Biological relevance of computationally predicted pathogenicity of noncoding variants

Liu, Li; Sanderford, Maxwell; Patel, Ravi; Chandrashekar, Pramod; Gibson, Greg; Kumar, Sudhir

doi:10.1038/s41467-018-08270-y

Cited by 47 publications

(52 citation statements)

References 75 publications

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“…Even though most eQTL span 100 or more polymorphisms in a credible interval, the general assumption is that prioritizing variants according to functional criteria and evolutionary conservation, using scores, such as CADD or LINSIGHT, reduces the search space to fewer than ten candidates. However, given that these variants are in tight linkage disequilibrium with similar frequencies [10], if they have similar functional scores, then it is possible that the observed univariate eQTL effect is actually due to the summation of two or smaller contributing effects. Under this scenario, the power to detect multiple causal variants is also reduced.…”

Section: Discussionmentioning

confidence: 99%

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Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

Tian

Pan

Etheridge

et al. 2020

Genes

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The majority of genetic variants affecting complex traits map to regulatory regions of genes, and typically lie in credible intervals of 100 or more SNPs. Fine mapping of the causal variant(s) at a locus depends on assays that are able to discriminate the effects of polymorphisms or mutations on gene expression. Here, we evaluated a moderate-throughput CRISPR-Cas9 mutagenesis approach, based on replicated measurement of transcript abundance in single-cell clones, by deleting candidate regulatory SNPs, affecting four genes known to be affected by large-effect expression Quantitative Trait Loci (eQTL) in leukocytes, and using Fluidigm qRT-PCR to monitor gene expression in HL60 pro-myeloid human cells. We concluded that there were multiple constraints that rendered the approach generally infeasible for fine mapping. These included the non-targetability of many regulatory SNPs, clonal variability of single-cell derivatives, and expense. Power calculations based on the measured variance attributable to major sources of experimental error indicated that typical eQTL explaining 10% of the variation in expression of a gene would usually require at least eight biological replicates of each clone. Scanning across credible intervals with this approach is not recommended.

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Section: Discussionmentioning

confidence: 99%

“…Third, the majority of the risk loci are located in non-coding regions of genes [7,8], where they exert their function through regulation of gene expression. Tools for predicting the function of such causal variants generally have low predictive value [9,10].…”

Section: Introductionmentioning

confidence: 99%

Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

Tian

Pan

Etheridge

et al. 2020

Genes

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“…Third, our S-LDSC analyses are inherently focused on common variants, but deep learning models have also shown promise in prioritizing rare pathogenic variants 4,8,51 . The value of deep learning models for prioritizing rare pathogenic variants has been questioned in a recent analysis focusing on Human Gene Mutation Database (HGMD) variants 52 , meriting further investigation. Fourth, we focused here on deep learning models trained using human data, but models trained using data from other species may also be informative for human disease 24,53 .…”

Section: Discussionmentioning

confidence: 99%

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

Dey

Kim

Gazal

et al. 2020

Preprint

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Deep learning models have achieved great success in predicting genome-wide regulatory effects from DNA sequence, but recent work has reported that SNP annotations derived from these predictions contribute limited unique information for human complex disease. Here, we explore three integrative approaches to improve the disease informativeness of allelic-effect annotations (predicted difference between reference and variant alleles) constructed using two previously trained deep learning models, DeepSEA and Basenji. First, we employ gradient boosting to learn optimal combinations of deep learning annotations, using (off-chromosome) fine-mapped SNPs and matched control SNPs for training. Second, we improve the specificity of these annotations by restricting them to SNPs implicated by (proximal and distal) SNP-to-gene (S2G) linking strategies, e.g. prioritizing SNPs involved in gene regulation. Third, we predict gene expression (and derive allelic-effect annotations) from deep learning annotations at SNPs implicated by S2G linking strategies — generalizing the previously proposed ExPecto approach, which incorporates deep learning annotations based on distance to TSS. We evaluated these approaches using stratified LD score regression, using functional data in blood and focusing on 11 autoimmune diseases and blood-related traits (average N=306K). We determined that the three approaches produced SNP annotations that were uniquely informative for these diseases/traits, despite the fact that linear combinations of the underlying DeepSEA and Basenji blood annotations were not uniquely informative for these diseases/traits. Our results highlight the benefits of integrating SNP annotations produced by deep learning models with other types of data, including data linking SNPs to genes.

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“…Although the distribution of CADD scores was significantly higher for the reported-peak variants, suggesting elevated likelihood that they are pathogenic ( Figure 4A for CAGE, and Figure 4B for FHS), the magnitude of the effect is small relative to the variance in CADD scores. Correspondingly, the positive predictive value for each SNP is low and functional discrimination of primary and secondary signals by this measure is poor (see also Liu et al 2019). Potential causal variants defined by fine-mapping also have only a slightly elevated probability of locating within regulatory enhancers in the human genome as defined by the deltaSVM score.…”

Section: Biological Annotation Of Detected Multiple Eqtlsmentioning

confidence: 99%

Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation

et al. 2019

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Expression QTL (eQTL) detection has emerged as an important tool for unraveling the relationship between genetic risk factors and disease or clinical phenotypes. Most studies are predicated on the assumption that only a single causal variant explains the association signal in each interval. This greatly simplifies the statistical modeling, but is liable to biases in scenarios where multiple local causal-variants are responsible. Here, our primary goal was to address the prevalence of secondary cis-eQTL signals regulating peripheral blood gene expression locally, utilizing two large human cohort studies, each >2500 samples with accompanying whole genome genotypes. The CAGE (Consortium for the Architecture of Gene Expression) dataset is a compendium of Illumina microarray studies, and the Framingham Heart Study is a two-generation Affymetrix dataset. We also describe Bayesian colocalization analysis of the extent of sharing of cis-eQTL detected in both studies as well as with the BIOS RNAseq dataset. Stepwise conditional modeling demonstrates that multiple eQTL signals are present for ∼40% of over 3500 eGenes in both microarray datasets, and that the number of loci with additional signals reduces by approximately two-thirds with each conditioning step. Although <20% of the peak signals across platforms fine map to the same credible interval, the colocalization analysis finds that as many as 50–60% of the primary eQTL are actually shared. Subsequently, colocalization of eQTL signals with GWAS hits detected 1349 genes whose expression in peripheral blood is associated with 591 human phenotype traits or diseases, including enrichment for genes with regulatory functions. At least 10%, and possibly as many as 40%, of eQTL-trait colocalized signals are due to nonprimary cis-eQTL peaks, but just one-quarter of these colocalization signals replicated across the gene expression datasets. Our results are provided as a web-based resource for visualization of multi-site regulation of gene expression and its association with human complex traits and disease states.

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Biological relevance of computationally predicted pathogenicity of noncoding variants

Cited by 47 publications

References 75 publications

Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

Pitfalls in Single Clone CRISPR-Cas9 Mutagenesis to Fine-Map Regulatory Intervals

Integrative approaches to improve the informativeness of deep learning models for human complex diseases

Comprehensive Multiple eQTL Detection and Its Application to GWAS Interpretation

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