scholarly journals Genetic Diagnosis Spectrum and Multigenic Burden of Exome-Level Rare Variants in a Childhood Epilepsy Cohort

2021 ◽  
Vol 12 ◽  
Author(s):  
Ruen Yao ◽  
Yunqing Zhou ◽  
Jie Tang ◽  
Niu Li ◽  
Tingting Yu ◽  
...  

Childhood epilepsy is a considerably heterogeneous neurological condition with a high worldwide incidence. Genetic diagnosis of childhood epilepsy provides the most accurate pathogenetic evidence; however, a large proportion of highly suspected cases remain undiagnosed. Accumulation of rare variants at the exome level as a multigenic burden contributing to childhood epilepsy should be further evaluated. In this retrospective analysis, exome-level sequencing was used to depict the mutation spectra of 294 childhood epilepsy patients from Shanghai Children’s Medical Center, Department of Neurology. Furthermore, variant information from exome sequencing data was analyzed apart from monogenic diagnostic purposes to elucidate the possible multigenic burden of rare variants related to epilepsy pathogenesis. Exome sequencing reached a diagnostic rate of 30.61% and identified six genes not currently listed in the epilepsy-associated gene list. A multigenic burden study revealed a three-fold possibility that deleterious missense mutations in ion channel and synaptic genes in the undiagnosed cohort may contribute to the genetic risk of childhood epilepsy, whereas variants in the gene categories of cell growth, metabolic, and regulatory function showed no significant difference. Our study provides a comprehensive overview of the genetic diagnosis of a Chinese childhood epilepsy cohort and provides novel insights into the genetic background of these patients. Harmful missense mutations in genes related to ion channels and synapses are most likely to produce a multigenic burden in childhood epilepsy.

2021 ◽  
Vol 22 (11) ◽  
pp. 5594
Author(s):  
Ting-Yi Lin ◽  
Yun-Chia Chang ◽  
Yu-Jer Hsiao ◽  
Yueh Chien ◽  
Ying-Chun Jheng ◽  
...  

Inherited retinal dystrophies (IRDs) are rare but highly heterogeneous genetic disorders that affect individuals and families worldwide. However, given its wide variability, its analysis of the driver genes for over 50% of the cases remains unexplored. The present study aims to identify novel driver genes, disease-causing variants, and retinitis pigmentosa (RP)-associated pathways. Using family-based whole-exome sequencing (WES) to identify putative RP-causing rare variants, we identified a total of five potentially pathogenic variants located in genes OR56A5, OR52L1, CTSD, PRF1, KBTBD13, and ATP2B4. Of the variants present in all affected individuals, genes OR56A5, OR52L1, CTSD, KBTBD13, and ATP2B4 present as missense mutations, while PRF1 and CTSD present as frameshift variants. Sanger sequencing confirmed the presence of the novel pathogenic variant PRF1 (c.124_128del) that has not been reported previously. More causal-effect or evidence-based studies will be required to elucidate the precise roles of these SNPs in the RP pathogenesis. Taken together, our findings may allow us to explore the risk variants based on the sequencing data and upgrade the existing variant annotation database in Taiwan. It may help detect specific eye diseases such as retinitis pigmentosa in East Asia.


2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Anna Ka-Yee Kwong ◽  
Mandy Ho-Yin Tsang ◽  
Jasmine Lee-Fong Fung ◽  
Christopher Chun-Yu Mak ◽  
Kate Lok-San Chan ◽  
...  

Abstract Background Movement disorders are a group of heterogeneous neurological diseases including hyperkinetic disorders with unwanted excess movements and hypokinetic disorders with reduction in the degree of movements. The objective of our study is to investigate the genetic etiology of a cohort of paediatric patients with movement disorders by whole exome sequencing and to review the potential treatment implications after a genetic diagnosis. Results We studied a cohort of 31 patients who have paediatric-onset movement disorders with unrevealing etiologies. Whole exome sequencing was performed and rare variants were interrogated for pathogenicity. Genetic diagnoses have been confirmed in 10 patients with disease-causing variants in CTNNB1, SPAST, ATP1A3, PURA, SLC2A1, KMT2B, ACTB, GNAO1 and SPG11. 80% (8/10) of patients with genetic diagnosis have potential treatment implications and treatments have been offered to them. One patient with KMT2B dystonia showed clinical improvement with decrease in dystonia after receiving globus pallidus interna deep brain stimulation. Conclusions A diagnostic yield of 32% (10/31) was reported in our cohort and this allows a better prediction of prognosis and contributes to a more effective clinical management. The study highlights the potential of implementing precision medicine in the patients.


2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
Floranne Boulogne ◽  
Laura Claus ◽  
Henry Wiersma ◽  
Roy Oelen ◽  
Floor Schukking ◽  
...  

Abstract Background and Aims Genetic testing in patients with suspected hereditary kidney disease does not always reveal the genetic cause for the patient's disorder. Potentially pathogenic variants can reside in genes that are not known to be involved in kidney disease, which makes it difficult to prioritize and interpret the relevance of these variants. As such, there is a clear need for methods that predict the phenotypic consequences of gene expression in a way that is as unbiased as possible. To help identify candidate genes we have developed KidneyNetwork, in which tissue-specific expression is utilized to predict kidney-specific gene functions. Method We combined gene co-expression in 878 publicly available kidney RNA-sequencing samples with the co-expression of a multi-tissue RNA-sequencing dataset of 31,499 samples to build KidneyNetwork. The expression patterns were used to predict which genes have a kidney-related function, and which (disease) phenotypes might be caused when these genes are mutated. By integrating the information from the HPO database, in which known phenotypic consequences of disease genes are annotated, with the gene co-expression network we obtained prediction scores for each gene per HPO term. As proof of principle, we applied KidneyNetwork to prioritize variants in exome-sequencing data from 13 kidney disease patients without a genetic diagnosis. Results We assessed the prediction performance of KidneyNetwork by comparing it to GeneNetwork, a multi-tissue co-expression network we previously developed. In KidneyNetwork, we observe a significantly improved prediction accuracy of kidney-related HPO-terms, as well as an increase in the total number of significantly predicted kidney-related HPO-terms (figure 1). To examine its clinical utility, we applied KidneyNetwork to 13 patients with a suspected hereditary kidney disease without a genetic diagnosis. Based on the HPO terms “Renal cyst” and “Hepatic cysts”, combined with a list of potentially damaging variants in one of the undiagnosed patients with mild ADPKD/PCLD, we identified ALG6 as a new candidate gene. ALG6 bears a high resemblance to other genes implicated in this phenotype in recent years. Through the 100,000 Genomes Project and collaborators we identified three additional patients with kidney and/or liver cysts carrying a suspected deleterious variant in ALG6. Conclusion We present KidneyNetwork, a kidney specific co-expression network that accurately predicts what genes have kidney-specific functions and may result in kidney disease. Gene-phenotype associations of genes unknown for kidney-related phenotypes can be predicted by KidneyNetwork. We show the added value of KidneyNetwork by applying it to exome sequencing data of kidney disease patients without a molecular diagnosis and consequently we propose ALG6 as a promising candidate gene. KidneyNetwork can be applied to clinically unsolved kidney disease cases, but it can also be used by researchers to gain insight into individual genes to better understand kidney physiology and pathophysiology. Acknowledgments This research was made possible through access to the data and findings generated by the 100,000 Genomes Project; http://www.genomicsengland.co.uk.


2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Tiziana Vaisitti ◽  
Monica Sorbini ◽  
Martina Callegari ◽  
Silvia Kalantari ◽  
Valeria Bracciamà ◽  
...  

Abstract Background and Aims Autosomal dominant PKD determines formation of multiple cysts predominantly in the kidneys and usually becomes symptomatic during adulthood and can lead to renal failure. In contrast, in autosomal recessive PKD cysts occur in both the kidneys and the liver and usually presents an earlier onset. Obtaining genetic diagnosis is important to confirm clinical diagnosis and is required before treating with vasopressin 2 receptor blockers, which are the only drugs known to slow down the disease. Furthermore, in the case of kidney transplant from a living family member it is essential to exclude the presence of the mutation in the donor. We used clinical exome sequencing to provide genetic diagnosis to a cohort of patients with a clinical suspicion of PKD. Method 175 patients were referred to the Immunogenetics and Transplant Biology Service of the Turin University Hospital through a network of nephrology centers operating in the Piedmont region. Some patients were referred following genetic counseling. All patients signed an informed consent and the referring physicians provided relevant clinical data. DNA from eligible patients was extracted, checked for integrity, quantified and used for library preparation. A clinical exome sequencing (CES) kit by Illumina was used, allowing the analysis of 6,700 clinically relevant genes. Results Out of the 175 recruited patients eligible for CES, 38 (21.7%) had a clinical suspicion or diagnosis of PKD, with 50% of them presenting family history. The majority of the cohort was represented by male subjects (60.5%) and included both children (34.2%) and adults. The analytical approach was based on initial analysis of genes responsible for PKD (PKD1, PKD2 and PKHD1). If no mutation could be identified, analysis was then extended to a panel of 99 genes responsible for ciliopathies. This approach led to the identification of causative variants in 33/38 (86.8%) of the PKD cohort, while no variant could be identified in 5/38 patients. In 5/33 (15.2%) patients, mutations were inconclusive as found in heterozygosity in genes known to have an autosomal recessive mode of inheritance, while 27/33 (81.8%) were in line with the initial clinical suspicion/diagnosis. Of these, the majority was represented by missense mutations (12), followed by frameshift and nonsense mutations (6 each) and 3 splicing variants. As expected, the majority of mutations were found in PKD1 17/27 (63%), PKD2 3/27 (11.1%) and PKHD1 2/27 (7.4%). In these two latter patients, variants were found as compound heterozygosity. We also found mutations in other genes known to cause cysts, including TSC2 and CPT2. Of note, in 7 patients carrying PKD1 mutations, we found a second variant in PKD1 or PKHD1. Interestingly, when looking at patients characterized by kidney failure but lacking a clinical suspicion at recruitment or diagnosed with other phenotypes (66/175), we found variants in PKD1 and in PKD2 in 11 patients (9 and 2, respectively). Of all identified variants in PKD1, PKD2 and PKHD1 genes, 17.6% were annotated as pathogenic (C5), 41.2% were likely pathogenic (C4) and 41.2% were variants of unknown significance (C3). 19 variants in these genes were not previously reported. All the variants found in genes responsible for PKD were validated and confirmed by Sanger sequencing. Family segregation studies are ongoing. Finally, it is worth mentioning that in a portion of cases (5/38) with clinical and phenotypic features of PKD, supported also by a positive family history, we could not detect mutations in causative genes. These results may be explained by the presence of intronic variants, in line with data reported in literature. Conclusion These results demonstrate that CES may be applied to PKD patients to identify causative variants during their routine diagnostic flow. Furthermore, CES may be a useful tool to detect mutations in PKD-related genes in patients with undiagnosed diseases, considering its rapidly decreasing costs.


2020 ◽  
Author(s):  
Quanli Wang ◽  
Ryan S. Dhindsa ◽  
Keren Carss ◽  
Andrew R Harper ◽  
Abhishek Nag ◽  
...  

The UK Biobank (UKB) represents an unprecedented population-based study of 502,543 participants with detailed phenotypic data and linkage to medical records. While the release of genotyping array data for this cohort has bolstered genomic discovery for common variants, the contribution of rare variants to this broad phenotype collection remains relatively unknown. Here, we use exome sequencing data from 177,882 UKB participants to evaluate the association between rare protein-coding variants with 10,533 binary and 1,419 quantitative phenotypes. We performed both a variant-level phenome-wide association study (PheWAS) and a gene-level collapsing analysis-based PheWAS tailored to detecting the aggregate contribution of rare variants. The latter revealed 911 statistically significant gene-phenotype relationships, with a median odds ratio of 15.7 for binary traits. Among the binary trait associations identified using collapsing analysis, 83% were undetectable using single variant association tests, emphasizing the power of collapsing analysis to detect signal in the setting of high allelic heterogeneity. As a whole, these genotype-phenotype associations were significantly enriched for loss-of-function mediated traits and currently approved drug targets. Using these results, we summarise the contribution of rare variants to common diseases in the context of the UKB phenome and provide an example of how novel gene-phenotype associations can aid in therapeutic target prioritisation.


2020 ◽  
Author(s):  
Anna Ka-Yee Kwong ◽  
Mandy Ho-Yin Tsang ◽  
Jasmine Lee-Fong Fung ◽  
Christopher Chun-Yu Mak ◽  
Kate Lok-San Chan ◽  
...  

Abstract Background: Movement disorders are a group of heterogeneous neurological diseases including hyperkinetic disorders with unwanted excess movements and hypokinetic disorders with reduction in the degree of movements. The objective of our study is to investigate the genetic etiology of a cohort of paediatric patients with movement disorders by whole exome sequencing and to review the potential treatment implications after a genetic diagnosis. Results: We studied a cohort of 31 patients who have paediatric-onset movement disorders with unrevealing etiologies. Whole exome sequencing was performed and rare variants were interrogated for pathogenicity. Genetic diagnoses have been confirmed in 10 patients with disease-causing variants in CTNNB1, SPAST, ATP1A3, PURA, SLC2A1, KMT2B, ACTB, GNAO1 and SPG11. 80% (8/10) of patients with genetic diagnosis have potential targeted treatment implications and treatments have been offered to them. One patient with KMT2B dystonia showed clinical improvement with decrease in dystonia after receiving globus pallidus interna deep brain stimulation. Conclusion: A diagnostic yield of 32% (10/31) was reported in our cohort and this allows a better prediction of prognosis and contributes to a more effective clinical management using targeted therapies. The study highlights the potential of implementing precision medicine in the patients.


2015 ◽  
Vol 100 (2) ◽  
pp. E333-E344 ◽  
Author(s):  
Ruth M. Baxter ◽  
Valerie A. Arboleda ◽  
Hane Lee ◽  
Hayk Barseghyan ◽  
Margaret P. Adam ◽  
...  

Abstract Context: Disorders of sex development (DSD) are clinical conditions where there is a discrepancy between the chromosomal sex and the phenotypic (gonadal or genital) sex of an individual. Such conditions can be stressful for patients and their families and have historically been difficult to diagnose, especially at the genetic level. In particular, for cases of 46,XY gonadal dysgenesis, once variants in SRY and NR5A1 have been ruled out, there are few other single gene tests available. Objective: We used exome sequencing followed by analysis with a list of all known human DSD-associated genes to investigate the underlying genetic etiology of 46,XY DSD patients who had not previously received a genetic diagnosis. Design: Samples were either submitted to the research laboratory or submitted as clinical samples to the UCLA Clinical Genomic Center. Sequencing data were filtered using a list of genes known to be involved in DSD. Results: We were able to identify a likely genetic diagnosis in more than a third of cases, including 22.5% with a pathogenic finding, an additional 12.5% with likely pathogenic findings, and 15% with variants of unknown clinical significance. Conclusions: Early identification of the genetic cause of a DSD will in many cases streamline and direct the clinical management of the patient, with more focused endocrine and imaging studies and better-informed surgical decisions. Exome sequencing proved an efficient method toward such a goal in 46,XY DSD patients.


2021 ◽  
Author(s):  
Orna Mizrahi Man ◽  
Marcos H Woehrmann ◽  
Teresa A Webster ◽  
Jeremy Gollub ◽  
Adrian Bivol ◽  
...  

Objective: To significantly improve the positive predictive value (PPV) and sensitivity of Applied Biosystems™ Axiom™ array variant calling, by means of novel improvement to genotyping algorithms and careful quality control of array probesets. The improvement makes array genotyping more suitable for very rare variants. Design: Retrospective evaluation of UK Biobank array data re-genotyped with improved algorithms for rare variants. Participant: 488,359 people recruited to the UK Biobank with Axiom array genotyping data including 200,630 with exome sequencing data. Main Outcome Measures: A comparison of genotyping calls from array data to genotyping calls on a subset of variants with exome sequencing data. Results: Axiom genotyping [18] performed well, based on comparison to sequencing data, for over 100,000 common variants directly genotyped on the Axiom UK Biobank array and also exome sequenced by the UK Biobank Exome Sequencing Consortium. However, in a comparison to the initial exome sequencing results of the first 50K individuals, Weedon et al. [1] observed that when grouping these variants by the minor allele frequency (MAF) observed in UK Biobank, the concordance with sequencing and resulting positive predictive value (PPV) decreased with the number of heterozygous (Het) array calls per variant. An improved genotyping algorithm, Rare Heterozygous Adjustment (RHA) [16], released mid-2020 for genotyping on Axiom arrays, significantly improves PPV in all MAF ranges for the 50K data as well as when compared to the exome sequencing of 200K individuals, released after Weedon et al. [1] performed their comparison. The RHA algorithm improved PPVs in the 200K data in the lowest three frequency groups [0, 0.001%), [0.001%, 0.005%) and [0.005%, 0.01%) to 83%, 82% and 88%; respectively. PPV was above 95% for higher MAF ranges without algorithm improvement. PPVs are somewhat higher in the 200K dataset, due to a different "truth set" from exome sequencing and because monomorphic exome loci are not included in the joint genotyping calls for the 200K data set, as explained in the methods section. Sensitivity was higher in the 200K data set than in the original 50K data as well, especially for low MAF ranges. This increase is in part due to the larger data set over which sensitivity could be computed and in part due to the different WES algorithms used for the 200K data [7]. Filtering of a relatively small number of non-performing probesets (determined without reference to the exome sequencing data) significantly improved sensitivities for all MAF ranges, resulting in 70%, 88% and 94% respectively in the three lowest MAF ranges and greater than 98% and 99.9% for the two higher MAF ranges ([0.01%, 1%), [1%, 50%]). Conclusions: Improved algorithms for genotyping along with enhanced quality control of array probesets, significantly improve the positive predictive value and the sensitivity of array data, making it suitable for the detection of very rare variants. The probeset filtering methods developed have resulted in better probe designs for arrays and the new genotyping algorithm is part of the standard algorithm for all Axiom arrays since early 2020.


Blood ◽  
2016 ◽  
Vol 127 (23) ◽  
pp. 2814-2823 ◽  
Author(s):  
Claire Lentaigne ◽  
Kathleen Freson ◽  
Michael A. Laffan ◽  
Ernest Turro ◽  
Willem H. Ouwehand

Abstract Variations in platelet number, volume, and function are largely genetically controlled, and many loci associated with platelet traits have been identified by genome-wide association studies (GWASs).1 The genome also contains a large number of rare variants, of which a tiny fraction underlies the inherited diseases of humans. Research over the last 3 decades has led to the discovery of 51 genes harboring variants responsible for inherited platelet disorders (IPDs). However, the majority of patients with an IPD still do not receive a molecular diagnosis. Alongside the scientific interest, molecular or genetic diagnosis is important for patients. There is increasing recognition that a number of IPDs are associated with severe pathologies, including an increased risk of malignancy, and a definitive diagnosis can inform prognosis and care. In this review, we give an overview of these disorders grouped according to their effect on platelet biology and their clinical characteristics. We also discuss the challenge of identifying candidate genes and causal variants therein, how IPDs have been historically diagnosed, and how this is changing with the introduction of high-throughput sequencing. Finally, we describe how integration of large genomic, epigenomic, and phenotypic datasets, including whole genome sequencing data, GWASs, epigenomic profiling, protein–protein interaction networks, and standardized clinical phenotype coding, will drive the discovery of novel mechanisms of disease in the near future to improve patient diagnosis and management.


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