@article{21488,
  abstract     = {Human height is a model for the genetic analysis of complex traits, and recent studies suggest the presence of thousands of common genetic variant associations and hundreds of low-frequency/rare variants. Here, we develop a new algorithmic paradigm based on approximate message passing (genomic vector approximate message passing [gVAMP]) for identifying DNA sequence variants associated with complex traits and common diseases in large-scale whole-genome sequencing (WGS) data. We show that gVAMP accurately localizes associations to variants with the correct frequency and position in the DNA, outperforming existing fine-mapping methods in selecting the appropriate genetic variants within WGS data. We then apply gVAMP to jointly model the relationship of tens of millions of WGS variants with human height in hundreds of thousands of UK Biobank individuals. We identify 59 rare variants and gene burden scores alongside many hundreds of DNA regions containing common variant associations and show that understanding the genetic basis of complex traits will require the joint analysis of hundreds of millions of variables measured on millions of people. The polygenic risk scores obtained from gVAMP have high accuracy (including a prediction accuracy of ∼46% for human height) and outperform current methods for downstream tasks such as mixed linear model association testing across 13 UK Biobank traits. In conclusion, gVAMP offers a scalable foundation for a wider range of analyses in WGS data.},
  author       = {Depope, Al and Bajzik, Jakub and Mondelli, Marco and Robinson, Matthew Richard},
  issn         = {2666-979X},
  journal      = {Cell Genomics},
  publisher    = {Elsevier},
  title        = {{Joint modeling of whole-genome sequencing data for human height via approximate message passing}},
  doi          = {10.1016/j.xgen.2026.101162},
  year         = {2026},
}

@article{20491,
  abstract     = {Global fibre production has expanded rapidly, with polyester and cotton dominating, significantly contributing to textile waste and increasing demand for sustainable solutions. This study presents innovative method to recycle polyester/cotton (PET/CO) blends using hydrophobic deep eutectic solvents (DESs), eliminating the need for toxic chemicals while achieving high dissolution yields. PET was completely dissolved within 5 min, substantially outperforming state-of-the-art methods and facilitating the efficient and selective recovery of both components, PET (97%) and CO (100%). SEM imaging confirmed no morphological changes in cotton fibres after treatment. The thermal stability of the recovered materials was validated using DSC and TGA analyses, while ATR-FTIR spectroscopy indicated no chemical changes. Mechanical testing confirmed recovered cotton’s tenacity and elongation are within expected ranges despite showing a decrease of 28% in tenacity and 34% in elongation. Hence, the proposed process provides an efficient and sustainable recycling solution for PET/CO blends, retaining both polymers in a condition similar to virgin materials used in textile manufacturing with minimal processing time.},
  author       = {Depope, Nika and Depope, Al and Archodoulaki, Vasiliki Maria and Ipsmiller, Wolfgang and Bartl, Andreas},
  issn         = {1879-2456},
  journal      = {Waste Management},
  publisher    = {Elsevier},
  title        = {{Deep eutectic solvent as a solution for polyester/cotton textile recycling}},
  doi          = {10.1016/j.wasman.2025.115177},
  volume       = {208},
  year         = {2025},
}

@inproceedings{17147,
  abstract     = {Efficient utilization of large-scale biobank data is crucial for inferring the genetic basis of disease and predicting health outcomes from the DNA. Yet we lack efficient, accurate methods that scale to data where electronic health records are linked to whole genome sequence information. To address this issue, our paper develops a new algorithmic paradigm based on Approximate Message Passing (AMP), which is specifically tailored for genomic prediction and association testing. Our method yields comparable out-of-sample prediction accuracy to the state of the art on UK Biobank traits, whilst dramatically improving computational complexity, with a 8x-speed up in the run time. In addition, AMP theory provides a joint association testing framework, which outperforms the currently used REGENIE method, in roughly a third of the compute time. This first, truly large-scale application of the AMP framework lays the foundations for a far wider range of statistical analyses for hundreds of millions of variables measured on millions of people.},
  author       = {Depope, Al and Mondelli, Marco and Robinson, Matthew Richard},
  booktitle    = {2024 IEEE International Conference on Acoustics, Speech, and Signal Processing},
  isbn         = {9798350344851},
  issn         = {1520-6149},
  location     = {Seoul, Korea},
  pages        = {13151--13155},
  publisher    = {IEEE},
  title        = {{Inference of genetic effects via approximate message passing}},
  doi          = {10.1109/ICASSP48485.2024.10447198},
  year         = {2024},
}