Beyond Mendel: an evolving view of human genetic disease transmission

A Copernican revolution of multigenic analysis: A retrospective study on clinical exome sequencing in unclear genetic disorders

Despite the inevitable shift in medical practice towards a deeper understanding of disease etiology and progression through multigenic analysis, the profound historical impact of Mendelian diseases cannot be overlooked. These diseases, such as cystic fibrosis and thalassemia, are characterized by a single variant in a single gene leading to clinical conditions, and have significantly shaped our medical knowledge and treatments. In this respect, the monogenic approach inevitably results in the underutilization of Next-Generation Sequencing (NGS) data. Herein, a retrospective study was performed to assess the diagnostic value of the clinical exome in 32 probands with specific phenotypic characteristics (patients with autoinflammation and immunological dysregulation, N = 20; patients diagnosed with Hemolytic uremic syndrome N = 9; and patients with Waldenström macroglobulinemia, N = 3). A gene enrichment analysis was performed using the *. VCF file generated by SOPHiA-DDM-v4. This analysis selected a subset of genes containing pathogenic or likely pathogenic variants with autosomal dominant (AD) inheritance. In addition, all variants of uncertain significance (VUS) were included, filtered by AD inheritance mode, the presence of compound heterozygotes, and a minor allele frequency (MAF) cutoff of 0.05 %. The aim of the pipeline described here is based on a perspective shift that focuses on analyzing patients' gene assets, offering new light on the complex interplay between genetics and disease presentation. Integrating this approach into clinical practices could significantly enhance the management of patients with rare genetic disorders.

Exploring the Genotype-Phenotype Correlations in a Child with Inherited Seizure and Thrombocytopenia by Digenic Network Analysis

Understanding the correlation between genotype and phenotype remains challenging for modern genetics. Digenic network analysis may provide useful models for understanding complex phenotypes that traditional Mendelian monogenic models cannot explain. Clinical data, whole exome sequencing data, in silico, and machine learning analysis were combined to construct a digenic network that may help unveil the complex genotype-phenotype correlations in a child presenting with inherited seizures and thrombocytopenia. The proband inherited a maternal heterozygous missense variant in SCN1A (NM_001165963.4:c.2722G>A) and a paternal heterozygous missense variant in MYH9 (NM_002473.6:c.3323A>C). In silico analysis showed that these two variants may be pathogenic for inherited seizures and thrombocytopenia in the proband. Moreover, focusing on 230 epilepsy-associated genes and 35 thrombopoiesis genes, variant call format data of the proband were analyzed using machine learning tools (VarCoPP 2.0) and Digenic Effect predictor. A digenic network was constructed, and SCN1A and MYH9 were found to be core genes in the network. Further analysis showed that MYH9 might be a modifier of SCN1A, and the variant in MYH9 might not only influence the severity of SCN1A-related seizure but also lead to thrombocytopenia in the bone marrow. In addition, another eight variants might also be co-factors that account for the proband's complex phenotypes. Our data show that as a supplement to the traditional Mendelian monogenic model, digenic network analysis may provide reasonable models for the explanation of complex genotype-phenotype correlations.

Understanding genomic medicine for thoracic aortic disease through the lens of induced pluripotent stem cells

Thoracic aortic disease (TAD) is often silent until a life-threatening complication occurs. However, genetic information can inform both identification and treatment at an early stage. Indeed, a diagnosis is important for personalised surveillance and intervention plans, as well as cascade screening of family members. Currently, only 20% of heritable TAD patients have a causative mutation identified and, consequently, further advances in genetic coverage are required to define the remaining molecular landscape. The rapid expansion of next generation sequencing technologies is providing a huge resource of genetic data, but a critical issue remains in functionally validating these findings. Induced pluripotent stem cells (iPSCs) are patient-derived, reprogrammed cell lines which allow mechanistic insights, complex modelling of genetic disease and a platform to study aortic genetic variants. This review will address the need for iPSCs as a frontline diagnostic tool to evaluate variants identified by genomic discovery studies and explore their evolving role in biological insight through to drug discovery.

Clinical and genetic characteristics of a large international cohort of individuals with rare NR5A1/SF-1 variants of sex development

BACKGROUND

Steroidogenic factor 1 (SF-1/NR5A1) is essential for human sex development. Heterozygous NR5A1/SF-1 variants manifest with a broad range of phenotypes of differences of sex development (DSD), which remain unexplained.

METHODS

We conducted a retrospective analysis on the so far largest international cohort of individuals with NR5A1/SF-1 variants, identified through the I-DSD registry and a research network.

FINDINGS

Among 197 individuals with NR5A1/SF-1 variants, we confirmed diverse phenotypes. Over 70% of 46, XY individuals had a severe DSD phenotype, while 90% of 46, XX individuals had female-typical sex development. Close to 100 different novel and known NR5A1/SF-1 variants were identified, without specific hot spots. Additionally, likely disease-associated variants in other genes were reported in 32 individuals out of 128 tested (25%), particularly in those with severe or opposite sex DSD phenotypes. Interestingly, 48% of these variants were found in known DSD or SF-1 interacting genes, but no frequent gene-clusters were identified. Sex registration at birth varied, with <10% undergoing reassignment. Gonadectomy was performed in 30% and genital surgery in 58%. Associated organ anomalies were observed in 27% of individuals with a DSD, mainly concerning the spleen. Intrafamilial phenotypes also varied considerably.

INTERPRETATION

The observed phenotypic variability in individuals and families with NR5A1/SF-1 variants is large and remains unpredictable. It may often not be solely explained by the monogenic pathogenicity of the NR5A1/SF-1 variants but is likely influenced by additional genetic variants and as-yet-unknown factors.

FUNDING

Swiss National Science Foundation (320030-197725) and Boveri Foundation Zürich, Switzerland.

Sequence variants in different genes underlying Bardet-Biedl syndrome in four consanguineous families

BACKGROUND

Bardet-Biedl Syndrome (BBS) is a rare (1:13,500-1-160,000) heterogeneous congenital disorder, characterized by postaxial polydactyly, obesity, hypogonadism, rod-cone dystrophy, cognitive impairment, and renal abnormalities (renal cystic dysplasia, anatomical malformation). To date about twenty-five genes have been identified to cause BBS, which accounts for about 80% of BBS diagnosis.

METHODS

In the current study, we have performed mutational screening of four Pakistani consanguineous families (A-D) with clinical manifestation of BBS by microsatellite-based genotyping and whole exome sequencing.

RESULTS

Analysis of the data revealed four variants, including a novel/unique inheritance pattern of compound heterozygous variants, p.(Ser40*) and p.(Thr259Leufs*21), in MKKS gene, novel homozygous variant, p.(Gly251Val)] in BBS7 gene and two previously reported p.(Thr259Leufs*21) in MKKS and p.(Met1Lys) in BBS5 gene. The variants were found segregated with the disorder within the families.

CONCLUSION

The study not only expanded mutations spectrum in the BBS genes, but this will facilitate diagnosis and genetic counselling of families carrying BBS related phenotypes in Pakistani population.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

Mutation profile of Bardet-Biedl syndrome patients from India: Implicative role of multiallelic rare variants and oligogenic inheritance pattern

Bardet-Biedl syndrome (BBS), a rare primary form of ciliopathy, with heterogeneous clinical and genetic presentation is characterized by rod cone dystrophy, obesity, polydactyly, urogenital abnormalities, and cognitive impairment. Here, we delineate the genetic profile in a cohort of 108 BBS patients from India by targeted gene sequencing-based approach for a panel of ciliopathy (including BBS) and other inherited retinal disease genes. We report here a higher frequency of BBS10 and BBS1 gene variations. A different spectrum of variations including a putatively novel gene TSPOAP1, for BBS was identified. Increased percentage frequency of digenic variants (36%) in the disease cohort, role of modifiers in familial cases are some of the salient observations in this work. This study appends the knowledge of BBS genetics pertaining to patients from India. We observed a different molecular epidemiology of BBS patients in this study cohort compared to other reports, which emphasizes the need for molecular testing in affected patients.

A knowledge graph approach to predict and interpret disease-causing gene interactions

BACKGROUND

Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.

RESULTS

We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.

CONCLUSION

Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

A dual sgRNA library design to probe genetic modifiers using genome-wide CRISPRi screens

Mapping genetic interactions is essential for determining gene function and defining novel biological pathways. We report a simple to use CRISPR interference (CRISPRi) based platform, compatible with Fluorescence Activated Cell Sorting (FACS)-based reporter screens, to query epistatic relationships at scale. This is enabled by a flexible dual-sgRNA library design that allows for the simultaneous delivery and selection of a fixed sgRNA and a second randomized guide, comprised of a genome-wide library, with a single transduction. We use this approach to identify epistatic relationships for a defined biological pathway, showing both increased sensitivity and specificity than traditional growth screening approaches.

Faster and more accurate pathogenic combination predictions with VarCoPP2.0

BACKGROUND

The prediction of potentially pathogenic variant combinations in patients remains a key task in the field of medical genetics for the understanding and detection of oligogenic/multilocus diseases. Models tailored towards such cases can help shorten the gap of missing diagnoses and can aid researchers in dealing with the high complexity of the derived data. The predictor VarCoPP (Variant Combinations Pathogenicity Predictor) that was published in 2019 and identified potentially pathogenic variant combinations in gene pairs (bilocus variant combinations), was the first important step in this direction. Despite its usefulness and applicability, several issues still remained that hindered a better performance, such as its False Positive (FP) rate, the quality of its training set and its complex architecture.

RESULTS

We present VarCoPP2.0: the successor of VarCoPP that is a simplified, faster and more accurate predictive model identifying potentially pathogenic bilocus variant combinations. Results from cross-validation and on independent data sets reveal that VarCoPP2.0 has improved in terms of both sensitivity (95% in cross-validation and 98% during testing) and specificity (5% FP rate). At the same time, its running time shows a significant 150-fold decrease due to the selection of a simpler Balanced Random Forest model. Its positive training set now consists of variant combinations that are more confidently linked with evidence of pathogenicity, based on the confidence scores present in OLIDA, the Oligogenic Diseases Database ( https://olida.ibsquare.be ). The improvement of its performance is also attributed to a more careful selection of up-to-date features identified via an original wrapper method. We show that the combination of different variant and gene pair features together is important for predictions, highlighting the usefulness of integrating biological information at different levels.

CONCLUSIONS

Through its improved performance and faster execution time, VarCoPP2.0 enables a more accurate analysis of larger data sets linked to oligogenic diseases. Users can access the ORVAL platform ( https://orval.ibsquare.be ) to apply VarCoPP2.0 on their data.

Digenic genotypes: The interface of inbreeding, linkage, and linkage disequilibrium

Many traits in populations are well understood as being Mendelian effects at single loci or additive polygenic effects across numerous loci. However, there are important phenomena and traits that are intermediate between these two extremes and are known as oligogenic traits. Here we investigate digenic, or two-locus, traits and how their frequencies in populations are affected by non-random mating, specifically inbreeding, linkage disequilibrium, and selection. These effects are examined both separately and in combination to demonstrate how many digenic traits, especially double homozygous ones, can show significant, sometimes unexpected, changes in population frequency with inbreeding, linkage, and linkage disequilibrium. The effects of selection on deleterious digenic traits are also detailed. These results are applied to both digenic traits of medical significance as well as measuring inbreeding in natural populations.

Dental Anomalies in Ciliopathies: Lessons from Patients with BBS2, BBS7, and EVC2 Mutations

Objective: To investigate dental anomalies and the molecular etiology of a patient with Ellis−van Creveld syndrome and two patients with Bardet−Biedl syndrome, two examples of ciliopathies. Patients and Methods: Clinical examination, radiographic evaluation, whole exome sequencing, and Sanger direct sequencing were performed. Results: Patient 1 had Ellis−van Creveld syndrome with delayed dental development or tooth agenesis, and multiple frenula, the feature found only in patients with mutations in ciliary genes. A novel homozygous mutation in EVC2 (c.703G>C; p.Ala235Pro) was identified. Patient 2 had Bardet−Biedl syndrome with a homozygous frameshift mutation (c.389_390delAC; p.Asn130ThrfsTer4) in BBS7. Patient 3 had Bardet−Biedl syndrome and carried a heterozygous mutation (c.389_390delAC; p.Asn130ThrfsTer4) in BBS7 and a homozygous mutation in BBS2 (c.209G>A; p.Ser70Asn). Her clinical findings included global developmental delay, disproportionate short stature, myopia, retinitis pigmentosa, obesity, pyometra with vaginal atresia, bilateral hydronephrosis with ureteropelvic junction obstruction, bilateral genu valgus, post-axial polydactyly feet, and small and thin fingernails and toenails, tooth agenesis, microdontia, taurodontism, and impaired dentin formation. Conclusions: EVC2, BBS2, and BBS7 mutations found in our patients were implicated in malformation syndromes with dental anomalies including tooth agenesis, microdontia, taurodontism, and impaired dentin formation.

Genetic susceptibility to autoimmunity-Current status and challenges

Autoimmune diseases (ADs) often arise from a combination of genetic and environmental triggers that disrupt the immune system's capability to properly tolerate body self-antigens. Familial studies provided the earliest insights into the risk loci of such diseases, while genome-wide association studies (GWAS) significantly broadened the horizons. A drug targeting a prominent pathological pathway can be applied to multiple indications sharing overlapping mechanisms. Advances in genomic technologies used in genetic studies provide critical insights into future research on gene-environment interactions in autoimmunity. This Review summarizes the history and recent advances in the understanding of genetic susceptibility to ADs and related immune disorders, including coronavirus disease 2019 (COVID-19), and their indications for the development of diagnostic or prognostic markers for translational applications.

An accurate prediction model of digenic interaction for estimating pathogenic gene pairs of human diseases

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Numerous pathogenic interactions are yet to be revealed efficiently due to dimension burden.

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Existing methods are underpowered and inaccurate to estimate pathogenic interactions.

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We developed an accurate bioinformatic method to predict digenic interaction effects based on gene-level features.

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The new method created a valuable resource of genome-wide pathogenic digenic interactions.

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We found that known causal genes in Mendelian and Oligogenic diseases may be enriched with interactive effects for the first time.

Increasing evidence shows that genetic interaction across the entire genome may explain a non-trivial fraction of genetic diseases. Digenic interaction is the simplest manifestation of genetic interaction among genes. However, systematic exploration of digenic interactive effects on the whole genome is often discouraged by the high dimension burden. Thus, numerous digenic interactions are yet to be identified for many diseases. Here, we propose a Digenic Interaction Effect Predictor (DIEP), an accurate machine-learning approach to identify the genome-wide pathogenic coding gene pairs with digenic interaction effects. This approach achieved high accuracy and sensitivity in independent testing datasets, outperforming another gene-level digenic predictor (DiGePred). DIEP was also able to discriminate digenic interaction effect from bi-locus effects dual molecular diagnosis (pseudo-digenic). Using DIEP, we provided a valuable resource of genome-wide digenic interactions and demonstrated the enrichment of the digenic interaction effect in Mendelian and Oligogenic diseases. Therefore, DIEP will play a useful role in facilitating the genomic mapping of interactive causal genes for human diseases.

Genetic Analysis of ABCB4 Mutations and Variants Related to the Pathogenesis and Pathophysiology of Low Phospholipid-Associated Cholelithiasis

Clinical studies have revealed that the ABCB4 gene encodes the phospholipid transporter on the canalicular membrane of hepatocytes, and its mutations and variants are the genetic basis of low phospholipid-associated cholelithiasis (LPAC), a rare type of gallstone disease caused by a single-gene mutation or variation. The main features of LPAC include a reduction or deficiency of phospholipids in bile, symptomatic cholelithiasis at <40 years of age, intrahepatic sludge and microlithiasis, mild chronic cholestasis, a high cholesterol/phospholipid ratio in bile, and recurrence of biliary symptoms after cholecystectomy. Needle-like cholesterol crystals, putatively “anhydrous” cholesterol crystallization at low phospholipid concentrations in model and native bile, are characterized in ABCB4 knockout mice, a unique animal model for LPAC. Gallbladder bile with only trace amounts of phospholipids in these mice is supersaturated with cholesterol, with lipid composition plotting in the left two-phase zone of the ternary phase diagram, consistent with “anhydrous” cholesterol crystallization. In this review, we summarize the molecular biology and physiological functions of ABCB4 and comprehensively discuss the latest advances in the genetic analysis of ABCB4 mutations and variations and their roles in the pathogenesis and pathophysiology of LPAC in humans, based on the results from clinical studies and mouse experiments. To date, approximately 158 distinct LPAC-causing ABCB4 mutations and variants in humans have been reported in the literature, indicating that it is a monogenic risk factor for LPAC. The elucidation of the ABCB4 function in the liver, the identification of ABCB4 mutations and variants in LPAC patients, and the exploration of gene therapy for ABCB4 deficiency in animal models can help us to better understand the cellular, molecular, and genetic mechanisms underlying the onset of the disease, and will pave the way for early diagnosis and prevention of susceptible subjects and effective intervention for LPAC in patients.

Generation and characterization of Ccdc28b mutant mice links the Bardet-Biedl associated gene with mild social behavioral phenotypes

CCDC28B (coiled-coil domain-containing protein 28B) was identified as a modifier in the ciliopathy Bardet-Biedl syndrome (BBS). Our previous work in cells and zebrafish showed that CCDC28B plays a role regulating cilia length in a mechanism that is not completely understood. Here we report the generation of a Ccdc28b mutant mouse using CRISPR/Cas9 (Ccdc28b mut). Depletion of CCDC28B resulted in a mild phenotype. Ccdc28b mut animals i) do not present clear structural cilia affectation, although we did observe mild defects in cilia density and cilia length in some tissues, ii) reproduce normally, and iii) do not develop retinal degeneration or obesity, two hallmark features of reported BBS murine models. In contrast, Ccdc28b mut mice did show clear social interaction defects as well as stereotypical behaviors. This finding is indeed relevant regarding CCDC28B as a modifier of BBS since behavioral phenotypes have been documented in BBS. Overall, this work reports a novel mouse model that will be key to continue evaluating genetic interactions in BBS, deciphering the contribution of CCDC28B to modulate the presentation of BBS phenotypes. In addition, our data underscores a novel link between CCDC28B and behavioral defects, providing a novel opportunity to further our understanding of the genetic, cellular, and molecular basis of these complex phenotypes.

BBS is caused by mutations in any one of 22 genes known to date. In some families, BBS can be inherited as an oligogenic trait whereby mutations in more than one BBS gene collaborate in the presentation of the syndrome. In addition, CCDC28B was originally identified as a modifier of BBS, whereby a reduction in CCDC28B levels was associated with a more severe presentation of the syndrome. Different mechanisms, all relying on functional redundancy, have been proposed to explain these genetic interactions. The characterization of BBS proteins supported this functional redundancy hypothesis: BBS proteins play a role in cilia maintenance/function and subsets of BBS proteins can even interact directly in multiprotein complexes.

We have previously shown that CCDC28B also participates in cilia biology regulating the length of the organelle: knockdown of CCDC28B in cells results in cilia shortening and targeting ccdc28b in zebrafish also results in early embryonic phenotypes characteristic of other cilia mutants. In this work, we generated a Ccdc28b mutant mouse to determine whether abrogating Ccdc28b function would be sufficient to cause a ciliopathy phenotype in mammals, and to generate a tool to continue dissecting its modifying role in the context of BBS. Overall, Ccdc28b mutant mice presented a mild phenotype, a finding fully compatible with its role as a modifier, rather than a causal BBS gene. In addition, we found that Ccdc28b mutants showed behavioral phenotypes, similar to the deficits observed in rodent autism spectrum disorder (ASD) models. Thus, our results underscore a novel causal link between CCDC28B and behavioral phenotypes in mice.

Scaling up oligogenic diseases research with OLIDA: the Oligogenic Diseases Database

Improving the understanding of the oligogenic nature of diseases requires access to high-quality, well-curated Findable, Accessible, Interoperable, Reusable (FAIR) data. Although first steps were taken with the development of the Digenic Diseases Database, leading to novel computational advancements to assist the field, these were also linked with a number of limitations, for instance, the ad hoc curation protocol and the inclusion of only digenic cases. The OLIgogenic diseases DAtabase (OLIDA) presents a novel, transparent and rigorous curation protocol, introducing a confidence scoring mechanism for the published oligogenic literature. The application of this protocol on the oligogenic literature generated a new repository containing 916 oligogenic variant combinations linked to 159 distinct diseases. Information extracted from the scientific literature is supplemented with current knowledge support obtained from public databases. Each entry is an oligogenic combination linked to a disease, labelled with a confidence score based on the level of genetic and functional evidence that supports its involvement in this disease. These scores allow users to assess the relevance and proof of pathogenicity of each oligogenic combination in the database, constituting markers for reporting improvements on disease-causing oligogenic variant combinations. OLIDA follows the FAIR principles, providing detailed documentation, easy data access through its application programming interface and website, use of unique identifiers and links to existing ontologies.

The Potential Role of Epigenetic Mechanisms in the Development of Retinitis Pigmentosa and Related Photoreceptor Dystrophies

Retinitis pigmentosa and related photoreceptor dystrophies (RPRPD) are rare retinal diseases caused by hereditary gene mutations resulting in photoreceptor death, followed by vision loss. While numerous genes involved in these diseases have been identified, many cases have still not been associated with any gene, indicating that new mechanisms may be involved in the pathogenesis of these photoreceptor dystrophies. Many genes associated with RPRPD regulate photoreceptor specification and maturation in the developing retina. Since retinal development begins with a population of equivalent, proliferating retinal progenitor cells (RPCs) having a specific “competence” in generating all types of retinal neurons, including cone and rod photoreceptors, we tested the epigenetic changes in promoters of genes required for photoreceptor development and genes associated with RPRPD during RPC differentiation into cone and rod photoreceptors. We found that promoters of many of these genes are epigenetically repressed in RPCs but have no epigenetic restrictions in photoreceptors. Our findings also suggest that DNA methylation as an epigenetic mark, and DNA demethylation as a process, are more important than other epigenetic marks or mechanisms in the pathogenesis of these diseases. Most notably, irregularities in the DNA demethylation process during the RPC-to-photoreceptor transition may significantly contribute to retinitis pigmentosa (RP) pathogenesis since genes with hypermethylated promoters in RPCs account for at least 40% of autosomal recessive RP cases and at least 30% of autosomal dominant RP cases. Thus, we proposed an epigenetic model according to which unsuccessful demethylation of regulatory sequences (e.g., promoters, enhancers) of genes required for photoreceptor development, maturation, and function during the RPC-to-photoreceptor transition may reduce or even eliminate their activity, leading to RPRPD without any inheritable mutations in these genes.

A guidance of model selection for genomic prediction based on linear mixed models for complex traits

Brain imaging outcomes are important for Alzheimer's disease (AD) detection, and their prediction based on both genetic and demographic risk factors can facilitate the ongoing prevention and treatment of AD. Existing studies have identified numerous significantly AD-associated SNPs. However, how to make the best use of them for prediction analyses remains unknown. In this research, we first explored the relationship between genetic architecture and prediction accuracy of linear mixed models via visualizing the Manhattan plots generated based on the data obtained from the Wellcome Trust Case Control Consortium, and then constructed prediction models for eleven AD-related brain imaging outcomes using data from United Kingdom Biobank and Alzheimer's Disease Neuroimaging Initiative studies. We found that the simple Manhattan plots can be informative for the selection of prediction models. For traits that do not exhibit any significant signals from the Manhattan plots, the simple genomic best linear unbiased prediction (gBLUP) model is recommended due to its robust and accurate prediction performance as well as its computational efficiency. For diseases and traits that show spiked signals on the Manhattan plots, the latent Dirichlet process regression is preferred, as it can flexibly accommodate both the oligogenic and omnigenic models. For the prediction of AD-related traits, the Manhattan plots suggest their polygenic nature, and gBLUP has achieved robust performance for all these traits. We found that for these AD-related traits, genetic factors themselves only explain a very small proportion of the heritability, and the well-known AD risk factors can substantially improve the prediction model.

Identifying digenic disease genes via machine learning in the Undiagnosed Diseases Network

Rare diseases affect millions of people worldwide, and discovering their genetic causes is challenging. More than half of the individuals analyzed by the Undiagnosed Diseases Network (UDN) remain undiagnosed. The central hypothesis of this work is that many of these rare genetic disorders are caused by multiple variants in more than one gene. However, given the large number of variants in each individual genome, experimentally evaluating combinations of variants for potential to cause disease is currently infeasible. To address this challenge, we developed the digenic predictor (DiGePred), a random forest classifier for identifying candidate digenic disease gene pairs by features derived from biological networks, genomics, evolutionary history, and functional annotations. We trained the DiGePred classifier by using DIDA, the largest available database of known digenic-disease-causing gene pairs, and several sets of non-digenic gene pairs, including variant pairs derived from unaffected relatives of UDN individuals. DiGePred achieved high precision and recall in cross-validation and on a held-out test set (PR area under the curve > 77%), and we further demonstrate its utility by using digenic pairs from the recent literature. In contrast to other approaches, DiGePred also appropriately controls the number of false positives when applied in realistic clinical settings. Finally, to enable the rapid screening of variant gene pairs for digenic disease potential, we freely provide the predictions of DiGePred on all human gene pairs. Our work enables the discovery of genetic causes for rare non-monogenic diseases by providing a means to rapidly evaluate variant gene pairs for the potential to cause digenic disease.

Deciphering pathogenicity of variants of uncertain significance with CRISPR-edited iPSCs

Genetic variants play an important role in conferring risk for cardiovascular diseases (CVDs). With the rapid development of next-generation sequencing (NGS), thousands of genetic variants associated with CVDs have been identified by genome-wide association studies (GWAS), but the function of more than 40% of genetic variants is still unknown. This gap of knowledge is a barrier to the clinical application of the genetic information. However, determining the pathogenicity of a variant of uncertain significance (VUS) is challenging due to the lack of suitable model systems and accessible technologies. By combining clustered regularly interspaced short palindromic repeats (CRISPR) and human induced pluripotent stem cells (iPSCs), unprecedented advances are now possible in determining the pathogenicity of VUS in CVDs. Here, we summarize recent progress and new strategies in deciphering pathogenic variants for CVDs using CRISPR-edited human iPSCs.

Revealing modifier variations characterizations for elucidating the genetic basis of human phenotypic variations

Epistatic interactions complicate the identification of variants involved in phenotypic effect. In-depth knowledge in modifiers and in pathogenic variants would benefit the mechanistic studies on the genetic basis of complex traits. We systematically compared the modifier variants which have evidence of modifier effect with the pathogenic variants from multiple angles. Our study found that genomic loci of modifier variations differ from pathogenic loci in many aspects, such as population genetics statistics, epigenetic features, evolutionary characteristics and functional properties of the variations. Genes containing modifier variation(s) exhibit higher probability of being haploinsufficient and higher probability of recessive disease causation, and they are relatively more important in network communication. Furthermore, we reinforced that co-expression analysis is an effective methodology to predict functional associations between modifier genes and their potential target genes. In many aspects, we detected statistically significant differences between modifier variants/genes and pathogenic variants/genes, and investigated relationships between modifiers and their potential targets. Our results offer some actionable insights that may provide appropriate guidelines to clinical genetics and researchers to elucidate the molecular mechanism underlying the human phenotypic variation.

The evolving genetic landscape of congenital disorders of glycosylation

Congenital Disorders of Glycosylation (CDG) are an expanding and complex group of rare genetic disorders caused by defects in the glycosylation of proteins and lipids. The genetic spectrum of CDG is extremely broad with mutations in over 140 genes leading to a wide variety of symptoms ranging from mild to severe and life-threatening. There has been an expansion in the genetic complexity of CDG in recent years. More specifically several examples of alternate phenotypes in recessive forms of CDG and new types of CDG following an autosomal dominant inheritance pattern have been identified. In addition, novel genetic mechanisms such as expansion repeats have been reported and several already known disorders have been classified as CDG as their pathophysiology was better elucidated. Furthermore, we consider the future and outlook of CDG genetics, with a focus on exploration of the non-coding genome using whole genome sequencing, RNA-seq and multi-omics technology.

Estimating the Posttest Probability of Long QT Syndrome Diagnosis for Rare KCNH2 Variants

BACKGROUND

The proliferation of genetic profiling has revealed many associations between genetic variations and disease. However, large-scale phenotyping efforts in largely healthy populations, coupled with DNA sequencing, suggest variants currently annotated as pathogenic are more common in healthy populations than previously thought. In addition, novel and rare variants are frequently observed in genes associated with disease both in healthy individuals and those under suspicion of disease. This raises the question of whether these variants can be useful predictors of disease. To answer this question, we assessed the degree to which the presence of a variant in the cardiac potassium channel gene KCNH2 was diagnostically predictive for the autosomal dominant long QT syndrome.

METHODS

We estimated the probability of a long QT diagnosis given the presence of each KCNH2 variant using Bayesian methods that incorporated variant features such as changes in variant function, protein structure, and in silico predictions. We call this estimate the posttest probability of disease. Our method was applied to over 4000 individuals heterozygous for 871 missense or in-frame insertion/deletion variants in KCNH2 and validated against a separate international cohort of 933 individuals heterozygous for 266 missense or in-frame insertion/deletion variants.

RESULTS

Our method was well-calibrated for the observed fraction of heterozygotes diagnosed with long QT syndrome. Heuristically, we found that the innate diagnostic information one learns about a variant from 3-dimensional variant location, in vitro functional data, and in silico predictors is equivalent to the diagnostic information one learns about that same variant by clinically phenotyping 10 heterozygotes. Most importantly, these data can be obtained in the absence of any clinical observations.

CONCLUSIONS

We show how variant-specific features can inform a prior probability of disease for rare variants even in the absence of clinically phenotyped heterozygotes.

Large-scale GWAS reveals genetic architecture of brain white matter microstructure and genetic overlap with cognitive and mental health traits (n = 17,706)

Individual variations of white matter (WM) tracts are known to be associated with various cognitive and neuropsychiatric traits. Diffusion tensor imaging (DTI) and genome-wide single-nucleotide polymorphism (SNP) data from 17,706 UK Biobank participants offer the opportunity to identify novel genetic variants of WM tracts and explore the genetic overlap with other brain-related complex traits. We analyzed the genetic architecture of 110 tract-based DTI parameters, carried out genome-wide association studies (GWAS), and performed post-GWAS analyses, including association lookups, gene-based association analysis, functional gene mapping, and genetic correlation estimation. We found that DTI parameters are substantially heritable for all WM tracts (mean heritability 48.7%). We observed a highly polygenic architecture of genetic influence across the genome (p value = 1.67 × 10^-05) as well as the enrichment of genetic effects for active SNPs annotated by central nervous system cells (p value = 8.95 × 10^-12). GWAS identified 213 independent significant SNPs associated with 90 DTI parameters (696 SNP-level and 205 locus-level associations; p value < 4.5 × 10^-10, adjusted for testing multiple phenotypes). Gene-based association study prioritized 112 significant genes, most of which are novel. More importantly, association lookups found that many of the novel SNPs and genes of DTI parameters have previously been implicated with cognitive and mental health traits. In conclusion, the present study identifies many new genetic variants at SNP, locus and gene levels for integrity of brain WM tracts and provides the overview of pleiotropy with cognitive and mental health traits.

Genotype-Phenotype Relationships in the Context of Transcriptional Adaptation and Genetic Robustness

Genetic manipulations with a robust and predictable outcome are critical to investigate gene function, as well as for therapeutic genome engineering. For many years, knockdown approaches and reagents including RNA interference and antisense oligonucleotides dominated functional studies; however, with the advent of precise genome editing technologies, CRISPR-based knockout systems have become the state-of-the-art tools for such studies. These technologies have helped decipher the role of thousands of genes in development and disease. Their use has also revealed how limited our understanding of genotype-phenotype relationships is. The recent discovery that certain mutations can trigger the transcriptional modulation of other genes, a phenomenon called transcriptional adaptation, has provided an additional explanation for the contradicting phenotypes observed in knockdown versus knockout models and increased awareness about the use of each of these approaches. In this review, we first cover the strengths and limitations of different gene perturbation strategies. Then we highlight the diverse ways in which the genotype-phenotype relationship can be discordant between these different strategies. Finally, we review the genetic robustness mechanisms that can lead to such discrepancies, paying special attention to the recently discovered phenomenon of transcriptional adaptation. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genome Editing Human Pluripotent Stem Cells to Model β-Cell Disease and Unmask Novel Genetic Modifiers

A mechanistic understanding of the genetic basis of complex diseases such as diabetes mellitus remain elusive due in large part to the activity of genetic disease modifiers that impact the penetrance and/or presentation of disease phenotypes. In the face of such complexity, rare forms of diabetes that result from single-gene mutations (monogenic diabetes) can be used to model the contribution of individual genetic factors to pancreatic β-cell dysfunction and the breakdown of glucose homeostasis. Here we review the contribution of protein coding and non-protein coding genetic disease modifiers to the pathogenesis of diabetes subtypes, as well as how recent technological advances in the generation, differentiation, and genome editing of human pluripotent stem cells (hPSC) enable the development of cell-based disease models. Finally, we describe a disease modifier discovery platform that utilizes these technologies to identify novel genetic modifiers using induced pluripotent stem cells (iPSC) derived from patients with monogenic diabetes caused by heterozygous mutations.

Genotype-Phenotype Correlations in RP1-Associated Retinal Dystrophies: A Multi-Center Cohort Study in JAPAN

Background: Little is known about genotype–phenotype correlations of RP1-associated retinal dystrophies in the Japanese population. We aimed to investigate the genetic spectrum of RP1 variants and provide a detailed description of the clinical findings in Japanese patients. Methods: In total, 607 patients with inherited retinal diseases were examined using whole-exome/whole-genome sequencing (WES/WGS). PCR-based screening for an Alu element insertion (c.4052_4053ins328/p.Tyr1352AlafsTer9) was performed in 18 patients with autosomal-recessive (AR)-retinitis pigmentosa (RP) or AR-cone dystrophy (COD)/cone-rod dystrophy (CORD), including seven patients with heterozygous RP1 variants identified by WES/WGS analysis, and 11 early onset AR-RP patients, in whom no pathogenic variant was identified. We clinically examined 25 patients (23 families) with pathogenic RP1 variants, including five patients (five families) with autosomal-dominant (AD)-RP, 13 patients (11 families) with AR-RP, and seven patients (seven families) with AR-COD/CORD. Results: We identified 18 pathogenic RP1 variants, including seven novel variants. Interestingly, the Alu element insertion was the most frequent variant (32.0%, 16/50 alleles). The clinical findings revealed that the age at onset and disease progression occurred significantly earlier and faster in AR-RP patients compared to AD-RP or AR-COD/CORD patients. Conclusions: Our results suggest a genotype–phenotype correlation between variant types/locations and phenotypes (AD-RP, AR-RP, and AR-COD/CORD), and the Alu element insertion was the most major variant in Japanese patients with RP1-associated retinal dystrophies.

Digenic mutations in ALDH2 and ADH5 impair formaldehyde clearance and cause a multisystem disorder, AMeD syndrome

Rs671 in the aldehyde dehydrogenase 2 gene (ALDH2) is the cause of Asian alcohol flushing response after drinking. ALDH2 detoxifies endogenous aldehydes, which are the major source of DNA damage repaired by the Fanconi anemia pathway. Here, we show that the rs671 defective allele in combination with mutations in the alcohol dehydrogenase 5 gene, which encodes formaldehyde dehydrogenase (ADH5FDH ), causes a previously unidentified disorder, AMeD (aplastic anemia, mental retardation, and dwarfism) syndrome. Cellular studies revealed that a decrease in the formaldehyde tolerance underlies a loss of differentiation and proliferation capacity of hematopoietic stem cells. Moreover, Adh5-/-Aldh2 E506K/E506K double-deficient mice recapitulated key clinical features of AMeDS, showing short life span, dwarfism, and hematopoietic failure. Collectively, our results suggest that the combined deficiency of formaldehyde clearance mechanisms leads to the complex clinical features due to overload of formaldehyde-induced DNA damage, thereby saturation of DNA repair processes.

Novel MYO15A variants are associated with hearing loss in the two Iranian pedigrees

BACKGROUND

Clinical genetic diagnosis of non-syndromic hearing loss (NSHL) is quite challenging. With regard to its high heterogeneity as well as large size of some genes, it is also really difficult to detect causative mutations using traditional approaches. One of the recent technologies called whole-exome sequencing (WES) has been thus developed in this domain to remove the limitations of conventional methods.

METHODS

This study was a report on a research study of two unrelated pedigrees with multiple affected cases of hearing loss (HL). Accordingly, clinical evaluations and genetic analysis were performed in both families.

RESULTS

The results of WES data analysis to uncover autosomal recessive non-syndromic hearing loss (ARNSHL) disease-causing variants was reported in the present study. Initial analysis identified two novel variants of MYO15A i.e. c.T6442A:p.W2148R and c.10504dupT:p.C3502Lfs*15 correspondingly which were later confirmed by Sanger validations and segregation analyses. According to online prediction tools, both identified variants seemed to have damaging effects.

CONCLUSION

In this study, whole exome sequencing were used as a first approach strategy to identify the two novel variants in MYO15A in two Iranian families with ARNSHL.

An Imperative Need for Further Genetic Studies of Alopecia Areata

Human genetic studies of diseases that are multifactorial and prevalent have generated a wealth of knowledge about the genetic architecture of chronic diseases. Generalizable attributes are shaping the development of models to explain how the human genome influences our health and can be leveraged to improve it. Importantly, both rare and common genetic variants contribute to disease risk and provide complementary information. Although initial genetic studies of alopecia areata have yielded insight with high clinical impact, there remains a number of important unanswered questions pertaining to disease biology and patient care that could be addressed by further genetic investigations.

Heritability of Regional Brain Volumes in Large-Scale Neuroimaging and Genetic Studies

Brain genetics is an active research area. The degree to which genetic variants impact variations in brain structure and function remains largely unknown. We examined the heritability of regional brain volumes (P ~ 100) captured by single-nucleotide polymorphisms (SNPs) in UK Biobank (n ~ 9000). We found that regional brain volumes are highly heritable in this study population and common genetic variants can explain up to 80% of their variabilities (median heritability 34.8%). We observed omnigenic impact across the genome and examined the enrichment of SNPs in active chromatin regions. Principal components derived from regional volume data are also highly heritable, but the amount of variance in brain volume explained by the component did not seem to be related to its heritability. Heritability estimates vary substantially across large-scale functional networks, exhibit a symmetric pattern across left and right hemispheres, and are consistent in females and males (correlation = 0.638). We repeated the main analysis in Alzheimer's Disease Neuroimaging Initiative (n ~ 1100), Philadelphia Neurodevelopmental Cohort (n ~ 600), and Pediatric Imaging, Neurocognition, and Genetics (n ~ 500) datasets, which demonstrated that more stable estimates can be obtained from the UK Biobank.

Hidden genetic variance contributes to increase the short-term adaptive potential of selfing populations

Standing genetic variation is considered a major contributor to the adaptive potential of species. The low heritable genetic variation observed in self-fertilizing populations has led to the hypothesis that species with this mating system would be less likely to adapt. However, a non-negligible amount of cryptic genetic variation for polygenic traits, accumulated through negative linkage disequilibrium, could prove to be an important source of standing variation in self-fertilizing species. To test this hypothesis, we simulated populations under stabilizing selection subjected to an environmental change. We demonstrate that, when the mutation rate is high (but realistic), selfing populations are better able to store genetic variance than outcrossing populations through genetic associations, notably due to the reduced effective recombination rate associated with predominant selfing. Following an environmental shift, this diversity can be partially remobilized, which increases the additive variance and adaptive potential of predominantly (but not completely) selfing populations. In such conditions, despite initially lower observed genetic variance, selfing populations adapt as readily as outcrossing ones within a few generations. For low mutation rates, purifying selection impedes the storage of diversity through genetic associations, in which case, as previously predicted, the lower genetic variance of selfing populations results in lower adaptability compared to their outcrossing counterparts. The population size and the mutation rate are the main parameters to consider, as they are the best predictors of the amount of stored diversity in selfing populations. Our results and their impact on our knowledge of adaptation under high selfing rates are discussed.

Preimplantation Genetic Testing for Polygenic Disease Relative Risk Reduction: Evaluation of Genomic Index Performance in 11,883 Adult Sibling Pairs

Preimplantation genetic testing for polygenic disease risk (PGT-P) represents a new tool to aid in embryo selection. Previous studies demonstrated the ability to obtain necessary genotypes in the embryo with accuracy equivalent to in adults. When applied to select adult siblings with known type I diabetes status, a reduction in disease incidence of 45-72% compared to random selection was achieved. This study extends analysis to 11,883 sibling pairs to evaluate clinical utility of embryo selection with PGT-P. Results demonstrate simultaneous relative risk reduction of all diseases tested in parallel, which included diabetes, cancer, and heart disease, and indicate applicability beyond patients with a known family history of disease.

Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4000 fitness interactions between expression variants of two metabolic genes, starting from various environmentally modulated expression levels. We detect a remarkable variety of interactions dependent on initial expression levels and demonstrate that they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity, and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.

ORVAL: a novel platform for the prediction and exploration of disease-causing oligogenic variant combinations

A tremendous amount of DNA sequencing data is being produced around the world with the ambition to capture in more detail the mechanisms underlying human diseases. While numerous bioinformatics tools exist that allow the discovery of causal variants in Mendelian diseases, little to no support is provided to do the same for variant combinations, an essential task for the discovery of the causes of oligogenic diseases. ORVAL (the Oligogenic Resource for Variant AnaLysis), which is presented here, provides an answer to this problem by focusing on generating networks of candidate pathogenic variant combinations in gene pairs, as opposed to isolated variants in unique genes. This online platform integrates innovative machine learning methods for combinatorial variant pathogenicity prediction with visualization techniques, offering several interactive and exploratory tools, such as pathogenic gene and protein interaction networks, a ranking of pathogenic gene pairs, as well as visual mappings of the cellular location and pathway information. ORVAL is the first web-based exploration platform dedicated to identifying networks of candidate pathogenic variant combinations with the sole ambition to help in uncovering oligogenic causes for patients that cannot rely on the classical disease analysis tools. ORVAL is available at https://orval.ibsquare.be.

Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications

An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.

Genetic Modifiers and Rare Mendelian Disease

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

A rewiring model of intratumoral interaction networks

Intratumoral heterogeneity (ITH) has been regarded as a key cause of the failure and resistance of cancer therapy, but how it behaves and functions remains unclear. Advances in single-cell analysis have facilitated the collection of a massive amount of data about genetic and molecular states of individual cancer cells, providing a fuel to dissect the mechanistic organization of ITH at the molecular, metabolic and positional level. Taking advantage of these data, we propose a computational model to rewire up a topological network of cell–cell interdependences and interactions that operate within a tumor mass. The model is grounded on the premise of game theory that each interactive cell (player) strives to maximize its fitness by pursuing a “rational self-interest” strategy, war or peace, in a way that senses and alters other cells to respond properly. By integrating this idea with genome-wide association studies for intratumoral cells, the model is equipped with a capacity to visualize, annotate and quantify how somatic mutations mediate ITH and the network of intratumoral interactions. Taken together, the model provides a topological flow by which cancer cells within a tumor cooperate or compete with each other to downstream pathogenesis. This topological flow can be potentially used as a blueprint for genetically intervening the pattern and strength of cell–cell interactions towards cancer control.

Identification of Gene Mutations in Primary Pediatric Cardiomyopathy by Whole Exome Sequencing

Pediatric primary cardiomyopathy is rare but serious, having high mortality; hypertrophic and dilated types are the most common. Its etiology has been mainly considered idiopathic; however, next generation sequencing techniques have revealed nearly half of idiopathic pediatric cases arose from specific genetic mutations. Therefore, our study aimed to identify the genetic causes of primary idiopathic cardiomyopathy. Newborns to 15-year old patients with this condition were recruited between March 2016 and May 2017 at Thammasat University Hospital. Complete patient history and physical examination data were collected by a geneticist with cardiac examinations and echocardiograms by pediatric cardiologists. Whole exome sequencing was performed for all. Of the 12 patients enrolled, 5 cases were dilated type and 7 hypertrophic. Two with dilated type were excluded during follow-up as cause was determined (hypocalcemia and pacemaker induced). A list of 118 genes for cardiomyopathy was analyzed in the remaining 10 cases. Pathogenic and likely pathogenic mutations were identified in 5 patients: HRAS, PTPN11, SOS1, FLNC and TXNRD2; half our patients were not actually idiopathic. Despite its high cost, genetic testing is useful for determining familial risk as well as predicting patient cardiomyopathy progress.

Using mechanistic models for the clinical interpretation of complex genomic variation

The sustained generation of genomic data in the last decade has increased the knowledge on the causal mutations of a large number of diseases, especially for highly penetrant Mendelian diseases, typically caused by a unique or a few genes. However, the discovery of causal genes in complex diseases has been far less successful. Many complex diseases are actually a consequence of the failure of complex biological modules, composed by interrelated proteins, which can happen in many different ways, which conferring a multigenic nature to the condition that can hardly be attributed to one or a few genes. We present a mechanistic model, Hipathia, implemented in a web server that allows estimating the effect that mutations, or changes in the expression of genes, have over the whole system of human signaling and the corresponding functional consequences. We show several use cases where we demonstrate how different the ultimate impact of mutations with similar loss-of-function potential can be and how the potential pathological role of a damaged gene can be inferred within the context of a signaling network. The use of systems biology-based approaches, such as mechanistic models, allows estimating the potential impact of loss-of-function mutations occurring in proteins that are part of complex biological interaction networks, such as signaling pathways. This holistic approach provides an elegant alternative to gene-centric approaches that can open new avenues in the interpretation of the genomic variability in complex diseases.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Using game theory and decision decomposition to effectively discern and characterise bi-locus diseases

In order to gain insight into oligogenic disorders, understanding those involving bi-locus variant combinations appears to be key. In prior work, we showed that features at multiple biological scales can already be used to discriminate among two types, i.e. disorders involving true digenic and modifier combinations. The current study expands this machine learning work towards dual molecular diagnosis cases, providing a classifier able to effectively distinguish between these three types. To reach this goal and gain an in-depth understanding of the decision process, game theory and tree decomposition techniques are applied to random forest predictors to investigate the relevance of feature combinations in the prediction. A machine learning model with high discrimination capabilities was developed, effectively differentiating the three classes in a biologically meaningful manner. Combining prediction interpretation and statistical analysis, we propose a biologically meaningful characterization of each class relying on specific feature strengths. Figuring out how biological characteristics shift samples towards one of three classes provides clinically relevant insight into the underlying biological processes as well as the disease itself.

Compensated pathogenic variants in coagulation factors VIII and IX present complex mapping between molecular impact and hemophilia severity

Compensated pathogenic deviations (CPDs) are sequence variants that are pathogenic in humans but neutral in other species. In recent years, our molecular understanding of CPDs has advanced substantially. For example, it is known that their impact on human proteins is generally milder than that of average pathogenic mutations and that their impact is suppressed in non-human carriers by compensatory mutations. However, prior studies have ignored the evolutionarily relevant relationship between molecular impact and organismal phenotype. Here, we explore this topic using CPDs from FVIII and FIX and data concerning carriers' hemophilia severity. We find that, regardless of their molecular impact, these mutations can be associated with either mild or severe disease phenotypes. Only a weak relationship is found between protein stability changes and severity. We also characterize the population variability of hemostasis proteins, which constitute the genetic background of FVIII and FIX, using data from the 1000 Genome project. We observe that genetic background can vary substantially between individuals in terms of both the amount and nature of genetic variants. Finally, we discuss how these results highlight the need to include new terms in present models of protein evolution to explain the origin of CPDs.

Early Onset Granulomatous Colitis Associated with a Mutation in NCF4 Resolved with Hematopoietic Stem Cell Transplantation

A 4-year-old boy presented with perianal abscess and granulomatous colitis, which led the diagnosis of Crohn's disease. He became refractory to all available therapies and required colectomy. Targeted sequencing revealed a deleterious variant in NCF4, causing severe neutrophil dysfunction. He underwent hematopoietic stem cell transplantation (HSCT) with an excellent outcome.

A frequent variant in the Japanese population determines quasi-Mendelian inheritance of rare retinal ciliopathy

Hereditary retinal degenerations (HRDs) are Mendelian diseases characterized by progressive blindness and caused by ultra-rare mutations. In a genomic screen of 331 unrelated Japanese patients, we identify a disruptive Alu insertion and a nonsense variant (p.Arg1933*) in the ciliary gene RP1, neither of which are rare alleles in Japan. p.Arg1933* is almost polymorphic (frequency = 0.6%, amongst 12,000 individuals), does not cause disease in homozygosis or heterozygosis, and yet is significantly enriched in HRD patients (frequency = 2.1%, i.e., a 3.5-fold enrichment; p-value = 9.2 × 10-5). Familial co-segregation and association analyses show that p.Arg1933* can act as a Mendelian mutation in trans with the Alu insertion, but might also associate with disease in combination with two alleles in the EYS gene in a non-Mendelian pattern of heredity. Our results suggest that rare conditions such as HRDs can be paradoxically determined by relatively common variants, following a quasi-Mendelian model linking monogenic and complex inheritance.