of whole reactive callus and the amounts of vessels in defined regions of the callus differed significantly between osteotomized and fractured animals at 10 d post surgery. A lower fraction of newly formed bone and cartilaginous tissue was obvious during this period in osteotomized animals and more inflammatory cells were observed in the callus. Newly formed bone tissue accumulated slowly on the anterior tibial side with both techniques. New formation of reparative cartilage was obviously inhibited on the anterior side, the surgical approach side, in osteotomized animals only.</p> <p>Conclusion</p> <p>Tibial osteotomy with intramedullary stabilisation in rats leads to pronounced delayed union and nonunion until 84 d post intervention. The early onset of this delay can already be detected histologically within 10 d post surgery. Moreover, the osteotomy technique is associated with cellular and vascular signs of persistent inflammation within the first 10 d after bone defect and may be a contributory factor to impaired healing. The model would be excellent to test agents to promote fracture healing.</p

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays

Multi-locus genome-wide association analysis supports the role of glutamatergic synaptic transmission in the etiology of major depressive disorder

Major depressive disorder (MDD) is a common psychiatric illness characterized by low mood and loss of interest in pleasurable activities. Despite years of effort, recent genome-wide association studies (GWAS) have identified few susceptibility variants or genes that are robustly associated with MDD. Standard single-SNP (single nucleotide polymorphism)-based GWAS analysis typically has limited power to deal with the extensive heterogeneity and substantial polygenic contribution of individually weak genetic effects underlying the pathogenesis of MDD. Here, we report an alternative, gene-set-based association analysis of MDD in an effort to identify groups of biologically related genetic variants that are involved in the same molecular function or cellular processes and exhibit a significant level of aggregated association with MDD. In particular, we used a text-mining-based data analysis to prioritize candidate gene sets implicated in MDD and conducted a multi-locus association analysis to look for enriched signals of nominally associated MDD susceptibility loci within each of the gene sets. Our primary analysis is based on the meta-analysis of three large MDD GWAS data sets (total N = 4346 cases and 4430 controls). After correction for multiple testing, we found that genes involved in glutamatergic synaptic neurotransmission were significantly associated with MDD (set-based association P = 6.9 X 10(-4)). This result is consistent with previous studies that support a role of the glutamatergic system in synaptic plasticity and MDD and support the potential utility of targeting glutamatergic neurotransmission in the treatment of MDD