Molecular Characterization of Juxtaglomerular Cell Tumors: Evidence of Alterations in MAPK-RAS Pathway.

Juxtaglomerular cell tumor (JGCT) is a rare neoplasm, part of the family of mesenchymal tumors of the kidney. Although the pathophysiological and clinical correlates of JGCT are well known, as these tumors are an important cause of early-onset arterial hypertension refractory to medical treatment, their molecular background is unknown, with only few small studies investigating their karyotype. Herein we describe a multi-institutional cohort of JGCTs diagnosed by experienced genitourinary pathologists, evaluating clinical presentation and outcome, morphologic diversity, and, importantly, the molecular features. Ten JGCTs were collected from 9 institutions, studied by immunohistochemistry, and submitted to whole exome sequencing. Our findings highlight the morphologic heterogeneity of JGCT, which can mimic several kidney tumor entities. Three cases showed concerning histologic features, but the patient course was unremarkable, which suggests that morphologic evaluation alone cannot reliably predict the clinical behavior. Gain-of-function variants in RAS GTPases were detected in JGCTs, with no evidence of additional recurrent genomic alterations. In conclusion, we present the largest series of JGCT characterized by whole exome sequencing, highlighting the putative role of the MAPK-RAS pathway.

Vitamin D status and variable responses to supplements depend in part on genetic factors in adults with cystic fibrosis.

Vitamin D sufficiency has been difficult to achieve consistently in patients with cystic fibrosis (CF), even with robust oral supplements. To assess vitamin D status and resistance to supplementation, we studied 80 adults using 25-hydroxyvitamin D (25OHD) determinations and whole genome sequencing to construct polygenic risk scores (PRS) that aggregate variants associated with vitamin D status. The results revealed that 30 % of patients were below the threshold of 30 ng/mL and thus should be regarded as insufficient despite normal vitamin E status, a reflection of adherence to fat soluble vitamin supplementation. The PRS values were significantly correlated with 25OHD concentrations, confirming our results in children with CF, and indicating that genetic factors play a role and have implications for therapy.

Impact of intrinsic and extrinsic risk factors on early-onset lung disease in cystic fibrosis.

BACKGROUND: Although respiratory pathology is known to develop in young children with cystic fibrosis (CF), the determinants of early-onset lung disease have not been elucidated. OBJECTIVE: We aimed to determine the impact of potential intrinsic and extrinsic risk factors during the first 3 years of life, testing the hypothesis that both contribute significantly to early-onset CF lung disease. DESIGN: We studied 104 infants born during 2012-2017, diagnosed through newborn screening by age 3 months, and evaluated comprehensively to 36 months of age. Lung disease manifestations were quantified with a new scoring system known as CFELD for Cystic Fibrosis Early-onset Lung Disease. The variants in the cystic fibrosis transmembrane conductance regulator (CFTR) gene were determined and categorized. Whole genome sequencing was performed on each subject and the data transformed to polygenic risk scores (PRS) that aggregate variants associated with lung function. Extrinsic factors included socioeconomic status (SES) indicators and environmental experiences such as exposures to smoking, pets, and daycare. RESULTS: We found by univariate analysis that CFTR genotype and genetic modifiers aggregated by the PRS method were significantly associated with early-onset CF lung disease. Ordinal logistic regression analysis demonstrated that high and stable SES (maternal education ≥community college, stable 2-parent home, and not receiving Medicaid) and better growth (weight-for-age and height-for-age z-scores) reduced risks, while exposure to smoking and daycare ≥20 h/week increased the risk of CFELD severity. CONCLUSIONS: Extrinsic, modifiable determinants are influential early and potentially as important as the intrinsic risk factors in the onset of CF lung disease.

Ten simple rules for using public biological data for your research.

With an increasing amount of biological data available publicly, there is a need for a guide on how to successfully download and use this data. The 10 simple rules for using public biological data are: (1) use public data purposefully in your research; (2) evaluate data for your use case; (3) check data reuse requirements and embargoes; (4) be aware of ethics for data reuse; (5) plan for data storage and compute requirements; (6) know what you are downloading; (7) download programmatically and verify integrity; (8) properly cite data; (9) make reprocessed data and models Findable, Accessible, Interoperable, and Reusable (FAIR) and share; and (10) make pipelines and code FAIR and share. These rules are intended as a guide for researchers wanting to make use of available data and to increase data reuse and reproducibility.

Genetic factors help explain the variable responses of young children with cystic fibrosis to vitamin D supplements.

BACKGROUND & AIMS: Children with cystic fibrosis (CF) are susceptible to fat-soluble vitamin deficiencies unless supplemented, but even large doses of vitamin D may not prevent low 25-hydroxyvitamin D (25OHD) concentrations. The explanation for these vitamin D non-responders has been elusive. We utilized data from whole genome sequencing (WGS) to test the hypothesis that genetic variations predict responsiveness to vitamin D supplementation in a prospective cohort study of children with CF in the first 3 years of life. METHODS: One hundred and one infants born during 2012-2017 and diagnosed with CF through newborn screening were studied. Serum 25OHD concentrations and vitamin D supplement doses were assessed during early infancy and annually thereafter. WGS was performed, the resultant variant calling files processed, and the summary statistics from a recent genome-wide association study were utilized to construct a polygenic risk score (PRS) for each subject. RESULTS: Overall, the prevalence of vitamin D insufficiency (<30 ng/mL) was 21% in the first 3 years of life. Among the 70 subjects who always adhered to vitamin D supplement doses recommended by the US CF Foundation guidelines, 89% were responders (achieved vitamin D sufficiency) by 3 years of age, while 11% were transient or non-responders. Multiple regression analysis revealed that PRS was a significant predictor of 25OHD concentrations (p < 0.001) and the likelihood of being an earlier responder in the first 3 years of life (p < 0.01). A limited SNP analysis revealed variants in four important genes (GC, LIPC, CYP24A1, and PDE3B) that were shown to be associated with 25OHD concentrations and vitamin D responder status. Other determinants included vitamin D supplement dose, season at 25OHD measurement, and pancreatic functional status. CONCLUSIONS: Applying WGS in conjunction with utilizing a PRS approach revealed genetic variations that partially explain the unresponsiveness of some children with CF to vitamin D supplementation. Our findings suggest that a nutrigenomics strategy could help promote personalized treatment in CF.

DOCKopathies: A systematic review of the clinical pathologies associated with human DOCK pathogenic variants.

The Dedicator of Cytokinesis (DOCK) family (DOCK1-11) of genes are essential mediators of cellular migration, growth, and fusion in a variety of cell types and tissues. Recent advances in whole-genome sequencing of patients with undiagnosed genetic disorders have identified several rare pathogenic variants in DOCK genes. We conducted a systematic review and performed a patient database and literature search of reported DOCK pathogenic variants that have been identified in association with clinical pathologies such as global developmental delay, immune cell dysfunction, muscle hypotonia, and muscle ataxia among other categories. We then categorized these pathogenic DOCK variants and their associated clinical phenotypes under several unique categories: developmental, cardiovascular, metabolic, cognitive, or neuromuscular. Our systematic review of DOCK variants aims to identify and analyze potential DOCK-regulated networks associated with neuromuscular diseases and other disease pathologies, which may identify novel therapeutic strategies and targets. This systematic analysis and categorization of human-associated pathologies with DOCK pathogenic variants is the first report to the best of our knowledge for a unique class in this understudied gene family that has important implications in furthering personalized genomic medicine, clinical diagnoses, and improve targeted therapeutic outcomes across many clinical pathologies.

Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data.

Developing an accurate and interpretable model to predict an individual's risk for Coronavirus Disease 2019 (COVID-19) is a critical step to efficiently triage testing and other scarce preventative resources. To aid in this effort, we have developed an interpretable risk calculator that utilized de-identified electronic health records (EHR) from the University of Alabama at Birmingham Informatics for Integrating Biology and the Bedside (UAB-i2b2) COVID-19 repository under the U-BRITE framework. The generated risk scores are analogous to commonly used credit scores where higher scores indicate higher risks for COVID-19 infection. By design, these risk scores can easily be calculated in spreadsheets or even with pen and paper. To predict risk, we implemented a Credit Scorecard modeling approach on longitudinal EHR data from 7,262 patients enrolled in the UAB Health System who were evaluated and/or tested for COVID-19 between January and June 2020. In this cohort, 912 patients were positive for COVID-19. Our workflow considered the timing of symptoms and medical conditions and tested the effects by applying different variable selection techniques such as LASSO and Elastic-Net. Within the two weeks before a COVID-19 diagnosis, the most predictive features were respiratory symptoms such as cough, abnormalities of breathing, pain in the throat and chest as well as other chronic conditions including nicotine dependence and major depressive disorder. When extending the timeframe to include all medical conditions across all time, our models also uncovered several chronic conditions impacting the respiratory, cardiovascular, central nervous and urinary organ systems. The whole pipeline of data processing, risk modeling and web-based risk calculator can be applied to any EHR data following the OMOP common data format. The results can be employed to generate questionnaires to estimate COVID-19 risk for screening in building entries or to optimize hospital resources.

Functional and structural analysis of cytokine-selective IL6ST defects that cause recessive hyper-IgE syndrome.

BACKGROUND: Biallelic variants in IL6ST, encoding GP130, cause a recessive form of hyper-IgE syndrome (HIES) characterized by high IgE level, eosinophilia, defective acute phase response, susceptibility to bacterial infections, and skeletal abnormalities due to cytokine-selective loss of function in GP130, with defective IL-6 and IL-11 and variable oncostatin M (OSM) and IL-27 levels but sparing leukemia inhibitory factor (LIF) signaling. OBJECTIVE: Our aim was to understand the functional and structural impact of recessive HIES-associated IL6ST variants. METHODS: We investigated a patient with HIES by using exome, genome, and RNA sequencing. Functional assays assessed IL-6, IL-11, IL-27, OSM, LIF, CT-1, CLC, and CNTF signaling. Molecular dynamics simulations and structural modeling of GP130 cytokine receptor complexes were performed. RESULTS: We identified a patient with compound heterozygous novel missense variants in IL6ST (p.Ala517Pro and the exon-skipping null variant p.Gly484_Pro518delinsArg). The p.Ala517Pro variant resulted in a more profound IL-6- and IL-11-dominated signaling defect than did the previously identified recessive HIES IL6ST variants p.Asn404Tyr and p.Pro498Leu. Molecular dynamics simulations suggested that the p.Ala517Pro and p.Asn404Tyr variants result in increased flexibility of the extracellular membrane-proximal domains of GP130. We propose a structural model that explains the cytokine selectivity of pathogenic IL6ST variants that result in recessive HIES. The variants destabilized the conformation of the hexameric cytokine receptor complexes, whereas the trimeric LIF-GP130-LIFR complex remained stable through an additional membrane-proximal interaction. Deletion of this membrane-proximal interaction site in GP130 consequently caused additional defective LIF signaling and Stüve-Wiedemann syndrome. CONCLUSION: Our data provide a structural basis to understand clinical phenotypes in patients with IL6ST variants.

Best practices for the analytical validation of clinical whole-genome sequencing intended for the diagnosis of germline disease.

Whole-genome sequencing (WGS) has shown promise in becoming a first-tier diagnostic test for patients with rare genetic disorders; however, standards addressing the definition and deployment practice of a best-in-class test are lacking. To address these gaps, the Medical Genome Initiative, a consortium of leading healthcare and research organizations in the US and Canada, was formed to expand access to high-quality clinical WGS by publishing best practices. Here, we present consensus recommendations on clinical WGS analytical validation for the diagnosis of individuals with suspected germline disease with a focus on test development, upfront considerations for test design, test validation practices, and metrics to monitor test performance. This work also provides insight into the current state of WGS testing at each member institution, including the utilization of reference and other standards across sites. Importantly, members of this initiative strongly believe that clinical WGS is an appropriate first-tier test for patients with rare genetic disorders, and at minimum is ready to replace chromosomal microarray analysis and whole-exome sequencing. The recommendations presented here should reduce the burden on laboratories introducing WGS into clinical practice, and support safe and effective WGS testing for diagnosis of germline disease.

Applying whole-genome sequencing in relation to phenotype and outcomes in siblings with cystic fibrosis.

Variations in disease onset and/or severity have often been observed in siblings with cystic fibrosis (CF), despite the same CFTR genotype and environment. We postulated that genomic variation (modifier and/or pharmacogenomic variants) might explain these clinical discordances. From a cohort of patients included in the Wisconsin randomized clinical trial (RCT) of newborn screening (NBS) for CF, we identified two brothers who showed discordant lung disease courses as children, with one milder and the other more severe than average, and a third, eldest brother, who also has severe lung disease. Leukocytes were harvested as the source of DNA, and whole-genome sequencing (WGS) was performed. Variants were identified and analyzed using in-house-developed informatics tools. Lung disease onset and severity were quantitatively different between brothers during childhood. The youngest, less severely affected brother is homozygous for HFE p.H63D. He also has a very rare PLG p.D238N variant that may influence host-pathogen interaction during chronic lung infection. Other variants of interest were found differentially between the siblings. Pharmacogenomics findings were consistent with the middle, most severely affected brother having poor outcomes to common CF treatments. We conclude that genomic variation between siblings with CF is expected. Variable lung disease severity may be associated with differences acting as genetic modifiers and/or pharmacogenomic factors, but large cohort studies are needed to assess this hypothesis.

Genome sequencing for early-onset or atypical dementia: high diagnostic yield and frequent observation of multiple contributory alleles.

We assessed the results of genome sequencing for early-onset dementia. Participants were selected from a memory disorders clinic. Genome sequencing was performed along with C9orf72 repeat expansion testing. All returned sequencing results were Sanger-validated. Prior clinical diagnoses included Alzheimer's disease, frontotemporal dementia, and unspecified dementia. The mean age of onset was 54 (41-76). Fifty percent of patients had a strong family history, 37.5% had some, and 12.5% had no known family history. Nine of 32 patients (28%) had a variant defined as pathogenic or likely pathogenic (P/LP) by American College of Medical Genetics and Genomics standards, including variants in APP, C9orf72, CSF1R, and MAPT Nine patients (including three with P/LP variants) harbored established risk alleles with moderate penetrance (odds ratios of ∼2-5) in ABCA7, AKAP9, GBA, PLD3, SORL1, and TREM2 All six patients harboring these moderate penetrance variants but not P/LP variants also had one or two APOE ε4 alleles. One patient had two APOE ε4 alleles with no other established contributors. In total, 16 patients (50%) harbored one or more genetic variants likely to explain symptoms. We identified variants of uncertain significance (VUSs) in ABI3, ADAM10, ARSA, GRID2IP, MME, NOTCH3, PLCD1, PSEN1, TM2D3, TNK1, TTC3, and VPS13C, also often along with other variants. In summary, genome sequencing for early-onset dementia frequently identified multiple established or possible contributory alleles. These observations add support for an oligogenic model for early-onset dementia.

VarSight: prioritizing clinically reported variants with binary classification algorithms.

BACKGROUND: When applying genomic medicine to a rare disease patient, the primary goal is to identify one or more genomic variants that may explain the patient's phenotypes. Typically, this is done through annotation, filtering, and then prioritization of variants for manual curation. However, prioritization of variants in rare disease patients remains a challenging task due to the high degree of variability in phenotype presentation and molecular source of disease. Thus, methods that can identify and/or prioritize variants to be clinically reported in the presence of such variability are of critical importance. METHODS: We tested the application of classification algorithms that ingest variant annotations along with phenotype information for predicting whether a variant will ultimately be clinically reported and returned to a patient. To test the classifiers, we performed a retrospective study on variants that were clinically reported to 237 patients in the Undiagnosed Diseases Network. RESULTS: We treated the classifiers as variant prioritization systems and compared them to four variant prioritization algorithms and two single-measure controls. We showed that the trained classifiers outperformed all other tested methods with the best classifiers ranking 72% of all reported variants and 94% of reported pathogenic variants in the top 20. CONCLUSIONS: We demonstrated how freely available binary classification algorithms can be used to prioritize variants even in the presence of real-world variability. Furthermore, these classifiers outperformed all other tested methods, suggesting that they may be well suited for working with real rare disease patient datasets.

A toolkit for genetics providers in follow-up of patients with non-diagnostic exome sequencing.

There are approximately 7,000 rare diseases affecting 25-30 million Americans, with 80% estimated to have a genetic basis. This presents a challenge for genetics practitioners to determine appropriate testing, make accurate diagnoses, and conduct up-to-date patient management. Exome sequencing (ES) is a comprehensive diagnostic approach, but only 25%-41% of the patients receive a molecular diagnosis. The remaining three-fifths to three-quarters of patients undergoing ES remain undiagnosed. The Stanford Center for Undiagnosed Diseases (CUD), a clinical site of the Undiagnosed Diseases Network, evaluates patients with undiagnosed and rare diseases using a combination of methods including ES. Frequently these patients have non-diagnostic ES results, but strategic follow-up techniques identify diagnoses in a subset. We present techniques used at the CUD that can be adopted by genetics providers in clinical follow-up of cases where ES is non-diagnostic. Solved case examples illustrate different types of non-diagnostic results and the additional techniques that led to a diagnosis. Frequent approaches include segregation analysis, data reanalysis, genome sequencing, additional variant identification, careful phenotype-disease correlation, confirmatory testing, and case matching. We also discuss prioritization of cases for additional analyses.

Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease.

BACKGROUND: Many patients remain without a diagnosis despite extensive medical evaluation. The Undiagnosed Diseases Network (UDN) was established to apply a multidisciplinary model in the evaluation of the most challenging cases and to identify the biologic characteristics of newly discovered diseases. The UDN, which is funded by the National Institutes of Health, was formed in 2014 as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. METHODS: We evaluated patients who were referred to the UDN over a period of 20 months. The patients were required to have an undiagnosed condition despite thorough evaluation by a health care provider. We determined the rate of diagnosis among patients who subsequently had a complete evaluation, and we observed the effect of diagnosis on medical care. RESULTS: A total of 1519 patients (53% female) were referred to the UDN, of whom 601 (40%) were accepted for evaluation. Of the accepted patients, 192 (32%) had previously undergone exome sequencing. Symptoms were neurologic in 40% of the applicants, musculoskeletal in 10%, immunologic in 7%, gastrointestinal in 7%, and rheumatologic in 6%. Of the 382 patients who had a complete evaluation, 132 received a diagnosis, yielding a rate of diagnosis of 35%. A total of 15 diagnoses (11%) were made by clinical review alone, and 98 (74%) were made by exome or genome sequencing. Of the diagnoses, 21% led to recommendations regarding changes in therapy, 37% led to changes in diagnostic testing, and 36% led to variant-specific genetic counseling. We defined 31 new syndromes. CONCLUSIONS: The UDN established a diagnosis in 132 of the 382 patients who had a complete evaluation, yielding a rate of diagnosis of 35%. (Funded by the National Institutes of Health Common Fund.).

Genome sequencing in the clinic: the past, present, and future of genomic medicine.

Genomic sequencing has undergone massive expansion in the past 10 yr, from a rarely used research tool into an approach that has broad applications in a clinical setting. From rare disease to cancer, genomics is transforming our knowledge of biology. The transition from targeted gene sequencing, to whole exome sequencing, to whole genome sequencing has only been made possible due to rapid advancements in technologies and informatics that have plummeted the cost per base of DNA sequencing and analysis. The tools of genomics have resolved the etiology of disease for previously undiagnosable conditions, identified cancer driver gene variants, and have impacted the understanding of pathophysiology for many diseases. However, this expansion of use has also highlighted research's current voids in knowledge. The lack of precise animal models for gene-to-function association, lack of tools for analysis of genomic structural changes, skew in populations used for genetic studies, publication biases, and the "Unknown Proteome" all contribute to voids needing filled for genomics to work in a fast-paced clinical setting. The future will hold the tools to fill in these voids, with new data sets and the continual development of new technologies allowing for expansion of genomic medicine, ushering in the days to come for precision medicine. In this review we highlight these and other points in hopes of advancing and guiding precision medicine into the future for optimal success.

Functional variants in TBX2 are associated with a syndromic cardiovascular and skeletal developmental disorder.

The 17 genes of the T-box family are transcriptional regulators that are involved in all stages of embryonic development, including craniofacial, brain, heart, skeleton and immune system. Malformation syndromes have been linked to many of the T-box genes. For example, haploinsufficiency of TBX1 is responsible for many structural malformations in DiGeorge syndrome caused by a chromosome 22q11.2 deletion. We report four individuals with an overlapping spectrum of craniofacial dysmorphisms, cardiac anomalies, skeletal malformations, immune deficiency, endocrine abnormalities and developmental impairments, reminiscent of DiGeorge syndrome, who are heterozygotes for TBX2 variants. The p.R20Q variant is shared by three affected family members in an autosomal dominant manner; the fourth unrelated individual has a de novo p.R305H mutation. Bioinformatics analyses indicate that these variants are rare and predict them to be damaging. In vitro transcriptional assays in cultured cells show that both variants result in reduced transcriptional repressor activity of TBX2. We also show that the variants result in reduced protein levels of TBX2. Heterologous over-expression studies in Drosophila demonstrate that both p.R20Q and p.R305H function as partial loss-of-function alleles. Hence, these and other data suggest that TBX2 is a novel candidate gene for a new multisystem malformation disorder.

Analysis and Annotation of Whole-Genome or Whole-Exome Sequencing Derived Variants for Clinical Diagnosis.

Over the last 10 years, next-generation sequencing (NGS) has transformed genomic research through substantial advances in technology and reduction in the cost of sequencing, and also in the systems required for analysis of these large volumes of data. This technology is now being used as a standard molecular diagnostic test in some clinical settings. The advances in sequencing have come so rapidly that the major bottleneck in identification of causal variants is no longer the sequencing or analysis (given access to appropriate tools), but rather clinical interpretation. Interpretation of genetic findings in a complex and ever changing clinical setting is scarcely a new challenge, but the task is increasingly complex in clinical genome-wide sequencing given the dramatic increase in dataset size and complexity. This increase requires application of appropriate interpretation tools, as well as development and application of appropriate methodologies and standard procedures. This unit provides an overview of these items. Specific challenges related to implementation of genome-wide sequencing in a clinical setting are discussed. © 2017 by John Wiley & Sons, Inc.

Successful Application of Whole Genome Sequencing in a Medical Genetics Clinic.

A pilot program was initiated using whole genome sequencing (WGS) to diagnose suspected genetic disorders in the Genetics Clinic at Children's Hospital of Wisconsin. Twenty-two patients underwent WGS between 2010 and 2013. Initially, we obtained a 14% (3/22) diagnosis rate over 2 years; with subsequent reanalysis, this increased to 36% (8/22). Disease causing variants were identified in SKIV2L, CECR1, DGKE, PYCR2, RYR1, PDGFRB, EFTUD2, and BCS1L. In 75% (6/8) of diagnosed cases, the diagnosis affected treatment and/or medical surveillance. Additionally, one case demonstrated a homozygous A18V variant in VLDLR that appears to be associated with a previously undescribed phenotype.

Principles and Recommendations for Standardizing the Use of the Next-Generation Sequencing Variant File in Clinical Settings.

A national workgroup convened by the Centers for Disease Control and Prevention identified principles and made recommendations for standardizing the description of sequence data contained within the variant file generated during the course of clinical next-generation sequence analysis for diagnosing human heritable conditions. The specifications for variant files were initially developed to be flexible with regard to content representation to support a variety of research applications. This flexibility permits variation with regard to how sequence findings are described and this depends, in part, on the conventions used. For clinical laboratory testing, this poses a problem because these differences can compromise the capability to compare sequence findings among laboratories to confirm results and to query databases to identify clinically relevant variants. To provide for a more consistent representation of sequence findings described within variant files, the workgroup made several recommendations that considered alignment to a common reference sequence, variant caller settings, use of genomic coordinates, and gene and variant naming conventions. These recommendations were considered with regard to the existing variant file specifications presently used in the clinical setting. Adoption of these recommendations is anticipated to reduce the potential for ambiguity in describing sequence findings and facilitate the sharing of genomic data among clinical laboratories and other entities.

Molecular modeling in the age of clinical genomics, the enterprise of the next generation.

Protein modeling and molecular dynamics hold a unique toolset to aide in the characterization of clinical variants that may result in disease. Not only do these techniques offer the ability to study under characterized proteins, but they do this with the speed that is needed for time-sensitive clinical cases. In this paper we retrospectively study a clinical variant in the XIAP protein, C203Y, while addressing additional variants seen in patients with similar gastrointestinal phenotypes as the C203Y mutation. In agreement with the clinical tests performed on the C203Y patient, protein modeling and molecular dynamics suggest that direct interactions with RIPK2 and Caspase3 are altered by the C203Y mutation and subsequent loss of Zn coordination in the second BIR domain of XIAP. Interestingly, the variant does not appear to alter interactions with SMAC, resulting in further damage to the caspase and NOD2 pathways. To expand the computational strategy designed when studying XIAP, we have applied the molecular modeling tools to a list of 140 variants seen in CFTR associated with cystic fibrosis, and a list of undiagnosed variants in 17 different genes. This paper shows the exciting applications of molecular modeling in the classification and characterization of genetic variants identified in next generation sequencing. Graphical abstract XIAP in Caspase 3 and NOD2 signaling pathways.