Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci

Trynka, Gosia; Westra, Harm Jan; Slowikowski, Kamil; Hu, Xinli; Han, Xu; Stranger, Barbara Elaine; Klein, Robert J.; Han, Buhm; Raychaudhuri, Soumya

doi:10.1016/j.ajhg.2015.05.016

Cited by 127 publications

(132 citation statements)

References 61 publications

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“…For 35 CS variants (out of 186 variants from 17 loci; 18.8%), we identified an overlap with hematopoietic NDRs, a significant enrichment compared with non-CS variants in moderate to high LD (r 2 > 0.5) (OR = 2.41, P = 0.0009). Additionally, a permutation test involving local shifting of the NDRs around the CS variants revealed a significant enrichment (1.87-fold change in overlap, P = 0.00042) (28). Furthermore, at 11 of 13 independent loci (85%), at least one CS variant overlapped a NDR ( Fig.…”

Section: −15mentioning

confidence: 99%

Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms

Guo

Nandakumar

Ulirsch

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Genetic variants affecting hematopoiesis can influence commonly measured blood cell traits. To identify factors that affect hematopoiesis, we performed association studies for blood cell traits in the population-based Estonian Biobank using highcoverage whole-genome sequencing (WGS) in 2,284 samples and SNP genotyping in an additional 14,904 samples. Using up to 7,134 samples with available phenotype data, our analyses identified 17 associations across 14 blood cell traits. Integration of WGS-based fine-mapping and complementary epigenomic datasets provided evidence for causal mechanisms at several loci, including at a previously undiscovered basophil countassociated locus near the master hematopoietic transcription factor CEBPA. The fine-mapped variant at this basophil count association near CEBPA overlapped an enhancer active in common myeloid progenitors and influenced its activity. In situ perturbation of this enhancer by CRISPR/Cas9 mutagenesis in hematopoietic stem and progenitor cells demonstrated that it is necessary for and specifically regulates CEBPA expression during basophil differentiation. We additionally identified basophil count-associated variation at another more pleiotropic myeloid enhancer near GATA2, highlighting regulatory mechanisms for ordered expression of master hematopoietic regulators during lineage specification. Our study illustrates how population-based genetic studies can provide key insights into poorly understood cell differentiation processes of considerable physiologic relevance.genome sequencing | GWAS | basophils | hematopoiesis | CEBPA

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Section: −15mentioning

confidence: 99%

Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms

Guo

Nandakumar

Ulirsch

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…However, current methods that aim to evaluate the contribution of such regions to genetic variation in disease cannot always do so robustly or are not readily applicable for systematic analysis and comparison of broad sets of features. In particular, it has been shown that LD, gene density and MAF can confound enrichment analysis results 12 . Here we further estimated the relative effect of each of those features and identified LD as the largest confounder.…”

Section: Discussionmentioning

confidence: 99%

“…In their simpler implementation, they estimate enrichment of association p--values based on comparisons of the full set of genetic variants analysed in the GWAS study [9][10][11] , or on subsets of highly associated variants, for instance variants achieving genome--wide significance [12][13][14] . These approaches have identified many biologically plausible patterns of correlation (for instance in open chromatin marks for lipid traits in liver cell types and Crohn's disease in immune cells) and are broadly used for ranking the relative contribution of features.…”

Section: Introductionmentioning

confidence: 99%

“…Genomic marks may cover a large proportion of the genome, and thus many disease--associated variants will be found within these marks by chance. In addition, the heterogeneous distribution of genetic variants and functional regions along the human genome, and thus non--random association with genomic features 7,8 , can create spurious correlations that again confound correct interpretation.Functional enrichment methods exploit experimentally derived regulatory genomic regions to assess the relative contribution of variation in each cell type and regulatory annotation to a given phenotype of interest.In their simpler implementation, they estimate enrichment of association p--values based on comparisons of the full set of genetic variants analysed in the GWAS study [9][10][11] , or on subsets of highly associated variants, for instance variants achieving genome--wide significance [12][13][14] . These approaches have identified many biologically plausible patterns of correlation (for instance in open chromatin marks for lipid traits in liver cell types and Crohn's disease in immune cells) and are broadly used for ranking the relative contribution of features.…”

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confidence: 99%

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GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction

Iotchkova

Grs.

Geihs

et al. 2016

Preprint

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Loci discovered by genome--wide association studies (GWAS) predominantly map outside protein--coding genes. The interpretation of functional consequences of non--coding variants can be greatly enhanced by catalogs of regulatory genomic regions in cell lines and primary tissues. However, robust and readily applicable methods are still lacking to systematically evaluate the contribution of these regions to genetic variation implicated in diseases or quantitative traits. Here we propose a novel approach that leverages GWAS findings with regulatory or functional annotations to classify features relevant to a phenotype of interest. Within our framework, we account for major sources of confounding that current methods do not offer. We further assess enrichment statistics for 27 GWAS traits within regulatory regions from the ENCODE and Roadmap projects. We characterise unique enrichment patterns for traits and annotations, driving novel biological insights. The method is implemented in standalone software and R package to facilitate its application by the research community. IntroductionGenome--wide association studies (GWAS) have discovered susceptibility variants for complex diseases and biomedical quantitative traits, with over 16 000 genotype--phenotype associations found to date 1,2 ,representing a large investment in resources, time and organisation to understanding human disease and other phenotypes. Despite the statistical soundness of the discovered associations, a large proportion (~90%) of implicated variants are classified as intronic or intergenic 3 and thus do not have a straightforward link to a cellular or molecular mechanism. This has prompted a number of efforts to annotate their putative functional . CC-BY-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/085738 doi: bioRxiv preprint first posted online Nov. 7, 2016; consequences in cell specific contexts from experimentally derived regulatory genomic regions (e.g. regions marked by histone modifications, of open chromatin and transcription factor binding [3][4][5][6] ), principally as a means to inform and accelerate functional validation efforts.The robust identification of which combinations of cells and marks are most informative for a given disease or quantitative trait of interest (henceforth referred generically to as 'phenotype') requires that one can confidently identify biologically meaningful correlations. Genomic marks may cover a large proportion of the genome, and thus many disease--associated variants will be found within these marks by chance. In addition, the heterogeneous distribution of genetic variants and functional regions along the human genome, and thus non--random association with genomic features 7,8 , can create spurious correlations that again confound correct interpretation.Functional enrichment methods exploit experimentally derived regulatory genomic regions to assess the relative contributio...

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“…If considering LD is crucial, we refer the user to the GoShifter enrichment tool (Trynka et al, 2015) that uses LD information from the Phase I 1000 genomes data release and estimates enrichments by local permutations. However, regulatory similarities among the 39 disease/trait-associated SNP sets obtained with GoShifter correlated less well with the shared genomic loci similarities (Supplementary Table S4).…”

Section: Discussionmentioning

confidence: 99%

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets

et al. 2016

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Motivation: The growing amount of regulatory data from the ENCODE, Roadmap Epigenomics and other consortia provides a wealth of opportunities to investigate the functional impact of single nucleotide polymorphisms (SNPs). Yet, given the large number of regulatory datasets, researchers are posed with a challenge of how to efficiently utilize them to interpret the functional impact of SNP sets. Results: We developed the GenomeRunner web server to automate systematic statistical analysis of SNP sets within a regulatory context. Besides defining the functional impact of SNP sets, GenomeRunner implements novel regulatory similarity/differential analyses, and cell type-specific regulatory enrichment analysis. Validated against literature-and disease ontology-based approaches, analysis of 39 disease/trait-associated SNP sets demonstrated that the functional impact of SNP sets corresponds to known disease relationships. We identified a group of autoimmune diseases with SNPs distinctly enriched in the enhancers of T helper cell subpopulations, and demonstrated relevant cell type-specificity of the functional impact of other SNP sets. In summary, we show how systematic analysis of genomic data within a regulatory context can help interpreting the functional impact of SNP sets. Availability and Implementation: GenomeRunner web server is freely available at http://www.inte grativegenomics.org/. Contact:

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Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci

Cited by 127 publications

References 61 publications

Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms

Comprehensive population-based genome sequencing provides insight into hematopoietic regulatory mechanisms

GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction

GenomeRunner web server: regulatory similarity and differences define the functional impact of SNP sets

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