Rare loss-of-function variants in matrisome genes are enriched in Ebstein's anomaly.

Ebstein's anomaly, a rare congenital heart disease, is distinguished by the failure of embryological delamination of the tricuspid valve leaflets from the underlying primitive right ventricle myocardium. Gaining insight into the genetic basis of Ebstein's anomaly allows a more precise definition of its pathogenesis. In this study, two distinct cohorts from the Chinese Han population were included: a case-control cohort consisting of 82 unrelated cases and 125 controls without cardiac phenotypes and a trio cohort comprising 36 parent-offspring trios. Whole-exome sequencing data from all 315 participants were utilized to identify qualifying variants, encompassing rare (minor allele frequency < 0.1% from East Asians in the gnomAD database) functional variants and high-confidence (HC) loss-of-function (LoF) variants. Various statistical models, including burden tests and variance-component models, were employed to identify rare variants, genes, and biological pathways associated with Ebstein's anomaly. Significant associations were noted between Ebstein's anomaly and rare HC LoF variants found in genes related to the matrisome, a collection of extracellular matrix (ECM) components. Specifically, 47 genes with HC LoF variants were exclusively or predominantly identified in cases, while nine genes showed such variants in the probands. Over half of unrelated cases (n = 42) and approximately one-third of probands (n = 12) were found to carry one or two LoF variants in these prioritized genes. These results highlight the role of the matrisome in the pathogenesis of Ebstein's anomaly, contributing to a better understanding of the genetic architecture underlying this condition. Our findings hold the potential to impact the genetic diagnosis and treatment approaches for Ebstein's anomaly.

Deep generative models of LDLR protein structure to predict variant pathogenicity.

The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.

An ancient viral epidemic involving host coronavirus interacting genes more than 20,000 years ago in East Asia.

The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has emphasized the vulnerability of human populations to novel viral pressures, despite the vast array of epidemiological and biomedical tools now available. Notably, modern human genomes contain evolutionary information tracing back tens of thousands of years, which may help identify the viruses that have impacted our ancestors-pointing to which viruses have future pandemic potential. Here, we apply evolutionary analyses to human genomic datasets to recover selection events involving tens of human genes that interact with coronaviruses, including SARS-CoV-2, that likely started more than 20,000 years ago. These adaptive events were limited to the population ancestral to East Asian populations. Multiple lines of functional evidence support an ancient viral selective pressure, and East Asia is the geographical origin of several modern coronavirus epidemics. An arms race with an ancient coronavirus, or with a different virus that happened to use similar interactions as coronaviruses with human hosts, may thus have taken place in ancestral East Asian populations. By learning more about our ancient viral foes, our study highlights the promise of evolutionary information to better predict the pandemics of the future. Importantly, adaptation to ancient viral epidemics in specific human populations does not necessarily imply any difference in genetic susceptibility between different human populations, and the current evidence points toward an overwhelming impact of socioeconomic factors in the case of coronavirus disease 2019 (COVID-19).

Mutations modifying sporadic Alzheimer's disease age of onset.

The identification of mutations modifying the age of onset (AOO) in Alzheimer's disease (AD) is crucial for understanding the natural history of AD and, therefore, for early interventions. Patients with sporadic AD (sAD) from a genetic isolate in the extremes of the AOO distribution were whole-exome genotyped. Single- and multi-locus linear mixed-effects models were used to identify functional variants modifying AOO. A posteriori enrichment and bioinformatic analyses were applied to evaluate the non-random clustering of the associate variants to physiopathological pathways involved in AD. We identified more than 20 pathogenic, genome-wide statistically significant mutations of major modifier effect on the AOO. These variants are harbored in genes implicated in neuron apoptosis, neurogenesis, inflammatory processes linked to AD, oligodendrocyte differentiation, and memory processes. This set of new genes harboring these mutations could be of importance for prediction, follow-up and eventually as therapeutical targets of AD. © 2016 Wiley Periodicals, Inc.

Novel and rare functional genomic variants in multiple autoimmune syndrome and Sjögren's syndrome.

BACKGROUND: Multiple autoimmune syndrome (MAS), an extreme phenotype of autoimmune disorders, is a very well suited trait to tackle genomic variants of these conditions. Whole exome sequencing (WES) is a widely used strategy for detection of protein coding and splicing variants associated with inherited diseases. METHODS: The DNA of eight patients affected by MAS [all of whom presenting with Sjögren's syndrome (SS)], four patients affected by SS alone and 38 unaffected individuals, were subject to WES. Filters to identify novel and rare functional (pathogenic-deleterious) homozygous and/or compound heterozygous variants in these patients and controls were applied. Bioinformatics tools such as the Human gene connectome as well as pathway and network analysis were applied to test overrepresentation of genes harbouring these variants in critical pathways and networks involved in autoimmunity. RESULTS: Eleven novel and rare functional variants were identified in cases but not in controls, harboured in: MACF1, KIAA0754, DUSP12, ICA1, CELA1, LRP1/STAT6, GRIN3B, ANKLE1, TMEM161A, and FKRP. These were subsequently subject to network analysis and their functional relatedness to genes already associated with autoimmunity was evaluated. Notably, the LRP1/STAT6 novel mutation was homozygous in one MAS affected patient and heterozygous in another. LRP1/STAT6 disclosed the strongest plausibility for autoimmunity. LRP1/STAT6 are involved in extracellular and intracellular anti-inflammatory pathways that play key roles in maintaining the homeostasis of the immune system. Further; networks, pathways, and interaction analyses showed that LRP1 is functionally related to the HLA-B and IL10 genes and it has a substantial impact within immunological pathways and/or reaction to bacterial and other foreign proteins (phagocytosis, regulation of phospholipase A2 activity, negative regulation of apoptosis and response to lipopolysaccharides). Further, ICA1 and STAT6 were also closely related to AIRE and IRF5, two very well known autoimmunity genes. CONCLUSIONS: Novel and rare exonic mutations that may account for autoimmunity were identified. Among those, the LRP1/STAT6 novel mutation has the strongest case for being categorised as potentially causative of MAS given the presence of intriguing patterns of functional interaction with other major genes shaping autoimmunity.

Candidate gene discovery in autoimmunity by using extreme phenotypes, next generation sequencing and whole exome capture.

Whole exome sequencing (WES) is a widely used strategy for detection of protein coding and splicing variants associated with inherited diseases. Many studies have shown that the strategy has been broad and proficient due to its ability in detecting a high proportion of disease causing variants, using only a small portion of the genome. In this review we outline the main steps involved in WES, the comprehensive analysis of the massive data obtained including the genomic capture, amplification, sequencing, alignment, curating, filtering and genetic analysis to determine the presence of candidate variants with potential pathogenic/functional effect. Further, we propose that the multiple autoimmune syndrome, an extreme phenotype of autoimmune disorders, is a very well suited trait to tackle genomic variants of major effect underpinning the lost of self-tolerance.

Whole exome sequencing of extreme morbid obesity patients: translational implications for obesity and related disorders.

Whole-exome sequencing (WES) is a new tool that allows the rapid, inexpensive and accurate exploration of Mendelian and complex diseases, such as obesity. To identify sequence variants associated with obesity, we performed WES of family trios of one male teenager and one female child with severe early-onset obesity. Additionally, the teenager patient had hypopituitarism and hyperprolactinaemia. A comprehensive bioinformatics analysis found de novo and compound heterozygote sequence variants with a damaging effect on genes previously associated with obesity in mice (LRP2) and humans (UCP2), among other intriguing mutations affecting ciliary function (DNAAF1). A gene ontology and pathway analysis of genes harbouring mutations resulted in the significant identification of overrepresented pathways related to ATP/ITP (adenosine/inosine triphosphate) metabolism and, in general, to the regulation of lipid metabolism. We discuss the clinical and physiological consequences of these mutations and the importance of these findings for either the clinical assessment or eventual treatment of morbid obesity.