Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Reshef, Yakir; Finucane, Hilary; Kelley, David R.; Gusev, Alexander; Kotliar, Dylan; Ulirsch, Jacob C.; Hormozdiari, Farhad; Nasser, Joseph; O’Connor, Luke; Geijn, Bryce van de; Loh, Po Ru; Grossman, Sharon R.; Bhatia, Gaurav; Gazal, Steven; Palamara, Pier Francesco; Pinello, Luca; Patterson, Nick; Adams, Ryan P.; Price, Alkes L.

doi:10.1038/s41588-018-0196-7

Cited by 64 publications

(91 citation statements)

References 130 publications

(142 reference statements)

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“…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 40 , meriting further investigation. Second, our analyses of allelic-effect annotations are restricted to unsigned analyses, but signed analyses have also proven valuable in linking deep learning annotations to molecular traits and complex disease 16,41,42 ; however, genome-wide signed relationships are unlikely to hold for the regulatory marks (DNase and histone marks) that we focus on here, which do not correspond to specific genes or pathways. Third, we focused here on deep learning models trained to predict specific regulatory marks, but deep learning models have also been used to predict a broader set of regulatory features, including gene expression levels and cryptic splicing 15,16,39 , that may be informative for complex disease.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the informativeness of deep learning annotations for human complex diseases

Dey

Geijn

Kim

et al. 2019

Preprint

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Deep learning models have shown great promise in predicting genome-wide regulatory effects from DNA sequence, but their informativeness for human complex diseases and traits is not fully understood. Here, we evaluate the disease informativeness of two types of deep learning annotations: (1) variant-level annotations (based on the reference allele), assessing whether they are more informative for complex disease than the underlying experimental data used to train the predictive models; and (2) allelic-effect annotations (absolute value of the predicted difference between reference and variant alleles), which have been a major focus of recent work. In each case, we primarily consider annotations constructed using two previously trained deep learning models, DeepSEA and Basenji. We apply stratified LD score regression (S-LDSC) to 41 independent diseases and complex traits (average N =320K) to evaluate each annotation's informativeness for disease heritability conditional on a broad set of coding, conserved, regulatory and LDrelated annotations from the baseline-LD model and other sources; as a secondary metric, we also evaluate the accuracy of models that incorporate deep learning annotations in predicting disease-associated or fine-mapped SNPs. We aggregated annotations across all tissues (resp. blood cell types or brain tissues) in metaanalyses across all 41 traits (resp. 11 blood-related traits or 8 brain-related traits). Variant-level annotations, despite being highly enriched for disease heritability, produced no conditionally significant results in meta-analyses across all 41 traits or 11 blood-related traits, but brain-specific DeepSEA-H3K4me3 and Basenji-H3K27ac annotations were conditionally significant in meta-analyses across 8 brain-related traits; a sequence motif analysis suggests that these annotations could be capturing unique information about nucleosome occupancy. Allelic-effect annotations were also highly enriched for disease heritability, and produced conditionally significant results for Basenji-H3K4me3 in meta-analyses across all 41 traits and brain-specific Basenji-H3K4me3 in meta-analyses across 8 brain-related traits. We conclude that deep learning models are informative for disease, but their informativeness cannot be inferred from metrics based on their accuracy in predicting regulatory annotations.

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Section: Discussionmentioning

confidence: 99%

Evaluating the informativeness of deep learning annotations for human complex diseases

Dey

Geijn

Kim

et al. 2019

Preprint

Self Cite

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“…This is mathematically equivalent to direct simulations but is substantially faster and allows modifying the residual variance for any desired sample size . Specifically, we sampled a vector of marginal effect sizes in each locus from ( , ⋅ / ) where is a matrix of summary LD information (computed via LDstore 30 ), are effect sizes sampled iid from (0, ) for causal SNPs (and set to 0 for non-causal SNPs), and = 1 − is the phenotypic variance not causally explained by SNPs in the locus 23,67 (see below). The causal variance was set to ℎ / where ℎ is the desired SNP heritability and is the expected number of causal SNPs.…”

Section: Fine-mapping Simulationsmentioning

confidence: 99%

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Weissbrod

Hormozdiari

Benner

et al. 2019

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162

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Fine-mapping aims to identify causal variants impacting complex traits. Several recent methods improve fine-mapping accuracy by prioritizing variants in enriched functional annotations. However, these methods can only use information at genome-wide significant loci (and/or a small number of functional annotations), severely limiting the benefit of functional data. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy using genome-wide functional data for a broad set of coding, conserved, regulatory and LD-related annotations. PolyFun prioritizes variants in enriched functional annotations by specifying prior causal probabilities for fine-mapping methods such as SuSiE or FINEMAP, employing special procedures to ensure robustness to model misspecification and winner's curse. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well-calibrated and identified >20% more variants with posterior causal probability >0.95 than their non-functionally informed counterparts (and >33% more fine-mapped variants than previous functionally-informed fine-mapping methods). In analyses of 47 UK Biobank traits (average N=317K), PolyFun + SuSiE identified 3,025 fine-mapped varianttrait pairs with posterior causal probability >0.95, a >32% improvement vs. SuSiE; 223 variants were finemapped for multiple genetically uncorrelated traits, indicating pervasive pleiotropy. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 25 (hair color) to 3,400 (height) to 550,000 (chronotype). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures.

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“…To generalize these observations, we compiled >1,300 traits with positive estimated SNP-based heritability from the UK Biobank project 27 and from curated published data 28 . Of these, 261 diseases and traits showed highly significant component-specific enrichment in heritability, particularly for pathophysiologically relevant regulatory components ( Fig.…”

Section: Annotation Of Trait-associated Genetic Variationmentioning

confidence: 99%

Index and biological spectrum of accessible DNA elements in the human genome

Meuleman

Muratov

Rynes

et al. 2019

Preprint

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DNase I hypersensitive sites (DHSs) are generic markers of regulatory DNA and harbor disease-and phenotypic trait-associated genetic variation. We established high-precision maps of DNase I hypersensitive sites from 733 human biosamples encompassing 439 cell and tissue types and states, and integrated these to precisely delineate and numerically index ~3.6 million DHSs encoded within the human genome, providing a common coordinate system for regulatory DNA. Here we show that the expansive scale of cell and tissue states sampled exposes an unprecedented degree of stereotyped actuation of large sets of elements, signaling the operation of distinct genome-scale regulatory programs. We show further that the complex actuation patterns of individual elements can be captured comprehensively by a simple regulatory vocabulary reflecting their dominant cellular manifestation. This vocabulary, in turn, enables comprehensive and quantitative regulatory annotation of both protein-coding genes and the vast array of well-defined but poorly-characterized non-coding RNA genes. Finally, we show that the combination of high-precision DHSs and regulatory vocabularies markedly concentrate disease-and trait-associated non-coding genetic signals both along the genome and across cellular compartments. Taken together, our results provide a common and extensible coordinate system and vocabulary for human regulatory DNA, and a new global perspective on the architecture of human gene regulation.

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Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk

Cited by 64 publications

References 130 publications

Evaluating the informativeness of deep learning annotations for human complex diseases

Evaluating the informativeness of deep learning annotations for human complex diseases

Functionally-informed fine-mapping and polygenic localization of complex trait heritability

Index and biological spectrum of accessible DNA elements in the human genome

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