Biology subjects

Haglund, A.

Publications and source records attributed to Haglund, A..

3 recordsLinked to original sources

bioRxiv2023

Shared and distinct molecular effects of regulatory genomic variants provide insight into mechanisms of distal enhancer-promoter communication

Gene enhancers often form long-range contacts with promoters, but it remains unclear if enhancer activity and their chromosomal contacts are mediated by the same DNA sequences and recruited factors. We studied the effects of expression quantitative trait loci (eQTLs) on enhancer activity and promoter contacts in primary monocytes isolated from 34 individuals. Using eQTL-Capture Hi-C and a Bayesian approach considering both intra- and inter-individual variation, we initially detected 19 eQTLs associated with enhancer-eGene promoter contacts, most of which also associated with enhancer accessibility and activity. Capitalising on these shared effects, we devised a multi-modality Bayesian strategy, which identified 629 "trimodal QTLs" jointly associated with enhancer accessibility, eGene promoter contact, and gene expression. Causal mediation analysis and CRISPR interference revealed causal relationships between these three modalities. Many detected QTLs overlapped disease susceptibility loci and influenced the predicted binding of myeloid transcription factors, including SPI1, GABPB and STAT3. Additionally, a variant associated with PCK2 promoter contact directly disrupted a CTCF binding motif and impacted promoter insulation from downstream enhancers. Jointly, our findings suggest an inherent genetic link between the activity and connectivity of enhancers with relevance for human disease, and highlight the role of genetically-determined chromatin boundaries in gene control.

genetics↗

bioRxiv2023

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce Multi-response Mendelian randomization (MR2), a novel MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and viceversa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

genetics↗

bioRxiv2022

Single-cell Mendelian randomisation identifies cell-type specific genetic effects on human brain disease and behaviour

Translating genome-wide association loci to therapies requires knowledge of the causal genes, their directionality of effect and the cell-types in which they act. To infer these relationships in the human brain, we implemented Mendelian randomisation using single cell-type expression quantitative trait loci (eQTLs) as genetic anchors. Expression QTLs were mapped across 8 major cell-types in brain tissue exclusively ascertained from donors with no history of brain disease. We report evidence for a causal association between the change in expression of 118 genes and one or more of 16 brain phenotypes, revealing candidate targets for risk mitigation and opportunities for shared preventative therapeutic strategies. We highlight key causal genes for neurodegenerative and neuropsychiatric disease and for each, we report its cellular context and the therapeutic directionality required for risk mitigation. Our use of control samples establishes a new resource for the causal interpretation of GWAS risk alleles for human brain phenotypes.

neuroscience↗