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.

Admixture has obscured signals of historical hard sweeps in humans.

The role of natural selection in shaping biological diversity is an area of intense interest in modern biology. To date, studies of positive selection have primarily relied on genomic datasets from contemporary populations, which are susceptible to confounding factors associated with complex and often unknown aspects of population history. In particular, admixture between diverged populations can distort or hide prior selection events in modern genomes, though this process is not explicitly accounted for in most selection studies despite its apparent ubiquity in humans and other species. Through analyses of ancient and modern human genomes, we show that previously reported Holocene-era admixture has masked more than 50 historic hard sweeps in modern European genomes. Our results imply that this canonical mode of selection has probably been underappreciated in the evolutionary history of humans and suggest that our current understanding of the tempo and mode of selection in natural populations may be inaccurate.

The immunogenetic impact of European colonization in the Americas.

The introduction of pathogens originating from Eurasia into the Americas during early European contact has been associated with high mortality rates among Indigenous peoples, likely contributing to their historical and precipitous population decline. However, the biological impacts of imported infectious diseases and resulting epidemics, especially in terms of pathogenic effects on the Indigenous immunity, remain poorly understood and highly contentious to this day. Here, we examine multidisciplinary evidence underpinning colonization-related immune genetic change, providing contextualization from anthropological studies, paleomicrobiological evidence of contrasting host-pathogen coevolutionary histories, and the timings of disease emergence. We further summarize current studies examining genetic signals reflecting post-contact Indigenous population bottlenecks, admixture with European and other populations, and the putative effects of natural selection, with a focus on ancient DNA studies and immunity-related findings. Considering current genetic evidence, together with a population genetics theoretical approach, we show that post-contact Indigenous immune adaptation, possibly influenced by selection exerted by introduced pathogens, is highly complex and likely to be affected by multifactorial causes. Disentangling putative adaptive signals from those of genetic drift thus remains a significant challenge, highlighting the need for the implementation of population genetic approaches that model the short time spans and complex demographic histories under consideration. This review adds to current understandings of post-contact immunity evolution in Indigenous peoples of America, with important implications for bettering our understanding of human adaptation in the face of emerging infectious diseases.

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).

Genetics of non-syndromic childhood obesity and the use of high-throughput DNA sequencing technologies.

BACKGROUND: Childhood obesity is a serious public health problem associated with the development of several chronic diseases, such as type 2 diabetes mellitus, dyslipidemia, and hypertension. The elevated prevalence of obesity is mostly due to inadequate diet and lifestyle, but it is also influenced by genetic factors. OBJECTIVES: To review recent advances in the field of the genetics of obesity. We summarize the list of genes associated with the rare non-syndromic forms of obesity, and explain their function. Furthermore, we discuss the technologies that are available for the genetic diagnosis of obesity. RESULTS: Several studies reported that single gene variants cause Mendelian forms of obesity, determined by mutations of major effect in single genes. Rare, non-syndromic forms of obesity are a result of loss-of-function mutations in genes that act on the development and function of the hypothalamus or the leptin-melanocortin pathway. These variants disrupt enzymes and receptors that play a role in energy homeostasis, resulting in severe early-onset obesity and endocrine dysfunctions. Different approaches and technologies have been used to understand the genetic background of obesity. Currently, whole genome and whole exome sequencing are important diagnostic tools to identify new genes and variants associated with severe obesity, but other approaches are also useful at individual or population levels, such as linkage analysis, candidate gene sequencing, chromosomal microarray analysis, and genome-wide association studies. CONCLUSIONS: The understanding of the genetic causes of obesity and the usefulness and limitations of the genetic diagnostic approaches can contribute to the development of new personalized therapeutic targets against obesity.

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.

Definition of mutations in polyautoimmunity.

OBJECTIVES: Familial autoimmunity and polyautoimmunity represent extreme phenotypes ideal for identifying major genomic variants contributing to autoimmunity. Whole exome sequencing (WES) and linkage analysis are well suited for this purpose due to its strong resolution upon familial segregation patterns of functional protein coding and splice variants. The primary objective of this study was to identify potentially autoimmune causative variants using WES data from extreme pedigrees segregating polyautoimmunity phenotypes. METHODS: DNA of 47 individuals across 10 extreme pedigrees, ascertained from probands affected with polyautoimmunity and familial autoimmunity, were selected for WES. Variant calls were obtained through Genome Analysis Toolkit. Filtration and prioritization framework to identify mutation(s) were applied, and later implemented for genetic linkage analysis. Sanger sequencing corroborated variants with significant linkage. RESULTS: Novel and mostly rare variants harbored in SRA1, MLL4, ABCB8, DHX34 and PLAUR showed significant linkage (LOD scores are >3.0). The strongest signal was in SRA1, with a LOD score of 5.48. Network analyses indicated that SRA1, PLAUR and ABCB8 contribute to regulation of apoptotic processes. CONCLUSIONS: Novel and rare variants in genetic linkage with polyautoimmunity were identified throughout WES. Genes harboring these variants might be major players of autoimmunity.

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.