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

Three novel mutations of the BCKDHA, BCKDHB and DBT genes in Chinese children with maple syrup urine disease

BACKGROUND

Maple syrup urine disease (MSUD) is a rare metabolic autosomal recessive disorder caused by deficiency of the branched-chain α-ketoacid dehydrogenase complex. Mutations in the BCKDHA, BCKDHB and DBT genes are responsible for MSUD. This study presents the clinical and molecular characterizations of four MSUD patients.

METHODS

Clinical data of patients were retrospectively analyzed, and genetic mutations were identified by whole-exome sequencing. CLUSTALX was employed to analyzed cross-species conservation of the mutant amino acid. The impact of the mutations was analyzed with PolyPhen-2 software. The I-TASSER website and PyMOL software were used to predict the protein three-position structure of the novel mutations carried by the patients.

RESULTS

Vomiting, irritability, feeding difficulties, seizures, dyspnoea, lethargy and coma were the main clinical presentations of MSUD. Cranial MRI showed abnormal symmetrical signals in accordance with the presentation of inherited metabolic encephalopathy. Seven mutations were detected in four patients, including three novel pathogenic mutations in the BCKDHA (c.656C>A), BCKDHB (deletion of a single-copy of BCKDHB) and DBT (c.1219dup) genes. Structural changes were compatible with the observed phenotypes.

CONCLUSIONS

Different types of MSUD can display heterogeneous clinical manifestations. Exhaustive molecular studies are necessary for a proper differential diagnosis. The newly identified mutation will play a key role in the prenatal diagnosis of MSUD in the future.

A study of elective genome sequencing and pharmacogenetic testing in an unselected population

BACKGROUND

Genome sequencing (GS) of individuals without a medical indication, known as elective GS, is now available at a number of centers around the United States. Here we report the results of elective GS and pharmacogenetic panel testing in 52 individuals at a private genomics clinic in Alabama.

METHODS

Individuals seeking elective genomic testing and pharmacogenetic testing were recruited through a private genomics clinic in Huntsville, AL. Individuals underwent clinical genome sequencing with a separate pharmacogenetic testing panel.

RESULTS

Six participants (11.5%) had pathogenic or likely pathogenic variants that may explain one or more aspects of their medical history. Ten participants (19%) had variants that altered the risk of disease in the future, including two individuals with clonal hematopoiesis of indeterminate potential. Forty-four participants (85%) were carriers of a recessive or X-linked disorder. All individuals with pharmacogenetic testing had variants that affected current and/or future medications.

CONCLUSION

Our study highlights the importance of collecting detailed phenotype information to interpret results in elective GS.

Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population

Background

The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of “silent” genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking.

Results

We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population.

Conclusions

These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a “Structural Predictivity Index” (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.

Set-theory based benchmarking of three different variant callers for targeted sequencing

Background

Next generation sequencing (NGS) technologies have improved the study of hereditary diseases. Since the evaluation of bioinformatics pipelines is not straightforward, NGS demands effective strategies to analyze data that is of paramount relevance for decision making under a clinical scenario. According to the benchmarking framework of the Global Alliance for Genomics and Health (GA4GH), we implemented a new simple and user-friendly set-theory based method to assess variant callers using a gold standard variant set and high confidence regions. As model, we used TruSight Cardio kit sequencing data of the reference genome NA12878. This targeted sequencing kit is used to identify variants in key genes related to Inherited Cardiac Conditions (ICCs), a group of cardiovascular diseases with high rates of morbidity and mortality.

Results

We implemented and compared three variant calling pipelines (Isaac, Freebayes, and VarScan). Performance metrics using our set-theory approach showed high-resolution pipelines and revealed: (1) a perfect recall of 1.000 for all three pipelines, (2) very high precision values, i.e. 0.987 for Freebayes, 0.928 for VarScan, and 1.000 for Isaac, when compared with the reference material, and (3) a ROC curve analysis with AUC > 0.94 for all cases. Moreover, significant differences were obtained between the three pipelines. In general, results indicate that the three pipelines were able to recognize the expected variants in the gold standard data set.

Conclusions

Our set-theory approach to calculate metrics was able to identify the expected ICCs related variants by the three selected pipelines, but results were completely dependent on the algorithms. We emphasize the importance to assess pipelines using gold standard materials to achieve the most reliable results for clinical application.

Implementation of exome sequencing in fetal diagnostics-Data and experiences from a tertiary center in Denmark

INTRODUCTION

Applying whole-exome sequencing (WES) for the diagnosis of diseases in children has shown significant diagnostic strength compared with chromosomal microarray. WES may also have the potential of adding clinically relevant prenatal information in cases where a fetus is found to have structural anomalies. We present results from the first fetal exomes performed in a tertiary center in Denmark.

MATERIAL AND METHODS

Couples/expectant parents were included in Central Denmark Region from July 2016 to March 2019. Inclusion was not systematic, but where one or more fetal malformations or severe fetal hydrops were detected, and a specific diagnosis had not been obtained by chromosomal microarray. WES was performed in ongoing pregnancies (N = 11), after intrauterine demise (N = 5), or after termination of pregnancy based on ultrasound findings (N = 19). In most cases, a trio format was applied comprising fetal and parental DNA.

RESULTS

WES was performed in 35 highly selected fetal cases. Pathogenic variants, or variants likely to explain the phenotype, were detected in 9/35 (26%). Variants of uncertain significance were detected in 7/35 (20%) and there was one secondary finding (3%). Out of the 11 ongoing pregnancies, four reached a genetic diagnosis (36%). Detection rate was highest in cases of multisystem anomalies (7/13, 54%). WES was completed in all three trimesters and both autosomal dominant, autosomal recessive and X-linked inheritance were revealed.

CONCLUSIONS

We present data from 35 cases of exome sequencing applied in a setting of fetal malformations. Importantly, though, we wish to share our personal experiences with implementing WES into a prenatal setting. As a medical society, we must continue to share what we do not understand, what went wrong, what is difficult, and what we do not agree upon. A common understanding and language are warranted. We also advocate that more research is needed concerning the clinical value, as well as costs and patient perspectives, of using WES in pregnancy. We believe that WES will lead to improved prenatal and perinatal care.

Rapid Phenotype-Driven Gene Sequencing with the NeoSeq Panel: A Diagnostic Tool for Critically Ill Newborns with Suspected Genetic Disease

New genomic sequencing techniques have shown considerable promise in the field of neonatology, increasing the diagnostic rate and reducing time to diagnosis. However, several obstacles have hindered the incorporation of this technology into routine clinical practice. We prospectively evaluated the diagnostic rate and diagnostic turnaround time achieved in newborns with suspected genetic diseases using a rapid phenotype-driven gene panel (NeoSeq) containing 1870 genes implicated in congenital malformations and neurological and metabolic disorders of early onset (<2 months of age). Of the 33 newborns recruited, a genomic diagnosis was established for 13 (39.4%) patients (median diagnostic turnaround time, 7.5 days), resulting in clinical management changes in 10 (76.9%) patients. An analysis of 12 previous prospective massive sequencing studies (whole genome (WGS), whole exome (WES), and clinical exome (CES) sequencing) in newborns admitted to neonatal intensive care units (NICUs) with suspected genetic disorders revealed a comparable median diagnostic rate (37.2%), but a higher median diagnostic turnaround time (22.3 days) than that obtained with NeoSeq. Our phenotype-driven gene panel, which is specific for genetic diseases in critically ill newborns is an affordable alternative to WGS and WES that offers comparable diagnostic efficacy, supporting its implementation as a first-tier genetic test in NICUs.

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.

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.

Identification of a de novo FOXP1 mutation and incidental discovery of inherited genetic variants contributing to a case of autism spectrum disorder and epilepsy

Background

Autism spectrum disorder is commonly co‐diagnosed intellectual disability, language disorder, anxiety, and epilepsy, however, symptom management is difficult due to the complex genetic nature of ASD.

Methods

We present a next‐generation sequencing‐based case study with both de novo and inherited genetic variants and highlight the impact of structural variants on post‐translational regulation of protein expression. Since management of symptoms has classically been through pharmaceutical therapies, a pharmacogenomics screen was also utilized to determine possible drug/gene interactions.

Results

A de novo variant was identified within the FOXP1 3′ untranslated regulatory region using exome sequencing. Additionally, inherited variants that likely contribute to the current and potential future traits were identified within the COMT, SLC6A4, CYP2C19, and CYP2D6 genes.

Conclusion

This study aims to elucidate how a collection of variant genotypes could potentially impact neural development resulting in a unique phenotype including ASD and epilepsy. Each gene's contribution to neural development is assessed, and the interplay of these genotypes is discussed. The results highlight the utility of exome sequencing in conjunction with pharmacogenomics screening when evaluating possible causes of and therapeutic treatments for ASD‐related symptoms.

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.