A genome sequencing system for universal newborn screening, diagnosis, and precision medicine for severe genetic diseases.

Newborn screening (NBS) dramatically improves outcomes in severe childhood disorders by treatment before symptom onset. In many genetic diseases, however, outcomes remain poor because NBS has lagged behind drug development. Rapid whole-genome sequencing (rWGS) is attractive for comprehensive NBS because it concomitantly examines almost all genetic diseases and is gaining acceptance for genetic disease diagnosis in ill newborns. We describe prototypic methods for scalable, parentally consented, feedback-informed NBS and diagnosis of genetic diseases by rWGS and virtual, acute management guidance (NBS-rWGS). Using established criteria and the Delphi method, we reviewed 457 genetic diseases for NBS-rWGS, retaining 388 (85%) with effective treatments. Simulated NBS-rWGS in 454,707 UK Biobank subjects with 29,865 pathogenic or likely pathogenic variants associated with 388 disorders had a true negative rate (specificity) of 99.7% following root cause analysis. In 2,208 critically ill children with suspected genetic disorders and 2,168 of their parents, simulated NBS-rWGS for 388 disorders identified 104 (87%) of 119 diagnoses previously made by rWGS and 15 findings not previously reported (NBS-rWGS negative predictive value 99.6%, true positive rate [sensitivity] 88.8%). Retrospective NBS-rWGS diagnosed 15 children with disorders that had been undetected by conventional NBS. In 43 of the 104 children, had NBS-rWGS-based interventions been started on day of life 5, the Delphi consensus was that symptoms could have been avoided completely in seven critically ill children, mostly in 21, and partially in 13. We invite groups worldwide to refine these NBS-rWGS conditions and join us to prospectively examine clinical utility and cost effectiveness.

Functional characterization of 105 factor H variants associated with aHUS: lessons for variant classification.

Atypical hemolytic uremic syndrome (aHUS) is a life-threatening thrombotic microangiopathy that can progress, when untreated, to end-stage renal disease. Most frequently, aHUS is caused by complement dysregulation due to pathogenic variants in genes that encode complement components and regulators. Among these genes, the factor H (FH) gene, CFH, presents with the highest frequency (15% to 20%) of variants and is associated with the poorest prognosis. Correct classification of CFH variants as pathogenic or benign is essential to clinical care but remains challenging owing to the dearth of functional studies. As a result, significant numbers of variants are reported as variants of uncertain significance. To address this knowledge gap, we expressed and functionally characterized 105 aHUS-associated FH variants. All FH variants were categorized as pathogenic or benign and, for each, we fully documented the nature of the pathogenicity. Twenty-six previously characterized FH variants were used as controls to validate and confirm the robustness of the functional assays used. Of the remaining 79 uncharacterized variants, only 29 (36.7%) alter FH expression or function in vitro and, therefore, are proposed to be pathogenic. We show that rarity in control databases is not informative for variant classification, and we identify important limitations in applying prediction algorithms to FH variants. Based on structural and functional data, we suggest ways to circumvent these difficulties and, thereby, improve variant classification. Our work highlights the need for functional assays to interpret FH variants accurately if clinical care of patients with aHUS is to be individualized and optimized.

Investigation of ALPL variant states and clinical outcomes: An analysis of adults and adolescents with hypophosphatasia treated with asfotase alfa.

BACKGROUND: Hypophosphatasia (HPP), a rare metabolic disease, can be inherited in an autosomal recessive (biallelic) or an autosomal dominant (monoallelic) manner. Most of the severe, early-onset, frequently lethal HPP in infants is acquired through recessive inheritance; less severe, later-onset, typically nonlethal HPP phenotypes are acquired through either dominant or recessive inheritance. HPP's variable clinical presentation arises from >400 identified ALPL pathogenic variants with likely variable penetrance, especially with autosomal dominant inheritance. This post hoc analysis investigated the relationship between ALPL variant state (biallelic and monoallelic) and clinical outcomes with asfotase alfa in HPP. METHODS: Data were pooled from two phase 2, randomized, open-label studies in adolescents and adults with HPP; one study evaluated the efficacy and safety of different doses of asfotase alfa (n = 25), and the other assessed the pharmacodynamics and safety of asfotase alfa (n = 19). Patients were grouped by ALPL variant state (biallelic or monoallelic). Available data from both studies included ALPL pathogenic variant state, Baseline characteristics, HPP-specific medical history, and Baseline TNSALP substrate levels (inorganic pyrophosphate [PPi] and pyridoxal 5'-phosphate [PLP]) concentrations). Clinical outcomes over 5 years of treatment were available from only the efficacy and safety study. RESULTS: In total, 44 patients with known variant status were included in the pooled analysis (biallelic, n = 30; monoallelic, n = 14). The most common pathogenic variant was c.571G > A (p.Glu191Lys) in biallelic patients (allele frequency: 19/60) and c.1133A > T (p.Asp378Val) in monoallelic patients (allele frequency: 7/28). Median (min, max) Baseline PPi concentrations were significantly higher in patients with a biallelic vs monoallelic variant state (5.3 [2.2, 12.1] vs 4.3 [3.5, 7.4] µM; P = 0.0113), as were Baseline PLP concentrations (221.4 [62.4, 1590.0] vs 75.1 [28.8, 577.0] ng/mL; P= 0.0022). HPP-specific medical history was generally similar between biallelic and monoallelic patients in terms of incidence and type of manifestations; notable exceptions included fractures, which were more common among monoallelic patients, and delayed walking and bone deformities such as abnormally shaped chest and head and bowing of arms or legs, which were more common among biallelic patients. Data from the efficacy and safety study (n = 19) showed that median PPi and PLP concentrations were normalized over 5 years of treatment in patients with both variant states. Median % predicted distance walked on the 6-Minute Walk Test remained within the normal range for monoallelic patients over 4 years of treatment, and improved from below normal (<84%) to normal in biallelic patients. CONCLUSIONS: Although patients with biallelic variants had significantly higher Baseline PPi and PLP levels than monoallelic variants, both groups generally showed similar pretreatment Baseline clinical characteristics. Treatment with asfotase alfa for up to 5 years normalized TNSALP substrate concentrations and improved functional outcomes, with no clear differences between biallelic and monoallelic variant states. This study suggests that patients with HPP have significant disease burden, regardless of ALPL variant state.

Large-scale in vitro functional testing and novel variant scoring via protein modeling provide insights into alkaline phosphatase activity in hypophosphatasia.

Hypophosphatasia (HPP) is a rare metabolic disorder characterized by low tissue-nonspecific alkaline phosphatase (TNSALP) typically caused by ALPL gene mutations. HPP is heterogeneous, with clinical presentation correlating with residual TNSALP activity and/or dominant-negative effects (DNE). We measured residual activity and DNE for 155 ALPL variants by transient transfection and TNSALP enzymatic activity measurement. Ninety variants showed low residual activity and 24 showed DNE. These results encompass all missense variants with carrier frequencies above 1/25,000 from the Genome Aggregation Database. We used resulting data as a reference to develop a new computational algorithm that scores ALPL missense variants and predicts high/low TNSALP enzymatic activity. Our approach measures the effects of amino acid changes on TNSALP dimer stability with a physics-based implicit solvent energy model. We predict mutation deleteriousness with high specificity, achieving a true-positive rate of 0.63 with false-positive rate of 0, with an area under receiver operating curve (AUC) of 0.9, better than all in silico predictors tested. Combining this algorithm with other in silico approaches can further increase performance, reaching an AUC of 0.94. This study expands our understanding of HPP heterogeneity and genotype/phenotype relationships with the aim of improving clinical ALPL variant interpretation.

Large-scale functional LIPA variant characterization to improve birth prevalence estimates of lysosomal acid lipase deficiency.

Lysosomal acid lipase (LAL) deficiency is an autosomal recessive disorder caused by LIPA gene mutations that disrupt LAL activity. We performed in vitro functional testing of 149 LIPA variants to increase the understanding of the variant effects on LAL deficiency and to improve disease prevalence estimates. Chosen variants had been reported in literature or population databases. Functional testing was done by plasmid transient transfection and LAL activity assessment. We assembled a set of 165 published LAL deficient patient genotypes to evaluate this assay's effectiveness to recapitulate genotype/phenotype relationships. Rapidly progressive LAL deficient patients showed negligible enzymatic activity (<1%), whereas patients with childhood/adult LAL deficiency typically have 1-7% average activity. We benchmarked six in silico variant effect prediction algorithms with these functional data. PolyPhen-2 was shown to have a superior area under the receiver operating curve performance. We used functional data along with Genome Aggregation Database (gnomAD) allele frequencies to estimate LAL deficiency birth prevalence, yielding a range of 3.45-5.97 cases per million births in European-ancestry populations. The low estimate only considers functionally assayed variants in gnomAD. The high estimate computes allele frequencies for variants absent in gnomAD, and uses in silico scores for unassayed variants. Prevalence estimates are lower than previously published, underscoring LAL deficiency's rarity.

The Global ALPL gene variant classification project: Dedicated to deciphering variants.

BACKGROUND: Hypophosphatasia (HPP) is an inherited multisystem disorder predominantly affecting the mineralization of bones and teeth. HPP is caused by pathogenic variants in ALPL, which encodes tissue non-specific alkaline phosphatase (TNSALP). Variants of uncertain significance (VUS) cause diagnostic delay and uncertainty amongst patients and health care providers. RESULTS: The ALPL gene variant database (https://alplmutationdatabase.jku.at/) is an open-access archive for interpretation of the clinical significance of variants reported in ALPL. The database contains coding and non-coding variants, including single nucleotide variants, insertions/deletions and structural variants affecting coding or non-coding sequences of ALPL. Each variant in the database is displayed with details explaining the corresponding pathogenicity, and all reported genotypes and phenotypes, including references. In 2021, the ALPL gene variant classification project was established to reclassify VUS and continuously assess and update genetic, phenotypic, and functional variant information in the database. For this purpose, the database provides a unique submission system for clinicians, geneticists, genetic counselors, and researchers to submit VUS within ALPL for classification. An international, multidisciplinary consortium of HPP experts has been established to reclassify the submitted VUS using a multi-step process adhering to the stringent ACMG/AMP variant classification guidelines. These steps include a clinical phenotype assessment, deep literature research including artificial intelligence technology, molecular genetic assessment, and in-vitro functional testing of variants in a co-transfection model to measure ALP residual activity. CONCLUSION: This classification project and the ALPL gene variant database will serve the global medical community, widen the genotypic and phenotypic HPP spectrum by reporting and characterizing new ALPL variants based on ACMG/AMP criteria and thus facilitate improved genetic counseling and medical decision-making for affected patients and families. The project may also serve as a gold standard framework for multidisciplinary collaboration for variant interpretation in other rare diseases.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA.

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function. Thus, the ability to distinguish these two classes of missense mutations would greatly aid drug discovery efforts in target identification and validation as well as medical diagnosis. Monitoring the co-occurrence of a given missense mutation and a disease phenotype provides a pathway for classifying functionally disrupting missense mutations. But, the occurrence of a specific missense variant is often extremely rare making statistical links challenging to infer. In this study, we benchmark a physics-based approach for predicting changes in stability, MM-GBSA, and apply it to classifying mutations as functionally disrupting. A large and diverse dataset of 990 residue mutations in beta-lactamase TEM1 is used to assess performance as it is rich in both functionally disrupting mutations and functionally neutral/beneficial mutations. On this dataset, we compare the performance of MM-GBSA to alternative strategies for predicting functionally disrupting mutations. We observe that the MM-GBSA method obtains an area under the curve (AUC) of 0.75 on the entire dataset, outperforming all other predictors tested. More importantly, MM-GBSA's performance is robust to various divisions of the dataset, speaking to the generality of the approach. Though there is one notable exception: Mutations on the surface of the protein are the mutations that are the most difficult to classify as functionally disrupting for all methods tested. This is likely due to the many mechanisms available to surface mutations to disrupt function, and thus provides a direction of focus for future studies.

From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline.

This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK.

A framework for variation discovery and genotyping using next-generation DNA sequencing data.

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.