scholarly journals A polyclonal allelic expression assay for detecting regulatory effects of transcript variants

2019 ◽  
Author(s):  
Margot Brandt ◽  
Alper Gokden ◽  
Marcello Ziosi ◽  
Tuuli Lappalainen
Keyword(s):  
Genetic Variants ◽  
Transcript Level ◽  
Allelic Expression ◽  
Targeted Sequencing ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Nonsense Mediated Decay ◽  
Transcript Variants ◽  
Causal Variants ◽  
Regulatory Effects

AbstractWe present an assay to experimentally test regulatory effects of genetic variants within transcripts using CRISPR/Cas9 followed by targeted sequencing. We applied the assay to 35 premature stop-gained variants across the genome and in two Mendelian disease genes, 33 putative causal variants of eQTLs and 65 control variants. We detected significant effects generally in the expected direction, demonstrating the ability of the assay to capture regulatory effects of eQTL variants and nonsense-mediated decay triggered by premature stop-gained variants. The results suggest a utility for validating transcript-level effects of genetic variants.

scholarly journals A polyclonal allelic expression assay for detecting regulatory effects of transcript variants

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Margot Brandt ◽  
Alper Gokden ◽  
Marcello Ziosi ◽  
Tuuli Lappalainen
Keyword(s):  
Hela Cells ◽  
Genetic Variants ◽  
Transcript Level ◽  
Allelic Expression ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Nonsense Mediated Decay ◽  
Transcript Variants ◽  
Causal Variants ◽  
Regulatory Effects

Abstract We present an assay to experimentally test the regulatory effects of genetic variants within transcripts using CRISPR/Cas9 followed by targeted sequencing. We applied the assay to 32 premature stop-gained variants across the genome and in two Mendelian disease genes, 33 putative causal variants of eQTLs, and 62 control variants in HEK293T cells, replicating a subset of variants in HeLa cells. We detected significant effects in the expected direction (in 60% of variants), demonstrating the ability of the assay to capture regulatory effects of eQTL variants and nonsense-mediated decay triggered by premature stop-gained variants. The results suggest a utility for validating transcript-level effects of genetic variants.

scholarly journals Clinical implementation of RNA sequencing for Mendelian disease diagnostics

2021 ◽  
Author(s):  
Vicente A. Yepez ◽  
Mirjana Gusic ◽  
Robert Kopajtich ◽  
Christian Mertes ◽  
Nicholas H. Smith ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Genetic Diagnosis ◽  
Allelic Expression ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Rna Seq ◽  
Aberrant Splicing ◽  
Aberrant Expression ◽  
Routine Diagnostics

Lack of functional evidence hampers variant interpretation, leaving a large proportion of cases with a suspected Mendelian disorder without genetic diagnosis after genome or whole exome sequencing (WES). Research studies advocate to further sequence transcriptomes to directly and systematically probe gene expression defects. However, collection of additional biopsies, and establishment of lab workflows, analytical pipelines, and defined concepts in clinical interpretation of aberrant gene expression are still needed for adopting RNA-sequencing (RNA-seq) in routine diagnostics. To address these issues, we implemented an automated RNA-seq protocol and a computational workflow with which we analyzed skin fibroblasts of 303 individuals with a suspected mitochondrial disease. We detected on average 12,500 genes per sample including around 60% disease genes - a coverage substantially higher than with whole blood, supporting the use of skin biopsies. We prioritized genes demonstrating aberrant expression, aberrant splicing, or mono-allelic expression. The pipeline required less than one week from sample preparation to result reporting and provided a median of eight disease genes per patient for inspection. A genetic diagnosis was established for 16% of the WES-inconclusive cases. Detection of aberrant expression was a major contributor to diagnosis including instances of 50% reduction, which, together with mono-allelic expression, allowed for the diagnosis of dominant disorders caused by haploinsufficiency. Moreover, calling aberrant splicing and variants from RNA-seq data enabled detecting and validating splice-disrupting variants, of which the majority fell outside WES-covered regions. Together, these results show that streamlined experimental and computational processes can accelerate the implementation of RNA-seq in routine diagnostics.

scholarly journals Editing GWAS: experimental approaches to dissect and exploit disease-associated genetic variation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Shuquan Rao ◽  
Yao Yao ◽  
Daniel E. Bauer
Keyword(s):  
Genome Editing ◽  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Functional Studies ◽  
Functional Genetics ◽  
Genome Wide ◽  
Causal Variants ◽  
Experimental Approaches

AbstractGenome-wide association studies (GWAS) have uncovered thousands of genetic variants that influence risk for human diseases and traits. Yet understanding the mechanisms by which these genetic variants, mainly noncoding, have an impact on associated diseases and traits remains a significant hurdle. In this review, we discuss emerging experimental approaches that are being applied for functional studies of causal variants and translational advances from GWAS findings to disease prevention and treatment. We highlight the use of genome editing technologies in GWAS functional studies to modify genomic sequences, with proof-of-principle examples. We discuss the challenges in interrogating causal variants, points for consideration in experimental design and interpretation of GWAS locus mechanisms, and the potential for novel therapeutic opportunities. With the accumulation of knowledge of functional genetics, therapeutic genome editing based on GWAS discoveries will become increasingly feasible.

scholarly journals Targeted sequencing reveals candidate causal variants for dairy bull subfertility

2021 ◽  
Author(s):  
R. Abdollahi‐Arpanahi ◽  
H. A. Pacheco ◽  
F. Peñagaricano
Keyword(s):  
Targeted Sequencing ◽  
Causal Variants ◽  
Dairy Bull

scholarly journals CAUSALdb: a database for disease/trait causal variants identified using summary statistics of genome-wide association studies

2019 ◽  
Author(s):  
Jianhua Wang ◽  
Dandan Huang ◽  
Yao Zhou ◽  
Hongcheng Yao ◽  
Huanhuan Liu ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Genome Wide ◽  
Credible Sets ◽  
Causal Variants

Abstract Genome-wide association studies (GWASs) have revolutionized the field of complex trait genetics over the past decade, yet for most of the significant genotype-phenotype associations the true causal variants remain unknown. Identifying and interpreting how causal genetic variants confer disease susceptibility is still a big challenge. Herein we introduce a new database, CAUSALdb, to integrate the most comprehensive GWAS summary statistics to date and identify credible sets of potential causal variants using uniformly processed fine-mapping. The database has six major features: it (i) curates 3052 high-quality, fine-mappable GWAS summary statistics across five human super-populations and 2629 unique traits; (ii) estimates causal probabilities of all genetic variants in GWAS significant loci using three state-of-the-art fine-mapping tools; (iii) maps the reported traits to a powerful ontology MeSH, making it simple for users to browse studies on the trait tree; (iv) incorporates highly interactive Manhattan and LocusZoom-like plots to allow visualization of credible sets in a single web page more efficiently; (v) enables online comparison of causal relations on variant-, gene- and trait-levels among studies with different sample sizes or populations and (vi) offers comprehensive variant annotations by integrating massive base-wise and allele-specific functional annotations. CAUSALdb is freely available at http://mulinlab.org/causaldb.

scholarly journals Whole-Exome Sequencing Identifies Novel Variants for Tooth Agenesis

2017 ◽  
Vol 97 (1) ◽  
pp. 49-59 ◽  
Author(s):  
N. Dinckan ◽  
R. Du ◽  
L.E. Petty ◽  
Z. Coban-Akdemir ◽  
S.N. Jhangiani ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Permanent Teeth ◽  
Tooth Agenesis ◽  
Disease Genes ◽  
Nonsense Mediated Decay ◽  
Pathogenic Variants ◽  
Whole Exome ◽  
Novel Variants ◽  
Complex Inheritance

Tooth agenesis is a common craniofacial abnormality in humans and represents failure to develop 1 or more permanent teeth. Tooth agenesis is complex, and variations in about a dozen genes have been reported as contributing to the etiology. Here, we combined whole-exome sequencing, array-based genotyping, and linkage analysis to identify putative pathogenic variants in candidate disease genes for tooth agenesis in 10 multiplex Turkish families. Novel homozygous and heterozygous variants in LRP6, DKK1, LAMA3, and COL17A1 genes, as well as known variants in WNT10A, were identified as likely pathogenic in isolated tooth agenesis. Novel variants in KREMEN1 were identified as likely pathogenic in 2 families with suspected syndromic tooth agenesis. Variants in more than 1 gene were identified segregating with tooth agenesis in 2 families, suggesting oligogenic inheritance. Structural modeling of missense variants suggests deleterious effects to the encoded proteins. Functional analysis of an indel variant (c.3607+3_6del) in LRP6 suggested that the predicted resulting mRNA is subject to nonsense-mediated decay. Our results support a major role for WNT pathways genes in the etiology of tooth agenesis while revealing new candidate genes. Moreover, oligogenic cosegregation was suggestive for complex inheritance and potentially complex gene product interactions during development, contributing to improved understanding of the genetic etiology of familial tooth agenesis.

Abstract 64: An Integrated Model for Titin Truncation Mutation Interpretation

2016 ◽  
Vol 119 (suppl_1) ◽  
Author(s):  
Jun Zou ◽  
Diana Tran ◽  
Angelo Pelonero ◽  
Rahul C Deo
Keyword(s):  
Large Scale ◽  
Characteristic Curve ◽  
Severe Disease ◽  
Predictive Performance ◽  
Transcript Level ◽  
Data Interpretation ◽  
Integrated Model ◽  
Integrative Model ◽  
Nonsense Mediated Decay ◽  
Cardiac Phenotype

Background: We recently discovered a conserved internal promoter in the Titin gene, which explains why truncating mutations in the C-terminal two thirds of the zebrafish ttna protein result in more severe disease, recapitulating a puzzling observation in human dilated cardiomyopathy (DCM) patients. Here we focus on the contribution of alternative splicing to the DCM phenotype, both in zebrafish Titin truncation mutants and in the context of an integrative model for Titin mutation interpretation. Methods and Results: Using CRISPR/Cas9, we disrupted an alternatively spliced exon in the I-band of Titin , normally present in zebrafish heart but absent in skeletal muscle. The resulting mutants had, on average, a milder cardiac phenotype than those with mutations in constitutive exons but also showed striking inter-sibling variability in disease expression, ranging from intact cardiac blood flow to severe early demise. The mutant exon demonstrated nonsense-altered splicing and disease severity paralleled selective deficiency in Titin transcript level, implying that variability in mutated exon inclusion coupled with nonsense-mediated decay (NMD) modulated phenotype. We next amassed Titin mutation information from 1785 human DCM cases and >68,000 controls to model mutation distribution and found three variance components 1) splicing; 2) internal isoform disruption; and 3) targeting of the C-terminal 2000 amino acids. An integrated model demonstrated strong predictive performance with an area under the receiver operating characteristic curve of 0.79 and correctly identified the highest risk individuals. Conclusions: We conclude that genetically targeted models and large-scale human data can be complementary in overcoming the challenges of genetic data interpretation.

scholarly journals Data-driven modelling of mutational hotspots and in silico predictors in hypertrophic cardiomyopathy

2020 ◽  
pp. jmedgenet-2020-106922
Author(s):  
Adam Waring ◽  
Andrew Harper ◽  
Silvia Salatino ◽  
Christopher Kramer ◽  
Stefan Neubauer ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
In Silico ◽  
Synthetic Data ◽  
Missense Variant ◽  
Statistical Modelling ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Modelling Framework ◽  
Mutational Hotspots ◽  
Anderson Darling

BackgroundAlthough rare missense variants in Mendelian disease genes often cluster in specific regions of proteins, it is unclear how to consider this when evaluating the pathogenicity of a gene or variant. Here we introduce methods for gene association and variant interpretation that use this powerful signal.MethodsWe present statistical methods to detect missense variant clustering (BIN-test) combined with burden information (ClusterBurden). We introduce a flexible generalised additive modelling (GAM) framework to identify mutational hotspots using burden and clustering information (hotspot model) and supplemented by in silico predictors (hotspot+ model). The methods were applied to synthetic data and a case–control dataset, comprising 5338 hypertrophic cardiomyopathy patients and 125 748 population reference samples over 34 putative cardiomyopathy genes.ResultsIn simulations, the BIN-test was almost twice as powerful as the Anderson-Darling or Kolmogorov-Smirnov tests; ClusterBurden was computationally faster and more powerful than alternative position-informed methods. For 6/8 sarcomeric genes with strong clustering, Clusterburden showed enhanced power over burden-alone, equivalent to increasing the sample size by 50%. Hotspot+ models that combine burden, clustering and in silico predictors outperform generic pathogenicity predictors and effectively integrate ACMG criteria PM1 and PP3 to yield strong or moderate evidence of pathogenicity for 31.8% of examined variants of uncertain significance.ConclusionGAMs represent a unified statistical modelling framework to combine burden, clustering and functional information. Hotspot models can refine maps of regional burden and hotspot+ models can be powerful predictors of variant pathogenicity. The BIN-test is a fast powerful approach to detect missense variant clustering that when combined with burden information (ClusterBurden) may enhance disease-gene discovery.

scholarly journals Sentinel interaction mapping – a generic approach for the functional analysis of human disease gene variants using yeast

2020 ◽  
Vol 13 (7) ◽  
pp. dmm044560
Author(s):  
Barry P. Young ◽  
Kathryn L. Post ◽  
Jesse T. Chao ◽  
Fabian Meili ◽  
Kurt Haas ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Human Disease ◽  
Disease Gene ◽  
Suppressor Gene ◽  
Tumour Suppressor Gene ◽  
Disease Genes ◽  
Gene Variants ◽  
Human Disease Gene ◽  
Simple Growth

ABSTRACTAdvances in sequencing technology have led to an explosion in the number of known genetic variants of human genes. A major challenge is to now determine which of these variants contribute to diseases as a result of their effect on gene function. Here, we describe a generic approach using the yeast Saccharomyces cerevisiae to quickly develop gene-specific in vivo assays that can be used to quantify the level of function of a genetic variant. Using synthetic dosage lethality screening, ‘sentinel’ yeast strains are identified that are sensitive to overexpression of a human disease gene. Variants of the gene can then be functionalized in a high-throughput fashion through simple growth assays using solid or liquid media. Sentinel interaction mapping (SIM) has the potential to create functional assays for the large majority of human disease genes that do not have a yeast orthologue. Using the tumour suppressor gene PTEN as an example, we show that SIM assays can provide a fast and economical means to screen a large number of genetic variants.

scholarly journals Copy number variants encompassing Mendelian disease genes in a large multigenerational family segregating bipolar disorder

BMC Genetics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Rachel L Kember ◽  
Benjamin Georgi ◽  
Joan E Bailey-Wilson ◽  
Dwight Stambolian ◽  
Steven M Paul ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Copy Number ◽  
Copy Number Variants ◽  
Disease Genes ◽  
Mendelian Disease ◽  
Multigenerational Family
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