Multiomics analysis identifies novel facilitators of human dopaminergic neuron differentiation.

Midbrain dopaminergic neurons (mDANs) control voluntary movement, cognition, and reward behavior under physiological conditions and are implicated in human diseases such as Parkinson's disease (PD). Many transcription factors (TFs) controlling human mDAN differentiation during development have been described, but much of the regulatory landscape remains undefined. Using a tyrosine hydroxylase (TH) human iPSC reporter line, we here generate time series transcriptomic and epigenomic profiles of purified mDANs during differentiation. Integrative analysis predicts novel regulators of mDAN differentiation and super-enhancers are used to identify key TFs. We find LBX1, NHLH1 and NR2F1/2 to promote mDAN differentiation and show that overexpression of either LBX1 or NHLH1 can also improve mDAN specification. A more detailed investigation of TF targets reveals that NHLH1 promotes the induction of neuronal miR-124, LBX1 regulates cholesterol biosynthesis, and NR2F1/2 controls neuronal activity.

Semantic Similarity Analysis Reveals Robust Gene-Disease Relationships in Developmental and Epileptic Encephalopathies.

More than 100 genetic etiologies have been identified in developmental and epileptic encephalopathies (DEEs), but correlating genetic findings with clinical features at scale has remained a hurdle because of a lack of frameworks for analyzing heterogenous clinical data. Here, we analyzed 31,742 Human Phenotype Ontology (HPO) terms in 846 individuals with existing whole-exome trio data and assessed associated clinical features and phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de novo variants in the same gene. Gene-specific phenotypic signatures included associations of SCN1A with "complex febrile seizures" (HP: 0011172; p = 2.1 × 10-5) and "focal clonic seizures" (HP: 0002266; p = 8.9 × 10-6), STXBP1 with "absent speech" (HP: 0001344; p = 1.3 × 10-11), and SLC6A1 with "EEG with generalized slow activity" (HP: 0010845; p = 0.018). Of 41 genes with de novo variants in two or more individuals, 11 genes showed significant phenotypic similarity, including SCN1A (n = 16, p < 0.0001), STXBP1 (n = 14, p = 0.0021), and KCNB1 (n = 6, p = 0.011). Including genetic and phenotypic data of control subjects increased phenotypic similarity for all genetic etiologies, whereas the probability of observing de novo variants decreased, emphasizing the conceptual differences between semantic similarity analysis and approaches based on the expected number of de novo events. We demonstrate that HPO-based phenotype analysis captures unique profiles for distinct genetic etiologies, reflecting the breadth of the phenotypic spectrum in genetic epilepsies. Semantic similarity can be used to generate statistical evidence for disease causation analogous to the traditional approach of primarily defining disease entities through similar clinical features.

The Human Phenotype Ontology in 2021.

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.

Benchmarking of univariate pleiotropy detection methods applied to epilepsy.

Pleiotropy is a widespread phenomenon that may increase insight into the etiology of biological and disease traits. Since genome-wide association studies frequently provide information on a single trait only, only univariate pleiotropy detection methods are applicable, with yet unknown comparative performance. Here, we compared five such methods with respect to their ability to detect pleiotropy, including meta-analysis, ASSET, conditional false discovery rate (cFDR), cross-phenotype Bayes (CPBayes), and pleiotropic analysis under the composite null hypothesis (PLACO), by performing extended computer simulations that varied the underlying etiological model for pleiotropy for a pair of traits, including the number of causal variants, degree of traits' overlap, effect sizes as well as trait prevalence, and varying sample sizes. Our results indicate that ASSET provides the best trade-off between power and protection against false positives. We then applied ASSET to a previously published International League Against Epilepsy (ILAE) consortium data set on complex epilepsies, comprising genetic generalized epilepsy and focal epilepsy cases and corresponding controls. We identified a novel candidate locus at 17q21.32 and confirmed locus 2q24.3, previously identified to act pleiotropically on both epilepsy subtypes by a mega-analysis. Functional annotation, tissue-specific expression, and regulatory function analysis as well as Bayesian colocalization analysis corroborated this result, rendering 17q21.32 a worthwhile candidate for follow-up studies on pleiotropy in epilepsies.

A Recurrent Missense Variant in AP2M1 Impairs Clathrin-Mediated Endocytosis and Causes Developmental and Epileptic Encephalopathy.

The developmental and epileptic encephalopathies (DEEs) are heterogeneous disorders with a strong genetic contribution, but the underlying genetic etiology remains unknown in a significant proportion of individuals. To explore whether statistical support for genetic etiologies can be generated on the basis of phenotypic features, we analyzed whole-exome sequencing data and phenotypic similarities by using Human Phenotype Ontology (HPO) in 314 individuals with DEEs. We identified a de novo c.508C>T (p.Arg170Trp) variant in AP2M1 in two individuals with a phenotypic similarity that was higher than expected by chance (p = 0.003) and a phenotype related to epilepsy with myoclonic-atonic seizures. We subsequently found the same de novo variant in two individuals with neurodevelopmental disorders and generalized epilepsy in a cohort of 2,310 individuals who underwent diagnostic whole-exome sequencing. AP2M1 encodes the µ-subunit of the adaptor protein complex 2 (AP-2), which is involved in clathrin-mediated endocytosis (CME) and synaptic vesicle recycling. Modeling of protein dynamics indicated that the p.Arg170Trp variant impairs the conformational activation and thermodynamic entropy of the AP-2 complex. Functional complementation of both the µ-subunit carrying the p.Arg170Trp variant in human cells and astrocytes derived from AP-2µ conditional knockout mice revealed a significant impairment of CME of transferrin. In contrast, stability, expression levels, membrane recruitment, and localization were not impaired, suggesting a functional alteration of the AP-2 complex as the underlying disease mechanism. We establish a recurrent pathogenic variant in AP2M1 as a cause of DEEs with distinct phenotypic features, and we implicate dysfunction of the early steps of endocytosis as a disease mechanism in epilepsy.

A pharmacogenomic assessment of psychiatric adverse drug reactions to levetiracetam.

OBJECTIVE: Levetiracetam (LEV) is an effective antiseizure medicine, but 10%-20% of people treated with LEV report psychiatric side-effects, and up to 1% may have psychotic episodes. Pharmacogenomic predictors of these adverse drug reactions (ADRs) have yet to be identified. We sought to determine the contribution of both common and rare genetic variation to psychiatric and behavioral ADRs associated with LEV. METHODS: This case-control study compared cases of LEV-associated behavioral disorder (n = 149) or psychotic reaction (n = 37) to LEV-exposed people with no history of psychiatric ADRs (n = 920). All samples were of European ancestry. We performed genome-wide association study (GWAS) analysis comparing those with LEV ADRs to controls. We estimated the polygenic risk scores (PRS) for schizophrenia and compared cases with LEV-associated psychotic reaction to controls. Rare variant burden analysis was performed using exome sequence data of cases with psychotic reactions (n = 18) and controls (n = 122). RESULTS: Univariate GWAS found no significant associations with either LEV-associated behavioural disorder or LEV-psychotic reaction. PRS analysis showed that cases of LEV-associated psychotic reaction had an increased PRS for schizophrenia relative to contr ols (p = .0097, estimate = .4886). The rare-variant analysis found no evidence of an increased burden of rare genetic variants in people who had experienced LEV-associated psychotic reaction relative to controls. SIGNIFICANCE: The polygenic burden for schizophrenia is a risk factor for LEV-associated psychotic reaction. To assess the clinical utility of PRS as a predictor, it should be tested in an independent and ideally prospective cohort. Larger sample sizes are required for the identification of significant univariate common genetic signals or rare genetic signals associated with psychiatric LEV ADRs.

Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders.

PURPOSE: Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype-phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed. METHODS: We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the NaV1.2 protein. RESULTS: We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance. CONCLUSION: Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype-phenotype correlations along a multidimensional spectrum.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable.

OBJECTIVE: The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics. METHODS: We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort's data were provided in a different format and harmonized using the two versions of the HPO. RESULTS: The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals' seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures. SIGNIFICANCE: We have generated a detailed contemporary conceptual map for harmonization of clinical seizure data, implemented in the official 2020-12-07 HPO release and freely available at hpo.jax.org. This will help to overcome the phenotypic bottleneck in genomics, facilitate reuse of valuable data, and ultimately improve diagnostics and precision treatment of the epilepsies.

Ten simple rules for making training materials FAIR.

Everything we do today is becoming more and more reliant on the use of computers. The field of biology is no exception; but most biologists receive little or no formal preparation for the increasingly computational aspects of their discipline. In consequence, informal training courses are often needed to plug the gaps; and the demand for such training is growing worldwide. To meet this demand, some training programs are being expanded, and new ones are being developed. Key to both scenarios is the creation of new course materials. Rather than starting from scratch, however, it's sometimes possible to repurpose materials that already exist. Yet finding suitable materials online can be difficult: They're often widely scattered across the internet or hidden in their home institutions, with no systematic way to find them. This is a common problem for all digital objects. The scientific community has attempted to address this issue by developing a set of rules (which have been called the Findable, Accessible, Interoperable and Reusable [FAIR] principles) to make such objects more findable and reusable. Here, we show how to apply these rules to help make training materials easier to find, (re)use, and adapt, for the benefit of all.

A framework to assess the quality and impact of bioinformatics training across ELIXIR.

ELIXIR is a pan-European intergovernmental organisation for life science that aims to coordinate bioinformatics resources in a single infrastructure across Europe; bioinformatics training is central to its strategy, which aims to develop a training community that spans all ELIXIR member states. In an evidence-based approach for strengthening bioinformatics training programmes across Europe, the ELIXIR Training Platform, led by the ELIXIR EXCELERATE Quality and Impact Assessment Subtask in collaboration with the ELIXIR Training Coordinators Group, has implemented an assessment strategy to measure quality and impact of its entire training portfolio. Here, we present ELIXIR's framework for assessing training quality and impact, which includes the following: specifying assessment aims, determining what data to collect in order to address these aims, and our strategy for centralised data collection to allow for ELIXIR-wide analyses. In addition, we present an overview of the ELIXIR training data collected over the past 4 years. We highlight the importance of a coordinated and consistent data collection approach and the relevance of defining specific metrics and answer scales for consortium-wide analyses as well as for comparison of data across iterations of the same course.

Testing association of rare genetic variants with resistance to three common antiseizure medications.

OBJECTIVE: Drug resistance is a major concern in the treatment of individuals with epilepsy. No genetic markers for resistance to individual antiseizure medication (ASM) have yet been identified. We aimed to identify the role of rare genetic variants in drug resistance for three common ASMs: levetiracetam (LEV), lamotrigine (LTG), and valproic acid (VPA). METHODS: A cohort of 1622 individuals of European descent with epilepsy was deeply phenotyped and underwent whole exome sequencing (WES), comprising 575 taking LEV, 826 LTG, and 782 VPA. We performed gene- and gene set-based collapsing analyses comparing responders and nonresponders to the three drugs to determine the burden of different categories of rare genetic variants. RESULTS: We observed a marginally significant enrichment of rare missense, truncating, and splice region variants in individuals who were resistant to VPA compared to VPA responders for genes involved in VPA pharmacokinetics. We also found a borderline significant enrichment of truncating and splice region variants in the synaptic vesicle glycoprotein (SV2) gene family in nonresponders compared to responders to LEV. We did not see any significant enrichment using a gene-based approach. SIGNIFICANCE: In our pharmacogenetic study, we identified a slightly increased burden of damaging variants in gene groups related to drug kinetics or targeting in individuals presenting with drug resistance to VPA or LEV. Such variants could thus determine a genetic contribution to drug resistance.

The Human Phenotype Ontology in 2017.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

70S-scanning initiation is a novel and frequent initiation mode of ribosomal translation in bacteria.

According to the standard model of bacterial translation initiation, the small ribosomal 30S subunit binds to the initiation site of an mRNA with the help of three initiation factors (IF1-IF3). Here, we describe a novel type of initiation termed "70S-scanning initiation," where the 70S ribosome does not necessarily dissociate after translation of a cistron, but rather scans to the initiation site of the downstream cistron. We detailed the mechanism of 70S-scanning initiation by designing unique monocistronic and polycistronic mRNAs harboring translation reporters, and by reconstituting systems to characterize each distinct mode of initiation. Results show that 70S scanning is triggered by fMet-tRNA and does not require energy; the Shine-Dalgarno sequence is an essential recognition element of the initiation site. IF1 and IF3 requirements for the various initiation modes were assessed by the formation of productive initiation complexes leading to synthesis of active proteins. IF3 is essential and IF1 is highly stimulating for the 70S-scanning mode. The task of IF1 appears to be the prevention of untimely interference by ternary aminoacyl (aa)-tRNA•elongation factor thermo unstable (EF-Tu)•GTP complexes. Evidence indicates that at least 50% of bacterial initiation events use the 70S-scanning mode, underscoring the relative importance of this translation initiation mechanism.

Rare GABRA3 variants are associated with epileptic seizures, encephalopathy and dysmorphic features.

Genetic epilepsies are caused by mutations in a range of different genes, many of them encoding ion channels, receptors or transporters. While the number of detected variants and genes increased dramatically in the recent years, pleiotropic effects have also been recognized, revealing that clinical syndromes with various degrees of severity arise from a single gene, a single mutation, or from different mutations showing similar functional defects. Accordingly, several genes coding for GABAA receptor subunits have been linked to a spectrum of benign to severe epileptic disorders and it was shown that a loss of function presents the major correlated pathomechanism. Here, we identified six variants in GABRA3 encoding the α3-subunit of the GABAA receptor. This gene is located on chromosome Xq28 and has not been previously associated with human disease. Five missense variants and one microduplication were detected in four families and two sporadic cases presenting with a range of epileptic seizure types, a varying degree of intellectual disability and developmental delay, sometimes with dysmorphic features or nystagmus. The variants co-segregated mostly but not completely with the phenotype in the families, indicating in some cases incomplete penetrance, involvement of other genes, or presence of phenocopies. Overall, males were more severely affected and there were three asymptomatic female mutation carriers compared to only one male without a clinical phenotype. X-chromosome inactivation studies could not explain the phenotypic variability in females. Three detected missense variants are localized in the extracellular GABA-binding NH2-terminus, one in the M2-M3 linker and one in the M4 transmembrane segment of the α3-subunit. Functional studies in Xenopus laevis oocytes revealed a variable but significant reduction of GABA-evoked anion currents for all mutants compared to wild-type receptors. The degree of current reduction correlated partially with the phenotype. The microduplication disrupted GABRA3 expression in fibroblasts of the affected patient. In summary, our results reveal that rare loss-of-function variants in GABRA3 increase the risk for a varying combination of epilepsy, intellectual disability/developmental delay and dysmorphic features, presenting in some pedigrees with an X-linked inheritance pattern.

A systems level analysis of epileptogenesis-associated proteome alterations.

Despite intense research efforts, the knowledge about the mechanisms of epileptogenesis and epilepsy is still considered incomplete and limited. However, an in-depth understanding of molecular pathophysiological processes is crucial for the rational selection of innovative biomarkers and target candidates. Here, we subjected proteomic data from different phases of a chronic rat epileptogenesis model to a comprehensive systems level analysis. Weighted Gene Co-expression Network analysis identified several modules of interconnected protein groups reflecting distinct molecular aspects of epileptogenesis in the hippocampus and the parahippocampal cortex. Characterization of these modules did not only further validate the data but also revealed regulation of molecular processes not described previously in the context of epilepsy development. The data sets also provide valuable information about temporal patterns, which should be taken into account for development of preventive strategies in particular when it comes to multi-targeting network pharmacology approaches. In addition, principal component analysis suggests candidate biomarkers, which might inform the design of novel molecular imaging approaches aiming to predict epileptogenesis during different phases or confirm epilepsy manifestation. Further studies are necessary to distinguish between molecular alterations, which correlate with epileptogenesis versus those reflecting a mere consequence of the status epilepticus.

Comparative effectiveness of antiepileptic drugs in patients with mesial temporal lobe epilepsy with hippocampal sclerosis.

OBJECTIVE: Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is a common epilepsy syndrome that is often poorly controlled by antiepileptic drug (AED) treatment. Comparative AED effectiveness studies in this condition are lacking. We report retention, efficacy, and tolerability in a cohort of patients with MTLE-HS. METHODS: Clinical data were collected from a European database of patients with epilepsy. We estimated retention, 12-month seizure freedom, and adverse drug reaction (ADR) rates for the 10 most commonly used AEDs in patients with MTLE-HS. RESULTS: Seven hundred sixty-seven patients with a total of 3,249 AED trials were included. The highest 12-month retention rates were observed with carbamazepine (85.9%), valproate (85%), and clobazam (79%). Twelve-month seizure freedom rates varied from 1.2% for gabapentin and vigabatrin to 11% for carbamazepine. Response rates were highest for AEDs that were prescribed as initial treatment and lowest for AEDs that were used in a third or higher instance. ADRs were reported in 47.6% of patients, with the highest rates observed with oxcarbazepine (35.7%), topiramate (30.9%), and pregabalin (27.4%), and the lowest rates with clobazam (6.5%), gabapentin (8.9%), and lamotrigine (16.6%). The most commonly reported ADRs were lethargy and drowsiness, dizziness, vertigo and ataxia, and blurred vision and diplopia. SIGNIFICANCE: Our results did not demonstrate any clear advantage of newer versus older AEDs. Our results provide useful insights into AED retention, efficacy, and ADR rates in patients with MTLE-HS.

CHD2 variants are a risk factor for photosensitivity in epilepsy.

Photosensitivity is a heritable abnormal cortical response to flickering light, manifesting as particular electroencephalographic changes, with or without seizures. Photosensitivity is prominent in a very rare epileptic encephalopathy due to de novo CHD2 mutations, but is also seen in epileptic encephalopathies due to other gene mutations. We determined whether CHD2 variation underlies photosensitivity in common epilepsies, specific photosensitive epilepsies and individuals with photosensitivity without seizures. We studied 580 individuals with epilepsy and either photosensitive seizures or abnormal photoparoxysmal response on electroencephalography, or both, and 55 individuals with photoparoxysmal response but no seizures. We compared CHD2 sequence data to publicly available data from 34 427 individuals, not enriched for epilepsy. We investigated the role of unique variants seen only once in the entire data set. We sought CHD2 variants in 238 exomes from familial genetic generalized epilepsies, and in other public exome data sets. We identified 11 unique variants in the 580 individuals with photosensitive epilepsies and 128 unique variants in the 34 427 controls: unique CHD2 variation is over-represented in cases overall (P = 2.17 × 10(-5)). Among epilepsy syndromes, there was over-representation of unique CHD2 variants (3/36 cases) in the archetypal photosensitive epilepsy syndrome, eyelid myoclonia with absences (P = 3.50 × 10(-4)). CHD2 variation was not over-represented in photoparoxysmal response without seizures. Zebrafish larvae with chd2 knockdown were tested for photosensitivity. Chd2 knockdown markedly enhanced mild innate zebrafish larval photosensitivity. CHD2 mutation is the first identified cause of the archetypal generalized photosensitive epilepsy syndrome, eyelid myoclonia with absences. Unique CHD2 variants are also associated with photosensitivity in common epilepsies. CHD2 does not encode an ion channel, opening new avenues for research into human cortical excitability.

Testing for pharmacogenomic predictors of ppRNFL thinning in individuals exposed to vigabatrin.

Background: The anti-seizure medication vigabatrin (VGB) is effective for controlling seizures, especially infantile spasms. However, use is limited by VGB-associated visual field loss (VAVFL). The mechanisms by which VGB causes VAVFL remains unknown. Average peripapillary retinal nerve fibre layer (ppRNFL) thickness correlates with the degree of visual field loss (measured by mean radial degrees). Duration of VGB exposure, maximum daily VGB dose, and male sex are associated with ppRNFL thinning. Here we test the hypothesis that common genetic variation is a predictor of ppRNFL thinning in VGB exposed individuals. Identifying pharmacogenomic predictors of ppRNFL thinning in VGB exposed individuals could potentially enable safe prescribing of VGB and broader use of a highly effective drug. Methods: Optical coherence topography (OCT) and GWAS data were processed from VGB-exposed individuals (n = 71) recruited through the EpiPGX Consortium. We conducted quantitative GWAS analyses for the following OCT measurements: (1) average ppRNFL, (2) inferior quadrant, (3) nasal quadrant, (4) superior quadrant, (5) temporal quadrant, (6) inferior nasal sector, (7) nasal inferior sector, (8) superior nasal sector, and (9) nasal superior sector. Using the summary statistics from the GWAS analyses we conducted gene-based testing using VEGAS2. We conducted nine different PRS analyses using the OCT measurements. To determine if VGB-exposed individuals were predisposed to having a thinner RNFL, we calculated their polygenic burden for retinal thickness. PRS alleles for retinal thickness were calculated using published summary statistics from a large-scale GWAS of inner retinal morphology using the OCT images of UK Biobank participants. Results: The GWAS analyses did not identify a significant association after correction for multiple testing. Similarly, the gene-based and PRS analyses did not reveal a significant association that survived multiple testing. Conclusion: We set out to identify common genetic predictors for VGB induced ppRNFL thinning. Results suggest that large-effect common genetic predictors are unlikely to exist for ppRNFL thinning (as a marker of VAVFL). Sample size was a limitation of this study. However, further recruitment is a challenge as VGB is rarely used today because of this adverse reaction. Rare variants may be predictors of this adverse drug reaction and were not studied here.

Exploring biological interaction networks with tailored weighted quasi-bicliques.

BACKGROUND: Biological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges. RESULTS: We introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks. CONCLUSIONS: We verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks.

Proteome survey reveals modularity of the yeast cell machinery.

Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.