Integration Of Functional Genomics Data Research Articles

Multiomic prioritisation of risk genes for anorexia nervosa.

Anorexia nervosa (AN) is a psychiatric disorder associated with marked morbidity. Whilst AN genetic studies could identify novel treatment targets, integration of functional genomics data, including transcriptomics and proteomics, would assist to disentangle correlated signals and reveal causally associated genes. We used models of genetically imputed expression and splicing from 14 tissues, leveraging mRNA, protein, and mRNA alternative splicing weights to identify genes, proteins, and transcripts, respectively, associated with AN risk. This was accomplished through transcriptome, proteome, and spliceosome-wide association studies, followed by conditional analysis and finemapping to prioritise candidate causal genes. We uncovered 134 genes for which genetically predicted mRNA expression was associated with AN after multiple-testing correction, as well as four proteins and 16 alternatively spliced transcripts. Conditional analysis of these significantly associated genes on other proximal association signals resulted in 97 genes independently associated with AN. Moreover, probabilistic finemapping further refined these associations and prioritised putative causal genes. The gene WDR6, for which increased genetically predicted mRNA expression was correlated with AN, was strongly supported by both conditional analyses and finemapping. Pathway analysis of genes revealed by finemapping identified the pathway regulation of immune system process (overlapping genes = MST1, TREX1, PRKAR2A, PROS1) as statistically overrepresented. We leveraged multiomic datasets to genetically prioritise novel risk genes for AN. Multiple-lines of evidence support that WDR6 is associated with AN, whilst other prioritised genes were enriched within immune related pathways, further supporting the role of the immune system in AN.

Integration of functional genomics data to uncover cell type-specific pathways affected in Parkinson's disease.

Parkinson's disease (PD) is the second most prevalent late-onset neurodegenerative disorder worldwide after Alzheimer's disease for which available drugs only deliver temporary symptomatic relief. Loss of dopaminergic neurons (DaNs) in the substantia nigra and intracellular alpha-synuclein inclusions are the main hallmarks of the disease but the events that cause this degeneration remain uncertain. Despite cell types other than DaNs such as astrocytes, microglia and oligodendrocytes have been recently associated with the pathogenesis of PD, we still lack an in-depth characterisation of PD-affected brain regions at cell-type resolution that could help our understanding of the disease mechanisms. Nevertheless, publicly available large-scale brain-specific genomic, transcriptomic and epigenomic datasets can be further exploited to extract different layers of cell type-specific biological information for the reconstruction of cell type-specific transcriptional regulatory networks. By intersecting disease risk variants within the networks, it may be possible to study the functional role of these risk variants and their combined effects at cell type- and pathway levels, that, in turn, can facilitate the identification of key regulators involved in disease progression, which are often potential therapeutic targets.

YeasTSS: an integrative web database of yeast transcription start sites.

The transcription initiation landscape of eukaryotic genes is complex and highly dynamic. In eukaryotes, genes can generate multiple transcript variants that differ in 5′ boundaries due to usages of alternative transcription start sites (TSSs), and the abundance of transcript isoforms are highly variable. Due to a large number and complexity of the TSSs, it is not feasible to depict details of transcript initiation landscape of all genes using text-format genome annotation files. Therefore, it is necessary to provide data visualization of TSSs to represent quantitative TSS maps and the core promoters (CPs). In addition, the selection and activity of TSSs are influenced by various factors, such as transcription factors, chromatin remodeling and histone modifications. Thus, integration and visualization of functional genomic data related to these features could provide a better understanding of the gene promoter architecture and regulatory mechanism of transcription initiation. Yeast species play important roles for the research and human society, yet no database provides visualization and integration of functional genomic data in yeast. Here, we generated quantitative TSS maps for 12 important yeast species, inferred their CPs and built a public database, YeasTSS (www.yeastss.org). YeasTSS was designed as a central portal for visualization and integration of the TSS maps, CPs and functional genomic data related to transcription initiation in yeast. YeasTSS is expected to benefit the research community and public education for improving genome annotation, studies of promoter structure, regulated control of transcription initiation and inferring gene regulatory network.

EPB41, a novel hepatoma susceptibility gene dysregulated by c-Myc: an integrative functional genomics study

BackgroundGenome-wide association studies (GWAS) have provided insight into cancer genetics. However, molecular mechanisms whereby many susceptibility single nucleotide polymorphisms (SNPs) identified by GWAS promote cancer heritability and risk are unknown. New research strategies to evaluate functionality are needed to systematically study causal genetic variants. MethodsIn this study, we developed an integrative functional genomics method to identify cancer susceptibility SNPs in transcription factor binding sites across the whole genome. By integration of functional genomic data from c-Myc cistromics, 1000 Genomes and the TRANSFAC matrix databases, we identified SNPs in the c-Myc cistrome that might modulate c-Myc binding affinity in hepatocellular carcinoma. We took blood samples from patients with hepatitis B-related hepatocellular carcinoma who were admitted to Shandong Cancer Hospital, Jinan, Shandong Province. China, or Huaian No. 2 Hospital, Huaian, Jiangsu Province, China. We extracted genomic DNA from these samples and did genotype analyses. Written informed consent was obtained from all participants and the study was ethically approved by the institutional review boards. FindingsWe identified 12 SNPs in 1806 people with hepatitis B-related hepatocellular carcinoma, we genotyped these SNPs and 1708 controls. We identified a novel hepatocellular carcinoma susceptibility SNP, EPB41 rs157224 G>T (OR of T allele 1·64 [95% CI 1·32–2·02]; p<0·00001), in Chinese patients with hepatocellular carcinoma. This SNP in EPB41 predisposes individuals to hepatocellular carcinoma through modification of c-Myc-mediated transcriptional regulation; the risk allele has decreased gene expression. EPB41 is a novel hepatocellular carcinoma susceptibility gene. Consistent with this notion, a EPB41 expression is decreased tissue samples from patients with hepatocellular carcinoma, especially portal vein metastasis or intrahepatic metastasis. InterpretationOur results show that SNP EPB41 rs157224 G>T could be associated with increased susceptibility to hepatocellular carcinoma and show that insight into malignancy pathology can be made using a genome-wide approach. FundingNational Natural Science Foundation of China (81201586 and 31271382), National High-Tech Research and Development Program of China (2015AA020950), State Key Laboratory of Molecular Oncology (SKL-KF-2015-05).

Integrative Functional Genomics Implicates EPB41 Dysregulation in Hepatocellular Carcinoma Risk

Genome-wide association studies (GWASs) have provided many insights into cancer genetics. However, the molecular mechanisms of many susceptibility SNPs defined by GWASs in cancer heritability and in promoting cancer risk remain elusive. New research strategies, including functional evaluations, are warranted to systematically explore truly causal genetic variants. In this study, we developed an integrative functional genomics methodology to identify cancer susceptibility SNPs in transcription factor-binding sites across the whole genome. Employing integration of functional genomic data from c-Myc cistromics, 1000 Genomes, and the TRANSFAC matrix, we successfully annotated 12 SNPs present in the c-Myc cistrome with properties consistent with modulating c-Myc binding affinity in hepatocellular carcinoma (HCC). After genotyping these 12 SNPs in 1,806 HBV-related HCC case subjects and 1,708 control subjects, we identified a HCC susceptibility SNP, rs157224G>T, in Chinese populations (T allele: odds ratio = 1.64, 95% confidence interval = 1.32-2.02; p = 5.2× 10(-6)). This polymorphism leads to HCC predisposition through modifying c-Myc-mediated transcriptional regulation of EPB41, with the risk rs157224T allele showing significantly decreased gene expression. Based on cell proliferation, wound healing, and transwell assays as well as the mouse xenograft model, we identify EPB41 as a HCC susceptibility gene invitro and invivo. Consistent with this notion, we note that EPB41 expression is significantly decreased in HCC tissue specimens, especially in portal vein metastasis or intrahepatic metastasis, compared to normal tissues. Our results highlight the involvement of regulatory genetic variants in HCC and provide pathogenic insights of this malignancy via a genome-wide approach.

Tissue-Specific Functional Networks for Prioritizing Phenotype and Disease Genes

Integrated analyses of functional genomics data have enormous potential for identifying phenotype-associated genes. Tissue-specificity is an important aspect of many genetic diseases, reflecting the potentially different roles of proteins and pathways in diverse cell lineages. Accounting for tissue specificity in global integration of functional genomics data is challenging, as “functionality” and “functional relationships” are often not resolved for specific tissue types. We address this challenge by generating tissue-specific functional networks, which can effectively represent the diversity of protein function for more accurate identification of phenotype-associated genes in the laboratory mouse. Specifically, we created 107 tissue-specific functional relationship networks through integration of genomic data utilizing knowledge of tissue-specific gene expression patterns. Cross-network comparison revealed significantly changed genes enriched for functions related to specific tissue development. We then utilized these tissue-specific networks to predict genes associated with different phenotypes. Our results demonstrate that prediction performance is significantly improved through using the tissue-specific networks as compared to the global functional network. We used a testis-specific functional relationship network to predict genes associated with male fertility and spermatogenesis phenotypes, and experimentally confirmed one top prediction, Mbyl1. We then focused on a less-common genetic disease, ataxia, and identified candidates uniquely predicted by the cerebellum network, which are supported by both literature and experimental evidence. Our systems-level, tissue-specific scheme advances over traditional global integration and analyses and establishes a prototype to address the tissue-specific effects of genetic perturbations, diseases and drugs.

Functional Genomics Complements Quantitative Genetics in Identifying Disease-Gene Associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.

Predictive modelling and integration of functional genomics data for applications in plant biology

Tree biotechnology will soon reach a mature state where it will influence the overall supply of fiber and wood products. Through technological advancements, it may be possible to introduce trees th ...