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Amino-terminal (Nt-) acetylation (NTA) is a common protein modification, affecting 80% of cytosolic proteins in humans. The human essential gene, NAA10, encodes for the enzyme NAA10, which is the catalytic subunit in the N-terminal acetyltransferase A (NatA) complex, also including the accessory protein, NAA15. The full spectrum of human genetic variation in this pathway is currently unknown. Here we reveal the genetic landscape of variation in NAA10 and NAA15 in humans. Through a genotype-first approach, one clinician interviewed the parents of 56 individuals with NAA10 variants and 19 individuals with NAA15 variants, which were added to all known cases (N = 106 for NAA10 and N = 66 for NAA15). Although there is clinical overlap between the two syndromes, functional assessment demonstrates that the overall level of functioning for the probands with NAA10 variants is significantly lower than the probands with NAA15 variants. The phenotypic spectrum includes variable levels of intellectual disability, delayed milestones, autism spectrum disorder, craniofacial dysmorphology, cardiac anomalies, seizures, and visual abnormalities (including cortical visual impairment and microphthalmia). One female with the p.Arg83Cys variant and one female with an NAA15 frameshift variant both have microphthalmia. The frameshift variants located toward the C-terminal end of NAA10 have much less impact on overall functioning, whereas the females with the p.Arg83Cys missense in NAA10 have substantial impairment. The overall data are consistent with a phenotypic spectrum for these alleles, involving multiple organ systems, thus revealing the widespread effect of alterations of the NTA pathway in humans.

BackgroundPolygenic risk scores (PRS) have been used to stratify colorectal cancer (CRC) risk in the general population, whereas its role in Lynch syndrome (LS), the most common type of hereditary CRC, is still conflicting. We aimed to assess the ability of PRS to refine CRC risk prediction in European-descendant LS individuals.Methods1,465 LS individuals (557MLH1, 517MSH2/EPCAM, 299MSH6, and 92PMS2)and 5,656 CRC-free population-based controls from two independent cohorts were included. A 91-Single Nucleotide Polymorphism PRS was applied. A Cox proportional hazard regression model with “family” as a random effect and a logistic regression analysis, followed by a meta-analysis combining both cohorts were conducted.ResultsOverall, we did not observe a statistically significant association between PRS and CRC risk in the entire cohort. Nevertheless, PRS was significantly associated with a slightly increased risk of CRC or advanced adenoma (AA), in those with CRC diagnosed < 50 years, and in individuals with multiple CRCs or AAs diagnosed < 60 years.ConclusionThe PRS may slightly influence CRC risk in LS individuals, in particular in more extreme phenotypes such as early-onset disease. However, the study design and recruitment strategy strongly influence the results of PRS studies. A separate analysis by genes and its combination with other genetic and non-genetic risk factors will help refine its role as a risk modifier in LS.KEY MESSAGESWHAT IS ALREADY KNOWN ON THIS TOPIC?-Great variability in the incidence of CRC has been described in LS individuals, even within the same family.-Polygenic risk scores (PRS) can help stratify colorectal cancer risk and, thus, adjust surveillance or treatment procedures.WHAT THIS STUDY ADDS-PRS performed on family-based registries slightly influences CRC risk in subgroups of LS individuals, even though with weak effects.-Our study showed a weak association of PRS with multiple and young CRC cases, pointing to a possible risk-modifying role in extreme phenotypes.HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY-Gene-based PRS analysis and its combination with other genetic and non-genetic factors may contribute to refining cancer risk in LS patients.

Most individuals with rare diseases initially consult their primary care physician. For a subset of rare diseases, efficient diagnostic pathways are available. However, ultra-rare diseases often require both expert clinical knowledge and comprehensive genetic diagnostics, which poses structural challenges for public healthcare systems. To address these challenges within Germany, a novel structured diagnostic concept, based on multidisciplinary expertise at established university hospital centers for rare diseases (CRDs), was evaluated in the three year prospective study TRANSLATE NAMSE. A key goal of TRANSLATE NAMSE was to assess the clinical value of exome sequencing (ES) in the ultra-rare disease population. The aims of the present study were to perform a systematic investigation of the phenotypic and molecular genetic data of TRANSLATE NAMSE patients who had undergone ES in order to determine the yield of both ultra-rare diagnoses and novel gene-disease associations; and determine whether the complementary use of machine learning and artificial intelligence (AI) tools improved diagnostic effectiveness and efficiency.ES was performed for 1,577 patients (268 adult and 1,309 pediatric). Molecular genetic diagnoses were established in 499 patients (74 adult and 425 pediatric). A total of 370 distinct molecular genetic causes were established. The majority of these concerned known disorders, most of which were ultra-rare. During the diagnostic process, 34 novel and 23 candidate genotype-phenotype associations were delineated, mainly in individuals with neurodevelopmental disorders.To determine the likelihood that ES will lead to a molecular diagnosis in a given patient, based on the respective clinical features only, we developed a statistical framework called YieldPred. The genetic data of a subcohort of 224 individuals that also gave consent to the computer-assisted analysis of their facial images were processed with the AI tool Prioritization of Exome Data by Image Analysis (PEDIA) and showed superior performance in variant prioritization.The present analyses demonstrated that the novel structured diagnostic concept facilitated the identification of ultra-rare genetic disorders and novel gene-disease associations on a national level and that the machine learning and AI tools improved diagnostic effectiveness and efficiency for ultra-rare genetic disorders.

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models that tend to be more robust regarding deviations from the target population.

Amino-terminal (Nt-) acetylation (NTA) is a common protein modification, affecting 80% of cytosolic proteins in humans. The human essential gene,NAA10,encodes for the enzyme NAA10, which is the catalytic subunit in the N-terminal acetyltransferase A (NatA) complex, also including the accessory protein, NAA15. The full spectrum of human genetic variation in this pathway is currently unknown. Here we reveal the genetic landscape of variation inNAA10andNAA15in humans. Through a genotype-first approach, one clinician interviewed the parents of 56 individuals withNAA10variants and 19 individuals withNAA15variants, which were added to all known cases (N=106 forNAA10and N=66 forNAA15). Although there is clinical overlap between the two syndromes, functional assessment demonstrates that the overall level of functioning for the probands withNAA10variants is significantly lower than the probands withNAA15variants. The phenotypic spectrum includes variable levels of intellectual disability, delayed milestones, autism spectrum disorder, craniofacial dysmorphology, cardiac anomalies, seizures, and visual abnormalities (including cortical visual impairment and microphthalmia). One female with the p.Arg83Cys variant and one female with anNAA15frameshift variant both have microphthalmia. The frameshift variants located toward the C-terminal end ofNAA10have much less impact on overall functioning, whereas the females with the p.Arg83Cys missense in NAA10 have substantial impairment. The overall data are consistent with a phenotypic spectrum for these alleles, involving multiple organ systems, thus revealing the widespread effect of alterations of the NTA pathway in humans.

Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in the field of clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands.We develop an adapted component-wise L2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types.

Motivation Missense variants are a frequent class of variation within the coding genome, and some of them cause Mendelian diseases. Despite advances in computational prediction, classifying missense variants into pathogenic or benign remains a major challenge in the context of personalized medicine. Recently, the structure of the human proteome was derived with unprecedented accuracy using the artificial intelligence system AlphaFold2. This raises the question of whether AlphaFold2 wild-type structures can improve the accuracy of computational pathogenicity prediction for missense variants. Results To address this, we first engineered a set of features for each amino acid from these structures. We then trained a random forest to distinguish between relatively common (proxy-benign) and singleton (proxy-pathogenic) missense variants from gnomAD v3.1. This yielded a novel AlphaFold2-based pathogenicity prediction score, termed AlphScore. Important feature classes used by AlphScore are solvent accessibility, amino acid network related features, features describing the physicochemical environment, and AlphaFold2’s quality parameter (predicted local distance difference test). AlphScore alone showed lower performance than existing in silico scores used for missense prediction, such as CADD or REVEL. However, when AlphScore was added to those scores, the performance increased, as measured by the approximation of deep mutational scan data, as well as the prediction of expert-curated missense variants from the ClinVar database. Overall, our data indicate that the integration of AlphaFold2-predicted structures can improve pathogenicity prediction of missense variants. Availability and implementation AlphScore, combinations of AlphScore with existing scores, as well as variants used for training and testing are publicly available.

Summary The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. Availability and implementation GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). Supplementary information Supplementary data are available at Bioinformatics online.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modelling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this ‘supervised’ approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes.

A large fraction of monogenic disorders causes craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 17,560 patients with 1,115 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism, as well as enable the delineation of novel phenotypes.