Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

Pickrell, Joseph K.

doi:10.1101/000752

Cited by 7 publications

(6 citation statements)

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“…We modeled the probability that a SNP is associated given its functional data using a logistic regression model in which the association status of the individual SNPs in the case blocks is assumed unknown. The model assumes, however, that each case block contains at least one truly associated SNP and that each control block contains none; as in [ 7 , 14 ], the identities of the associated SNPs are not specified as they are unknown (details of the model are provided in Section “Model”); a prior distribution on the overall fraction of associated SNPs was employed to encourage their number to be close to one on average. In this way, the model is free to identify multivariate patterns in the annotation data (‘signatures’) of individual SNPs that would be diluted by averaging the annotation measurements of all SNPs within a block.…”

Section: Resultsmentioning

confidence: 99%

“…This may explain, at least in part, the relatively modest nature of the improvements observed in our GWAS analysis and the failure of general summaries of eQTL status to contribute meaningfully to the functional signatures. Indeed, many regulatory processes are cell–type–specific [ 11 , 12 ] and hence will be more informative for a given phenotype if measured in the appropriate context [ 14 ]. However, determining the relevant annotation data, assuming it exists, for a given phenotype requires domain expertise and more careful modeling to create functional signatures.…”

Section: Discussionmentioning

confidence: 99%

“…It is becoming increasingly clear that a widening array of annotation data correlates with a variant’s having been associated with a human phenotype [ 7 – 14 ]. In what follows, we describe a formal approach to inference for association that combines functional annotation data (through a prior distribution) with genotype data (through a sampling model for the phenotype given genetic and other covariate data).…”

Section: Introductionmentioning

confidence: 99%

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Functional annotation signatures of disease susceptibility loci improve SNP association analysis

et al. 2014

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BackgroundGenetic association studies are conducted to discover genetic loci that contribute to an inherited trait, identify the variants behind these associations and ascertain their functional role in determining the phenotype. To date, functional annotations of the genetic variants have rarely played more than an indirect role in assessing evidence for association. Here, we demonstrate how these data can be systematically integrated into an association study’s analysis plan.ResultsWe developed a Bayesian statistical model for the prior probability of phenotype–genotype association that incorporates data from past association studies and publicly available functional annotation data regarding the susceptibility variants under study. The model takes the form of a binary regression of association status on a set of annotation variables whose coefficients were estimated through an analysis of associated SNPs in the GWAS Catalog (GC). The functional predictors examined included measures that have been demonstrated to correlate with the association status of SNPs in the GC and some whose utility in this regard is speculative: summaries of the UCSC Human Genome Browser ENCODE super–track data, dbSNP function class, sequence conservation summaries, proximity to genomic variants in the Database of Genomic Variants and known regulatory elements in the Open Regulatory Annotation database, PolyPhen–2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotations would contribute to predicting association, we employed a penalized likelihood method to reduce the impact of non–informative predictors and evaluated the model’s ability to predict GC SNPs not used to construct the model. We show that the functional data alone are predictive of a SNP’s presence in the GC. Further, using data from a genome–wide study of ovarian cancer, we demonstrate that their use as prior data when testing for association is practical at the genome–wide scale and improves power to detect associations.ConclusionsWe show how diverse functional annotations can be efficiently combined to create ‘functional signatures’ that predict the a priori odds of a variant’s association to a trait and how these signatures can be integrated into a standard genome–wide–scale association analysis, resulting in improved power to detect truly associated variants.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-398) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Functional annotation signatures of disease susceptibility loci improve SNP association analysis

et al. 2014

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“…Mendelian traits [50]. Recent approaches have used functional annotation to boost GWAS power in identifying SNP associations [51], stratify heritability of complex disease by functional annotation [52],…”

Section: Discussionmentioning

confidence: 99%

Liver-Specific Polygenic Risk Score Is Associated with Alzheimer’s Disease Diagnosis

Panyard

Deming

Darst

et al. 2023

JAD

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Background: Our understanding of the pathophysiology underlying Alzheimer’s disease (AD) has benefited from genomic analyses, including those that leverage polygenic risk score (PRS) models of disease. The use of functional annotation has been able to improve the power of genomic models. Objective: We sought to leverage genomic functional annotations to build tissue-specific AD PRS models and study their relationship with AD and its biomarkers. Methods: We built 13 tissue-specific AD PRS and studied the scores’ relationships with AD diagnosis, cerebrospinal fluid (CSF) amyloid, CSF tau, and other CSF biomarkers in two longitudinal cohort studies of AD. Results: The AD PRS model that was most predictive of AD diagnosis (even without APOE) was the liver AD PRS: n = 1,115; odds ratio = 2.15 (1.67–2.78), p = 3.62×10–9. The liver AD PRS was also statistically significantly associated with cerebrospinal fluid biomarker evidence of amyloid-β (Aβ 42:Aβ 40 ratio, p = 3.53×10–6) and the phosphorylated tau:amyloid-β ratio (p = 1.45×10–5). Conclusion: These findings provide further evidence of the role of the liver-functional genome in AD and the benefits of incorporating functional annotation into genomic research.

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“…Aggregation was done to keep SNPs with the lowest p-value for each LD block and if multiple with the same, the shortest distance to the effect region (to avoid biases towards distal interactions). The Pickrell LD blocks 44 (available from https://bitbucket.org/nygcresearch/ldetect-data) were used for this analysis (Additional file 1: Fig. S2).…”

Section: Calculating Genetic Variant Distance From Epigenetic Effect ...mentioning

confidence: 99%

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Murphy,

Beardall,

Rei

et al. 2024

Preprint

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Understanding genetic variants effects on the epigenome is crucial for interpreting genome-wide association studies (GWAS) results, yet profiling these effects across the non-coding genome remains challenging due to the scalability limits of experimental methods. This necessitates accurate computational models. Existing machine learning approaches, while progressively improving, are confined to the cell types they were trained on, limiting their applicability. Here, we propose the development of a deep learning model, Enformer Celltyping, which can both incorporate distal effects of DNA interactions, up to 100,000 base-pairs away, and predict epigenetic signals in a cell type-agnostic fashion. We demonstrate that Enformer Celltypings predictions of the epigenome out-perform the current best-in-class approach, have strong performance in a range of different cell types and biological regions and generalise to cell types assayed independently of the original training set. Finally, we propose an approach to test epigenetic models performance on genetic variant effect predictions using regulatory quantitative trait loci mapping studies, highlighting Enformer Celltypings and other genomic deep learning models limitations for this task. Our work introduces a new, accurate model for cell type-agnostic histone mark predictions and highlights the benefit of transfer learning for more efficient model development for deep learning in genomics. We make our customisable and efficient transfer learning approach for Enformer available to the community.

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Joint analysis of functional genomic data and genome-wide association studies of 18 human traits

Cited by 7 publications

References 57 publications

Functional annotation signatures of disease susceptibility loci improve SNP association analysis

Functional annotation signatures of disease susceptibility loci improve SNP association analysis

Liver-Specific Polygenic Risk Score Is Associated with Alzheimer’s Disease Diagnosis

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

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