scholarly journals Modified penetrance of coding variants by cis-regulatory variation shapes human traits

2017 ◽  
Author(s):  
Stephane E. Castel ◽  
Alejandra Cervera ◽  
Pejman Mohammadi ◽  
François Aguet ◽  
Ferran Reverter ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Target Gene ◽  
Direct Evidence ◽  
Disease Risk ◽  
Purifying Selection ◽  
Functional Genomic ◽  
Regulatory Variation ◽  
Regulatory Variant ◽  
Coding Variants ◽  
Variable Penetrance

SummaryCoding variants represent many of the strongest associations between genotype and phenotype, however they exhibit inter-individual differences in effect, known as variable penetrance. In this work, we study how cis-regulatory variation modifies the penetrance of coding variants in their target gene. Using functional genomic and genetic data from GTEx, we observed that in the general population, purifying selection has depleted haplotype combinations that lead to higher penetrance of pathogenic coding variants. Conversely, in cancer and autism patients, we observed an enrichment of haplotype combinations that lead to higher penetrance of pathogenic coding variants in disease implicated genes, which provides direct evidence that regulatory haplotype configuration of causal coding variants affects disease risk. Finally, we experimentally demonstrated that a regulatory variant can modify the penetrance of a coding variant by introducing a Mendelian SNP using CRISPR/Cas9 on distinct expression haplotypes and using the transcriptome as a phenotypic readout. Our results demonstrate that joint effects of regulatory and coding variants are an important part of the genetic architecture of human traits, and contribute to modified penetrance of disease-causing variants.

scholarly journals Modified penetrance of coding variants by cis-regulatory variation contributes to disease risk

2018 ◽  
Vol 50 (9) ◽  
pp. 1327-1334 ◽  
Author(s):  
Stephane E. Castel ◽  
Alejandra Cervera ◽  
Pejman Mohammadi ◽  
François Aguet ◽  
Ferran Reverter ◽  
...  
Keyword(s):  
Disease Risk ◽  
Regulatory Variation ◽  
Coding Variants

scholarly journals CHOP-Dependent Stress-Inducible Expression of a Novel Form of Carbonic Anhydrase VI

1999 ◽  
Vol 19 (1) ◽  
pp. 495-504 ◽  
Author(s):  
John Sok ◽  
Xiao-Zhong Wang ◽  
Nikoleta Batchvarova ◽  
Masahiko Kuroda ◽  
Heather Harding ◽  
...  
Keyword(s):  
Carbonic Anhydrase ◽  
Target Gene ◽  
Target Genes ◽  
Direct Evidence ◽  
Nuclear Protein ◽  
Intracellular Form ◽  
Carbonic Anhydrase Vi ◽  
Responsive Element

ABSTRACT CHOP (also called GADD153) is a stress-inducible nuclear protein that dimerizes with members of the C/EBP family of transcription factors and was initially identified as an inhibitor of C/EBP binding to classic C/EBP target genes. Subsequent experiments suggested a role for CHOP-C/EBP heterodimers in positively regulating gene expression; however, direct evidence that this is the case has so far not been uncovered. Here we describe the identification of a positively regulated direct CHOP-C/EBP target gene, that encoding murine carbonic anhydrase VI (CA-VI). The stress-inducible form of the gene is expressed from an internal promoter and encodes a novel intracellular form of what is normally a secreted protein. Stress-induced expression of CA-VI is both CHOP and C/EBPβ dependent in that it does not occur in cells deficient in either gene. A CHOP-responsive element was mapped to the inducibleCA-VI promoter, and in vitro footprinting revealed binding of CHOP-C/EBP heterodimers to that site. Rescue of CA-VIexpression in c/ebpβ−/− cells by exogenous C/EBPβ and a shorter, normally inhibitory isoform of the protein known as LIP suggests that the role of the C/EBP partner is limited to targeting the CHOP-containing heterodimer to the response element and points to a preeminent role for CHOP in CA-VI induction during stress.

scholarly journals Genetic architecture of schizophrenia: a review of major advancements

2021 ◽  
pp. 1-10
Author(s):  
Sophie E. Legge ◽  
Marcos L. Santoro ◽  
Sathish Periyasamy ◽  
Adeniran Okewole ◽  
Arsalan Arsalan ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Copy Number Variants ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Loci ◽  
Significant Enrichment ◽  
High Heritability ◽  
Coding Variants

Abstract Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

scholarly journals Exome Chip Meta-analysis Fine Maps Causal Variants and Elucidates the Genetic Architecture of Rare Coding Variants in Smoking and Alcohol Use

2019 ◽  
Vol 85 (11) ◽  
pp. 946-955 ◽  
Author(s):  
David M. Brazel ◽  
Yu Jiang ◽  
Jordan M. Hughey ◽  
Valérie Turcot ◽  
Xiaowei Zhan ◽  
...  
Keyword(s):  
Alcohol Use ◽  
Genetic Architecture ◽  
Meta Analysis ◽  
Causal Variants ◽  
Coding Variants ◽  
Exome Chip

Performance Comparison of Computational Prediction Methods for the Function and Pathogenicity of Non-coding Variants

2021 ◽  
Author(s):  
Zheng Wang ◽  
Guihu Zhao ◽  
Bin Li ◽  
Zhenghuan Fang ◽  
Qian Chen ◽  
...  
Keyword(s):  
Computational Methods ◽  
Method Development ◽  
De Novo ◽  
Autism Spectrum ◽  
Common Variant ◽  
Tool Selection ◽  
Somatic Variant ◽  
Germline Variant ◽  
Regulatory Variant ◽  
Coding Variants

Non-coding variants in the human genome greatly influence some traits and complex diseases by their own regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in the human non-coding sequences. However, it is difficult for users with insufficient knowledge about the performances of computational methods to select appropriate computational methods from dozens of methods. In order to solve this problem, we assessed 12 performance measures of 24 methods on four independent non-coding variant benchmark datasets: (Ⅰ) rare germline variant from ClinVar, (Ⅱ) rare somatic variant from COSMIC, (Ⅲ) common regulatory variant dataset, and (Ⅳ) disease associated common variant dataset. All 24 tested methods performed differently under various conditions, indicating that these methods have varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable in the rare germline variant from ClinVar with area under curves (AUCs) of 0.4481 - 0.8033 and poor in the rare somatic variant from COSMIC (AUCs: 0.4984 - 0.7131), common regulatory variant dataset (AUCs: 0.4837 - 0.6472), and disease associated common variant dataset (AUCs: 0.4766 -0.5188). We also compared the prediction performance among 24 methods for non-coding de novo mutations in autism spectrum disorder and found that the CADD and CDTS methods showed better performance. Summarily, we assessed the performances of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and new method development in interpreting non-coding variants.

scholarly journals Disease Risk Factors Identified Through Shared Genetic Architecture and Electronic Medical Records

2014 ◽  
Vol 6 (234) ◽  
pp. 234ra57-234ra57 ◽  
Author(s):  
L. Li ◽  
D. J. Ruau ◽  
C. J. Patel ◽  
S. C. Weber ◽  
R. Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Electronic Medical Records ◽  
Medical Records ◽  
Genetic Architecture ◽  
Disease Risk ◽  
Shared Genetic

scholarly journals Challenges and progress in interpretation of non-coding genetic variants associated with human disease

2017 ◽  
Vol 242 (13) ◽  
pp. 1325-1334 ◽  
Author(s):  
Yizhou Zhu ◽  
Cagdas Tazearslan ◽  
Yousin Suh
Keyword(s):  
Molecular Mechanisms ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genetic Associations ◽  
Functional Interpretation ◽  
Genome Wide ◽  
Coding Variants

Genome-wide association studies have shown that the far majority of disease-associated variants reside in the non-coding regions of the genome, suggesting that gene regulatory changes contribute to disease risk. To identify truly causal non-coding variants and their affected target genes remains challenging but is a critical step to translate the genetic associations to molecular mechanisms and ultimately clinical applications. Here we review genomic/epigenomic resources and in silico tools that can be used to identify causal non-coding variants and experimental strategies to validate their functionalities. Impact statement Most signals from genome-wide association studies (GWASs) map to the non-coding genome, and functional interpretation of these associations remained challenging. We reviewed recent progress in methodologies of studying the non-coding genome and argued that no single approach allows one to effectively identify the causal regulatory variants from GWAS results. By illustrating the advantages and limitations of each method, our review potentially provided a guideline for taking a combinatorial approach to accurately predict, prioritize, and eventually experimentally validate the causal variants.

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