Trajectory of exonic variant discovery in a large clinical population: implications for variant curation

Development of a Polygenic Risk Score to Predict Diverticulitis

BACKGROUND

Despite its prevalence and associated morbidity, we remain limited in our ability to predict the course of a patient with diverticular disease. Although several clinical and genetic risk factors have been identified, we do not know how these factors relate to one another.

OBJECTIVE

Our aim was to determine whether a polygenic risk score could improve risk prediction for diverticulitis and recurrent diverticulitis compared with a model using only clinical factors.

DESIGN

This is an observational study.

SETTING

The study examines the predictive ability of a polygenic risk score for diverticulitis developed using prior genome-wide association studies and validated using the MyCode biobank.

PATIENTS

This study included patients of European ancestry in the Geisinger Health System who were enrolled in the MyCode Community Health biobanking program.

MAIN OUTCOME MEASURES

The ability of a polygenic risk score to predict diverticulosis, diverticulitis, and recurrent diverticulitis was the main outcome measure of this study.

RESULTS

A total of 60,861 patients were included, of whom 9912 (16.3%) had diverticulosis or diverticulitis (5015 with diverticulosis and 4897 with diverticulitis). When divided into deciles, our polygenic risk score stratified patients by risk of both diverticulosis and diverticulitis with a 2-fold difference in disease risk between the highest and lowest deciles for diverticulitis and a 4.8-fold difference for recurrent complicated diverticulitis. When compared with clinical factors alone, our polygenic risk score was able to improve risk prediction of recurrent diverticulitis.

LIMITATIONS

Our population is largely located in a single geographic region and were classified by disease status, using international classification of diseases codes.

CONCLUSIONS

This predictive model stratifies patients based on genetic risk for diverticular disease. The increased frequency of recurrent disease in our high-risk patients suggests that a polygenic risk score, in addition to other factors, may help guide the discussion regarding surgical intervention. See Video Abstract .

DESARROLLO DE UNA PUNTUACIN DE RIESGO POLIGNICO PARA PREDECIR LA DIVERTICULITIS

ANTECEDENTES:A pesar de su prevalencia y morbilidad asociada, nuestra capacidad para predecir el curso en un paciente con enfermedad diverticular sigue siendo limitada. Si bien se han identificado varios factores de riesgo clínicos y genéticos, no sabemos cómo se relacionan estos factores entre sí.OBJETIVO:Determinar si una puntuación de riesgo poligénico podría mejorar la predicción del riesgo de diverticulitis y diverticulitis recurrente en comparación con un modelo que utiliza solo factores clínicos.DISEÑO:Un estudio observacional que examina la capacidad predictiva de una puntuación de riesgo poligénico para la diverticulitis desarrollada usando estudios previos de asociación amplia del genoma y validada usando el biobanco MyCode.ÁMBITOS Y PACIENTES:Pacientes de ascendencia europea en el Sistema de Salud Geisinger que estaban inscritos en el programa de biobancos MyCode Community Health.PRINCIPALES MEDIDAS DE VALORACIÓN:La capacidad de una puntuación de riesgo poligénico para predecir diverticulosis, diverticulitis y diverticulitis recurrente.RESULTADOS:Se incluyeron un total de 60.861 pacientes, de los cuales 9.912 (16,3%) presentaban diverticulosis o diverticulitis (5.015 con diverticulosis y 4.897 con diverticulitis). Cuando se dividió en deciles, nuestra puntuación de riesgo poligénico estratificó a los pacientes según el riesgo de diverticulosis y diverticulitis con una diferencia de 2 veces en el riesgo de enfermedad entre los deciles más alto y más bajo para diverticulitis y una diferencia de 4,8 veces para diverticulitis complicada recurrente. En comparación con los factores clínicos solos, nuestra puntuación de riesgo poligénico pudo mejorar la predicción del riesgo de diverticulitis recurrente.LIMITACIONES:Nuestra población se encuentra en gran parte en una sola región geográfica y se clasificó por estado de enfermedad utilizando códigos de clasificación internacional de enfermedades.CONCLUSIONES:Este modelo predictivo estratifica a los pacientes en función del riesgo genético de enfermedad diverticular. La mayor frecuencia de enfermedad recurrente en nuestros pacientes de alto riesgo sugiere que un puntaje de riesgo poligénico, además de otros factores, puede ayudar a guiar la discusión sobre la intervención quirúrgica. (Traducción- Dr. Ingrid Melo ).

Reduced penetrance of MODY-associated HNF1A/HNF4A variants but not GCK variants in clinically unselected cohorts

The true prevalence and penetrance of monogenic disease variants are often not known because of clinical-referral ascertainment bias. We comprehensively assess the penetrance and prevalence of pathogenic variants in HNF1A, HNF4A, and GCK that account for >80% of monogenic diabetes. We analyzed clinical and genetic data from 1,742 clinically referred probands, 2,194 family members, clinically unselected individuals from a US health system-based cohort (n = 132,194), and a UK population-based cohort (n = 198,748). We show that one in 1,500 individuals harbor a pathogenic variant in one of these genes. The penetrance of diabetes for HNF1A and HNF4A pathogenic variants was substantially lower in the clinically unselected individuals compared to clinically referred probands and was dependent on the setting (32% in the population, 49% in the health system cohort, 86% in a family member, and 98% in probands for HNF1A). The relative risk of diabetes was similar across the clinically unselected cohorts highlighting the role of environment/other genetic factors. Surprisingly, the penetrance of pathogenic GCK variants was similar across all cohorts (89%-97%). We highlight that pathogenic variants in HNF1A, HNF4A, and GCK are not ultra-rare in the population. For HNF1A and HNF4A, we need to tailor genetic interpretation and counseling based on the setting in which a pathogenic monogenic variant was identified. GCK is an exception with near-complete penetrance in all settings. This along with the clinical implication of diagnosis makes it an excellent candidate for the American College of Medical Genetics secondary gene list.

Framework From a Multidisciplinary Approach for Transitioning Variants of Unknown Significance From Clinical Genetic Testing in Kidney Disease to a Definitive Classification

Introduction

Monogenic causes in over 300 kidney-associated genes account for approximately 12% of end stage kidney disease (ESKD) cases. Advances in sequencing and large customized panels enable the noninvasive diagnosis of monogenic kidney disease at relatively low cost, thereby allowing for more precise management for patients and their families. A major challenge is interpreting rare variants, many of which are classified as variants of unknown significance (VUS). We present a framework in which we thoroughly evaluated and provided evidence of pathogenicity for HNF1B-p.Arg303His, a VUS returned from clinical diagnostic testing for a kidney transplant candidate.

Methods

A blueprint was designed by a multidisciplinary team of clinicians, molecular biologists, and diagnostic geneticists. The blueprint included using a health system-based cohort with genetic and clinical information to perform deep phenotyping of VUS heterozygotes to identify the candidate VUS and rule out other VUS, examination of existing genetic databases, as well as functional testing.

Results

Our approach demonstrated evidence for pathogenicity for HNF1B-p.Arg303His by showing similar burden of kidney manifestations in this variant to known HNF1B pathogenic variants, and greater burden compared to noncarriers.

Conclusion

Determination of a molecular diagnosis for the example family allows for proper surveillance and management of HNF1B-related manifestations such as kidney disease, diabetes, and hypomagnesemia with important implications for safe living-related kidney donation. The candidate gene-variant pair also allows for clinical biomarker testing for aberrations of linked pathways. This working model may be applicable to other diseases of genetic etiology.

Leveraging population-based exome screening to impact clinical care: The evolution of variant assessment in the Geisinger MyCode research project

Exome and genome sequencing are increasingly utilized in research studies and clinical care and can provide clinically relevant information beyond the initial intent for sequencing, including medically actionable secondary findings. Despite ongoing debate about sharing this information with patients and participants, a growing number of clinical laboratories and research programs routinely report secondary findings that increase the risk for selected diseases. Recently, there has been a push to maximize the potential benefit of this practice by implementing proactive genomic screening at the population level irrespective of medical history, but the feasibility of deploying population-scale proactive genomic screening requires scaling key elements of the genomic data evaluation process. Herein, we describe the motivation, development, and implementation of a population-scale variant-first screening pipeline combining bioinformatics-based filtering with a manual review process to screen for clinically relevant findings in research exomes generated through the DiscovEHR collaboration within Geisinger's MyCode® research project. Consistent with other studies, this pipeline yields a screen-positive detection rate between 2.1 and 2.6% (depending on inclusion of those with prior indication-based testing) in 130,048 adult MyCode patient-participants screened for clinically relevant findings in 60 genes. Our variant-first pipeline affords cost and time savings by filtering out negative cases, thereby avoiding analysis of each exome one-by-one, as typically employed in the diagnostic setting. While research is still needed to fully appreciate the benefits of population genomic screening, MyCode provides the first demonstration of a program at scale to help shape how population genomic screening is integrated into routine clinical care.

A Genome-First Approach to Characterize DICER1 Pathogenic Variant Prevalence, Penetrance, and Phenotype

IMPORTANCE

Genetic disorders are historically defined through phenotype-first approaches. However, risk estimates derived from phenotype-linked ascertainment may overestimate severity and penetrance. Pathogenic variants in DICER1 are associated with increased risks of rare and common neoplasms and thyroid disease in adults and children. This study explored how effectively a genome-first approach could characterize the clinical traits associated with germline DICER1 putative loss-of-function (pLOF) variants in an unselected clinical cohort.

OBJECTIVE

To examine the prevalence, penetrance, and phenotypic characteristics of carriers of germline DICER1 pLOF variants via genome-first ascertainment.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study classifies DICER1 variants in germline exome sequence data from 92 296 participants of the Geisinger MyCode Community Health Initiative. Data for each MyCode participant were used from the start of the Geisinger electronic health record to February 1, 2018.

MAIN OUTCOMES AND MEASURES

Prevalence of germline DICER1 variation; penetrance of malignant tumors and thyroid disease in carriers of germline DICER1 variation; structured, manual review of electronic health records; and DICER1 sequencing of available tumors from an associated cancer registry.

RESULTS

A total of 92 296 adults (mean [SD] age, 59 [18] years; 98% white; 60% female) participated in the study. Germline DICER1 pLOF variants were observed in 1 in 3700 to 1 in 4600 participants, more than double the expected prevalence. Malignant tumors (primarily thyroid carcinoma) were observed in 4 of 25 participants (16%) with DICER1 pLOF variants, which is comparable (by 50 years of age) to the frequency of neoplasms in the largest registry- and clinic-based (phenotype-first) DICER1 studies published to date. DICER1 pLOF variants were significantly associated with risks of thyroidectomy (odds ratio [OR], 6.0; 95% CI, 2.2-16.3; P = .007) and thyroid cancer (OR, 9.2; 95% CI, 2.1-34.7; P = .02) compared with controls, but there was not a significant increase in the risk of goiter (OR, 1.8; 95% CI, 0.7-4.9). A female patient in her 80s who was a carrier of a germline DICER1 hotspot variant was apparently healthy on electronic health record review. The term DICER1 did not appear in any of the medical records of the 25 participants with a pLOF DICER1 variant, even in those affected with a known DICER1-associated tumor or thyroid phenotype.

CONCLUSIONS AND RELEVANCE

This cohort study was able to ascertain individuals with germline DICER1 variants based on a genome-first approach rather than through a previously established DICER1-related phenotype. Use of the genome-first approach may complement more traditional approaches to syndrome delineation and may be an efficient approach for risk estimation.

Bosch-Boonstra-Schaaf optic atrophy syndrome (BBSOAS) initially diagnosed as ALG6-CDG: Functional evidence for benignity of the ALG6 c.391T>C (p.Tyr131His) variant and further expanding the BBSOAS phenotype

Bosch-Boonstra-Schaaf optic atrophy syndrome (BBSOAS) is a recently described autosomal dominant syndrome of developmental delay, cortical vision loss with optic nerve atrophy, epilepsy, and autism spectrum disorder. Due to its many overlapping features with congenital disorders of glycosylation (CDG), the differential diagnosis between these disorders may be difficult and relies on molecular genetic testing. We report on a 31-year-old female initially diagnosed with ALG6-CDG based on glycosylation abnormalities on transferrin isoelectrofocusing and targeted genetic testing, and later diagnosed with BBSOAS by whole-exome sequencing (WES). Functional studies on cultured fibroblasts including Western blotting and RT-qPCR, as well as mass spectrometry of glycosylated transferrin and MALDI-TOF glycan analysis in serum, demonstrated normal glycosylation in this patient. In this report, we extend the phenotype of BBSOAS with ataxia and protein-losing enteropathy. This case is illustrative of the utility of whole exome sequencing in the diagnostic odyssey, and the potential pitfalls of relying on focused genetic testing results for diagnosis of conditions with complex overlapping phenotypes.

Predictive value of genomic screening: cross-sectional study of cystic fibrosis in 50,788 electronic health records

Doubts have been raised about the value of DNA-based screening for low-prevalence monogenic conditions following reports of testing this approach using available electronic health record (EHR) as the sole phenotyping source. We hypothesized that a better model for EHR-focused examination of DNA-based screening is Cystic Fibrosis (CF) since the diagnosis is proactively sought within the healthcare system. We reviewed CFTR variants in 50,778 exomes. In 24 cases with bi-allelic pathogenic CFTR variants, there were 21 true-positives. We considered three cases "potential" false-positives due to limitations in available EHR phenotype data. This genomic screening exhibited a positive predictive value of 87.5%, negative predictive value of 99.9%, sensitivity of 95.5%, and a specificity of 99.9%. Despite EHR-based phenotyping limitations in three cases, the presence or absence of pathogenic CFTR variants has strong predictive value for CF diagnosis when EHR data is used as the sole phenotyping source. Accurate ascertainment of the predictive value of DNA-based screening requires condition-specific phenotyping beyond available EHR data.

A data-driven evaluation of the size and content of expanded carrier screening panels

PURPOSE

The American College of Obstetricians and Gynecologists (ACOG) proposed seven criteria for expanded carrier screening (ECS) panel design. To ensure that screening for a condition is sufficiently sensitive to identify carriers and reduce residual risk of noncarriers, one criterion requires a per-condition carrier rate greater than 1 in 100. However, it is unestablished whether this threshold corresponds with a loss in clinical detection. The impact of the proposed panel design criteria on at-risk couple detection warrants data-driven evaluation.

METHODS

Carrier rates and at-risk couple rates were calculated in 56,281 patients who underwent a 176-condition ECS and were evaluated for panels satisfying various criteria. Condition-specific clinical detection rates were estimated via simulation.

RESULTS

Different interpretations of the 1-in-100 criterion have variable impact: a compliant panel would include between 3 and 38 conditions, identify 11-81% fewer at-risk couples, and detect 36-79% fewer carriers than a 176-condition panel. If the carrier rate threshold must be exceeded in all ethnicities, ECS panels would lack prevalent conditions like cystic fibrosis. Simulations suggest that the clinical detection rate remains >84% for conditions with carrier rates as low as 1 in 1000.

CONCLUSION

The 1-in-100 criterion limits at-risk couple detection and should be reconsidered.

Future directions for high-throughput splicing assays in precision medicine

Classification of variants of unknown significance is a challenging technical problem in clinical genetics. As up to one-third of disease-causing mutations are thought to affect pre-mRNA splicing, it is important to accurately classify splicing mutations in patient sequencing data. Several consortia and healthcare systems have conducted large-scale patient sequencing studies, which discover novel variants faster than they can be classified. Here, we compare the advantages and limitations of several high-throughput splicing assays aimed at mitigating this bottleneck, and describe a data set of ~5,000 variants that we analyzed using our Massively Parallel Splicing Assay (MaPSy). The Critical Assessment of Genome Interpretation group (CAGI) organized a challenge, in which participants submitted machine learning models to predict the splicing effects of variants in this data set. We discuss the winning submission of the challenge (MMSplice) which outperformed existing software. Finally, we highlight methods to overcome the limitations of MaPSy and similar assays, such as tissue-specific splicing, the effect of surrounding sequence context, classifying intronic variants, synthesizing large exons, and amplifying complex libraries of minigene species. Further development of these assays will greatly benefit the field of clinical genetics, which lack high-throughput methods for variant interpretation.

Exome-Wide Rare Variant Analysis From the DiscovEHR Study Identifies Novel Candidate Predisposition Genes for Endometrial Cancer

Endometrial cancer is the fourth most commonly diagnosed cancer in women. Family history is a known risk factor for endometrial cancer. The incidence of endometrial cancer in a first-degree relative elevates the relative risk to range between 1.3 and 2.8. It is unclear to what extent or what other novel germline variants are at play in endometrial cancer. We aim to address this question by utilizing whole exome sequencing as a means to identify novel, rare variant associations between exonic regions and endometrial cancer. The MyCode community health initiative is an excellent resource for this study with germline whole exome data for 60,000 patients available in the first phase, and further 30,000 patients independently sequenced in the second phase as part of DiscovEHR study. We conducted exome-wide rare variant association using 472 cases and 4,110 controls in 60,000 patients (discovery cohort); and 261 cases and 1,531 controls from 30,000 patients (replication cohort). After binning rare germline variants into genes, case-control association tests performed using Optimal Unified Approach for Rare-Variant Association, SKAT-O. Seven genes, including RBM12, NDUFB6, ATP6V1A, RECK, SLC35E1, RFX3 (Bonferroni-corrected P < 0.05) and ATP8A1 (suggestive P < 10⁻⁵), and one long non-coding RNA, DLGAP4-AS1 (Bonferroni-corrected P < 0.05), were associated with endometrial cancer. Notably, RECK, and ATP8A1 were replicated from the replication cohort (suggestive threshold P < 0.05). Additionally, a pathway-based rare variant analysis, using pathogenic and likely pathogenic variants, identified two significant pathways, pyrimidine metabolism and protein processing in the endoplasmic reticulum (Bonferroni-corrected P < 0.05). In conclusion, our results using the single-source electronic health records (EHR) linked to genomic data highlights candidate genes and pathways associated with endometrial cancer and indicates rare variants involvement in endometrial cancer predisposition, which could help in personalized prognosis and also further our understanding of its genetic etiology.