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Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures.

Wang X, Tucker NR, Rizki G, Mills R, Krijger PH, de Wit E, Subramanian V, Bartell E, Nguyen XX, Ye J, Leyton-Mange J, Dolmatova EV, van der Harst P, de Laat W, Ellinor PT, Newton-Cheh C, Milan DJ, Kellis M, Boyer LA - Elife (2016)

Bottom Line: We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies.We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse.Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Massachusetts Institute of Technology, Cambridge, United States.

ABSTRACT
Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

No MeSH data available.


Related in: MedlinePlus

Sub-threshold SNP alleles affect enhancer activity.For each sub-threshold locus, enhancers carrying one of two haplotypes were cloned upstream of a minimal promoter and firefly luciferase reporter gene. Blue: enhancer carrying reference allele; red: enhancer carrying alternate allele. Error bars represent standard error of the mean.DOI:http://dx.doi.org/10.7554/eLife.10557.014
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fig4s1: Sub-threshold SNP alleles affect enhancer activity.For each sub-threshold locus, enhancers carrying one of two haplotypes were cloned upstream of a minimal promoter and firefly luciferase reporter gene. Blue: enhancer carrying reference allele; red: enhancer carrying alternate allele. Error bars represent standard error of the mean.DOI:http://dx.doi.org/10.7554/eLife.10557.014

Mentions: Because the enrichment of sub-threshold SNPs in cardiac enhancers suggests that epigenetic prioritization can be used as a starting point for more in-depth investigations of sub-threshold signals from GWAS, we sought to directly test the molecular hypothesis that these sub-threshold loci impact the transcriptional regulation of cardiac genes (Figure 4a). We grouped all 2075 sub-threshold SNPs using linkage disequilibrium data (minimum r2=0.2) to identify 287 independent sub-threshold loci in the genome (Materials and methods). We prioritized loci where a sub-threshold SNP overlapped an active LV enhancer and either (i) also overlapped a fetal heart DNase I hypersensitivity peak or (ii) was an expression quantitative trait locus (eQTL) for a nearby gene. In total, we cloned allele-specific enhancer fragments from 22 cardiac enhancers that overlap SNPs from 18 independent sub-threshold loci, and performed quantitative luciferase assays in human iPSC-derived cardiomyocytes to determine whether the sub-threshold SNP genotypes influence enhancer activity (Materials and methods). We observed that 13 of 18 sub-threshold loci (72.2%) contain an enhancer that drives luciferase activity in an allele-specific manner (Figure 4b,d, Figure 4—figure supplement 1). Moreover, we estimate that between 51.1%-89.8% (95% Bayesian confidence interval) of prioritized sub-threshold loci show allele-specific activity on transcription, suggesting that the majority of sub-threshold loci identified by epigenomic prioritization do in fact have an impact on transcriptional enhancer activity.10.7554/eLife.10557.013Figure 4.Sub-threshold loci prioritized by epigenomics alter enhancer activity.


Discovery and validation of sub-threshold genome-wide association study loci using epigenomic signatures.

Wang X, Tucker NR, Rizki G, Mills R, Krijger PH, de Wit E, Subramanian V, Bartell E, Nguyen XX, Ye J, Leyton-Mange J, Dolmatova EV, van der Harst P, de Laat W, Ellinor PT, Newton-Cheh C, Milan DJ, Kellis M, Boyer LA - Elife (2016)

Sub-threshold SNP alleles affect enhancer activity.For each sub-threshold locus, enhancers carrying one of two haplotypes were cloned upstream of a minimal promoter and firefly luciferase reporter gene. Blue: enhancer carrying reference allele; red: enhancer carrying alternate allele. Error bars represent standard error of the mean.DOI:http://dx.doi.org/10.7554/eLife.10557.014
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862755&req=5

fig4s1: Sub-threshold SNP alleles affect enhancer activity.For each sub-threshold locus, enhancers carrying one of two haplotypes were cloned upstream of a minimal promoter and firefly luciferase reporter gene. Blue: enhancer carrying reference allele; red: enhancer carrying alternate allele. Error bars represent standard error of the mean.DOI:http://dx.doi.org/10.7554/eLife.10557.014
Mentions: Because the enrichment of sub-threshold SNPs in cardiac enhancers suggests that epigenetic prioritization can be used as a starting point for more in-depth investigations of sub-threshold signals from GWAS, we sought to directly test the molecular hypothesis that these sub-threshold loci impact the transcriptional regulation of cardiac genes (Figure 4a). We grouped all 2075 sub-threshold SNPs using linkage disequilibrium data (minimum r2=0.2) to identify 287 independent sub-threshold loci in the genome (Materials and methods). We prioritized loci where a sub-threshold SNP overlapped an active LV enhancer and either (i) also overlapped a fetal heart DNase I hypersensitivity peak or (ii) was an expression quantitative trait locus (eQTL) for a nearby gene. In total, we cloned allele-specific enhancer fragments from 22 cardiac enhancers that overlap SNPs from 18 independent sub-threshold loci, and performed quantitative luciferase assays in human iPSC-derived cardiomyocytes to determine whether the sub-threshold SNP genotypes influence enhancer activity (Materials and methods). We observed that 13 of 18 sub-threshold loci (72.2%) contain an enhancer that drives luciferase activity in an allele-specific manner (Figure 4b,d, Figure 4—figure supplement 1). Moreover, we estimate that between 51.1%-89.8% (95% Bayesian confidence interval) of prioritized sub-threshold loci show allele-specific activity on transcription, suggesting that the majority of sub-threshold loci identified by epigenomic prioritization do in fact have an impact on transcriptional enhancer activity.10.7554/eLife.10557.013Figure 4.Sub-threshold loci prioritized by epigenomics alter enhancer activity.

Bottom Line: We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies.We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse.Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology, Massachusetts Institute of Technology, Cambridge, United States.

ABSTRACT
Genetic variants identified by genome-wide association studies explain only a modest proportion of heritability, suggesting that meaningful associations lie 'hidden' below current thresholds. Here, we integrate information from association studies with epigenomic maps to demonstrate that enhancers significantly overlap known loci associated with the cardiac QT interval and QRS duration. We apply functional criteria to identify loci associated with QT interval that do not meet genome-wide significance and are missed by existing studies. We demonstrate that these 'sub-threshold' signals represent novel loci, and that epigenomic maps are effective at discriminating true biological signals from noise. We experimentally validate the molecular, gene-regulatory, cellular and organismal phenotypes of these sub-threshold loci, demonstrating that most sub-threshold loci have regulatory consequences and that genetic perturbation of nearby genes causes cardiac phenotypes in mouse. Our work provides a general approach for improving the detection of novel loci associated with complex human traits.

No MeSH data available.


Related in: MedlinePlus