Identification of deleterious and regulatory genomic variations in known asthma loci

Copy Number Variation in Asthma: An Integrative Review

Asthma is a complex disease with varied clinical manifestations resulting from the interaction between environmental and genetic factors. While chronic airway inflammation and hyperresponsiveness are central features, the etiology of asthma is multifaceted, leading to a diversity of phenotypes and endotypes. Although most research into the genetics of asthma focused on the analysis of single nucleotide polymorphisms (SNPs), studies highlight the importance of structural variations, such as copy number variations (CNVs), in the inheritance of complex characteristics, but their role has not yet been fully elucidated in asthma. In this context, an integrative review was conducted to identify the genes and pathways involved, the location, size, and classes of CNVs, as well as their contribution to asthma risk, severity, control, and response to treatment. As a result of the review, 16 articles were analyzed, from different types of observational studies, such as case-control, cohort studies and genotyped-proband or trios design, that have been carried out in populations from different countries, ethnicities, and ages. Chromosomes 12 and 17 were the most studied in three publications each. CNVs located on 12 chromosomes were associated with asthma, the majority being found on chromosome 6p and 17q, of the deletion type, encompassing 30 different coding-protein genes and one pseudogene region. Six genes with CNVs were identified as significant expression quantitative locus (eQTLs) with mean expression in asthma-related tissues, such as the lung and whole blood. The phenotypic variability of asthma may hinder the clinical application of these findings, but the research shows the importance of investigating these genetic variations as possible biomarkers in asthma patients.

Shared genetic architecture of blood eosinophil counts and asthma in UK Biobank

Rationale

Asthma is a complex, heterogeneous disease strongly associated with type 2 inflammation, and blood eosinophil counts guide therapeutic interventions in moderate and severe asthma. Eosinophils are leukocytes involved in type 2 immune responses. Despite these critical associations between asthma and blood eosinophil counts, the shared genetic architecture of these two traits remains unknown. The objective of the present study was to characterise the genetic architecture of blood eosinophil counts and asthma in the UK Biobank.

Methods

We performed genome-wide association studies (GWAS) of doctor-diagnosed asthma, blood eosinophil, neutrophil, lymphocyte and monocyte counts in the UK Biobank. Genetic correlation analysis was performed on GWAS results and validated in the Trans-National Asthma Genetic Consortium (TAGC) study of asthma.

Results

GWAS of doctor-diagnosed asthma and blood eosinophil counts in the UK Biobank identified 585 and 3429 significant variants, respectively. STAT6, a transcription factor involved in interleukin-4 signalling, was a key shared pathway between asthma and blood eosinophil counts. Genetic correlation analysis demonstrated a positive correlation between doctor-diagnosed asthma and blood eosinophil counts (r=0.38±0.10, correlation±se; p=4.7×10-11). As a validation of this association, we found a similar correlation between TAGC and blood eosinophil counts in the UK Biobank (0.37±0.08, correlation±se; p=1.2×10-6).

Conclusions

These findings define the shared genetic architecture between blood eosinophil counts and asthma risk in subjects of European ancestry and point to a genetic link to the STAT6 signalling pathway in these two traits.

Decoding the genetic and epigenetic basis of asthma

Asthma is a complex and heterogeneous chronic inflammatory disease of the airways. Alongside environmental factors, asthma susceptibility is strongly influenced by genetics. Given its high prevalence and our incomplete understanding of the mechanisms underlying disease susceptibility, asthma is frequently studied in genome-wide association studies (GWAS), which have identified thousands of genetic variants associated with asthma development. Virtually all these genetic variants reside in non-coding genomic regions, which has obscured the functional impact of asthma-associated variants and their translation into disease-relevant mechanisms. Recent advances in genomics technology and epigenetics now offer methods to link genetic variants to gene regulatory elements embedded within non-coding regions, which have started to unravel the molecular mechanisms underlying the complex (epi)genetics of asthma. Here, we provide an integrated overview of (epi)genetic variants associated with asthma, focusing on efforts to link these disease associations to biological insight into asthma pathophysiology using state-of-the-art genomics methodology. Finally, we provide a perspective as to how decoding the genetic and epigenetic basis of asthma has the potential to transform clinical management of asthma and to predict the risk of asthma development.

Functional genomic analysis delineates regulatory mechanisms of GWAS-identified bipolar disorder risk variants

BACKGROUND

Genome-wide association studies (GWASs) have identified multiple risk loci for bipolar disorder (BD). However, pinpointing functional (or causal) variants in the reported risk loci and elucidating their regulatory mechanisms remain challenging.

METHODS

We first integrated chromatin immunoprecipitation sequencing (ChIP-Seq) data from human brain tissues (or neuronal cell lines) and position weight matrix (PWM) data to identify functional single-nucleotide polymorphisms (SNPs). Then, we verified the regulatory effects of these transcription factor (TF) binding-disrupting SNPs (hereafter referred to as "functional SNPs") through a series of experiments, including reporter gene assays, allele-specific expression (ASE) analysis, TF knockdown, CRISPR/Cas9-mediated genome editing, and expression quantitative trait loci (eQTL) analysis. Finally, we overexpressed PACS1 (whose expression was most significantly associated with the identified functional SNPs rs10896081 and rs3862386) in mouse primary cortical neurons to investigate if PACS1 affects dendritic spine density.

RESULTS

We identified 16 functional SNPs (in 9 risk loci); these functional SNPs disrupted the binding of 7 TFs, for example, CTCF and REST binding was frequently disrupted. We then identified the potential target genes whose expression in the human brain was regulated by these functional SNPs through eQTL analysis. Of note, we showed dysregulation of some target genes of the identified TF binding-disrupting SNPs in BD patients compared with controls, and overexpression of PACS1 reduced the density of dendritic spines, revealing the possible biological mechanisms of these functional SNPs in BD.

CONCLUSIONS

Our study identifies functional SNPs in some reported risk loci and sheds light on the regulatory mechanisms of BD risk variants. Further functional characterization and mechanistic studies of these functional SNPs and candidate genes will help to elucidate BD pathogenesis and develop new therapeutic approaches and drugs.

Functional genomics elucidates regulatory mechanisms of Parkinson's disease-associated variants

BACKGROUND

Genome-wide association studies (GWASs) have identified multiple risk loci for Parkinson's disease (PD). However, identifying the functional (or potential causal) variants in the reported risk loci and elucidating their roles in PD pathogenesis remain major challenges. To identify the potential causal (or functional) variants in the reported PD risk loci and to elucidate their regulatory mechanisms, we report a functional genomics study of PD.

METHODS

We first integrated chromatin immunoprecipitation sequencing (ChIP-Seq) (from neuronal cells and human brain tissues) data and GWAS-identified single-nucleotide polymorphisms (SNPs) in PD risk loci. We then conducted a series of experiments and analyses to validate the regulatory effects of these (i.e., functional) SNPs, including reporter gene assays, allele-specific expression (ASE), transcription factor (TF) knockdown, CRISPR-Cas9-mediated genome editing, and expression quantitative trait loci (eQTL) analysis.

RESULTS

We identified 44 SNPs (from 11 risk loci) affecting the binding of 12 TFs and we validated the regulatory effects of 15 TF binding-disrupting SNPs. In addition, we also identified the potential target genes regulated by these TF binding-disrupting SNPs through eQTL analysis. Finally, we showed that 4 eQTL genes of these TF binding-disrupting SNPs were dysregulated in PD cases compared with controls.

CONCLUSION

Our study systematically reveals the gene regulatory mechanisms of PD risk variants (including widespread disruption of CTCF binding), generates the landscape of potential PD causal variants, and pinpoints promising candidate genes for further functional characterization and drug development.

Integrating Multimorbidity into a Whole-Body Understanding of Disease Using Spatial Genomics

Multimorbidity is characterized by multidimensional complexity emerging from interactions between multiple diseases across levels of biological (including genetic) and environmental determinants and the complex array of interactions between and within cells, tissues and organ systems. Advances in spatial genomic research have led to an unprecedented expansion in our ability to link alterations in genome folding with changes that are associated with human disease. Studying disease-associated genetic variants in the context of the spatial genome has enabled the discovery of transcriptional regulatory programmes that potentially link dysregulated genes to disease development. However, the approaches that have been used have typically been applied to uncover pathological molecular mechanisms occurring in a specific disease-relevant tissue. These forms of reductionist, targeted investigations are not appropriate for the molecular dissection of multimorbidity that typically involves contributions from multiple tissues. In this perspective, we emphasize the importance of a whole-body understanding of multimorbidity and discuss how spatial genomics, when integrated with additional omic datasets, could provide novel insights into the molecular underpinnings of multimorbidity.