A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity

Zhang, Peng; Cobat, Aurélie; Lee, Yoon-Seung; Wu, Yiming; Bayrak, Çiğdem Sevim; Boccon‐Gibod, Clémentine; Matuozzo, Daniela; Lorenzo, Lazaro; Jain, Aayushee; Boucherit, Soraya; Vallée, Louis; Stüve, Burkhard; Chabrier, Stéphane; Casanova, Jean‐Laurent; Abel, Laurent; Zhang, Shen-Ying; Itan, Yuval

doi:10.1016/j.ajhg.2021.04.023

Cited by 8 publications

(11 citation statements)

References 63 publications

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“…In the search for candidate variants underlying human disease, current approaches mostly focus on nonsynonymous variants in coding regions and variants residing in essential splice sites. If an investigator wishes to screen for candidate variants among the vast number of intronic variants, a combination of approaches may be applied, e.g., testing for enrichment of intronic variants in a case-control study, computing and ranking the deleteriousness/missplicing scores ( 40 – 42 ) of intronic variants, referencing the MAF of population variations ( 32 , 60 ), and clustering genetic heterogeneity (presumably deleterious intronic variants together with coding/splice-site variants) underlying physiological homogeneity ( 61 ). However, enrichment of intronic variants relies on having a group of carriers in cases versus controls, so individual variants (albeit functionally impactful) in sporadic cases are rarely captured.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide detection of human variants that disrupt intronic branchpoints

Zhang

Philippot

Ren

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Pre-messenger RNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Forty-eight rare variants in 43 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach was available to efficiently detect such variants in massively parallel sequencing data. We established a comprehensive human genome-wide BP database by integrating existing BP data and generating new BP data from RNA sequencing of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We characterized multiple features of BP in major and minor introns and found that BP and BP-2 (two nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, while being comparable to the exonic background. We developed BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition. BPHunter retrospectively identified 40 of the 48 known pathogenic BP variants, in which we summarized a strategy for prioritizing BP variant candidates. The remaining eight variants all create AG-dinucleotides between the BP and acceptor site, which is the likely reason for missplicing. We demonstrated the practical utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which were validated experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter .

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Section: Discussionmentioning

confidence: 99%

Genome-wide detection of human variants that disrupt intronic branchpoints

Zhang

Philippot

Ren

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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“…If an investigator wishes to screen for candidate mutations among the vast number of intronic variants, a combination of approaches may be applied, e.g. testing for enrichment of intronic variants in a case-control study, computing and ranking the mutation deleteriousness/mis-splicing scores (Cheng et al 2019; Jaganathan et al 2019; Rentzsch et al 2019) of intronic variants, referencing the MAF of population variations (Zhang et al 2018a; Karczewski et al 2020), and searching for genetic heterogeneity (presumably deleterious intronic variants together with coding/splice-site variants) underlying physiological homogeneity (Zhang et al 2021). However, enrichment of intronic variants relies on having a group of carriers in cases versus controls, so individual variants (albeit functionally impactful) in sporadic cases are rarely captured.…”

Section: Discussionmentioning

confidence: 99%

“…It precomputed and ranked all possible variants in the human genome, and then assigned a score of 10 to the top 10% of predicted deleterious variants, a score of 20 to the top 1% of variants, and a score of 30 to the top 0.1% of variants, etc. Usually, a high-cutoff 20 or a moderate-cutoff 10 were used for large-scale variant filtration for deleterious candidate mutations (Zhang et al 2018a; Zhang et al 2021). We were also aware of some mis-splicing prediction scores (Xiong et al 2015; Cheng et al 2019; Jagadeesh et al 2019; Jaganathan et al 2019), and in this study we recruited SpliceAI (Jaganathan et al 2019) and MMSplice (Cheng et al 2019) to evaluate the BP mutations.…”

Section: Methodsmentioning

confidence: 99%

Section: Mutation Deleteriousness Mis-splicing Prediction Scores and ...mentioning

confidence: 99%

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Genome-wide detection of human variants that disrupt intronic branchpoints

Zhang

Philippot

Ren

et al. 2022

Preprint

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Pre-mRNA splicing is initiated with the recognition of a single-nucleotide intronic branchpoint (BP) within a BP motif by spliceosome elements. Fifty-six rare variants in 44 human genes have been reported to alter splicing and cause disease by disrupting BP. However, until now, no computational approach has been available to efficiently detect such variants in next-generation sequencing (NGS) data. We established a comprehensive human genome-wide BP database by integrating existing BP data, and by generating new BP data from RNA-seq of lariat debranching enzyme DBR1-mutated patients and from machine-learning predictions. We in-depth characterize multiple features of BP in major and minor introns, and find that BP and BP-2 (two-nucleotides upstream of BP) positions exhibit a lower rate of variation in human populations and higher evolutionary conservation than the intronic background, whilst being comparable to the exonic background. We develop BPHunter as a genome-wide computational approach to systematically and efficiently detect intronic variants that may disrupt BP recognition in NGS data. BPHunter retrospectively identifies 48 of the 56 known pathogenic BP mutations in which we summarize a strategy for prioritizing BP mutation candidates, and the remaining 8 all create AG dinucleotides between BP and acceptor site which is probably the reason for mis-splicing. We demonstrate the utility of BPHunter prospectively by using it to identify a novel germline heterozygous BP variant of STAT2 in a patient with critical COVID-19 pneumonia, and a novel somatic intronic 59-nucleotide deletion of ITPKB in a lymphoma patient, both of which we validate experimentally. BPHunter is publicly available from https://hgidsoft.rockefeller.edu/BPHunter and https://github.com/casanova-lab/BPHunter.

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“…A few existing tools employ a disease-specific approach, for somatic mutations in cancers [ 15–18 ], or predict the new pathogenic variants by employing possible association with known pathogenic variants [ 19 , 20 ]. Genes causing a specific disease or a group of similar diseases tend to be involved in common biological processes, and we hypothesize that they have a higher probability of sharing common characteristics [ 21 ]. For instance, many genes associated with Alzheimer’s disease and some other neurodegenerative diseases, including amyotrophic lateral sclerosis, Parkinson’s disease and Huntington’s disease, are all involved in shared pathways with similar protein structures and molecular properties [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases

Fang

Abolhassani

et al. 2022

Briefings in Bioinformatics

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Distinguishing pathogenic variants from non-pathogenic ones remains a major challenge in clinical genetic testing of primary immunodeficiency (PID) patients. Most of the existing mutation pathogenicity prediction tools treat all mutations as homogeneous entities, ignoring the differences in characteristics of different genes, and use the same model for genes in different diseases. In this study, we developed a single nucleotide variant (SNV) pathogenicity prediction tool, Variant Impact Predictor for PIDs (VIPPID; https://mylab.shinyapps.io/VIPPID/), which was tailored for PIDs genes and used a specific model for each of the most prevalent PID known genes. It employed a Conditional Inference Forest model and utilized information of 85 features of SNVs and scores from 20 existing prediction tools. Evaluation of VIPPID showed that it had superior performance (area under the curve = 0.91) over non-specific conventional tools. In addition, we also showed that the gene-specific model outperformed the non-gene-specific models. Our study demonstrated that disease-specific and gene-specific models can improve SNV pathogenicity prediction performance. This observation supports the notion that each feature of mutations in the model can be potentially used, in a new algorithm, to investigate the characteristics and function of the encoded proteins.

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A computational approach for detecting physiological homogeneity in the midst of genetic heterogeneity

Cited by 8 publications

References 63 publications

Genome-wide detection of human variants that disrupt intronic branchpoints

Genome-wide detection of human variants that disrupt intronic branchpoints

Genome-wide detection of human variants that disrupt intronic branchpoints

VIPPID: a gene-specific single nucleotide variant pathogenicity prediction tool for primary immunodeficiency diseases

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