scholarly journals Allele-specific open chromatin in human iPSC neurons elucidates functional disease variants

Science ◽  
2020 ◽  
Vol 369 (6503) ◽  
pp. 561-565 ◽  
Author(s):  
Siwei Zhang ◽  
Hanwen Zhang ◽  
Yifan Zhou ◽  
Min Qiao ◽  
Siming Zhao ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Disease Risk ◽  
Induced Pluripotent Stem Cell ◽  
Open Chromatin ◽  
Regulatory Sequences ◽  
Functional Interpretation ◽  
Neuropsychiatric Disease ◽  
Risk Variants ◽  
Allele Specific

Most neuropsychiatric disease risk variants are in noncoding sequences and lack functional interpretation. Because regulatory sequences often reside in open chromatin, we reasoned that neuropsychiatric disease risk variants may affect chromatin accessibility during neurodevelopment. Using human induced pluripotent stem cell (iPSC)–derived neurons that model developing brains, we identified thousands of genetic variants exhibiting allele-specific open chromatin (ASoC). These neuronal ASoCs were partially driven by altered transcription factor binding, overrepresented in brain gene enhancers and expression quantitative trait loci, and frequently associated with distal genes through chromatin contacts. ASoCs were enriched for genetic variants associated with brain disorders, enabling identification of functional schizophrenia risk variants and their cis-target genes. This study highlights ASoC as a functional mechanism of noncoding neuropsychiatric risk variants, providing a powerful framework for identifying disease causal variants and genes.

scholarly journals Allele-specific open chromatin in human iPSC neurons elucidates functional non-coding disease variants

2019 ◽  
Author(s):  
Siwei Zhang ◽  
Hanwen Zhang ◽  
Min Qiao ◽  
Yifan Zhou ◽  
Siming Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Variants ◽  
Induced Pluripotent Stem Cell ◽  
Chromatin Accessibility ◽  
Adult Brain ◽  
Open Chromatin ◽  
Brain Disorders ◽  
Functional Interpretation ◽  
Allele Specific ◽  
Induced Pluripotent

AbstractFunctional interpretation of noncoding disease variants, which likely regulate gene expression, has been challenging. Chromatin accessibility strongly influences gene expression during neurodevelopment; however, to what extent genetic variants can alter chromatin accessibility in the context of brain disorders/traits is unknown. Using human induced pluripotent stem cell (iPSC)-derived neurons as a neurodevelopmental model, we identified abundant open-chromatin regions absent in adult brain samples and thousands of genetic variants exhibiting allele-specific open-chromatin (ASoC). ASoC variants are overrepresented in brain enhancers, transcription-factor-binding sites, and quantitative-trait-loci associated with gene expression, histone modification, and DNA methylation. Notably, compared to open chromatin regions and other commonly used functional annotations, neuronal ASoC variants showed much stronger enrichments of risk variants for various brain disorders/traits. Our study provides the first snapshot of the neuronal ASoC landscape and a powerful framework for prioritizing functional disease variants.One Sentence SummaryAllele-specific open chromatin informs functional disease variants

scholarly journals Functional Interpretation of Genetic Variants Using Deep Learning Predicts Impact on Epigenome

2018 ◽  
Author(s):  
Gabriel E. Hoffman ◽  
Eric E. Schadt ◽  
Panos Roussos
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Genetic Variants ◽  
Disease Risk ◽  
Functional Interpretation ◽  
Protein Coding ◽  
Functional Consequence ◽  
Risk Variants ◽  
Causal Variants ◽  
Coding Variants

ABSTRACTIdentifying causal variants underling disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequences for 438 million variants. These impact scores are assay-specific, are predictive of allele-specific transcription factor binding and are enriched for variants associated with gene expression and disease risk. Nucleotide-level functional consequence scores for non-coding variants can refine the mechanism of known causal variants, identify novel risk variants and prioritize downstream experiments.

scholarly journals Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification

2019 ◽  
Vol 47 (20) ◽  
pp. 10597-10611 ◽  
Author(s):  
Gabriel E Hoffman ◽  
Jaroslav Bendl ◽  
Kiran Girdhar ◽  
Eric E Schadt ◽  
Panos Roussos
Keyword(s):  
Deep Learning ◽  
Dna Sequence ◽  
Genetic Variants ◽  
Disease Risk ◽  
Underlying Disease ◽  
Functional Interpretation ◽  
Functional Consequence ◽  
Functional Variants ◽  
Risk Variants ◽  
Coding Variants

Abstract Identifying functional variants underlying disease risk and adoption of personalized medicine are currently limited by the challenge of interpreting the functional consequences of genetic variants. Predicting the functional effects of disease-associated protein-coding variants is increasingly routine. Yet, the vast majority of risk variants are non-coding, and predicting the functional consequence and prioritizing variants for functional validation remains a major challenge. Here, we develop a deep learning model to accurately predict locus-specific signals from four epigenetic assays using only DNA sequence as input. Given the predicted epigenetic signal from DNA sequence for the reference and alternative alleles at a given locus, we generate a score of the predicted epigenetic consequences for 438 million variants observed in previous sequencing projects. These impact scores are assay-specific, are predictive of allele-specific transcription factor binding and are enriched for variants associated with gene expression and disease risk. Nucleotide-level functional consequence scores for non-coding variants can refine the mechanism of known functional variants, identify novel risk variants and prioritize downstream experiments.

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.

scholarly journals Which genetic variants in DNase I sensitive regions are functional?

2014 ◽  
Author(s):  
Gregory A Moyerbrailean ◽  
Chris T Harvey ◽  
Cynthia A Kalita ◽  
Xiaoquan Wen ◽  
Francesca Luca ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Computational Models ◽  
Specific Binding ◽  
Dnase I ◽  
Regulatory Sequences ◽  
Dnase I Footprinting ◽  
Show Evidence ◽  
Allele Specific ◽  
The Impact

Ongoing large experimental characterization is crucial to determine all regulatory sequences, yet we do not know which genetic variants in those regions are non-silent. Here, we present a novel analysis integrating sequence and DNase I footprinting data for 653 samples to predict the impact of a sequence change on transcription factor binding for a panel of 1,372 motifs. Most genetic variants in footprints (5,810,227) do not show evidence of allele-specific binding (ASB). In contrast, functional genetic variants predicted by our computational models are highly enriched for ASB (3,217 SNPs at 20% FDR). Comparing silent to functional non-coding genetic variants, the latter are 1.22-fold enriched for GWAS traits, have lower allele frequencies, and affect footprints more distal to promoters or active in fewer tissues. Finally, integration of the annotations into 18 GWAS meta-studies improves identification of likely causal SNPs and transcription factors relevant for complex traits.

scholarly journals Associating disease-related genetic variants in intergenic regions to the genes they impact

2014 ◽  
Author(s):  
Geoff Macintyre ◽  
Antonio Jimeno Yepes ◽  
Cheng Soon Ong ◽  
Karin Verspoor
Keyword(s):  
Genetic Variants ◽  
Target Genes ◽  
Three Dimensional ◽  
Biomedical Literature ◽  
Dimensional Structure ◽  
Chromatin Conformation ◽  
Functional Interpretation ◽  
Chromatin Conformation Capture ◽  
Intergenic Regions ◽  
Small Test

We present a method to assist in interpretation of the functional impact of intergenic disease-associated SNPs that is not limited to search strategies proximal to the SNP. The method builds on two sources of external knowledge: the growing understanding of three-dimensional spatial relationships in the genome, and the substantial repository of information about relationships among genetic variants, genes, and diseases captured in the published biomedical literature. We integrate chromatin conformation capture data (HiC) with literature support to rank putative target genes of intergenic disease-associated SNPs. We demonstrate that this hybrid method outperforms a genomic distance baseline on a small test set of expression quantitative trait loci, as well as either method individually. In addition, we show the potential for this method to uncover relationships between intergenic SNPs and target genes across chromosomes. With more extensive chromatin conformation capture data becoming readily available, this method provides a way forward towards functional interpretation of SNPs in the context of the three dimensional structure of the genome in the nucleus.

scholarly journals Glucocorticoids Unmask Silent Non-Coding Genetic Risk Variants for Common Diseases

2021 ◽  
Author(s):  
Thanh Thanh Le Nguyen ◽  
Huanyao Gao ◽  
Duan Liu ◽  
Zhenqing Ye ◽  
Jeong-Heon Lee ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Genetic Variants ◽  
Disease Risk ◽  
Regulatory Elements ◽  
Risk Genes ◽  
3 Dimensional ◽  
Risk Variants ◽  
Expression Of Genes ◽  
Formidable Challenge ◽  
A Genome

AbstractUnderstanding the function of non-coding genetic variants represents a formidable challenge for biomedicine. We previously identified genetic variants that influence gene expression only after exposure to a hormone or drug. Using glucocorticoid signaling as a model system, we have now demonstrated, in a genome-wide manner, that exposure to glucocorticoids triggered disease risk variants with previously unclear function to influence the expression of genes involved in autoimmunity, metabolic and mood disorders, osteoporosis and cancer. Integrating a series of genomic and epigenomic assays, we identified the cis-regulatory elements and 3-dimensional interactions underlying the ligand-dependent associations between those genetic variants and distant risk genes. These observations increase our understanding of mechanisms of non-coding genetic variant-chemical environment interactions and advance the fine-mapping of disease risk and pharmacogenomic loci.One Sentence SummaryGenomic and epigenomic fine-mapping of ligand-dependent genetic variants unmasks novel disease risk genes

scholarly journals Combining SNP-to-gene linking strategies to pinpoint disease genes and assess disease omnigenicity

2021 ◽  
Author(s):  
Steven Gazal ◽  
Omer Weissbrod ◽  
Farhad Hormozdiari ◽  
Kushal Dey ◽  
Joseph Nasser ◽  
...  
Keyword(s):  
Fine Mapping ◽  
Complex Traits ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Common Disease ◽  
Disease Genes ◽  
Genome Wide Association Studies ◽  
Functional Interpretation ◽  
Genome Wide

Although genome-wide association studies (GWAS) have identified thousands of disease-associated common SNPs, these SNPs generally do not implicate the underlying target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis, but it is unclear how these strategies should be applied in the context of interpreting common disease risk variants. We developed a framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk, leveraging polygenic analyses of disease heritability to define and estimate their precision and recall. We applied our framework to GWAS summary statistics for 63 diseases and complex traits (average N=314K), evaluating 50 S2G strategies. Our optimal combined S2G strategy (cS2G) included 7 constituent S2G strategies (Exon, Promoter, 2 fine-mapped cis-eQTL strategies, EpiMap enhancer-gene linking, Activity-By-Contact (ABC), and Cicero), and achieved a precision of 0.75 and a recall of 0.33, more than doubling the precision and/or recall of any individual strategy; this implies that 33% of SNP-heritability can be linked to causal genes with 75% confidence. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 7,111 causal SNP-gene-disease triplets (with S2G-derived functional interpretation) with high confidence. Finally, we applied cS2G to genome-wide fine-mapping results for these traits (not restricted to GWAS loci) to rank genes by the heritability linked to each gene, providing an empirical assessment of disease omnigenicity; averaging across traits, we determined that the top 200 (1%) of ranked genes explained roughly half of the heritability linked to all genes. Our results highlight the benefits of our cS2G strategy in providing functional interpretation of GWAS findings; we anticipate that precision and recall will increase further under our framework as improved functional assays lead to improved S2G strategies. 

scholarly journals High throughput characterization of genetic effects on DNA:protein binding and gene transcription

2018 ◽  
Author(s):  
Cynthia A. Kalita ◽  
Christopher D. Brown ◽  
Andrew Freiman ◽  
Jenna Isherwood ◽  
Xiaoquan Wen ◽  
...  
Keyword(s):  
High Throughput ◽  
Complex Traits ◽  
Disease Risk ◽  
Specific Binding ◽  
P Value ◽  
Regulatory Sequences ◽  
Specific Expression ◽  
Pcr Duplicates ◽  
Allele Specific ◽  
Altered Gene

Many variants associated with complex traits are in non-coding regions, and contribute to phenotypes by disrupting regulatory sequences. To characterize these variants, we developed a streamlined protocol for a high-throughput reporter assay, BiT-STARR-seq (Biallelic Targeted STARR-seq), that identifies allele-specific expression (ASE) while accounting for PCR duplicates through unique molecular identifiers. We tested 75,501 oligos (43,500 SNPs) and identified 2,720 SNPs with significant ASE (FDR 10%). To validate disruption of binding as one of the mechanisms underlying ASE, we developed a new high throughput allele specific binding assay for NFKB-p50. We identified 2,951 SNPs with allele-specific binding (ASB) (FDR 10%); 173 of these SNPs also had ASE (OR=1.97, p-value=0.0006). Of variants associated with complex traits, 1,531 resulted in ASE and 1,662 showed ASB. For example, we characterized that the Crohn’s disease risk variant for rs3810936 increases NFKB binding and results in altered gene expression.

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