Genetic interactions have been recognized as a potentially important contributor to the heritability of complex diseases. Nevertheless, due to small effect sizes and stringent multiple-testing ...correction, identifying genetic interactions in complex diseases is particularly challenging. To address the above challenges, many genomic research initiatives collaborate to form large-scale consortia and develop open access to enable sharing of genome-wide association study (GWAS) data. Despite the perceived benefits of data sharing from large consortia, a number of practical issues have arisen, such as privacy concerns on individual genomic information and heterogeneous data sources from distributed GWAS databases. In the context of large consortia, we demonstrate that the heterogeneously appearing marginal effects over distributed GWAS databases can offer new insights into genetic interactions for which conventional methods have had limited success. In this paper, we develop a novel two-stage testing procedure, named phylogenY-based effect-size tests for interactions using first 2 moments (YETI2), to detect genetic interactions through both pooled marginal effects, in terms of averaging site-specific marginal effects, and heterogeneity in marginal effects across sites, using a meta-analytic framework. YETI2 can not only be applied to large consortia without shared personal information but also can be used to leverage underlying heterogeneity in marginal effects to prioritize potential genetic interactions. We investigate the performance of YETI2 through simulation studies and apply YETI2 to bladder cancer data from dbGaP.
Abstract Epigenetics is the regulation of gene expression (transcription) in response to changes in the cell environment through genomic modifications that largely involve the non-coding fraction of ...the human genome and that cannot be attributed to modification of the primary DNA sequence. Epigenetics is dominant in establishing cell fate and positioning during programmed embryonic development. However the same pathways are used by mature postnatal and adult mammalian cells during normal physiology and are implicated in disease mechanisms. Recent research demonstrates that blood flow and pressure are cell environments that can influence transcription via epigenetic pathways. The principal epigenetic pathways are chemical modification of cytosine residues of DNA (DNA methylation) and of the amino tails of histone proteins associated with DNA in nucleosomes. They also encompass the post-transcriptional degradation of mRNA transcripts by non-coding RNAs (ncRNA). In vascular endothelium, epigenetic pathways respond to temporal and spatial variations of flow and pressure, particularly hemodynamic disturbed blood flow, with important consequences for gene expression. The biofluid environment is linked by mechanotransduction and solute transport to cardiovascular cell phenotypes via signaling pathways and epigenetic regulation for which there is an adequate interdisciplinary infrastructure with robust tools and methods available. Epigenetic mechanisms may be less familiar than acute genomic signaling to Investigators at the interface of biofluids, biomechanics and cardiovascular biology. Here we introduce a biofluids / cellular biomechanics readership to the principal epigenetic pathways and provide a contextual overview of endothelial epigenetic plasticity in the regulation of flow-responsive transcription.
Abstract
Background
Previous studies of Alzheimer’s Disease (AD) have primarily implicated neurons and microglia as disease‐conferring cell types due to obvious links with degeneration and ...inflammation, respectively. However, astrocytes may also play a role in AD pathogenesis, but have been less studied in this context. Indeed, genome‐wide association studies (GWAS) have identified multiple genomic variants that reside near genes with astrocyte‐related functions, such as lipid processing and synaptic function. However, GWAS only reports genomic variants associated with a given trait and not necessarily the precise localization of culprit genes. High resolution chromatin conformation capture‐based techniques detect 3D genomic interactions between GWAS‐implicated signals and their effector genes, and allow for the characterization of non‐coding variants in the context of their regulatory activity.
Method
To improve on the low resolution of available Hi‐C data, we employed a high resolution DpnII‐based Capture‐C method to simultaneously characterize the physical genome‐wide interactions of 36,691 baited regions covering human promoters genome‐wide (including non‐coding transcripts) ‐ which we applied to a human primary astrocyte line (NHA, Lonza). Following sequencing, we investigated significant interactions at two different resolutions (1‐fragment and 4‐fragment resolutions; median fragment size = 265bp and 1,441bp, respectively). In parallel, we generated ATAC‐seq open chromatin maps to filter for informative (r
2
>0.7) proxy single nucleotide polymorphisms (SNPs) for each of the 45 AD independent signals reported to date.
Result
ATAC‐seq fine‐mapping yielded 100 candidate SNPs in open chromatin for 30 of these loci. By further constraining on our promoter Capture C data in astrocytes, at both one and four DpnII fragment resolution (median distance between interacting regions 9kb and 71kb, respectively), we observed contacts to “open” promoters for 6 putative target genes, corresponding to 6 of the original GWAS loci. These included
PICALM, FERMT2, CASS4
, and
CLU
.
Conclusion
We observed informative contacts between proxy SNPs and putative effector genes in the human astrocyte context for ∼13% of AD GWAS loci. Further efforts in other relevant cell types, where many of the other loci may in fact be principally operating, should shed light on additional signals. Follow‐up functional studies are warranted to validate these findings.
Background
Previous studies of Alzheimer’s Disease (AD) have primarily implicated neurons and microglia as disease‐conferring cell types due to obvious links with degeneration and inflammation, ...respectively. However, astrocytes may also play a role in AD pathogenesis, but have been less studied in this context. Indeed, genome‐wide association studies (GWAS) have identified multiple genomic variants that reside near genes with astrocyte‐related functions, such as lipid processing and synaptic function. However, GWAS only reports genomic variants associated with a given trait and not necessarily the precise localization of culprit genes. High resolution chromatin conformation capture‐based techniques detect 3D genomic interactions between GWAS‐implicated signals and their effector genes, and allow for the characterization of non‐coding variants in the context of their regulatory activity.
Method
To improve on the low resolution of available Hi‐C data, we employed a high resolution DpnII‐based Capture‐C method to simultaneously characterize the physical genome‐wide interactions of 36,691 baited regions covering human promoters genome‐wide (including non‐coding transcripts) ‐ which we applied to a human primary astrocyte line (NHA, Lonza). Following sequencing, we investigated significant interactions at two different resolutions (1‐fragment and 4‐fragment resolutions; median fragment size = 265bp and 1,441bp, respectively). In parallel, we generated ATAC‐seq open chromatin maps to filter for informative (r2>0.7) proxy single nucleotide polymorphisms (SNPs) for each of the 45 AD independent signals reported to date.
Result
ATAC‐seq fine‐mapping yielded 100 candidate SNPs in open chromatin for 30 of these loci. By further constraining on our promoter Capture C data in astrocytes, at both one and four DpnII fragment resolution (median distance between interacting regions 9kb and 71kb, respectively), we observed contacts to “open” promoters for 6 putative target genes, corresponding to 6 of the original GWAS loci. These included PICALM, FERMT2, CASS4, and CLU.
Conclusion
We observed informative contacts between proxy SNPs and putative effector genes in the human astrocyte context for ∼13% of AD GWAS loci. Further efforts in other relevant cell types, where many of the other loci may in fact be principally operating, should shed light on additional signals. Follow‐up functional studies are warranted to validate these findings.
Earlier pubertal onset in girls is associated with later-life type 2 diabetes (T2D) risk. Studies have shown that age at menarche (AAM), a marker of girls’ pubertal timing, and T2D are genetically ...correlated genome-wide. Since both AAM and T2D also correlate with BMI, the biological link between the two traits could be mediated via BMI. As the most recent genome-wide association study (GWAS) for T2D also adjusted for BMI, we queried if AAM and T2D remain correlated after removing the effect of BMI on T2D. Leveraging such large-scale GWAS data, LD Score Regression revealed that the genetic correlation between AAM and T2D (rg (SE)=-0.24 (0.02) P=2.2x10-22) was only partly attenuated after BMI adjustment (rg (SE)=-0.1 (0.03) P=0.0002), suggesting that BMI does not entirely explain the puberty-T2D link. Five of the 13 significant GWAS-implicated loci associated with both AAM and T2D do not associate with BMI, further supporting that BMI is not the only mediator between the two traits. Given that causal effector genes are unknown at most GWAS loci, identification of target genes at these shared loci should provide key biological insights. To implicate effector genes, we first identified all SNPs in LD with the sentinel SNPs. We then extracted the subsets of proxy SNPs in open chromatin, determined by ATAC-seq, in primary cells or cell lines relevant to this trait area beta cells (EndoC-βH1), adipose (SGBS and MSC-derived adipocytes), and—given recent findings in both AAM and BMI studies—brain (neural precursors, microglia, astrocytes and hypothalamic neurons). Using high resolution Capture-C, we detected consistent physical contacts between open-proxy SNPs and candidate effector genes. Our results support direct SNP-to-gene promoter contacts for at least four of these shared loci (one not associated with BMI) including TSPAN3 at ‘AC046168.1’ and TFAP2D at ‘TFAP2B’, along with leads at the ‘MTCH2/CELF1’ and ‘MAP2K5’ loci. Follow-up experiments are needed to determine the physiological roles of implicated target genes.
Disclosure
D.L. Cousminer: None. R. Mishra: None. M.A. Argenziano: None. E. Manduchi: None. K.M. Hodge: None. C. Su: None. M. Leonard: None. S. Lu: None. J.A. Pippin: None. M. Johnson: None. A.D. Wells: Research Support; Self; GlaxoSmithKline plc. A. Chesi: None. B.F. Voight: None. S.F. Grant: None.
Funding
American Diabetes Association (1-17-PDF-077 to D.L.C.); National Institutes of Health
Background
Alzheimer’s disease (AD) research has principally focused on neurons due to their role in neurodegeneration. In contrast, recent studies suggest that genetic mechanisms drive microglia to ...prolonged inflammation in AD brains, exacerbating neurodegeneration. Indeed, recent genome‐wide association studies (GWAS) of AD have identified multiple loci near genes related to microglial function, such as TREM2 and CR1. However, GWAS does not have the sensitivity to identify causal variants or effector genes. We used a combination of ATAC‐seq and high‐resolution promoter‐focused Capture‐C in two human microglial cell models to map interactions between GWAS‐implicated variants and their putative effector genes. We then validated an observed interaction at the ‘CASS4’ locus using CRISPR‐Cas9 genome editing.
Method
Improving on the relatively low resolution of available Hi‐C data, we employed high resolution Capture‐C to characterize the physical genome‐wide interactions of all human promoters. We performed this in the human microglial cell line HMC3 and human iPSC‐derived microglia (iMg). We confirmed the enhancer activity of a SNP we elected to pursue and its associated regulatory element using dual‐luciferase assays. To confirm the regulatory interaction with the Capture‐C implicated effector gene, we used lentiviral CRISPR‐Cas9 to delete an approximately 300bp region containing the SNP in HMC3 cells. We then confirmed changes in RNA and protein expression using RNA‐seq and Western blotting, respectively.
Result
Variant‐to‐gene mapping in both microglial cell models identified 67 putative effector genes (51 coding) across both cell types, with 14 observed in both models. We identified a novel proxy SNP, rs6024870 (r2 = 0.93 to sentinel SNP rs6014724), at the ‘CASS4’ locus, which coincided with open chromatin and directly contacted the promoter of RTFDC1, a gene not previously implicated in AD. Deletion of the putative enhancer region harboring rs6024870 by CRISPR‐Cas9 in HMC3 reduced the expression of RTFDC1 at both the mRNA and protein level. We also note that CASS4 levels were modestly influenced by this CRISPR‐mediated perturbation.
Conclusion
We implicate RTFDC1 as a novel effector gene at the AD ‘CASS4’ GWAS locus. Further efforts will characterize the phenotypic effect of this variant in microglial cell models, including on inflammation and phagocytic activity.
Motivation: We address the problem of identifying differentially expressed genes between two conditions in the scenario where the data arise from an observational study, in which confounding factors ...are likely to be present. Results: We suggest to use matching methods to balance two groups of observed cases on measured covariates, and to identify differentially expressed genes using a test suited to matched data. We illustrate this approach on two microarray studies: the first study consists of data from patients with two cancer subtypes, and the second study consists of data from AMKL patients with and without Down syndrome. Availability: R code (www.r-project.org) for implementing our approach is included as Supplementary Material. Contact: ruheller@whatron.upenn.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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