@article{21987,
  abstract     = {We introduce JODIE, a genetic joint modeling approach that estimates how DNA loci influence human traits by partitioning genetic effects into four components: direct effects (from a child’s alleles), indirect maternal and paternal effects (from parents’ alleles), and parent-of-origin (PofO) effects (dependent on parental transmission of alleles), while uniquely accounting for assortative mating. We analyze 30,000 child-mother-father trios from the Estonian Biobank and the Norwegian Mother, Father, and Child Cohort, focusing on height, body mass index, and childhood educational test scores. We find direct effects to be the largest contributor to trait variation, but combined, indirect parental and PofO effects are similarly substantial. We support our results by within-family genome-wide association testing and identify 276 independently associated DNA regions with a complex interplay between direct, indirect, and PofO effects. By joint modeling, we show that direct, indirect, and PofO effects collectively shape human phenotypic variation across loci genome-wide.},
  author       = {Krätschmer, Ilse and Hegemann, Laura and Hofmeister, Robin J. and Corfield, Elizabeth C. and Mahmoudi, Mahdi and Delaneau, Olivier and Andreassen, Ole A. and Campbell, Archie and Hayward, Caroline and Marioni, Riccardo E. and Ystrom, Eivind and Havdahl, Alexandra and Robinson, Matthew Richard},
  issn         = {2666-979X},
  journal      = {Cell Genomics},
  publisher    = {Elsevier},
  title        = {{Separating direct, indirect, and parent-of-origin genetic effects in the human population}},
  doi          = {10.1016/j.xgen.2026.101277},
  year         = {2026},
}

@article{21484,
  abstract     = {An individual's phenotype reflects a complex interplay of the direct effects of their DNA, epigenetic modifications of their DNA induced by their parents, and indirect effects of their parents' DNA. Here, we derive how the genetic variance within a population is changed under the influence of indirect maternal, paternal and parent-of-origin effects under random mating. We also consider indirect effects of a sibling, in particular how the genetic variance is altered when looking at the phenotypic difference between two siblings. The calculations are then extended to include assortative mating (AM), which alters the variance by inducing increased homozygosity and correlations within and across loci. AM likely leads to covariance of parental genetic effects, a measure of the similarity of parents in the indirect effects they have on their children. We propose that this assortment for parental characteristics, where biological parents create similar environments for their children, can create shared parental effects across traits and the appearance of cross-trait AM. Our theory shows how the resemblance among relatives increases under both AM, indirect and parent-of-origin effects. When our model is used to predict correlations among relatives in human height, we find that explaining the patterns observed in real data requires both indirect genetic effects and assortative mating. The degree to which direct, indirect and epigenetic effects shape the phenotypic variance of complex traits remains an open question that requires large-scale family data to be resolved.},
  author       = {Krätschmer, Ilse and Robinson, Matthew Richard},
  issn         = {1943-2631},
  journal      = {Genetics},
  publisher    = {Oxford University Press},
  title        = {{A quantitative genetic model for indirect genetic effects and genomic imprinting under random and assortative mating}},
  doi          = {10.1093/genetics/iyag042},
  year         = {2026},
}

@article{21503,
  abstract     = {Currently, pharmacogenetics relies on partially annotated star alleles, leaving novel variants and complex haplotypes uninterpretable. Computational scoring frameworks could overcome these limitations. Here, we comprehensively evaluated the ability of existing (CADD, FATHMM-XF, PROVEAN, MutationAssessor, SIFT, PhyloP100, APF, APF2) and novel (PharmGScore and PharmMLScore) variant effect predictors to assess pharmacogenetic alleles in multiple scenarios. Altogether we analyzed 541 PharmVar alleles, high‑throughput CYP2C9 and CYP2C19 mutational maps, and 200 642 UK Biobank exomes linked with health records containing antidepressant treatment outcomes. Many evaluated tools, especially ensemble frameworks, matched or exceeded star allele classifications (ROC‑AUC up to 0.85 for allele definitions, 0.95 in vitro; TPR up to 0.99 for exomes) and accurately predicted severe antidepressant adverse events for carriers of deleterious variants in CYP2C19 (OR 1.20–1.35). Our findings show that computational predictors deliver star allele accuracy while overcoming their limitations. With additional validation, computational tools could enhance clinical decision frameworks by enabling continuous scoring, incorporating previously unknown variants, and providing genome-wide applicability.},
  author       = {Hajto, Jacek and Piechota, Marcin and Krätschmer, Ilse and Konowalska, Paula and Boyle, Gabriel E. and Fowler, Douglas M. and Borczyk, Malgorzata and Korostynski, Michal},
  issn         = {1473-1150},
  journal      = {Pharmacogenomics Journal},
  number       = {2},
  publisher    = {Springer Nature},
  title        = {{Computational variant predictors for pharmacogenomics: From evaluation of single alleles to assessment of adverse drug reactions to antidepressants}},
  doi          = {10.1038/s41397-026-00399-0},
  volume       = {26},
  year         = {2026},
}

@unpublished{18648,
  abstract     = {Statistical causal learning in genomics relies on the instrumental variable method of
Mendelian Randomization (MR). Currently, an overwhelming number of MR studies
purport to show causal relationships among a wide range of risk factors and outcomes.
Here, we show that selecting instrument variables from genome-wide association study
estimates leads to high false discovery rates for many MR approaches, which can be
greatly reduced by employing a graphical inference approach which: (i) explicitly tests
instrumental variable assumptions; (ii) distinguishes direct from indirect factors in very
high-dimensional data; (iii) discriminates pleiotropic from trait-specific markers, controlling for LD genome-wide; (iv) accommodates rare variants and binary outcomes in a
principled way; and (v) identifies potential unobserved latent confounding. For 17 traits
and 8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that
standard MR analysis gives an abundance of findings that disappear under stringent
assumption checks, with many relationships reflecting potential unmeasured confounding. This implies that mixtures of temporal precedence and potential for reverse-causality
prohibit understanding the underlying nature of phenotypic and genetic correlations in
biobank data. We propose that well-curated longitudinal records are likely needed and
that our approach provides a first-step toward robust principled screening for potential
causal links.
},
  author       = {Machnik, Nick N and Mahmoudi, Seyed Mahdi and Borczyk, Malgorzata and Krätschmer, Ilse and Bauer, Markus J. and Robinson, Matthew Richard},
  booktitle    = {bioRxiv},
  title        = {{Causal inference for multiple risk factors and diseases from genomics data}},
  doi          = {10.1101/2023.12.06.570392},
  year         = {2024},
}

@article{14753,
  abstract     = {Several fixed-target experiments reported J/ψ and ϒ polarizations, as functions of Feynman x (xF) and transverse momentum (PT), in three different frames, using different combinations of beam particles, target nuclei, and collision energies. Despite the diverse and heterogeneous picture formed by these measurements, a detailed look allows us to discern qualitative physical patterns that inspire a simple empirical model. This data-driven scenario offers a good quantitative description of the J/ψ and ϒ(1S) polarizations measured in proton- and pion-nucleus collisions, in the xF 0.5 domain: more than 80 data points (not statistically independent) are well reproduced with only one free parameter. This study sets the context for future low-PT
 quarkonium polarization measurements in proton- and pion-nucleus collisions, such as those to be made by the AMBER experiment, and shows that such measurements provide significant constraints on the poorly-known parton distribution functions of the pion.},
  author       = {Faccioli, Pietro and Krätschmer, Ilse and Lourenço, Carlos},
  issn         = {1873-2445},
  journal      = {Physics Letters B},
  keywords     = {Nuclear and High Energy Physics},
  publisher    = {Elsevier},
  title        = {{Low-pT quarkonium polarization measurements: Challenges and opportunities}},
  doi          = {10.1016/j.physletb.2023.137871},
  volume       = {840},
  year         = {2023},
}