Comparison of predicted and actual consequences of missense mutations

Identification of AGO2 and PLEC genes polymorphisms in Hu sheep and their relationship with body size traits

The body size traits are major traits in livestock, which intuitively displays the development of the animal's bones and muscles. This study used PCR amplification, Sanger sequencing, KASPar genotyping, and quantitative real-time reverse transcription PCR (qRT-PCR) to analyze the Single-nucleotide polymorphism and expression characteristics of Argonaute RISC catalytic component 2 (AGO2) and Plectin (PLEC) genes in Hu sheep. Two intron mutations were found in Hu sheep, which were AGO2 g.51700 A > C and PLEC g.23157 C > T, respectively. Through association analysis of two mutation sites and body size traits, it was found that AGO2 g.51700 A > C mainly affects the chest and cannon circumference of Hu sheep of while PLEC g.23157 C mainly affects body height and body length. The combined genotypes of AGO2 and PLEC genes with body size traits showed SNPs at the AGO2 g.51700 A > C and PLEC g.23157 C > T loci significantly improved the body size traits of Hu sheep. In addition, the AGO2 gene has the highest expression levels in the heart, rumen, and tail fat, and the PLEC gene is highly expressed in the heart. These two loci can provide new research ideas for improving the body size traits of Hu sheep.

Prediction of Deleterious Single Amino Acid Polymorphisms with a Consensus Holdout Sampler

Background

Single Amino Acid Polymorphisms (SAPs) or nonsynonymous Single Nucleotide Variants (nsSNVs) are the most common genetic variations. They result from missense mutations where a single base pair substitution changes the genetic code in such a way that the triplet of bases (codon) at a given position is coding a different amino acid. Since genetic mutations sometimes cause genetic diseases, it is important to comprehend and foresee which variations are harmful and which ones are neutral (not causing changes in the phenotype). This can be posed as a classification problem.

Methods

Computational methods using machine intelligence are gradually replacing repetitive and exceedingly overpriced mutagenic tests. By and large, uneven quality, deficiencies, and irregularities of nsSNVs datasets debase the convenience of artificial intelligence-based methods. Subsequently, strong and more exact approaches are needed to address these problems. In the present work paper, we show a consensus classifier built on the holdout sampler, which appears strong and precise and outflanks all other popular methods.

Results

We produced 100 holdouts to test the structures and diverse classification variables of diverse classifiers during the training phase. The finest performing holdouts were chosen to develop a consensus classifier and tested using a k-fold (1 ≤ k ≤5) cross-validation method. We also examined which protein properties have the biggest impact on the precise prediction of the effects of nsSNVs.

Conclusion

Our Consensus Holdout Sampler outflanks other popular algorithms, and gives excellent results, highly accurate with low standard deviation. The advantage of our method emerges from using a tree of holdouts, where diverse LM/AI-based programs are sampled in diverse ways.

Structure-based network analysis predicts pathogenic variants in human proteins associated with inherited retinal disease

Advances in gene sequencing technologies have accelerated the identification of genetic variants, but better tools are needed to understand which are causal of disease. This would be particularly useful in fields where gene therapy is a potential therapeutic modality for a disease-causing variant such as inherited retinal disease (IRD). Here, we apply structure-based network analysis (SBNA), which has been successfully utilized to identify variant-constrained amino acid residues in viral proteins, to identify residues that may cause IRD if subject to missense mutation. SBNA is based entirely on structural first principles and is not fit to specific outcome data, which makes it distinct from other contemporary missense prediction tools. In 4 well-studied human disease-associated proteins (BRCA1, HRAS, PTEN, and ERK2) with high-quality structural data, we find that SBNA scores correlate strongly with deep mutagenesis data. When applied to 47 IRD genes with available high-quality crystal structure data, SBNA scores reliably identified disease-causing variants according to phenotype definitions from the ClinVar database. Finally, we applied this approach to 63 patients at Massachusetts Eye and Ear (MEE) with IRD but for whom no genetic cause had been identified. Untrained models built using SBNA scores and BLOSUM62 scores for IRD-associated genes successfully predicted the pathogenicity of novel variants (AUC = 0.851), allowing us to identify likely causative disease variants in 40 IRD patients. Model performance was further augmented by incorporating orthogonal data from EVE scores (AUC = 0.927), which are based on evolutionary multiple sequence alignments. In conclusion, SBNA can used to successfully identify variants as causal of disease in human proteins and may help predict variants causative of IRD in an unbiased fashion.

Functional validation of EIF2AK4 (GCN2) missense variants associated with pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a disorder with a large genetic component. Biallelic mutations of EIF2AK4, which encodes the kinase GCN2, are causal in two ultra-rare subtypes of PAH, pulmonary veno-occlusive disease and pulmonary capillary haemangiomatosis. EIF2AK4 variants of unknown significance have also been identified in patients with classical PAH, though their relationship to disease remains unclear. To provide patients with diagnostic information and enable family testing, the functional consequences of such rare variants must be determined, but existing computational methods are imperfect. We applied a suite of bioinformatic and experimental approaches to sixteen EIF2AK4 variants that had been identified in patients. By experimentally testing the functional integrity of the integrated stress response (ISR) downstream of GCN2, we determined that existing computational tools have insufficient sensitivity to reliably predict impaired kinase function. We determined experimentally that several EIF2AK4 variants identified in patients with classical PAH had preserved function and are therefore likely to be non-pathogenic. The dysfunctional variants of GCN2 that we identified could be subclassified into three groups: misfolded, kinase-dead, and hypomorphic. Intriguingly, members of the hypomorphic group were amenable to paradoxical activation by a type-1½ GCN2 kinase inhibitor. This experiment approach may aid in the clinical stratification of EIF2AK4 variants and potentially identify hypomorophic alleles receptive to pharmacological activation.

Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population

Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

The importance of protein domain mutations in cancer therapy

Cancer is a complex disease that is caused by multiple genetic factors. Researchers have been studying protein domain mutations to understand how they affect the progression and treatment of cancer. These mutations can significantly impact the development and spread of cancer by changing the protein structure, function, and signalling pathways. As a result, there is a growing interest in how these mutations can be used as prognostic indicators for cancer prognosis. Recent studies have shown that protein domain mutations can provide valuable information about the severity of the disease and the patient's response to treatment. They may also be used to predict the response and resistance to targeted therapy in cancer treatment. The clinical implications of protein domain mutations in cancer are significant, and they are regarded as essential biomarkers in oncology. However, additional techniques and approaches are required to characterize changes in protein domains and predict their functional effects. Machine learning and other computational tools offer promising solutions to this challenge, enabling the prediction of the impact of mutations on protein structure and function. Such predictions can aid in the clinical interpretation of genetic information. Furthermore, the development of genome editing tools like CRISPR/Cas9 has made it possible to validate the functional significance of mutants more efficiently and accurately. In conclusion, protein domain mutations hold great promise as prognostic and predictive biomarkers in cancer. Overall, considerable research is still needed to better define genetic and molecular heterogeneity and to resolve the challenges that remain, so that their full potential can be realized.

A Human Homozygous HELQ Missense Variant Does Not Cause Premature Ovarian Insufficiency in a Mouse Model

Disruption of meiosis and DNA repair genes is associated with female fertility disorders like premature ovarian insufficiency (POI). In this study, we identified a homozygous missense variant in the HELQ gene (c.596 A>C; p.Gln199Pro) through whole exome sequencing in a POI patient, a condition associated with disrupted ovarian function and female infertility. HELQ, an enzyme involved in DNA repair, plays a crucial role in repairing DNA cross-links and has been linked to germ cell maintenance, fertility, and tumour suppression in mice. To explore the potential association of the HELQ variant with POI, we used CRISPR/Cas9 to create a knock-in mouse model harbouring the equivalent of the human HELQ variant identified in the POI patient. Surprisingly, Helq knock-in mice showed no discernible phenotype, with fertility levels, histological features, and follicle development similar to wild-type mice. Despite the lack of observable effects in mice, the potential role of HELQ in human fertility, especially in the context of POI, should not be dismissed. Larger studies encompassing diverse ethnic populations and alternative functional approaches will be necessary to further examine the role of HELQ in POI. Our results underscore the potential uncertainties associated with genomic variants and the limitations of in vivo animal modelling.

MLe-KCNQ2: An Artificial Intelligence Model for the Prognosis of Missense KCNQ2 Gene Variants

Despite the increasing availability of genomic data and enhanced data analysis procedures, predicting the severity of associated diseases remains elusive in the absence of clinical descriptors. To address this challenge, we have focused on the KV7.2 voltage-gated potassium channel gene (KCNQ2), known for its link to developmental delays and various epilepsies, including self-limited benign familial neonatal epilepsy and epileptic encephalopathy. Genome-wide tools often exhibit a tendency to overestimate deleterious mutations, frequently overlooking tolerated variants, and lack the capacity to discriminate variant severity. This study introduces a novel approach by evaluating multiple machine learning (ML) protocols and descriptors. The combination of genomic information with a novel Variant Frequency Index (VFI) builds a robust foundation for constructing reliable gene-specific ML models. The ensemble model, MLe-KCNQ2, formed through logistic regression, support vector machine, random forest and gradient boosting algorithms, achieves specificity and sensitivity values surpassing 0.95 (AUC-ROC > 0.98). The ensemble MLe-KCNQ2 model also categorizes pathogenic mutations as benign or severe, with an area under the receiver operating characteristic curve (AUC-ROC) above 0.67. This study not only presents a transferable methodology for accurately classifying KCNQ2 missense variants, but also provides valuable insights for clinical counseling and aids in the determination of variant severity. The research context emphasizes the necessity of precise variant classification, especially for genes like KCNQ2, contributing to the broader understanding of gene-specific challenges in the field of genomic research. The MLe-KCNQ2 model stands as a promising tool for enhancing clinical decision making and prognosis in the realm of KCNQ2-related pathologies.

Utility of long-read sequencing for All of Us

The All of Us (AoU) initiative aims to sequence the genomes of over one million Americans from diverse ethnic backgrounds to improve personalized medical care. In a recent technical pilot, we compare the performance of traditional short-read sequencing with long-read sequencing in a small cohort of samples from the HapMap project and two AoU control samples representing eight datasets. Our analysis reveals substantial differences in the ability of these technologies to accurately sequence complex medically relevant genes, particularly in terms of gene coverage and pathogenic variant identification. We also consider the advantages and challenges of using low coverage sequencing to increase sample numbers in large cohort analysis. Our results show that HiFi reads produce the most accurate results for both small and large variants. Further, we present a cloud-based pipeline to optimize SNV, indel and SV calling at scale for long-reads analysis. These results lead to widespread improvements across AoU.

Comprehensive analysis reveals potential therapeutic targets and an integrated risk stratification model for solitary fibrous tumors

Solitary fibrous tumors (SFTs) are rare mesenchymal tumors with unpredictable evolution and with a recurrence or metastasis rate of 10-40%. Current medical treatments for relapsed SFTs remain ineffective. Here, we identify potential therapeutic targets and risk factors, including IDH1 p.R132S, high PD-L1 expression, and predominant macrophage infiltration, suggesting the potential benefits of combinational immune therapy and targeted therapy for SFTs. An integrated risk model incorporating mitotic count, density of Ki-67+ cells and CD163+ cells, MTOR mutation is developed, applying a discovery cohort of 101 primary non-CNS patients with negative tumor margins (NTM) and validated in three independent cohorts of 210 SFTs with the same criteria, and in 36 primary CNS SFTs with NTM. Compared with the existing models, our model shows significantly improved efficacy in identifying high-risk primary non-CNS and CNS SFTs with NTM for tumor progression.Our findings hold promise for advancing therapeutic strategies and refining risk prediction in SFTs.

Finding significance: New perspectives in variant classification of the RAD51 regulators, BRCA2 and beyond

For many individuals harboring a variant of uncertain functional significance (VUS) in a homologous recombination (HR) gene, their risk of developing breast and ovarian cancer is unknown. Integral to the process of HR are BRCA1 and regulators of the central HR protein, RAD51, including BRCA2, PALB2, RAD51C and RAD51D. Due to advancements in sequencing technology and the continued expansion of cancer screening panels, the number of VUS identified in these genes has risen significantly. Standard practices for variant classification utilize different types of predictive, population, phenotypic, allelic and functional evidence. While variant analysis is improving, there remains a struggle to keep up with demand. Understanding the effects of an HR variant can aid in preventative care and is critical for developing an effective cancer treatment plan. In this review, we discuss current perspectives in the classification of variants in the breast and ovarian cancer genes BRCA1, BRCA2, PALB2, RAD51C and RAD51D.

In vivo versus in silico assessment of potentially pathogenic missense variants in human reproductive genes

Infertility is a heterogeneous condition, with genetic causes thought to underlie a substantial fraction of cases. Genome sequencing is becoming increasingly important for genetic diagnosis of diseases including idiopathic infertility; however, most rare or minor alleles identified in patients are variants of uncertain significance (VUS). Interpreting the functional impacts of VUS is challenging but profoundly important for clinical management and genetic counseling. To determine the consequences of these variants in key fertility genes, we functionally evaluated 11 missense variants in the genes ANKRD31, BRDT, DMC1, EXO1, FKBP6, MCM9, M1AP, MEI1, MSH4 and SEPT12 by generating genome-edited mouse models. Nine variants were classified as deleterious by most functional prediction algorithms, and two disrupted a protein-protein interaction (PPI) in the yeast two hybrid (Y2H) assay. Though these genes are essential for normal meiosis or spermiogenesis in mice, only one variant, observed in the MCM9 gene of a male infertility patient, compromised fertility or gametogenesis in the mouse models. To explore the disconnect between predictions and outcomes, we compared pathogenicity calls of missense variants made by ten widely used algorithms to 1) those annotated in ClinVar and 2) those evaluated in mice. All the algorithms performed poorly in terms of predicting the effects of human missense variants modeled in mice. These studies emphasize caution in the genetic diagnoses of infertile patients based primarily on pathogenicity prediction algorithms and emphasize the need for alternative and efficient in vitro or in vivo functional validation models for more effective and accurate VUS description to either pathogenic or benign categories.

Actionability classification of variants of unknown significance correlates with functional effect

Genomically-informed therapy requires consideration of the functional impact of genomic alterations on protein expression and/or function. However, a substantial number of variants are of unknown significance (VUS). The MD Anderson Precision Oncology Decision Support (PODS) team developed an actionability classification scheme that categorizes VUS as either "Unknown" or "Potentially" actionable based on their location within functional domains and/or proximity to known oncogenic variants. We then compared PODS VUS actionability classification with results from a functional genomics platform consisting of mutant generation and cell viability assays. 106 (24%) of 438 VUS in 20 actionable genes were classified as oncogenic in functional assays. Variants categorized by PODS as Potentially actionable (N = 204) were more likely to be oncogenic than those categorized as Unknown (N = 230) (37% vs 13%, p = 4.08e-09). Our results demonstrate that rule-based actionability classification of VUS can identify patients more likely to have actionable variants for consideration with genomically-matched therapy.

Discovering Deleterious Single Nucleotide Polymorphisms of Human AKT1 Oncogene: An In Silico Study

BACKGROUND

AKT1 is a serine/threonine kinase necessary for the mediation of apoptosis, angiogenesis, metabolism, and cell proliferation in both normal and cancerous cells. The mutations in the AKT1 gene have been associated with different types of cancer. Further, the AKT1 gene mutations are also reported to be associated with other diseases such as Proteus syndrome and Cowden syndromes. Hence, this study aims to identify the deleterious AKT1 missense SNPs and predict their effect on the function and structure of the AKT1 protein using various computational tools.

METHODS

Extensive in silico approaches were applied to identify deleterious SNPs of the human AKT1 gene and assessment of their impact on the function and structure of the AKT1 protein. The association of these highly deleterious missense SNPs with different forms of cancers was also analyzed. The in silico approach can help in reducing the cost and time required to identify SNPs associated with diseases.

RESULTS

In this study, 12 highly deleterious SNPs were identified which could affect the structure and function of the AKT1 protein. Out of the 12, four SNPs-namely, G157R, G159V, G336D, and H265Y-were predicted to be located at highly conserved residues. G157R could affect the ligand binding to the AKT1 protein. Another highly deleterious SNP, R273Q, was predicted to be associated with liver cancer.

CONCLUSIONS

This study can be useful for pharmacogenomics, molecular diagnosis of diseases, and developing inhibitors of the AKT1 oncogene.

Structure-based network analysis predicts mutations associated with inherited retinal disease

With continued advances in gene sequencing technologies comes the need to develop better tools to understand which mutations cause disease. Here we validate structure-based network analysis (SBNA)1,2 in well-studied human proteins and report results of using SBNA to identify critical amino acids that may cause retinal disease if subject to missense mutation. We computed SBNA scores for genes with high-quality structural data, starting with validating the method using 4 well-studied human disease-associated proteins. We then analyzed 47 inherited retinal disease (IRD) genes. We compared SBNA scores to phenotype data from the ClinVar database and found a significant difference between benign and pathogenic mutations with respect to network score. Finally, we applied this approach to 65 patients at Massachusetts Eye and Ear (MEE) who were diagnosed with IRD but for whom no genetic cause was found. Multivariable logistic regression models built using SBNA scores for IRD-associated genes successfully predicted pathogenicity of novel mutations, allowing us to identify likely causative disease variants in 37 patients with IRD from our clinic. In conclusion, SBNA can be meaningfully applied to human proteins and may help predict mutations causative of IRD.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Identification of Three Human POLH Germline Variants Defective in Complementing the UV- and Cisplatin-Sensitivity of POLH-Deficient Cells

DNA polymerase (pol) η is responsible for error-free translesion DNA synthesis (TLS) opposite ultraviolet light (UV)-induced cis-syn cyclobutane thymine dimers (CTDs) and cisplatin-induced intrastrand guanine crosslinks. POLH deficiency causes one form of the skin cancer-prone disease xeroderma pigmentosum variant (XPV) and cisplatin sensitivity, but the functional impacts of its germline variants remain unclear. We evaluated the functional properties of eight human POLH germline in silico-predicted deleterious missense variants, using biochemical and cell-based assays. In enzymatic assays, utilizing recombinant pol η (residues 1-432) proteins, the C34W, I147N, and R167Q variants showed 4- to 14-fold and 3- to 5-fold decreases in specificity constants (kcat/Km) for dATP insertion opposite the 3'-T and 5'-T of a CTD, respectively, compared to the wild-type, while the other variants displayed 2- to 4-fold increases. A CRISPR/Cas9-mediated POLH knockout increased the sensitivity of human embryonic kidney 293 cells to UV and cisplatin, which was fully reversed by ectopic expression of wild-type pol η, but not by that of an inactive (D115A/E116A) or either of two XPV-pathogenic (R93P and G263V) mutants. Ectopic expression of the C34W, I147N, and R167Q variants, unlike the other variants, did not rescue the UV- and cisplatin-sensitivity in POLH-knockout cells. Our results indicate that the C34W, I147N, and R167Q variants-substantially reduced in TLS activity-failed to rescue the UV- and cisplatin-sensitive phenotype of POLH-deficient cells, which also raises the possibility that such hypoactive germline POLH variants may increase the individual susceptibility to UV irradiation and cisplatin chemotherapy.

Lynch syndrome, molecular mechanisms and variant classification

Patients with the heritable cancer disease, Lynch syndrome, carry germline variants in the MLH1, MSH2, MSH6 and PMS2 genes, encoding the central components of the DNA mismatch repair system. Loss-of-function variants disrupt the DNA mismatch repair system and give rise to a detrimental increase in the cellular mutational burden and cancer development. The treatment prospects for Lynch syndrome rely heavily on early diagnosis; however, accurate diagnosis is inextricably linked to correct clinical interpretation of individual variants. Protein variant classification traditionally relies on cumulative information from occurrence in patients, as well as experimental testing of the individual variants. The complexity of variant classification is due to (1) that variants of unknown significance are rare in the population and phenotypic information on the specific variants is missing, and (2) that individual variant testing is challenging, costly and slow. Here, we summarise recent developments in high-throughput technologies and computational prediction tools for the assessment of variants of unknown significance in Lynch syndrome. These approaches may vastly increase the number of interpretable variants and could also provide important mechanistic insights into the disease. These insights may in turn pave the road towards developing personalised treatment approaches for Lynch syndrome.

Assessing the relationship between the in silico predicted consequences of 97 missense mutations mapping to 68 genes related to lipid metabolism and their association with porcine fatness traits

In general, the relationship between the predicted functional consequences of missense mutations mapping to genes known to be involved in human diseases and the severity of disease manifestations is weak. In this study, we tested in pigs whether missense single nucleotide polymorphisms (SNPs), predicted to have consequences on the function of genes related to lipid metabolism are associated with lipid phenotypes. Association analysis demonstrated that nine out of 72 nominally associated SNPs were classified as "highly" or "very highly consistent" in silico-predicted functional mutations and did not show association with lipid traits expected to be affected by inactivation of the corresponding gene. Although the lack of endophenotypes and the limited sample size of certain genotypic classes might have limited to some extent the reach of the current study, our data indicate that present-day bioinformatic tools have a modest ability to predict the impact of missense mutations on complex phenotypes.

Analyses of Mutation Displacements from Homology Models

Evaluation of the structural perturbations introduced by a single amino acid mutation is the main issue for protein structural biology. We propose here to present some recent advances in methods, allowing the splitting of distortion between the actual substitution effect and the contribution of the local flexibility of the position where the mutation occurs. Its main drawback is the need of many structures with a single mutation in each of them. To bypass this difficulty, we propose to use molecular modeling tools, with several software enabling us to build a model from a template, given the sequence. As a proof of concept, we rely on a gold standard, the human lysozyme. Both wild-type and three mutant structures are available in the PDB. Two of these mutations result in amyloid fibril formation, and the last one is neutral. As a conclusion, irrespective of the algorithm used for modeling, side chain conformations at the site of mutation are reliable, although long-range effects are out of reach of these tools.

Interpreting the spectrum of gamma-secretase complex missense variation in the context of hidradenitis suppurativa—An in-silico study

Hidradenitis suppurativa (HS) is a disease of the pilosebaceous unit characterized by recurrent nodules, abscesses and draining tunnels with a predilection to intertriginous skin. The pathophysiology of HS is complex. However, it is known that inflammation and hyperkeratinization at the hair follicle play crucial roles in disease manifestation. Genetic and environmental factors are considered the main drivers of these two pathophysiological processes. Despite a considerable proportion of patients having a positive family history of disease, only a minority of patients suffering from HS have been found to harbor monogenic variants which segregate to affected kindreds. Most of these variants are in the ɣ secretase complex (GSC) protein-coding genes. In this manuscript, we set out to characterize the burden of missense pathogenic variants in healthy reference population using large scale genomic dataset thereby providing a standard for comparing genomic variation in GSC protein-coding genes in the HS patient cohort.

A massively parallel assay accurately discriminates between functionally normal and abnormal variants in a hotspot domain of KCNH2

Many genes, including KCNH2, contain "hotspot" domains associated with a high density of variants associated with disease. This has led to the suggestion that variant location can be used as evidence supporting classification of clinical variants. However, it is not known what proportion of all potential variants in hotspot domains cause loss of function. Here, we have used a massively parallel trafficking assay to characterize all single-nucleotide variants in exon 2 of KCNH2, a known hotspot for variants that cause long QT syndrome type 2 and an increased risk of sudden cardiac death. Forty-two percent of KCNH2 exon 2 variants caused at least 50% reduction in protein trafficking, and 65% of these trafficking-defective variants exerted a dominant-negative effect when co-expressed with a WT KCNH2 allele as assessed using a calibrated patch-clamp electrophysiology assay. The massively parallel trafficking assay was more accurate (AUC of 0.94) than bioinformatic prediction tools (REVEL and CardioBoost, AUC of 0.81) in discriminating between functionally normal and abnormal variants. Interestingly, over half of variants in exon 2 were found to be functionally normal, suggesting a nuanced interpretation of variants in this "hotspot" domain is necessary. Our massively parallel trafficking assay can provide this information prospectively.

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we investigate the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors, even those based solely on sequence conservation, underperform on non-LOF mutations. However, we do show that non-LOF mutations could potentially be identified by their tendency to cluster in three-dimensional space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mechanisms are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Here the authors analyse the locations of thousands of human disease mutations and their predicted effects on protein structure and show that,while loss-of-function mutations tend to be highly disruptive, non-loss-of-function mutations are in general much milder at a protein structural level.

High-throughput functional evaluation of human cancer-associated mutations using base editors

Comprehensive phenotypic characterization of the many mutations found in cancer tissues is one of the biggest challenges in cancer genomics. In this study, we evaluated the functional effects of 29,060 cancer-related transition mutations that result in protein variants on the survival and proliferation of non-tumorigenic lung cells using cytosine and adenine base editors and single guide RNA (sgRNA) libraries. By monitoring base editing efficiencies and outcomes using surrogate target sequences paired with sgRNA-encoding sequences on the lentiviral delivery construct, we identified sgRNAs that induced a single primary protein variant per sgRNA, enabling linking those mutations to the cellular phenotypes caused by base editing. The functions of the vast majority of the protein variants (28,458 variants, 98%) were classified as neutral or likely neutral; only 18 (0.06%) and 157 (0.5%) variants caused outgrowing and likely outgrowing phenotypes, respectively. We expect that our approach can be extended to more variants of unknown significance and other tumor types.

Interpreting ciliopathy-associated missense variants of uncertain significance (VUS) in Caenorhabditis elegans

Better methods are required to interpret the pathogenicity of disease-associated variants of uncertain significance (VUS), which cannot be actioned clinically. In this study, we explore the use of an animal model (Caenorhabditis elegans) for in vivo interpretation of missense VUS alleles of TMEM67, a cilia gene associated with ciliopathies. CRISPR/Cas9 gene editing was used to generate homozygous knock-in C. elegans worm strains carrying TMEM67 patient variants engineered into the orthologous gene (mks-3). Quantitative phenotypic assays of sensory cilia structure and function (neuronal dye filling, roaming and chemotaxis assays) measured how the variants impacted mks-3 gene function. Effects of the variants on mks-3 function were further investigated by looking at MKS-3::GFP localization and cilia ultrastructure. The quantitative assays in C. elegans accurately distinguished between known benign (Asp359Glu, Thr360Ala) and known pathogenic (Glu361Ter, Gln376Pro) variants. Analysis of eight missense VUS generated evidence that three are benign (Cys173Arg, Thr176Ile and Gly979Arg) and five are pathogenic (Cys170Tyr, His782Arg, Gly786Glu, His790Arg and Ser961Tyr). Results from worms were validated by a genetic complementation assay in a human TMEM67 knock-out hTERT-RPE1 cell line that tests a TMEM67 signalling function. We conclude that efficient genome editing and quantitative functional assays in C. elegans make it a tractable in vivo animal model for rapid, cost-effective interpretation of ciliopathy-associated missense VUS alleles.

Integration of Protein Structure and Population-Scale DNA Sequence Data for Disease Gene Discovery and Variant Interpretation

The experimental and computational techniques for capturing information about protein structures and genetic variation within the human genome have advanced dramatically in the past 20 years, generating extensive new data resources. In this review, we discuss these advances, along with new approaches for determining the impact a genetic variant has on protein function. We focus on the potential of new methods that integrate human genetic variation into protein structures to discover relationships to disease, including the discovery of mutational hotspots in cancer-related proteins, the localization of protein-altering variants within protein regions for common complex diseases, and the assessment of variants of unknown significance for Mendelian traits. We expect that approaches that integrate these data sources will play increasingly important roles in disease gene discovery and variant interpretation. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Bioinformatic Challenges Detecting Genetic Variation in Precision Medicine Programs

Precision medicine programs to identify clinically relevant genetic variation have been revolutionized by access to increasingly affordable high-throughput sequencing technologies. A decade of continual drops in per-base sequencing costs means it is now feasible to sequence an individual patient genome and interrogate all classes of genetic variation for < $1,000 USD. However, while advances in these technologies have greatly simplified the ability to obtain patient sequence information, the timely analysis and interpretation of variant information remains a challenge for the rollout of large-scale precision medicine programs. This review will examine the challenges and potential solutions that exist in identifying predictive genetic biomarkers and pharmacogenetic variants in a patient and discuss the larger bioinformatic challenges likely to emerge in the future. It will examine how both software and hardware development are aiming to overcome issues in short read mapping, variant detection and variant interpretation. It will discuss the current state of the art for genetic disease and the remaining challenges to overcome for complex disease. Success across all types of disease will require novel statistical models and software in order to ensure precision medicine programs realize their full potential now and into the future.

Current and Future Approaches to Classify VUSs in LGMD-Related Genes

Next-generation sequencing (NGS) has revealed large numbers of genetic variants in LGMD-related genes, with most of them classified as variants of uncertain significance (VUSs). VUSs are genetic changes with unknown pathological impact and present a major challenge in genetic test interpretation and disease diagnosis. Understanding the phenotypic consequences of VUSs can provide clinical guidance regarding LGMD risk and therapy. In this review, we provide a brief overview of the subtypes of LGMD, disease diagnosis, current classification systems for investigating VUSs, and a potential deep mutational scanning approach to classify VUSs in LGMD-related genes.

The Location of Missense Variants in the Human GIP Gene Is Indicative for Natural Selection

The intestinal hormone, glucose-dependent insulinotropic polypeptide (GIP), is involved in important physiological functions, including postprandial blood glucose homeostasis, bone remodeling, and lipid metabolism. While mutations leading to physiological changes can be identified in large-scale sequencing, no systematic investigation of GIP missense variants has been performed. Here, we identified 168 naturally occurring missense variants in the human GIP genes from three independent cohorts comprising ~720,000 individuals. We examined amino acid changing variants scattered across the pre-pro-GIP peptide using in silico effect predictions, which revealed that the sequence of the fully processed GIP hormone is more protected against mutations than the rest of the precursor protein. Thus, we observed a highly species-orthologous and population-specific conservation of the GIP peptide sequence, suggestive of evolutionary constraints to preserve the GIP peptide sequence. Elucidating the mutational landscape of GIP variants and how they affect the structural and functional architecture of GIP can aid future biological characterization and clinical translation.

Investigation of discordant sibling pairs from hereditary breast cancer families and analysis of a rare PMS1 variant

BACKGROUND

It is likely that additional genes for hereditary breast cancer can be identified using a discordant sib pair design. Using this design we identified individuals harboring a rare PMS1 c.605G>A variant previously predicted to result in loss of function.

OBJECTIVES

A family-based design and predictive algorithms were used to prioritize candidate variants possibly associated with an increased risk of hereditary breast cancer. Functional analyses were performed for one of the candidate variants, PMS1 c.605G>A.

METHODS

1) 14 discordant sister-pairs from hereditary breast cancer families were identified. 2) Whole exome sequencing was performed and candidate risk variants identified. 3) A rare PMS variant was identified in 2 unrelated affected sisters but no unaffected siblings. 4) Functional analysis of this variant was carried out using targeted mRNA sequencing.

RESULTS

Genotype-phenotype correlation did not demonstrate tracking of the variant with cancer in the family. Functional analysis revealed no difference in exon 6 incorporation, which was validated by analyzing PMS1 allele specific expression.

CONCLUSIONS

The PMS1 c.605G>A variant did not segregate with disease, and there was no variant-dependent impact on PMS1 exon 6 splicing, supporting this variant is likely benign. Functional analyses are imperative to understanding the clinical significance of predictive algorithms.

Biochemically deleterious human NFKB1 variants underlie an autosomal dominant form of common variable immunodeficiency

Autosomal dominant (AD) NFKB1 deficiency is thought to be the most common genetic etiology of common variable immunodeficiency (CVID). However, the causal link between NFKB1 variants and CVID has not been demonstrated experimentally and genetically, as there has been insufficient biochemical characterization and enrichment analysis. We show that the cotransfection of NFKB1-deficient HEK293T cells (lacking both p105 and its cleaved form p50) with a κB reporter, NFKB1/p105, and a homodimerization-defective RELA/p65 mutant results in p50:p65 heterodimer–dependent and p65:p65 homodimer–independent transcriptional activation. We found that 59 of the 90 variants in patients with CVID or related conditions were loss of function or hypomorphic. By contrast, 258 of 260 variants in the general population or patients with unrelated conditions were neutral. None of the deleterious variants displayed negative dominance. The enrichment in deleterious NFKB1 variants of patients with CVID was selective and highly significant (P = 2.78 × 10⁻¹⁵). NFKB1 variants disrupting NFKB1/p50 transcriptional activity thus underlie AD CVID by haploinsufficiency, whereas neutral variants in this assay should not be considered causal.

The clinical and functional effects of TERT variants in myelodysplastic syndrome

Germline pathogenic TERT variants are associated with short telomeres and an increased risk of developing myelodysplastic syndrome (MDS) among patients with a telomere biology disorder. We identified TERT rare variants in 41 of 1514 MDS patients (2.7%) without a clinical diagnosis of a telomere biology disorder who underwent allogeneic transplantation. Patients with a TERT rare variant had shorter telomere length (P < .001) and younger age at MDS diagnosis (52 vs 59 years, P = .03) than patients without a TERT rare variant. In multivariable models, TERT rare variants were associated with inferior overall survival (P = .034) driven by an increased incidence of nonrelapse mortality (NRM; P = .015). Death from a noninfectious pulmonary cause was more frequent among patients with a TERT rare variant. Most variants were missense substitutions and classified as variants of unknown significance. Therefore, we cloned all rare missense variants and quantified their impact on telomere elongation in a cell-based assay. We found that 90% of TERT rare variants had severe or intermediate impairment in their capacity to elongate telomeres. Using a homology model of human TERT bound to the shelterin protein TPP1, we inferred that TERT rare variants disrupt domain-specific functions, including catalysis, protein-RNA interactions, and recruitment to telomeres. Our results indicate that the contribution of TERT rare variants to MDS pathogenesis and NRM risk is underrecognized. Routine screening for TERT rare variants in MDS patients regardless of age or clinical suspicion may identify clinically inapparent telomere biology disorders and improve transplant outcomes through risk-adapted approaches.

Evolutionary and functional lessons from human-specific amino acid substitution matrices

Human genetic variation in coding regions is fundamental to the study of protein structure and function. Most methods for interpreting missense variants consider substitution measures derived from homologous proteins across different species. In this study, we introduce human-specific amino acid (AA) substitution matrices that are based on genetic variations in the modern human population. We analyzed the frequencies of >4.8M single nucleotide variants (SNVs) at codon and AA resolution and compiled human-centric substitution matrices that are fundamentally different from classic cross-species matrices (e.g. BLOSUM, PAM). Our matrices are asymmetric, with some AA replacements showing significant directional preference. Moreover, these AA matrices are only partly predicted by nucleotide substitution rates. We further test the utility of our matrices in exposing functional signals of experimentally-validated protein annotations. A significant reduction in AA transition frequencies was observed across nine post-translational modification (PTM) types and four ion-binding sites. Our results propose a purifying selection signal in the human proteome across a diverse set of functional protein annotations and provide an empirical baseline for interpreting human genetic variation in coding regions.

A scalable Drosophila assay for clinical interpretation of human PTEN variants in suppression of PI3K/AKT induced cellular proliferation

Gene variant discovery is becoming routine, but it remains difficult to usefully interpret the functional consequence or disease relevance of most variants. To fill this interpretation gap, experimental assays of variant function are becoming common place. Yet, it remains challenging to make these assays reproducible, scalable to high numbers of variants, and capable of assessing defined gene-disease mechanism for clinical interpretation aligned to the ClinGen Sequence Variant Interpretation (SVI) Working Group guidelines for 'well-established assays'. Drosophila melanogaster offers great potential as an assay platform, but was untested for high numbers of human variants adherent to these guidelines. Here, we wished to test the utility of Drosophila as a platform for scalable well-established assays. We took a genetic interaction approach to test the function of ~100 human PTEN variants in cancer-relevant suppression of PI3K/AKT signaling in cellular growth and proliferation. We validated the assay using biochemically characterized PTEN mutants as well as 23 total known pathogenic and benign PTEN variants, all of which the assay correctly assigned into predicted functional categories. Additionally, function calls for these variants correlated very well with our recent published data from a human cell line. Finally, using these pathogenic and benign variants to calibrate the assay, we could set readout thresholds for clinical interpretation of the pathogenicity of 70 other PTEN variants. Overall, we demonstrate that Drosophila offers a powerful assay platform for clinical variant interpretation, that can be used in conjunction with other well-established assays, to increase confidence in the accurate assessment of variant function and pathogenicity.

L-Type Calcium Channel: Predicting Pathogenic/Likely Pathogenic Status for Variants of Uncertain Clinical Significance

(1) Background: Defects in gene CACNA1C, which encodes the pore-forming subunit of the human Cav1.2 channel (hCav1.2), are associated with cardiac disorders such as atrial fibrillation, long QT syndrome, conduction disorders, cardiomyopathies, and congenital heart defects. Clinical manifestations are known only for 12% of CACNA1C missense variants, which are listed in public databases. Bioinformatics approaches can be used to predict the pathogenic/likely pathogenic status for variants of uncertain clinical significance. Choosing a bioinformatics tool and pathogenicity threshold that are optimal for specific protein families increases the reliability of such predictions. (2) Methods and Results: We used databases ClinVar, Humsavar, gnomAD, and Ensembl to compose a dataset of pathogenic/likely pathogenic and benign variants of hCav1.2 and its 20 paralogues: voltage-gated sodium and calcium channels. We further tested the performance of sixteen in silico tools in predicting pathogenic variants. ClinPred demonstrated the best performance, followed by REVEL and MCap. In the subset of 309 uncharacterized variants of hCav1.2, ClinPred predicted the pathogenicity for 188 variants. Among these, 36 variants were also categorized as pathogenic/likely pathogenic in at least one paralogue of hCav1.2. (3) Conclusions: The bioinformatics tool ClinPred and the paralogue annotation method consensually predicted the pathogenic/likely pathogenic status for 36 uncharacterized variants of hCav1.2. An analogous approach can be used to classify missense variants of other calcium channels and novel variants of hCav1.2.

An overview of germline variations in genes of primary immunodeficiences through integrative analysis of ClinVar, HGMD® and dbSNP databases

Primary immunodeficiencies (PID) are a diverse group of genetic disorders caused by inadequate development and function of immune system. Identifying genetic etiology is important for genetic counselling and treatment decisions. Clinical relevance of genetic variants is a complex problem depending on gene-specific and variant specific genotype-phenotype interactions. To address this challenge, we aimed to characterize the pathogenic landscape of PID genes by combining the analysis of germline variations reported in ClinVar and HGMD^® and identification of damaging variations available in dbSNP. We generated a joint ClinVar/HGMD database, which included 111,940 variants, among them 32,452 were classified as pathogenic/likely pathogenic. From a total of 5,415,794 bi- or multiallelic variants in PID genes recorded in dbSNP, we retrieved 38,291 high impact (HI) biallelic variants with presumably disruptive impact in the protein, of them 25,500 variants were not present in ClinVar/HGMD. Using a functional prediction algorithm, we additionally identified 28,507 deleterious and 56,016 neutral missense variants among dbSNP variants and created a collection of damaging and neutral variations in PID genes, not currently present in ClinVar/HGMD, with their allele frequencies and mappings to protein domains. The distribution of pathogenic variants from ClinVar/HGMD, HI variants and deleterious missense variants from dbSNP was analyzed in the context of hereditary pattern and gene specific metrics, such as pLI and haploinsufficiency. Our report summarized data on complex gene-specific variability in PID genes and might be useful for the identification of the most promising variants and gene regions for further study.

Efficacy of computational predictions of the functional effect of idiosyncratic pharmacogenetic variants

Background

Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools-these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect.

Methods

Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations.

Results

As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as 'benign'. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as 'benign'. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms.

Conclusion

In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.

A method to delineate de novo missense variants across pathways prioritizes genes linked to autism

Genotype-phenotype relationships shape health and population fitness but remain difficult to predict and interpret. Here, we apply an evolutionary action method to de novo missense variants in whole-exome sequences of individuals with autism spectrum disorder (ASD) to unravel genes and pathways connected to ASD. Evolutionary action predicts the impact of missense variants on protein function by measuring the fitness effect based on phylogenetic distances and substitution odds in homologous gene sequences. By examining de novo missense variants in 2384 individuals with ASD (probands) compared to matched siblings without ASD, we found missense variants in 398 genes representing 23 pathways that were biased toward higher evolutionary action scores than expected by random chance; these pathways were involved in axonogenesis, synaptic transmission, and neurodevelopment. The predicted fitness impact of de novo and inherited missense variants in candidate genes correlated with the IQ of individuals with ASD, even for new gene candidates. Taking an evolutionary action method, we detected those missense variants most likely to contribute to ASD pathogenesis and elucidated their phenotypic impact. This approach could be applied to integrate missense variants across a patient cohort to identify genes contributing to a shared phenotype in other complex diseases.

A systematic analysis of protein-altering exonic variants in chronic obstructive pulmonary disease

Genome-wide association studies (GWASs) have identified regions associated with chronic obstructive pulmonary disease (COPD). GWASs of other diseases have shown an approximately 10-fold overrepresentation of nonsynonymous variants, despite limited exonic coverage on genotyping arrays. We hypothesized that a large-scale analysis of coding variants could discover novel genetic associations with COPD, including rare variants with large effect sizes. We performed a meta-analysis of exome arrays from 218,399 controls and 33,851 moderate-to-severe COPD cases. All exome-wide significant associations were present in regions previously identified by GWAS. We did not identify any novel rare coding variants with large effect sizes. Within GWAS regions on chromosomes 5q, 6p, and 15q, four coding variants were conditionally significant (P < 0.00015) when adjusting for lead GWAS single-nucleotide polymorphisms A common gasdermin B (GSDMB) splice variant (rs11078928) previously associated with a decreased risk for asthma was nominally associated with a decreased risk for COPD [minor allele frequency (MAF) = 0.46, P = 1.8e-4]. Two stop variants in coiled-coil α-helical rod protein 1 (CCHCR1), a gene involved in regulating cell proliferation, were associated with COPD (both P < 0.0001). The SERPINA1 Z allele was associated with a random-effects odds ratio of 1.43 for COPD (95% confidence interval = 1.17-1.74), though with marked heterogeneity across studies. Overall, COPD-associated exonic variants were identified in genes involved in DNA methylation, cell-matrix interactions, cell proliferation, and cell death. In conclusion, we performed the largest exome array meta-analysis of COPD to date and identified potential functional coding variants. Future studies are needed to identify rarer variants and further define the role of coding variants in COPD pathogenesis.

Mutational Landscape of the Proglucagon-Derived Peptides

Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.

Focused Strategies for Defining the Genetic Architecture of Congenital Heart Defects

Congenital heart defects (CHD) are malformations present at birth that occur during heart development. Increasing evidence supports a genetic origin of CHD, but in the process important challenges have been identified. This review begins with information about CHD and the importance of detailed phenotyping of study subjects. To facilitate appropriate genetic study design, we review DNA structure, genetic variation in the human genome and tools to identify the genetic variation of interest. Analytic approaches powered for both common and rare variants are assessed. While the ideal outcome of genetic studies is to identify variants that have a causal role, a more realistic goal for genetic analytics is to identify variants in specific genes that influence the occurrence of a phenotype and which provide keys to open biologic doors that inform how the genetic variants modulate heart development. It has never been truer that good genetic studies start with good planning. Continued progress in unraveling the genetic underpinnings of CHD will require multidisciplinary collaboration between geneticists, quantitative scientists, clinicians, and developmental biologists.

Missense3D-DB web catalogue: an atom-based analysis and repository of 4M human protein-coding genetic variants

The interpretation of human genetic variation is one of the greatest challenges of modern genetics. New approaches are urgently needed to prioritize variants, especially those that are rare or lack a definitive clinical interpretation. We examined 10,136,597 human missense genetic variants from GnomAD, ClinVar and UniProt. We were able to perform large-scale atom-based mapping and phenotype interpretation of 3,960,015 of these variants onto 18,874 experimental and 84,818 in house predicted three-dimensional coordinates of the human proteome. We demonstrate that 14% of amino acid substitutions from the GnomAD database that could be structurally analysed are predicted to affect protein structure (n = 568,548, of which 566,439 rare or extremely rare) and may, therefore, have a yet unknown disease-causing effect. The same is true for 19.0% (n = 6266) of variants of unknown clinical significance or conflicting interpretation reported in the ClinVar database. The results of the structural analysis are available in the dedicated web catalogue Missense3D-DB ( http://missense3d.bc.ic.ac.uk/ ). For each of the 4 M variants, the results of the structural analysis are presented in a friendly concise format that can be included in clinical genetic reports. A detailed report of the structural analysis is also available for the non-experts in structural biology. Population frequency and predictions from SIFT and PolyPhen are included for a more comprehensive variant interpretation. This is the first large-scale atom-based structural interpretation of human genetic variation and offers geneticists and the biomedical community a new approach to genetic variant interpretation.

Whole-exome sequencing reveals the etiology of the rare primary hepatic mucoepidermoid carcinoma

Background

Primary hepatic mucoepidermoid carcinoma (HMEC) is extremely rare and the molecular etiology is still unknown. The CRTC1-MAML2 fusion gene was previously detected in a primary HMEC, which is often associated with MEC of salivary gland in the literature.

Methods

A 64-year-old male was diagnosed with HMEC based on malignant squamous cells and mucus-secreting cells in immunohistochemical examination. Fluorescence in situ hybridization (FISH) was used to detect the CRTC1-MAML2 fusion gene in HMEC. Whole-exome sequencing and Sanger sequencing were used to reveal the molecular characteristics of HMEC and analysis was performed with public data. Pedigree investigation was performed to identify susceptibility genes.

Results

Hematoxylin–eosin staining and immunohistochemistry revealed that the tumor cells were composed of malignant epidermoid malignant cells and mucous cells, indicating a diagnosis of HMEC. The CRTC1-MAML2 fusion gene was not detected in the primary HMEC, and somatic mutations in GNAS, KMT2C and ELF3 genes were identified by sequencing. Analyses of public data revealed somatic GNAS alterations in 2.1% hepatobiliary tumors and relation with parasite infection. Heterozygous germline mutations of FANCA, FANCI, FANCJ/BRIP1 and FAN1 genes were also identified. Pedigree investigation verified that mutation of Fanconi’s anemia susceptibility genes were present in the pedigree.

Conclusions

Here we provide the first evidence of the molecular etiology of a rare HMEC associated with germline Fanconi’s anemia gene mutations and somatic GNAS R201H mutation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13000-021-01086-3.

A Point Mutation in IKAROS ZF1 Causes a B Cell Deficiency in Mice

IKZF1 (IKAROS) is essential for normal lymphopoiesis in both humans and mice. Previous Ikzf1 mouse models have demonstrated the dual role for IKZF1 in both B and T cell development and have indicated differential requirements of each zinc finger. Furthermore, mutations in IKZF1 are known to cause common variable immunodeficiency in patients characterized by a loss of B cells and reduced Ab production. Through N-ethyl-N-nitrosourea mutagenesis, we have discovered a novel Ikzf1 mutant mouse with a missense mutation (L132P) in zinc finger 1 (ZF1) located in the DNA binding domain. Unlike other previously reported murine Ikzf1 mutations, this L132P point mutation (Ikzf1^L132P ) conserves overall protein expression and has a B cell-specific phenotype with no effect on T cell development, indicating that ZF1 is not required for T cells. Mice have reduced Ab responses to immunization and show a progressive loss of serum Igs compared with wild-type littermates. IKZF1^L132P overexpressed in NIH3T3 or HEK293T cells failed to localize to pericentromeric heterochromatin and bind target DNA sequences. Coexpression of wild-type and mutant IKZF1, however, allows for localization to pericentromeric heterochromatin and binding to DNA indicating a haploinsufficient mechanism of action for IKZF1^L132P Furthermore, Ikzf1^+/L132P mice have late onset defective Ig production, similar to what is observed in common variable immunodeficiency patients. RNA sequencing revealed a total loss of Hsf1 expression in follicular B cells, suggesting a possible functional link for the humoral immune response defects observed in Ikzf1^L132P/L132P mice.

Detecting Causal Variants in Mendelian Disorders Using Whole-Genome Sequencing

Increasingly affordable sequencing technologies are revolutionizing the field of genomic medicine. It is now feasible to interrogate all major classes of variation in an individual across the entire genome for less than $1000 USD. While the generation of patient sequence information using these technologies has become routine, the analysis and interpretation of this data remains the greatest obstacle to widespread clinical implementation. This chapter summarizes the steps to identify, annotate, and prioritize variant information required for clinical report generation. We discuss methods to detect each variant class and describe strategies to increase the likelihood of detecting causal variant(s) in Mendelian disease. Lastly, we describe a sample workflow for synthesizing large amount of genetic information into concise clinical reports.

The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing

We characterize the landscape of somatic mutations-mutations occurring after fertilization-in the human brain using ultra-deep (~250×) whole-genome sequencing of prefrontal cortex from 59 donors with autism spectrum disorder (ASD) and 15 control donors. We observe a mean of 26 somatic single-nucleotide variants per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.4 mutations, followed by 2-3 mutations in subsequent generations. This suggests that a typical individual possesses ~80 somatic single-nucleotide variants present in ≥2% of cells-comparable to the number of de novo germline mutations per generation-with about half of individuals having at least one potentially function-altering somatic mutation somewhere in the cortex. ASD brains show an excess of somatic mutations in neural enhancer sequences compared with controls, suggesting that mosaic enhancer mutations may contribute to ASD risk.

Insights From Genetic Studies of Cerebral Palsy

Cohort-based whole exome and whole genome sequencing and copy number variant (CNV) studies have identified genetic etiologies for a sizable proportion of patients with cerebral palsy (CP). These findings indicate that genetic mutations collectively comprise an important cause of CP. We review findings in CP genomics and propose criteria for CP-associated genes at the level of gene discovery, research study, and clinical application. We review the published literature and report 18 genes and 5 CNVs from genomics studies with strong evidence of for the pathophysiology of CP. CP-associated genes often disrupt early brain developmental programming or predispose individuals to known environmental risk factors. We discuss the overlap of CP-associated genes with other neurodevelopmental disorders and related movement disorders. We revisit diagnostic criteria for CP and discuss how identification of genetic etiologies does not preclude CP as an appropriate diagnosis. The identification of genetic etiologies improves our understanding of the neurobiology of CP, providing opportunities to study CP pathogenesis and develop mechanism-based interventions.