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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): unidentified plasmid Homo sapiens

PROVIDER: GSE136703 | GEO | 2020/02/11

REPOSITORIES: GEO

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

Project description:Genome-wide association studies have associated thousands of genetic variants with complex traits and diseases, but pinpointing the causal variant(s) among those in tight linkage disequilibrium with each associated variant remains a major challenge. Here, we used seven experimental assays to characterize all common variants at the multiple disease-associated TNFAIP3 locus in three disease-relevant immune cell types, based on a set of features related to regulatory potential. Trait/disease-associated variants were enriched among SNPs prioritized based on either: (1) residing within CRISPRi-sensitive regulatory regions, or (2) localizing in a chromatin accessible region while displaying allele-specific reporter activity. Of the 15 trait/disease-associated haplotypes at TNFAIP3, 9 had at least one variant meeting one or both of these criteria, with 3 of these haplotypes having a single prioritized variant. 5 of the 9 prioritized variants were further supported by genetic fine-mapping in our and other studies. Our work provides evidence for the efficacy and limitations of strategies for prioritizing disease- and trait-associated genetic variants.

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [ATAC-seq]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [CRISPR guide-seq]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

Project description:Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

Project description:Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [MPRA]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [ATAC-seq]

Project description:Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [ATAC-seq]

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Assessing the ability of various genomic features to prioritize causal non-coding variants associated with diseases and traits [CRISPR guide-seq]

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Mapping short tandem repeats for liver gene expression traits helps prioritize potential causal variants for complex traits in pigs.

Project description:BackgroundShort tandem repeats (STRs) were recently found to have significant impacts on gene expression and diseases in humans, but their roles on gene expression and complex traits in pigs remain unexplored. This study investigates the effects of STRs on gene expression in liver tissues based on the whole-genome sequences and RNA-Seq data of a discovery cohort of 260 F6 individuals and a validation population of 296 F7 individuals from a heterogeneous population generated from crosses among eight pig breeds.ResultsWe identified 5203 and 5868 significantly expression STRs (eSTRs, FDR < 1%) in the F6 and F7 populations, respectively, most of which could be reciprocally validated (π1 = 0.92). The eSTRs explained 27.5% of the cis-heritability of gene expression traits on average. We further identified 235 and 298 fine-mapped STRs through the Bayesian fine-mapping approach in the F6 and F7 pigs, respectively, which were significantly enriched in intron, ATAC peak, compartment A and H3K4me3 regions. We identified 20 fine-mapped STRs located in 100 kb windows upstream and downstream of published complex trait-associated SNPs, which colocalized with epigenetic markers such as H3K27ac and ATAC peaks. These included eSTR of the CLPB, PGLS, PSMD6 and DHDH genes, which are linked with genome-wide association study (GWAS) SNPs for blood-related traits, leg conformation, growth-related traits, and meat quality traits, respectively.ConclusionsThis study provides insights into the effects of STRs on gene expression traits. The identified eSTRs are valuable resources for prioritizing causal STRs for complex traits in pigs.

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Integrating functional data to prioritize causal variants in statistical fine-mapping studies.

Project description:Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

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Openness weighted association studies: leveraging personal genome information to prioritize non-coding variants.

Project description:MotivationIdentification and interpretation of non-coding variations that affect disease risk remain a paramount challenge in genome-wide association studies (GWAS) of complex diseases. Experimental efforts have provided comprehensive annotations of functional elements in the human genome. On the other hand, advances in computational biology, especially machine learning approaches, have facilitated accurate predictions of cell-type-specific functional annotations. Integrating functional annotations with GWAS signals has advanced the understanding of disease mechanisms. In previous studies, functional annotations were treated as static of a genomic region, ignoring potential functional differences imposed by different genotypes across individuals.ResultsWe develop a computational approach, Openness Weighted Association Studies (OWAS), to leverage and aggregate predictions of chromosome accessibility in personal genomes for prioritizing GWAS signals. The approach relies on an analytical expression we derived for identifying disease associated genomic segments whose effects in the etiology of complex diseases are evaluated. In extensive simulations and real data analysis, OWAS identifies genes/segments that explain more heritability than existing methods, and has a better replication rate in independent cohorts than GWAS. Moreover, the identified genes/segments show tissue-specific patterns and are enriched in disease relevant pathways. We use rheumatic arthritis and asthma as examples to demonstrate how OWAS can be exploited to provide novel insights on complex diseases.Availability and implementationThe R package OWAS that implements our method is available at https://github.com/shuangsong0110/OWAS.Supplementary informationSupplementary data are available at Bioinformatics online.

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