Sheep genome functional annotation reveals proximal regulatory elements contributed to the evolution of modern breeds

AbstractBackgroundGenome-wide association and fine-mapping studies have enabled the discovery of single nucleotide polymorphisms (SNPs) and other variants that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the SNPs lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression.MethodsAutoimmunity results from a failure of immune tolerance, suggesting that regulatory T cells (Treg) are likely a significant point of impact for this genetic risk, as Treg are critical for immune tolerance. Focusing on T1D as a model of defective function of Treg in autoimmunity, we designed a SNPs filtering workflow called 3 Dimensional Functional Annotation of Accessible Cell Type Specific SNPs (3DFAACTS-SNP) that utilises overlapping profiles of Treg-specific epigenomic data (ATAC-seq, Hi-C and FOXP3-ChIP) to identify regulatory elements potentially driving the effect of variants associated with T1D, and the gene(s) that they control.ResultsUsing 3DFAACTS-SNP we identified 36 SNPs with plausible Treg-specific mechanisms of action contributing to T1D from 1,228 T1D fine-mapped variants, identifying 119 novel interacting regions resulting in the identification of 51 candidate target genes. We further demonstrated the utility of the workflow by applying it to three other fine-mapped/meta-analysed SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of any genetic variation using all common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research.ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. The 3DFAACTS-SNP workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

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Transcriptome Analysis Reveals Differentially Expressed Genes and Long Non-coding RNAs Associated With Fecundity in Sheep Hypothalamus With Different FecB Genotypes

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.633747 ◽

2021 ◽

Vol 9 ◽

Author(s):

Si Chen ◽

Xiaofei Guo ◽

Xiaoyun He ◽

Ran Di ◽

Xiaosheng Zhang ◽

...

Keyword(s):

Functional Annotation ◽

Target Genes ◽

Expression Profiles ◽

Regulatory Elements ◽

Reproductive Hormones ◽

Differentially Expressed ◽

Wild Type ◽

Reproductive Process ◽

Gnrh Secretion ◽

Non Coding Rnas

Small-tailed Han sheep, with different FecB genotypes, manifest distinct ovulation rates and fecundities, which are due to differences in reproductive hormones secreted by the hypothalamic–pituitary–ovarian axis. Nevertheless, the function of the hypothalamus against a FecB mutant background on increasing ovulation rate is rarely reported. Therefore, we determined the expression profiles of hypothalamus tissue collected from six wild-type (WW) and six FecB mutant homozygous (BB) ewes at the follicular and luteal phases by whole-transcriptome sequencing. We identified 53 differentially expressed mRNAs (DEGs) and 40 differentially expressed long non-coding RNAs (DELs) between the two estrus states. Functional annotation analysis revealed that one of the DEGs, PRL, was particularly enriched in the hypothalamic function, hormone-related, and reproductive pathways. The lncRNA–target gene interaction networks and KEGG analysis in combination suggest that the lncRNAs LINC-676 and WNT3-AS cis-acting on DRD2 and WNT9B in different phases may induce gonadotropin-releasing hormone (GnRH) secretion. Furthermore, there were differences of regulatory elements and WNT gene family members involved in the follicular–luteal transition in the reproductive process between wild-type (WNT7A) and FecB mutant sheep (WNT9B). We combined the DEG and DEL data sets screened from different estrus states and genotypes. The overlap of these two sets was identified to select the mRNAs and lncRNAs that have major effects on ovulation. Among the overlapping molecules, seven DEGs and four DELs were involved in the follicular–luteal transition regulated by FecB mutation. Functional annotation analysis showed that two DEGs (FKBP5 and KITLG) were enriched in melanogenesis, oxytocin, and GnRH secretion. LINC-219386 and IGF2-AS were highly expressed in the BB ewes compared with WW ewes, modulating their target genes (DMXL2 and IGF2) to produce more GnRH during follicular development, which explains why mutated ewes produced more mature follicles. These results from expression profiling of the hypothalamus with the FecB mutation at different estrus states provide new insights into how the hypothalamus regulates ovulation under the effect of the FecB mutation.

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Deep Post-GWAS Analysis Identifies Potential Risk Genes and Causal Variants for Alzheimer’s Disease, Providing New Insights Into Its Disease Mechanisms

10.21203/rs.3.rs-31864/v1 ◽

2020 ◽

Author(s):

ZHEN WANG ◽

Quanwei Zhang ◽

Jhih-Rong Lin ◽

M.Reza Jabalameli ◽

Joydeep Mitra ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Annotation ◽

Expression Profiles ◽

Regulatory Elements ◽

Coagulation Cascade ◽

Genome Wide Association Studies ◽

Common Variants ◽

Risk Genes ◽

Causal Variants

Abstract Background:Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the causal variants underlying GWAS AD-association signals remain unknown.Methods:We developed a computational pipeline that integrates 936 AD-associated SNPs, linkage disequilibrium and genomic data from multiple sources – e.g., disease genes databases, functional annotation of genetic variants, GTEx, and the 1000 Genomes Project – to predict both AD risk genes and their causal variants.Results:We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS, and 115 of them are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 causal variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%)of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms – e.g., the complement and coagulation cascade andphosphorylation and activation of immune response – and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes – e.g., IL1RAP, PMAIP1, LAMTOR4 – as predictors for the prognosis of AD survival and genes such as ARL6IP5with altered network connectivity between AD patients and normal individuals involved in AD progressionConclusions: Our findings provide novel biological insights into the genetic architecture, expression profiles, functional pathways involved in the AD etiology, and open new strategies for developing therapeutics targeting AD risk genes or causal variants to influence AD pathogenesis.

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Functional annotation of cis-regulatory elements in human cells by dCas9/sgRNA

Cell Research ◽

10.1038/cr.2015.70 ◽

2015 ◽

Vol 25 (7) ◽

pp. 877-880 ◽

Cited By ~ 2

Author(s):

Yinan Du ◽

Qingzhou Meng ◽

Jun Zhang ◽

Man Sun ◽

Bin Shen ◽

...

Keyword(s):

Functional Annotation ◽

Regulatory Elements ◽

Human Cells

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Pig genome functional annotation enhances the biological interpretation of complex traits and human disease

Nature Communications ◽

10.1038/s41467-021-26153-7 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Zhangyuan Pan ◽

Yuelin Yao ◽

Hongwei Yin ◽

Zexi Cai ◽

Ying Wang ◽

...

Keyword(s):

Adaptive Evolution ◽

Complex Traits ◽

Functional Annotation ◽

Molecular Mechanisms ◽

Association Studies ◽

Regulatory Elements ◽

Neutral Evolution ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Slow Evolution

AbstractThe functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide biological insights on tissue-specific regulatory conservation, and by integrating 47 human genome-wide association studies, we demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases.

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Pig genome functional annotation enhances biological interpretations of complex traits and comparative epigenomics

10.21203/rs.3.rs-253276/v1 ◽

2021 ◽

Author(s):

Huaijun Zhou ◽

Zhangyuan Pan ◽

Yuelin Yao ◽

Hongwei Ying ◽

Zexi Cai ◽

...

Keyword(s):

Adaptive Evolution ◽

Complex Traits ◽

Functional Annotation ◽

Molecular Mechanisms ◽

Association Studies ◽

Regulatory Elements ◽

Neutral Evolution ◽

Genome Wide Association Studies ◽

Tissue Specific ◽

Slow Evolution

Abstract The functional annotation of livestock genomes is crucial for understanding the molecular mechanisms that underpin complex traits of economic importance, adaptive evolution and comparative genomics. Here, we provide the most comprehensive catalogue to date of regulatory elements in the pig (Sus scrofa) by integrating 223 epigenomic and transcriptomic data sets, representing 14 biologically important tissues. We systematically describe the dynamic epigenetic landscape across tissues by functionally annotating 15 different chromatin states and defining their tissue-specific regulatory activities. We demonstrate that genomic variants associated with complex traits and adaptive evolution in pig are significantly enriched in active promoters and enhancers. Furthermore, we reveal distinct tissue-specific regulatory selection between Asian and European pig domestication processes. Compared with human and mouse epigenomes, we show that porcine regulatory elements are more conserved in DNA sequence, under both rapid and slow evolution, than those under neutral evolution across pig, mouse, and human. Finally, we provide novel biological insights on tissue-specific regulatory conservation and demonstrate that, depending on the traits, mouse or pig might be more appropriate biomedical models for different complex traits and diseases in humans through integrating comparative epigenomes with 47 human genome-wide association studies.

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Deep Post-GWAS Analysis Identifies Potential Risk Genes and Causal Variants for Alzheimer’s Disease, Providing New Insights Into Its Disease Mechanisms

10.21203/rs.3.rs-96273/v1 ◽

2020 ◽

Author(s):

ZHEN WANG ◽

Quanwei Zhang ◽

Jhih-Rong Lin ◽

M.Reza Jabalamel ◽

Joydeep Mitra ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Functional Annotation ◽

Expression Profiles ◽

Regulatory Elements ◽

Coagulation Cascade ◽

Genome Wide Association Studies ◽

Common Variants ◽

Risk Genes ◽

Causal Variants

Abstract Background: Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the causal variants underlying GWAS AD-association signals remain unknown.Methods: We developed a computational pipeline that integrates 936 AD-associated SNPs, linkage disequilibrium and genomic data from multiple sources – e.g., disease genes databases, functional annotation of genetic variants, GTEx, and the 1000 Genomes Project – to predict both AD risk genes and their causal variants.Results: We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS, and 115 of them are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 causal variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms – e.g., the complement and coagulation cascade and phosphorylation and activation of immune response – and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes – e.g., IL1RAP, PMAIP1, LAMTOR4 – as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression.Conclusions: Our findings provide novel biological insights into the genetic architecture, expression profiles, functional pathways involved in the AD etiology, and open new strategies for developing therapeutics targeting AD risk genes or causal variants to influence AD pathogenesis.

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Functional Annotation Signatures of Disease Susceptibility Loci Improve SNP Association Analysis

10.1101/000158 ◽

2013 ◽

Author(s):

Edwin S Iversen ◽

Gary Lipton ◽

Merlise A. Clyde ◽

Alvaro N. A. Monteiro

Keyword(s):

Functional Annotation ◽

Association Studies ◽

Function Class ◽

Regulatory Elements ◽

Likelihood Method ◽

Annotation Database ◽

Genomic Variants ◽

Genome Wide ◽

A Genome ◽

The Impact

We describe the development and application of 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 housed in the GWAS Catalog (GC). The set of functional predictors we examined includes 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 included in the Database of Genomic Variants (DGV) and known regulatory elements included in the Open Regulatory Annotation database (ORegAnno), PolyPhen-2 probabilities and RegulomeDB categories. Because we expected that only a fraction of the annotation variables 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.

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CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens

Nature Communications ◽

10.1038/s41467-021-26722-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Alexendar R. Perez ◽

Laura Sala ◽

Richard K. Perez ◽

Joana A. Vidigal

Keyword(s):

Open Source ◽

Sensitivity And Specificity ◽

Open Source Software ◽

Functional Annotation ◽

Negative Selection ◽

Regulatory Elements ◽

Computational Method ◽

Target Effects

AbstractOff-target effects are well established confounders of CRISPR negative selection screens that impair the identification of essential genomic loci. In particular, non-coding regulatory elements and repetitive regions are often difficult to target with specific gRNAs, effectively precluding the unbiased screening of a large portion of the genome. To address this, we developed CRISPR Specificity Correction (CSC), a computational method that corrects for the effect of off-targeting on gRNA depletion. We benchmark CSC with data from the Cancer Dependency Map and show that it significantly improves the overall sensitivity and specificity of viability screens while preserving known essentialities, particularly for genes targeted by highly promiscuous gRNAs. We believe this tool will further enable the functional annotation of the genome as it represents a robust alternative to the traditional filtering strategy of discarding unspecific guides from the analysis. CSC is an open-source software that can be seamlessly integrated into current CRISPR analysis pipelines.

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Hungarian small business tax and possibilities to minimize distortions from capital income taxation

Society and Economy ◽

10.1556/204.2015.37.s.6 ◽

2015 ◽

Vol 37 (s1) ◽

pp. 87-105

Author(s):

Benedek Nobilis ◽

András Svraka

Keyword(s):

Small Business ◽

Cash Flow ◽

Capital Accumulation ◽

Value Added ◽

Regulatory Elements ◽

Capital Income ◽

Potential Growth ◽

Elegant Method ◽

Business Tax ◽

Working Out

Governments throughout the EU and OECD countries rely on revenues raised on capital income. Albeit several arguments can be made for keeping these taxes, in their widespread form they hinder capital accumulation and significantly lower potential growth due to their savings and investment distorting nature. At the same time, the actual economic impact of tax types is largely influenced by their structure. An elegant method, which is also simple in its concept, for eliminating the economic distortions of profit taxes is cash-flow taxation which moves income taxes closer to the more growth-friendly value-added taxes. The small business tax, which was introduced in Hungary in 2013, was designed along these principles. In this paper we review the theoretical literature on cash-flow taxation and discuss the main regulatory elements of the small business tax, as well as the solutions elaborated for working out the challenges related to its implementation.

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