High-Throughput Assays to Assess the Functional Impact of Genetic Variants: A Road Towards Genomic-Driven Medicine

Basic science methods for the characterization of variants of uncertain significance in hypertrophic cardiomyopathy

With the advent of next-generation whole genome sequencing, many variants of uncertain significance (VUS) have been identified in individuals suffering from inheritable hypertrophic cardiomyopathy (HCM). Unfortunately, this classification of a genetic variant results in ambiguity in interpretation, risk stratification, and clinical practice. Here, we aim to review some basic science methods to gain a more accurate characterization of VUS in HCM. Currently, many genomic data-based computational methods have been developed and validated against each other to provide a robust set of resources for researchers. With the continual improvement in computing speed and accuracy, in silico molecular dynamic simulations can also be applied in mutational studies and provide valuable mechanistic insights. In addition, high throughput in vitro screening can provide more biologically meaningful insights into the structural and functional effects of VUS. Lastly, multi-level mathematical modeling can predict how the mutations could cause clinically significant organ-level dysfunction. We discuss emerging technologies that will aid in better VUS characterization and offer a possible basic science workflow for exploring the pathogenicity of VUS in HCM. Although the focus of this mini review was on HCM, these basic science methods can be applied to research in dilated cardiomyopathy (DCM), restrictive cardiomyopathy (RCM), arrhythmogenic cardiomyopathy (ACM), or other genetic cardiomyopathies.

Gene Hunting Approaches through the Combination of Linkage Analysis with Whole-Exome Sequencing in Mendelian Diseases: From Darwin to the Present Day

BACKGROUND

In the context of medical genetics, gene hunting is the process of identifying and functionally characterizing genes or genetic variations that contribute to disease phenotypes. In this review, we would like to summarize gene hunting process in terms of historical aspects from Darwin to now. For this purpose, different approaches and recent developments will be detailed.

SUMMARY

Linkage analysis and association studies are the most common methods in use for explaining the genetic background of hereditary diseases and disorders. Although linkage analysis is a relatively old approach, it is still a powerful method to detect disease-causing rare variants using family-based data, particularly for consanguineous marriages. As is known that, consanguineous marriages or endogamy poses a social problem in developing countries, however, this same condition also provides a unique opportunity for scientists to identify and characterize pathogenic variants. The rapid advancements in sequencing technologies and their parallel implementation together with linkage analyses now allow us to identify the candidate variants related to diseases in a relatively short time. Furthermore, we can now go one step further and functionally characterize the causative variant through in vitro and in vivo studies and unveil the variant-phenotype relationships on a molecular level more robustly. Key Messages: Herein, we suggest that the combined analysis of linkage and exome analysis is a powerful and precise tool to diagnose clinically rare and recessively inherited conditions.

De novo Splice Site Mutation of the CHD7 Gene in a Chinese Patient with Typical CHARGE Syndrome

INTRODUCTION

CHARGE syndrome (CS, OMIM 214800) is a rare genetic disease characterized by multiple congenital abnormalities, including coloboma, heart defect, atresia of the choanae, retardation of development, genital anomalies, and ear anomalies/deafness. The syndrome is mainly caused by a heterozygous variant in the chromodomain helicase DNA-binding protein 7 (CHD7) gene that encodes the CHD7 protein, involved in the ATP-dependent remodeling of chromatin.

METHODS

In this study, the next-generation sequencing targeted panel was used to detect a de novo variant c.3523-2A>G in the CHD7 gene in a patient with severe CS, congenital heart disease, left coloboma of the choroid, cryptorchidism, and congenital deafness. The Sanger sequencing confirmed the variant and clarified it as de novo variant by short tandem repeat analysis in the patient family. We analyzed the effect of a variant by Minigene assay to evaluate the pathogenicity of the variant.

RESULTS

In summary, cDNA analysis confirmed that c.3523-2A>G variant activates a cryptic splice site, resulting in 172 base pair missing in exon 15, leading to the premature truncation of the CHD7 protein (p.V1175Afs*11).

CONCLUSION

The present study functionally characterized the novel c.3523-2A>G variant in CHD7, providing further confirmatory evidence that it is associated with CS.

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Genetic Investigation of Inverse Psoriasis

Inverse psoriasis is considered to be a rare variant of plaque-type psoriasis and is associated with significantly impaired quality of life. Clinical manifestations and treatment options are somewhat different for each subtype. Identifying genetic variants that contribute to the susceptibility of different types of psoriasis might improve understanding of the etiology of the disease. Since we have no current knowledge about the genetic background of inverse psoriasis, whole exome sequencing was used to comprehensively assess genetic variations in five patients with exclusively inverse lesions. We detected six potentially pathogenic rare (MAF < 0.01) sequence variants that occurred in all investigated patients. The corresponding mutated genes were FN1, FBLN1, MYH7B, MST1R, RHOD, and SCN10A. Several mutations identified in this study are known to cause disease, but roles in psoriasis or other papulosquamous diseases have not previously been reported. Interestingly, potentially causative variants of established psoriasis-susceptibility genes were not identified. These outcomes are in agreement with our hypothesis that the inverse subtype is a different entity from plaque-type psoriasis.

Pharmacogenomics in the era of next generation sequencing - from byte to bedside

Pharmacogenetic research has resulted in the identification of a multitude of genetic variants that impact drug response or toxicity. These polymorphisms are mostly common and have been included as actionable information in the labels of numerous drugs. In addition to common variants, recent advances in Next Generation Sequencing (NGS) technologies have resulted in the identification of a plethora of rare and population-specific pharmacogenetic variations with unclear functional consequences that are not accessible by conventional forward genetics strategies. In this review, we discuss how comprehensive sequencing information can be translated into personalized pharmacogenomic advice in the age of NGS. Specifically, we provide an update of the functional impacts of rare pharmacogenetic variability and how this information can be leveraged to improve pharmacogenetic guidance. Furthermore, we critically discuss the current status of implementation of pharmacogenetic testing across drug development and layers of care. We identify major gaps and provide perspectives on how these can be minimized to optimize the utilization of NGS data for personalized clinical decision-support.

New Strategies for Clinical Trials in Autism Spectrum Disorder

BACKGROUND

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed.

METHODS

The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for the characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD.

RESULTS

In order to analyze the big data collected by assays such as the whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and, therefore, establish a functional informative network among the different data sources.

CONCLUSION

The potential advantage provided by these new integrated omics-based strategies is better characterization of the genetic background of ASD cohorts, to identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

A novel splice site indel alteration in the EIF2AK3 gene is responsible for the first cases of Wolcott-Rallison syndrome in Hungary

BACKGROUND

Wolcott-Rallison Syndrome (WRS) is a rare autosomal recessive disease that is the most common cause of neonatal diabetes in consanguineous families. WRS is caused by various genetic alterations of the Eukaryotic Translation Initiation Factor 2-Alpha Kinase 3 (EIF2AK3) gene.

METHODS

Genetic analysis of a consanguineous family where two children were diagnosed with WRS was performed by Sanger sequencing. The altered protein was investigated by in vitro cloning, expression and immunohistochemistry.

RESULTS

The first cases in Hungary, - two patients in one family, where the parents were fourth-degree cousins - showed the typical clinical features of WRS: early onset diabetes mellitus with hyperglycemia, growth retardation, infection-induced multiple organ failure. The genetic background of the disease was a novel alteration in the EIF2AK3 gene involving the splice site of exon 11- intron 11-12 boundary: g.53051_53062delinsTG. According to cDNA sequencing this created a new splice site and resulted in a frameshift and the development of an early termination codon at amino acid position 633 (p.Pro627AspfsTer7). Based on in vitro cloning and expression studies, the truncated protein was functionally inactive. Immunohistochemistry revealed that the intact protein was absent in the islets of pancreas, furthermore insulin expressing cells were also dramatically diminished. Elevated GRP78 and reduced CHOP protein expression were observed in the liver.

CONCLUSIONS

The novel genetic alteration causing the absence of the EIF2AK3 protein resulted in insufficient handling of severe endoplasmic reticulum stress, leading to liver failure and demise of the patients.

Hunting for genes that shape human faces: Initial successes and challenges for the future

There is ample evidence from heritability studies, genetic syndromes and experimental animal models that facial morphology is strongly influenced by genes. In this brief review, we present an up-to-date overview of the efforts to identify genes associated with the size and shape of human facial features. We discuss recent methodological advances that have led to breakthroughs, but also the multitude of challenges facing the field. We offer perspective on possible applications of this line of research, particularly in the context of the precision genomics movement.

What Do Neighbors Tell About You: The Local Context of Cis-Regulatory Modules Complicates Prediction of Regulatory Variants

Many problems of modern genetics and functional genomics require the assessment of functional effects of sequence variants, including gene expression changes. Machine learning is considered to be a promising approach for solving this task, but its practical applications remain a challenge due to the insufficient volume and diversity of training data. A promising source of valuable data is a saturation mutagenesis massively parallel reporter assay, which quantitatively measures changes in transcription activity caused by sequence variants. Here, we explore the computational predictions of the effects of individual single-nucleotide variants on gene transcription measured in the massively parallel reporter assays, based on the data from the recent "Regulation Saturation" Critical Assessment of Genome Interpretation challenge. We show that the estimated prediction quality strongly depends on the structure of the training and validation data. Particularly, training on the sequence segments located next to the validation data results in the "information leakage" caused by the local context. This information leakage allows reproducing the prediction quality of the best CAGI challenge submissions with a fairly simple machine learning approach, and even obtaining notably better-than-random predictions using irrelevant genomic regions. Validation scenarios preventing such information leakage dramatically reduce the measured prediction quality. The performance at independent regulatory regions entirely excluded from the training set appears to be much lower than needed for practical applications, and even the performance estimation will become reliable only in the future with richer data from multiple reporters. The source code and data are available at https://bitbucket.org/autosomeru_cagi2018/cagi2018_regsat and https://genomeinterpretation.org/content/expression-variants.

Single-nucleotide polymorphism rs13426236 contributes to an increased prostate cancer risk via regulating MLPH splicing variant 4

A prostate cancer risk single-nucleotide polymorphism (SNP), rs13426236, is significantly associated with melanophilin (MLPH) expression. To functionally characterize role of the rs13426236 in prostate cancer, we first performed splicing-specific expression quantitative trait loci analysis and refined the significant association of rs13426236 allele G with an increased expression of MLPH splicing transcript variant 4 (V4) (P = 7.61E-5) but not other protein-coding variants (V1-V3) (P > .05). We then performed an allele-specific reporter assay to determine if SNP-containing sequences functioned as an active enhancer. Compared to allele A, allele G of rs13426236 showed significantly higher luciferase activity on the promoter of the splicing transcript V4 (P < .03) but not on the promoter of transcript V1 (P > .05) in two prostate cancer cell lines (DU145 and 22Rv1). Cell transfection assays showed stronger effect of transcript V4 than V1 on promoting cell proliferation, invasion, and antiapoptotic activities. RNA profiling analysis demonstrated that transcript V4 overexpression caused significant expression changes in glycosylation/glycoprotein and metal-binding gene ontology pathways (FDR < 0.01). We also found that both transcripts V4 and V1 were significantly upregulated in prostate adenocarcinoma (P ≤ 2.49E-6) but only transcript V4 upregulation was associated with poor recurrence-free survival (P = .028, hazard ratio = 1.63, 95% confidence interval = 1.05-2.42) in The Cancer Genome Atlas data. This study provides strong evidence showing that prostate cancer risk SNP rs13426236 upregulates expression of MLPH transcript V4, which may function as a candidate oncogene in prostate cancer.

Advances in genetics of migraine

Background

Migraine is a complex neurovascular disorder with a strong genetic component. There are rare monogenic forms of migraine, as well as more common polygenic forms; research into the genes involved in both types has provided insights into the many contributing genetic factors. This review summarises advances that have been made in the knowledge and understanding of the genes and genetic variations implicated in migraine etiology.

Findings

Migraine is characterised into two main types, migraine without aura (MO) and migraine with aura (MA). Hemiplegic migraine is a rare monogenic MA subtype caused by mutations in three main genes - CACNA1A, ATP1A2 and SCN1A - which encode ion channel and transport proteins. Functional studies in cellular and animal models show that, in general, mutations result in impaired glutamatergic neurotransmission and cortical hyperexcitability, which make the brain more susceptible to cortical spreading depression, a phenomenon thought to coincide with aura symptoms. Variants in other genes encoding ion channels and solute carriers, or with roles in regulating neurotransmitters at neuronal synapses, or in vascular function, can also cause monogenic migraine, hemiplegic migraine and related disorders with overlapping symptoms. Next-generation sequencing will accelerate the finding of new potentially causal variants and genes, with high-throughput bioinformatics analysis methods and functional analysis pipelines important in prioritising, confirming and understanding the mechanisms of disease-causing variants.

With respect to common migraine forms, large genome-wide association studies (GWAS) have greatly expanded our knowledge of the genes involved, emphasizing the role of both neuronal and vascular pathways. Dissecting the genetic architecture of migraine leads to greater understanding of what underpins relationships between subtypes and comorbid disorders, and may have utility in diagnosis or tailoring treatments. Further work is required to identify causal polymorphisms and the mechanism of their effect, and studies of gene expression and epigenetic factors will help bridge the genetics with migraine pathophysiology.

Conclusions

The complexity of migraine disorders is mirrored by their genetic complexity. A comprehensive knowledge of the genetic factors underpinning migraine will lead to improved understanding of molecular mechanisms and pathogenesis, to enable better diagnosis and treatments for migraine sufferers.

The Bardet-Biedl syndrome protein complex regulates cell migration and tissue repair through a Cullin-3/RhoA pathway

Cell motility and migration play critical roles in various physiological processes and disease states. Here, we show that the BBBsome, a macromolecule composed of eight Bardet-Biedl syndrome (BBS) proteins including BBS1, is a critical determinant of cell migration and wound healing. Fibroblast cells derived from mice or humans harboring a homozygous missense mutation (BBS1^M390R/M390R) that disrupt the BBSome exhibit defects in migration and wound healing. Furthermore, we demonstrate that BBS1^M390R/M390R mice have significantly delayed wound closure. In line with this, we provide data suggesting that BBS1^M390R/M390R fibroblasts have impaired platelet-derived growth factor-AA (PDGF) receptor-α signaling, a key regulator of directional cell migration acting as a chemoattractant during postnatal migration responses such as wound healing. In addition, we show that BBS1^M390R/M390R fibroblasts have upregulated RhoA expression and activity. The relevance of RhoA upregulation is demonstrated by the ability of RhoA-kinase inhibitor Y27632 to partially rescue the migration defect of BBS1^M390R/M390R fibroblasts cells. We also show that accumulation of RhoA protein in BBS1^M390R/M390R fibroblasts cells is associated with reduction and inactivation of the ubiquitin ligase Cullin-3. Consistent with this, Cullin-3 inhibition with MLN4924 is sufficient to reduce migration of normal fibroblasts. These data implicate the BBSome in cell motility and tissue repair through a mechanism that involves PDGF receptor signaling and Cullin-3-mediated control of RhoA.

Massively Parallel Assays and Quantitative Sequence-Function Relationships

Over the last decade, a rich variety of massively parallel assays have revolutionized our understanding of how biological sequences encode quantitative molecular phenotypes. These assays include deep mutational scanning, high-throughput SELEX, and massively parallel reporter assays. Here, we review these experimental methods and how the data they produce can be used to quantitatively model sequence-function relationships. In doing so, we touch on a diverse range of topics, including the identification of clinically relevant genomic variants, the modeling of transcription factor binding to DNA, the functional and evolutionary landscapes of proteins, and cis-regulatory mechanisms in both transcription and mRNA splicing. We further describe a unified conceptual framework and a core set of mathematical modeling strategies that studies in these diverse areas can make use of. Finally, we highlight key aspects of experimental design and mathematical modeling that are important for the results of such studies to be interpretable and reproducible.

Cooperation between non-essential DNA polymerases contributes to genome stability in Saccharomyces cerevisiae

DNA polymerases influence genome stability through their involvement in DNA replication, response to DNA damage, and DNA repair processes. Saccharomyces cerevisiae possess four non-essential DNA polymerases, Pol λ, Pol η, Pol ζ, and Rev1, which have varying roles in genome stability. In order to assess the contribution of the non-essential DNA polymerases in genome stability, we analyzed the pol4Δ rev1Δ rev3Δ rad30Δ quadruple mutant in microhomology mediated repair, due to recent studies linking some of these DNA polymerases to this repair pathway. Our results suggest that the length and quality of microhomology influence both the overall efficiency of repair and the involvement of DNA polymerases. Furthermore, the non-essential DNA polymerases demonstrate overlapping and redundant functions when repairing double-strand breaks using short microhomologies containing mismatches. Then, we examined genome-wide mutation accumulation in the pol4Δ rev1Δ rev3Δ rad30Δ quadruple mutant compared to wild type cells. We found a significant decrease in the overall rate of mutation accumulation in the quadruple mutant cells compared to wildtype, but an increase in frameshift mutations and a shift towards transversion base-substitution with a preference for G:C to T:A or C:G. Thus, the non-essential DNA polymerases have an impact on the nature of the mutational spectrum. The sequence and functional homology shared between human and S. cerevisiae non-essential DNA polymerases suggest these DNA polymerases may have a similar role in human cells.

Prediction of drug response and adverse drug reactions: From twin studies to Next Generation Sequencing

Understanding and predicting inter-individual differences related to the success of drug therapy is of tremendous importance, both during drug development and for clinical applications. Importantly, while seminal twin studies indicate that the majority of inter-individual differences in drug disposition are driven by hereditary factors, common genetic polymorphisms explain only less than half of this genetically encoded variability. Recent progress in Next Generation Sequencing (NGS) technologies has for the first time allowed to comprehensively map the genetic landscape of human pharmacogenes. Importantly, these projects have unveiled vast numbers of rare genetic variants, which are estimated to contribute substantially to the missing heritability of drug metabolism phenotypes. However, functional interpretation of these rare variants remains challenging and constitutes one of the important frontiers of contemporary pharmacogenomics. Furthermore, NGS technologies face challenges in the interrogation of genes residing in complex genomic regions, such as CYP2D6 and HLA genes. We here provide an update of the implementation of pharmacogenomic variations in the clinical setting and present emerging strategies that facilitate the translation of NGS data into clinically useful information. Importantly, we anticipate that these developments will soon result in a paradigm shift of pre-emptive genotyping away from the interrogation to candidate variants and towards the comprehensive profiling of an individuals genotype, thus allowing for a true individualization of patient drug treatment regimens.

Population-scale genomics-Enabling precision public health

The current excitement for affordable genomics technologies and national precision medicine initiatives marks a turning point in worldwide healthcare practices. The last decade of global population sequencing efforts has defined the enormous extent of genetic variation in the human population resulting in insights into differential disease burden and response to therapy within and between populations. Population-scale pharmacogenomics helps to provide insights into the choice of optimal therapies and an opportunity to estimate, predict and minimize adverse events. Such an approach can potentially empower countries to formulate national selection and dosing policies for therapeutic agents thereby promoting public health with precision. We review the breadth and depth of worldwide population-scale sequencing efforts and its implications for the implementation of clinical pharmacogenetics toward making precision medicine a reality.

Genomics for paediatricians: promises and pitfalls

In recent years, there have been significant advances in genetic technologies, evolving the field of genomics from genetics. This has huge diagnostic potential, as genomic testing increasingly becomes part of mainstream medicine. However, there are numerous potential pitfalls in the interpretation of genomic data. It is therefore essential that we educate clinicians more widely about the appropriate interpretation and utilisation of genomic testing.

Comprehensive Cancer-Predisposition Gene Testing in an Adult Multiple Primary Tumor Series Shows a Broad Range of Deleterious Variants and Atypical Tumor Phenotypes

Multiple primary tumors (MPTs) affect a substantial proportion of cancer survivors and can result from various causes, including inherited predisposition. Currently, germline genetic testing of MPT-affected individuals for variants in cancer-predisposition genes (CPGs) is mostly targeted by tumor type. We ascertained pre-assessed MPT individuals (with at least two primary tumors by age 60 years or at least three by 70 years) from genetics centers and performed whole-genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment and molecular investigations, pathogenic variants in moderate- and high-risk CPGs were detected in 67/440 (15.2%) probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies, including low-frequency structural variants (6/440 [1.4%] probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant), the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice-site CPG variants and at least one discordant tumor type was significantly higher than in a control population (χ2 = 43.642; p ≤ 0.0001). 2/67 (3%) probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Together with variant detection rates from a previous series of similarly ascertained MPT-affected individuals, the present results suggest that first-line comprehensive CPG analysis in an MPT cohort referred to clinical genetics services would detect a deleterious variant in about a third of individuals.

PASSPORT-seq: A Novel High-Throughput Bioassay to Functionally Test Polymorphisms in Micro-RNA Target Sites

Next-generation sequencing (NGS) studies have identified large numbers of genetic variants that are predicted to alter miRNA–mRNA interactions. We developed a novel high-throughput bioassay, PASSPORT-seq, that can functionally test in parallel 100s of these variants in miRNA binding sites (mirSNPs). The results are highly reproducible across both technical and biological replicates. The utility of the bioassay was demonstrated by testing 100 mirSNPs in HEK293, HepG2, and HeLa cells. The results of several of the variants were validated in all three cell lines using traditional individual luciferase assays. Fifty-five mirSNPs were functional in at least one of three cell lines (FDR ≤ 0.05); 11, 36, and 27 of them were functional in HEK293, HepG2, and HeLa cells, respectively. Only four of the variants were functional in all three cell lines, which demonstrates the cell-type specific effects of mirSNPs and the importance of testing the mirSNPs in multiple cell lines. Using PASSPORT-seq, we functionally tested 111 variants in the 3′ UTR of 17 pharmacogenes that are predicted to alter miRNA regulation. Thirty-three of the variants tested were functional in at least one cell line.

Functional Assays Are Essential for Interpretation of Missense Variants Associated with Variable Expressivity

Missense DNA variants have variable effects upon protein function. Consequently, interpreting their pathogenicity is challenging, especially when they are associated with disease variability. To determine the degree to which functional assays inform interpretation, we analyzed 48 CFTR missense variants associated with variable expressivity of cystic fibrosis (CF). We assessed function in a native isogenic context by evaluating CFTR mutants that were stably expressed in the genome of a human airway cell line devoid of endogenous CFTR expression. 21 of 29 variants associated with full expressivity of the CF phenotype generated <10% wild-type CFTR (WT-CFTR) function, a conservative threshold for the development of life-limiting CF lung disease, and five variants had moderately decreased function (10% to ∼25% WT-CFTR). The remaining three variants in this group unexpectedly had >25% WT-CFTR function; two were higher than 75% WT-CFTR. As expected, 14 of 19 variants associated with partial expressivity of CF had >25% WT-CFTR function; however, four had minimal to no effect on CFTR function (>75% WT-CFTR). Thus, 6 of 48 (13%) missense variants believed to be disease causing did not alter CFTR function. Functional studies substantially refined pathogenicity assignment with expert annotation and criteria from the American College of Medical Genetics and Genomics and Association for Molecular Pathology. However, four algorithms (CADD, REVEL, SIFT, and PolyPhen-2) could not differentiate between variants that caused severe, moderate, or minimal reduction in function. In the setting of variable expressivity, these results indicate that functional assays are essential for accurate interpretation of missense variants and that current prediction tools should be used with caution.

Inferring the effect of genomic variation in the new era of genomics

Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene-variant-disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next-generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as "synergistic heterozygosity," digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large-scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.

Enlightening the Association between Bicuspid Aortic Valve and Aortopathy

Bicuspid aortic valve (BAV) patients have an increased incidence of developing aortic dilation. Despite its importance, the pathogenesis of aortopathy in BAV is still largely undetermined. Nowadays, intense focus falls both on BAV morphology and progression of valvular dysfunction and on the development of aortic dilation. However, less is known about the relationship between aortic valve morphology and aortic dilation. A better understanding of the molecular pathways involved in the homeostasis of the aortic wall, including the extracellular matrix, the plasticity of the vascular smooth cells, TGFβ signaling, and epigenetic dysregulation, is key to enlighten the mechanisms underpinning BAV-aortopathy development and progression. To date, there are two main theories on this subject, i.e., the genetic and the hemodynamic theory, with an ongoing debate over the pathogenesis of BAV-aortopathy. Furthermore, the lack of early detection biomarkers leads to challenges in the management of patients affected by BAV-aortopathy. Here, we critically review the current knowledge on the driving mechanisms of BAV-aortopathy together with the current clinical management and lack of available biomarkers allowing for early detection and better treatment optimization.

Understanding Neurodevelopmental Disorders: The Promise of Regulatory Variation in the 3'UTRome

Neurodevelopmental disorders have a strong genetic component, but despite widespread efforts, the specific genetic factors underlying these disorders remain undefined for a large proportion of affected individuals. Given the accessibility of exome sequencing, this problem has thus far been addressed from a protein-centric standpoint; however, protein-coding regions only make up ∼1% to 2% of the human genome. With the advent of whole genome sequencing we are in the midst of a paradigm shift as it is now possible to interrogate the entire sequence of the human genome (coding and noncoding) to fill in the missing heritability of complex disorders. These new technologies bring new challenges, as the number of noncoding variants identified per individual can be overwhelming, making it prudent to focus on noncoding regions of known function, for which the effects of variation can be predicted and directly tested to assess pathogenicity. The 3'UTRome is a region of the noncoding genome that perfectly fulfills these criteria and is of high interest when searching for pathogenic variation related to complex neurodevelopmental disorders. Herein, we review the regulatory roles of the 3'UTRome as binding sites for microRNAs or RNA binding proteins, or during alternative polyadenylation. We detail existing evidence that these regions contribute to neurodevelopmental disorders and outline strategies for identification and validation of novel putatively pathogenic variation in these regions. This evidence suggests that studying the 3'UTRome will lead to the identification of new risk factors, new candidate disease genes, and a better understanding of the molecular mechanisms contributing to neurodevelopmental disorders.

Variant Interpretation: Functional Assays to the Rescue

Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.