Evaluating the pathogenic potential of genes with de novo variants in epileptic encephalopathies

He, Na; Lin, Zhi-Jian; Wang, Jie; Wei, Feng; Meng, Heng; Liu, Xiaorong; Chen, Qian; Su, Tao; Shi, Yi‐Wu; Yi, Yong‐Hong; Liao, Wei‐Ping

doi:10.1038/s41436-018-0011-y

Cited by 50 publications

(54 citation statements)

References 75 publications

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“…The pathogenicity of p.Pro1031His could therefore be suspected and was reclassified as likely benign (Baldassari et al, 2019) or variant of undetermined significance. Evaluation of the pathogenicity of a variant is currently challenging (Tang et al, 2019), even for genes with de novo mutations (He et al, 2019). Generally, the MAFs are closely related to the prevalence of the phenotypes (Richards et al, 2015), among which mild phenotypes potentially have higher prevalence than severe ones.…”

Section: Genotype-phenotype Correlationmentioning

confidence: 99%

DEPDC5 Variants Associated Malformations of Cortical Development and Focal Epilepsy With Febrile Seizure Plus/Febrile Seizures: The Role of Molecular Sub-Regional Effect

Liu

Chen

et al. 2020

Front. Neurosci.

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Liu et al. MCD/FS & Molecular Sub-Regions of DEPDC5 whereas those associated with FEFS + /FS were a distance away from the binding sites. Evidence from four aspects and one possible evidence from sub-regional implication suggested MCD and FEFS + /FS as phenotypes of DEPDC5 variants. This study suggested that the phenotypes of DEPDC5 variants vary from mild FEFS + /FS to severe MCD. Heterozygous DEPDC5 mutations are generally less pathogenic and commonly associated with mild phenotypes. Bi-allelic mutations and second hit of somatic mutations, together with the genotype-phenotype correlation and sub-regional implication of DEPDC5 variants, explain severe phenotypes.

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Section: Genotype-phenotype Correlationmentioning

confidence: 99%

DEPDC5 Variants Associated Malformations of Cortical Development and Focal Epilepsy With Febrile Seizure Plus/Febrile Seizures: The Role of Molecular Sub-Regional Effect

Liu

Chen

et al. 2020

Front. Neurosci.

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“…Epileptic encephalopathies comprise group of conditions clinically characterized by refractory seizures and psychomotor developmental delay and/or decline (He et al, ). Inborn errors of metabolism are one of the leading causes of epileptic encephalopathies.…”

Section: Introductionmentioning

confidence: 99%

Identification of a novel homozygous variant confirms ITPA as a developmental and epileptic encephalopathy gene

Kaur

Kausthubham

Kumble³

et al. 2019

American J of Med Genetics Pt A

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ITPA related epileptic encephalopathy (epileptic encephalopathy, early infantile, 35) is a rare inborn error of metabolism. All reported individuals with this condition, including the present case manifest global developmental delay, seizures, progressive postnatal microcephaly, hypotonia, thin corpus callosum, cerebral atrophy, delayed myelination and white matter changes in posterior limb of internal capsule. Cataract and dilated cardiomyopathy are other characteristic findings.Currently, a single publication describes this condition in four families. Three truncating and two missense variants in ITPA have been identified in these families. We hereby report another family with ITPA related disorder and review the genotype and phenotype of the reported subjects. K E Y W O R D Sepileptic encephalopathy, hypomyelination, ITPA, ITPase, purine metabolism disorders

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“…Additionally, only the manic subtype shares a high degree of pleiotropy with schizophrenia [17]. Aside from psychiatric traits, heterogeneity of genomic associations between known subtypes has been observed in diseases such as lupus [18], multiple sclerosis [19], epilepsy [20], encephalopathy [21], and juvenile idiopathic arthritis [22]. Elucidating the nature of heterogeneity in these traits may also play a at the following link: https://github.com/jyuan1322/ CLiP Due to patient confidentiality protections, genotype data of schizophrenia patients are available on request from The Schizophrenia Working Group of the Psychiatric Genomics Consortium (PGC) for researchers who meet the criteria for access to confidential data.…”

Section: Introductionmentioning

confidence: 99%

Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts

et al. 2020

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Evidence from both GWAS and clinical observation has suggested that certain psychiatric, metabolic, and autoimmune diseases are heterogeneous, comprising multiple subtypes with distinct genomic etiologies and Polygenic Risk Scores (PRS). However, the presence of subtypes within many phenotypes is frequently unknown. We present CLiP (Correlated Liability Predictors), a method to detect heterogeneity in single GWAS cohorts. CLiP calculates a weighted sum of correlations between SNPs contributing to a PRS on the case/control liability scale. We demonstrate mathematically and through simulation that among i.i.d. homogeneous cases generated by a liability threshold model, significant anti-correlations are expected between otherwise independent predictors due to ascertainment on the hidden liability score. In the presence of heterogeneity from distinct etiologies, confounding by covariates, or mislabeling, these correlation patterns are altered predictably. We further extend our method to two additional association study designs: CLiP-X for quantitative predictors in applications such as transcriptome-wide association, and CLiP-Y for quantitative phenotypes, where there is no clear distinction between cases and controls. Through simulations, we demonstrate that CLiP and its extensions reliably distinguish between homogeneous and heterogeneous cohorts when the PRS explains as low as 3% of variance on the liability scale and cohorts comprise 50, 000 − 100, 000 samples, an increasingly practical size for modern GWAS. We apply CLiP to heterogeneity detection in schizophrenia cohorts totaling > 50, 000 cases and controls collected by the Psychiatric Genomics Consortium. We observe significant heterogeneity in mega-analysis of the combined PGC data (p-value 8.54 × 0 −4), as well as in individual cohorts meta-analyzed using Fisher's method (p-value 0.03), based on significantly associated variants. We also apply CLiP-Y to detect heterogeneity in neuroticism in over 10, 000 individuals from the UK Biobank and detect heterogeneity with a p-value of 1.68 × 10 −9. Scores were not significantly reduced when partitioning by known subclusters ("Depression" and "Worry"), suggesting that these factors are not the primary source of observed heterogeneity.

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Evaluating the pathogenic potential of genes with de novo variants in epileptic encephalopathies

Cited by 50 publications

References 75 publications

DEPDC5 Variants Associated Malformations of Cortical Development and Focal Epilepsy With Febrile Seizure Plus/Febrile Seizures: The Role of Molecular Sub-Regional Effect

DEPDC5 Variants Associated Malformations of Cortical Development and Focal Epilepsy With Febrile Seizure Plus/Febrile Seizures: The Role of Molecular Sub-Regional Effect

Identification of a novel homozygous variant confirms ITPA as a developmental and epileptic encephalopathy gene

Leveraging correlations between variants in polygenic risk scores to detect heterogeneity in GWAS cohorts

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