Markus Loeffler

Trans-eQTLs reveal the architecture of human gene regulatory networks

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

Genome- and Transcriptome-wide association meta-analysis reveals new insights into genes affecting coronary and peripheral artery disease

This study identifies new gene loci and pathways associated with ankle-brachial index through genome- and transcriptome-wide association analyses, revealing shared genetic mechanisms with coronary artery disease and inflammation.

Trans-eQTL mapping prioritises USP18 as a negative regulator of interferon response at a lupus risk locus

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

The utility of explainable A.I. for MRI analysis: Relating model predictions to neuroimaging features of the aging brain

This study uses explainable AI to link brain-age prediction models with neuroimaging features of aging, revealing correlations between voxel relevance and structural brain markers.

OTTERS: A powerful TWAS framework leveraging summary-level reference data

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

Towards the Interpretability of Deep Learning Models for Multi-modal Neuroimaging: Finding Structural Changes of the Ageing Brain

This paper uses deep learning to estimate brain age from MRI data and identifies structural brain changes associated with aging.

Investigating the genetic and environmental basis of head micromovements during MRI

This study investigates the genetic and environmental factors influencing head motion during MRI, finding no significant single variants and no mediation by physiological or psychological factors.

Unraveling the polygenic architecture of complex traits using blood eQTL meta-analysis

The paper uses blood eQTL meta-analysis to explore the polygenic architecture of complex traits, finding that trans-eQTLs are more informative for identifying causal genes than cis-eQTLs.

Mendelian Randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

This paper introduces TWMR, a Mendelian Randomization method that identifies gene-trait associations by integrating GWAS and eQTL data, revealing 3,913 novel associations across 43 human phenotypes.

Genetic Determinants of Cortical Structure (Thickness, Surface Area and Volumes) among Disease Free Adults in the CHARGE Consortium

The paper identifies 161 genome-wide significant genetic associations for cortical structure traits across 34 brain regions, highlighting key biological pathways and gene functions.

Planar cell polarity pathway and development of the human visual cortex

The paper identifies genetic associations with cortical surface area using PCA and GWAS, linking DAAM1 to visual cortex development through mitochondrial pathways and thalamocortical input.

The age-dependent relationship between resting heart rate variability and functional brain connectivity

The paper examines how resting heart rate variability (HRV) relates to brain structure and function across different age groups, finding age-dependent changes in functional connectivity.

Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes

This study identifies 139 common and 4 rare genetic variants associated with type 2 diabetes through GWAS and integrates gene expression and epigenomic data to highlight potential functional genes and regulatory mechanisms.

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood

The paper identifies gene targets for brain-related traits using transcriptomic and methylomic data from blood, finding strong correlations between genetic effects in brain and blood tissues.

Predicting brain-age from multimodal imaging data captures cognitive impairment

This study predicts brain-age from multimodal imaging data and finds it captures cognitive impairment with robustness to motion and generalizability across datasets.