Browsing by Author "Schultz, Patrick"

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    Baculovirus expression: tackling the complexity challenge
    (Elsevier, 2013-06) Barford, David; Takagi, Yuichiro; Schultz, Patrick; Berger, Imre; Biochemistry and Molecular Biology, School of Medicine
    Most essential functions in eukaryotic cells are catalyzed by complex molecular machines built of many subunits. To fully understand their biological function in health and disease, it is imperative to study these machines in their entirety. The provision of many essential multiprotein complexes of higher eukaryotes including humans, can be a considerable challenge, as low abundance and heterogeneity often rule out their extraction from native source material. The baculovirus expression vector system (BEVS), specifically tailored for multiprotein complex production, has proven itself to be uniquely suited for overcoming this impeding bottleneck. Here we highlight recent major achievements in multiprotein complex structure research that were catalyzed by this versatile recombinant complex expression tool.
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    Principled distillation of UK Biobank phenotype data reveals underlying structure in human variation
    (Springer Nature, 2024) Carey, Caitlin E.; Shafee, Rebecca; Wedow, Robbee; Elliott, Amanda; Palmer, Duncan S.; Compitello, John; Kanai, Masahiro; Abbott, Liam; Schultz, Patrick; Karczewski, Konrad J.; Bryant, Samuel C.; Cusick, Caroline M.; Churchhouse, Claire; Howrigan, Daniel P.; King, Daniel; Smith, George Davey; Neale, Benjamin M.; Walters, Raymond K.; Robinson, Elise B.; Medical and Molecular Genetics, School of Medicine
    Data within biobanks capture broad yet detailed indices of human variation, but biobank-wide insights can be difficult to extract due to complexity and scale. Here, using large-scale factor analysis, we distill hundreds of variables (diagnoses, assessments and survey items) into 35 latent constructs, using data from unrelated individuals with predominantly estimated European genetic ancestry in UK Biobank. These factors recapitulate known disease classifications, disentangle elements of socioeconomic status, highlight the relevance of psychiatric constructs to health and improve measurement of pro-health behaviours. We go on to demonstrate the power of this approach to clarify genetic signal, enhance discovery and identify associations between underlying phenotypic structure and health outcomes. In building a deeper understanding of ways in which constructs such as socioeconomic status, trauma, or physical activity are structured in the dataset, we emphasize the importance of considering the interwoven nature of the human phenome when evaluating public health patterns.