Biology subjects

Krebs, M. D.

Publications and source records attributed to Krebs, M. D..

3 recordsLinked to original sources

bioRxiv2026

LT-FGRS: a unifying R-package for the estimation of family-based genetic liabilities at population-scale

Estimates of per-individual genetic liability from large-scale family data are routinely used in biomedical research to describe genetic etiology of traits and disorders, boost the power of gene-mapping studies, and improve risk predictions. Here we present LT-FGRS, an R package for handling population-scale pedigrees and implementing multiple state-of-the-field methods for estimating genetic liability from such data. Benchmarking in population-scale Nordic registry data demonstrates that LT-FGRS reproduces estimates from existing implementations at manageable computational cost. LT-FGRS unifies previous parallel implementations into a single framework, lowering barriers for methodological comparison and applied use. Availability and ImplementationLT-FGRS is available as an R-package on CRAN. (https://CRAN.R-project.org/package=LTFGRS) Contactemp@au.dk; morten.dybdahl.krebs@regionh.dk Supplementary informationhttps://emilmip.github.io/LTFGRS

genetics↗

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2017

Quantification of cellular distribution as Poisson process in 3D matrix using a multiview light-sheet microscope

The absence of quantitative in vitro cell-extracellular matrix models represents an important bottleneck for basic research and human health. Randomness of cellular distributions provides an opportunity for the development of a quantitative in vitro model. However, quantification of the randomness of random cell distributions is still lacking. In this paper, we have imaged cellular distributions in an alginate matrix using a multiview light-sheet microscope and developed quantification metrics of randomness by modeling it as a Poisson process, a process that has constant probability of occurring in space or time. Our light-sheet microscope can image more than 5 mm thick optically clear samples with 2.9 {+/-}0.4 m depth-resolution. We applied our method to image fluorescently labeled human mesenchymal stem cells (hMSCs) embedded in an alginate matrix. Simulated randomness agrees well with the experiments. Quantification of distributions and validation by simulations will enable quantitative study of cell-matrix interactions in tissue models.

biophysics↗