---
DOAJ_listed: '1'
OA_place: publisher
OA_type: gold
_id: '21987'
abstract:
- lang: eng
text: '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.'
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik, Peter Visscher, and members of the Robinson
group at ISTA for their comments, which improved this manuscript. This work was
funded by an SNSF Eccellenza Grant to M.R.R. (PCEGP3-181181) and by core funding
from the Institute of Science and Technology Austria.\r\nThe Norwegian Mother, Father,
and Child Cohort Study is supported by the Norwegian Ministry of Health and Care
Services and the Ministry of Education and Research. We are grateful to all the
participating families in Norway who take part in this on-going cohort study. We
thank the Norwegian Institute of Public Health (NIPH) for generating high-quality
genomic data. The research is part of the HARVEST collaboration, supported by the
Research Council of Norway (#229624). We also thank the NORMENT Center for providing
genotype data, funded by the Research Council of Norway (#223273), South East Norway
Health Authorities, and Stiftelsen Kristian Gerhard Jebsen, and in collaboration
with deCODE Genetics. We further thank the Center for Diabetes Research, the University
of Bergen for providing genotype data funded by the ERC AdG project SELECTionPREDISPOSED,
Stiftelsen Kristian Gerhard Jebsen, Trond Mohn Foundation, the Research Council
of Norway, the Novo Nordisk Foundation, the University of Bergen, and the Western
Norway Health Authorities. The MoBa work was performed on the TSD (Tjeneste for
Sensitive Data) facilities, owned by the University of Oslo, operated and developed
by the TSD service group at the University of Oslo, IT Department (USIT, tsd-drift@usit.uio.no).
E.Y. is supported by the European Union (grant numbers 101045526 and 101073237)
and the Research Council of Norway (grant numbers 336078, 288083, and 331640).\r\nWe
would like to acknowledge the participants and investigators of the Generation Scotland
Cohort study. Generation Scotland received core support from the Chief Scientist
Office of the Scottish Government Health Directorates (CZD/16/6) and the Scottish
Funding Council (HR03006). Genotyping and methylation typing of the GS:SFHS samples
was carried out by the Genetics Core Laboratory at the Wellcome Trust Clinical Research
Facility, Edinburgh, Scotland and was funded by the Medical Research Council UK
and the Wellcome Trust (Wellcome Trust Strategic Award “STratifying Resilience and
Depression Longitudinally” [STRADL] ref. 104036/Z/14/Z).\r\nWe would like to thank
and acknowledge the participants and investigators of the Estonian Biobank (EstBB)
study. The research was conducted using the Estonian Center of Genomics/Roadmap
II funded by the Estonian Research Council (project number TT17).\r\nNorwegian analyses
were performed on resources provided by Sigma2 - the National Infrastructure for
High-Performance Computing and Data Storage in Norway. Estonian Data analysis was
carried out in the High-Performance Computing Center cloud provided by University
of Tartu. Analysis of the Generation Scotland data and the summary statistics obtained
from the other analyses was conducted at IST Austria and is supported by the Scientific
Service Units (SSU) of IST Austria through resources provided by Scientific Computing
(SciComp)."
article_number: '101277'
article_processing_charge: Yes
article_type: original
author:
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Laura
full_name: Hegemann, Laura
last_name: Hegemann
- first_name: Robin J.
full_name: Hofmeister, Robin J.
last_name: Hofmeister
- first_name: Elizabeth C.
full_name: Corfield, Elizabeth C.
last_name: Corfield
- first_name: Mahdi
full_name: Mahmoudi, Mahdi
last_name: Mahmoudi
- first_name: Olivier
full_name: Delaneau, Olivier
last_name: Delaneau
- first_name: Ole A.
full_name: Andreassen, Ole A.
last_name: Andreassen
- first_name: Archie
full_name: Campbell, Archie
last_name: Campbell
- first_name: Caroline
full_name: Hayward, Caroline
last_name: Hayward
- first_name: Riccardo E.
full_name: Marioni, Riccardo E.
last_name: Marioni
- first_name: Eivind
full_name: Ystrom, Eivind
last_name: Ystrom
- first_name: Alexandra
full_name: Havdahl, Alexandra
last_name: Havdahl
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Krätschmer I, Hegemann L, Hofmeister RJ, et al. Separating direct, indirect,
and parent-of-origin genetic effects in the human population. Cell Genomics.
doi:10.1016/j.xgen.2026.101277
apa: Krätschmer, I., Hegemann, L., Hofmeister, R. J., Corfield, E. C., Mahmoudi,
M., Delaneau, O., … Robinson, M. R. (n.d.). Separating direct, indirect, and parent-of-origin
genetic effects in the human population. Cell Genomics. Elsevier. https://doi.org/10.1016/j.xgen.2026.101277
chicago: Krätschmer, Ilse, Laura Hegemann, Robin J. Hofmeister, Elizabeth C. Corfield,
Mahdi Mahmoudi, Olivier Delaneau, Ole A. Andreassen, et al. “Separating Direct,
Indirect, and Parent-of-Origin Genetic Effects in the Human Population.” Cell
Genomics. Elsevier, n.d. https://doi.org/10.1016/j.xgen.2026.101277.
ieee: I. Krätschmer et al., “Separating direct, indirect, and parent-of-origin
genetic effects in the human population,” Cell Genomics. Elsevier.
ista: Krätschmer I, Hegemann L, Hofmeister RJ, Corfield EC, Mahmoudi M, Delaneau
O, Andreassen OA, Campbell A, Hayward C, Marioni RE, Ystrom E, Havdahl A, Robinson
MR. Separating direct, indirect, and parent-of-origin genetic effects in the human
population. Cell Genomics., 101277.
mla: Krätschmer, Ilse, et al. “Separating Direct, Indirect, and Parent-of-Origin
Genetic Effects in the Human Population.” Cell Genomics, 101277, Elsevier,
doi:10.1016/j.xgen.2026.101277.
short: I. Krätschmer, L. Hegemann, R.J. Hofmeister, E.C. Corfield, M. Mahmoudi,
O. Delaneau, O.A. Andreassen, A. Campbell, C. Hayward, R.E. Marioni, E. Ystrom,
A. Havdahl, M.R. Robinson, Cell Genomics (n.d.).
corr_author: '1'
date_created: 2026-06-10T07:39:08Z
date_published: 2026-06-09T00:00:00Z
date_updated: 2026-06-19T07:00:47Z
day: '09'
department:
- _id: MaRo
doi: 10.1016/j.xgen.2026.101277
external_id:
pmid:
- '40909755'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.xgen.2026.101277
month: '06'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
publication: Cell Genomics
publication_identifier:
eissn:
- 2666-979X
publication_status: inpress
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: Separating direct, indirect, and parent-of-origin genetic effects in the human
population
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
PlanS_conform: '1'
_id: '21484'
abstract:
- lang: eng
text: 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.
acknowledgement: We thank members of the Medical Genomics group at ISTA for their
comments, which improved this manuscript. This work was funded by an SNSF Eccellenza
Grant to MRR (PCEGP3-181181), and by core funding from the Institute of Science
and Technology Austria.
article_number: iyag042
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Krätschmer I, Robinson MR. A quantitative genetic model for indirect genetic
effects and genomic imprinting under random and assortative mating. Genetics.
2026. doi:10.1093/genetics/iyag042
apa: Krätschmer, I., & Robinson, M. R. (2026). A quantitative genetic model
for indirect genetic effects and genomic imprinting under random and assortative
mating. Genetics. Oxford University Press. https://doi.org/10.1093/genetics/iyag042
chicago: Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic
Model for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative
Mating.” Genetics. Oxford University Press, 2026. https://doi.org/10.1093/genetics/iyag042.
ieee: I. Krätschmer and M. R. Robinson, “A quantitative genetic model for indirect
genetic effects and genomic imprinting under random and assortative mating,” Genetics.
Oxford University Press, 2026.
ista: Krätschmer I, Robinson MR. 2026. A quantitative genetic model for indirect
genetic effects and genomic imprinting under random and assortative mating. Genetics.,
iyag042.
mla: Krätschmer, Ilse, and Matthew Richard Robinson. “A Quantitative Genetic Model
for Indirect Genetic Effects and Genomic Imprinting under Random and Assortative
Mating.” Genetics, iyag042, Oxford University Press, 2026, doi:10.1093/genetics/iyag042.
short: I. Krätschmer, M.R. Robinson, Genetics (2026).
corr_author: '1'
date_created: 2026-03-23T15:02:54Z
date_published: 2026-02-12T00:00:00Z
date_updated: 2026-06-18T08:31:14Z
day: '12'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1093/genetics/iyag042
external_id:
pmid:
- '41677404'
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1093/genetics/iyag042
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
publication: Genetics
publication_identifier:
issn:
- 1943-2631
publication_status: epub_ahead
publisher: Oxford University Press
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/medical-genomics-group/familyMC
status: public
title: A quantitative genetic model for indirect genetic effects and genomic imprinting
under random and assortative mating
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2026'
...
---
OA_place: publisher
OA_type: hybrid
_id: '21503'
abstract:
- lang: eng
text: 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.
acknowledgement: "This research has been conducted using the UK Biobank Resource under
Application Number 62979. We are grateful to the UK Biobank and all its voluntary
participants. This work used data provided by patients and collected by the NHS
as part of their care and support.\r\n\r\nThis study was funded by the National
Science Center, Poland: PRELUDIUM BIS-3 grant no. 2021/43/O/NZ7/01187 (development
and benchmarking of variant scores) and SONATINA 5 grant 2021/40/C/NZ2/00218 (UKB
analyses). Additional support came from the statutory funds of the Maj Institute
of Pharmacology PAS. We gratefully acknowledge Poland’s high-performance Infrastructure
PLGrid ACK Cyfronet AGH, for providing computer facilities and support within computational
grant no PLG/2022/015861. DMF and GEB were funded by NIH grants NIH R35GM152106
and UM1HG011969."
article_number: '8'
article_processing_charge: Yes (in subscription journal)
article_type: original
author:
- first_name: Jacek
full_name: Hajto, Jacek
last_name: Hajto
- first_name: Marcin
full_name: Piechota, Marcin
last_name: Piechota
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Paula
full_name: Konowalska, Paula
last_name: Konowalska
- first_name: Gabriel E.
full_name: Boyle, Gabriel E.
last_name: Boyle
- first_name: Douglas M.
full_name: Fowler, Douglas M.
last_name: Fowler
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Michal
full_name: Korostynski, Michal
last_name: Korostynski
citation:
ama: 'Hajto J, Piechota M, Krätschmer I, et al. Computational variant predictors
for pharmacogenomics: From evaluation of single alleles to assessment of adverse
drug reactions to antidepressants. Pharmacogenomics Journal. 2026;26(2).
doi:10.1038/s41397-026-00399-0'
apa: 'Hajto, J., Piechota, M., Krätschmer, I., Konowalska, P., Boyle, G. E., Fowler,
D. M., … Korostynski, M. (2026). Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
Pharmacogenomics Journal. Springer Nature. https://doi.org/10.1038/s41397-026-00399-0'
chicago: 'Hajto, Jacek, Marcin Piechota, Ilse Krätschmer, Paula Konowalska, Gabriel
E. Boyle, Douglas M. Fowler, Malgorzata Borczyk, and Michal Korostynski. “Computational
Variant Predictors for Pharmacogenomics: From Evaluation of Single Alleles to
Assessment of Adverse Drug Reactions to Antidepressants.” Pharmacogenomics
Journal. Springer Nature, 2026. https://doi.org/10.1038/s41397-026-00399-0.'
ieee: 'J. Hajto et al., “Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants,”
Pharmacogenomics Journal, vol. 26, no. 2. Springer Nature, 2026.'
ista: 'Hajto J, Piechota M, Krätschmer I, Konowalska P, Boyle GE, Fowler DM, Borczyk
M, Korostynski M. 2026. Computational variant predictors for pharmacogenomics:
From evaluation of single alleles to assessment of adverse drug reactions to antidepressants.
Pharmacogenomics Journal. 26(2), 8.'
mla: 'Hajto, Jacek, et al. “Computational Variant Predictors for Pharmacogenomics:
From Evaluation of Single Alleles to Assessment of Adverse Drug Reactions to Antidepressants.”
Pharmacogenomics Journal, vol. 26, no. 2, 8, Springer Nature, 2026, doi:10.1038/s41397-026-00399-0.'
short: J. Hajto, M. Piechota, I. Krätschmer, P. Konowalska, G.E. Boyle, D.M. Fowler,
M. Borczyk, M. Korostynski, Pharmacogenomics Journal 26 (2026).
date_created: 2026-03-29T22:07:08Z
date_published: 2026-03-09T00:00:00Z
date_updated: 2026-03-30T07:10:50Z
day: '09'
ddc:
- '570'
department:
- _id: MaRo
doi: 10.1038/s41397-026-00399-0
external_id:
pmid:
- '41803106'
file:
- access_level: open_access
checksum: 2fd3d7e48b779ac24245f6c35449b89a
content_type: application/pdf
creator: dernst
date_created: 2026-03-30T07:04:08Z
date_updated: 2026-03-30T07:04:08Z
file_id: '21506'
file_name: 2026_PharmacogenomicsJour_Hajto.pdf
file_size: 2618963
relation: main_file
success: 1
file_date_updated: 2026-03-30T07:04:08Z
has_accepted_license: '1'
intvolume: ' 26'
issue: '2'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
month: '03'
oa: 1
oa_version: Published Version
pmid: 1
publication: Pharmacogenomics Journal
publication_identifier:
eissn:
- 1473-1150
issn:
- ' 1470-269X'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Computational variant predictors for pharmacogenomics: From evaluation of
single alleles to assessment of adverse drug reactions to antidepressants'
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 26
year: '2026'
...
---
OA_place: repository
OA_type: free access
_id: '18648'
abstract:
- lang: eng
text: "Statistical causal learning in genomics relies on the instrumental variable
method of\r\nMendelian Randomization (MR). Currently, an overwhelming number of
MR studies\r\npurport to show causal relationships among a wide range of risk
factors and outcomes.\r\nHere, we show that selecting instrument variables from
genome-wide association study\r\nestimates leads to high false discovery rates
for many MR approaches, which can be\r\ngreatly reduced by employing a graphical
inference approach which: (i) explicitly tests\r\ninstrumental variable assumptions;
(ii) distinguishes direct from indirect factors in very\r\nhigh-dimensional data;
(iii) discriminates pleiotropic from trait-specific markers, controlling for LD
genome-wide; (iv) accommodates rare variants and binary outcomes in a\r\nprincipled
way; and (v) identifies potential unobserved latent confounding. For 17 traits\r\nand
8.4M variants recorded for 458,747 individuals in the UK Biobank, we show that\r\nstandard
MR analysis gives an abundance of findings that disappear under stringent\r\nassumption
checks, with many relationships reflecting potential unmeasured confounding. This
implies that mixtures of temporal precedence and potential for reverse-causality\r\nprohibit
understanding the underlying nature of phenotypic and genetic correlations in\r\nbiobank
data. We propose that well-curated longitudinal records are likely needed and\r\nthat
our approach provides a first-step toward robust principled screening for potential\r\ncausal
links.\r\n"
acknowledged_ssus:
- _id: ScienComp
acknowledgement: "We thank Zoltan Kutalik and members of the Robinson group \r\nat
ISTA for their comments, which improved this manuscript. This work was funded \r\nby
a research collaboration agreement between Boehringer Ingelheim and the research
\r\ngroup of MRR at the Institute of Science and Technology Austria. Additional
funding \r\nwas also provided by an SNSF Eccellenza Grant to MRR (PCEGP3-181181),
and by \r\ncore funding from the Institute of Science and Technology Austria. We
would like \r\nto acknowledge the participants and investigators of the UK Biobank
study. High- \r\nperformance computing was supported by the Scientific Service Units
(SSU) of IST \r\nAustria through resources provided by Scientific Computing (SciComp). "
article_processing_charge: No
author:
- first_name: Nick N
full_name: Machnik, Nick N
id: 3591A0AA-F248-11E8-B48F-1D18A9856A87
last_name: Machnik
orcid: 0000-0001-6617-9742
- first_name: Seyed Mahdi
full_name: Mahmoudi, Seyed Mahdi
id: b9f6d5ef-7774-11eb-a47f-df2c75c02ee7
last_name: Mahmoudi
- first_name: Malgorzata
full_name: Borczyk, Malgorzata
last_name: Borczyk
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Markus J.
full_name: Bauer, Markus J.
last_name: Bauer
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
citation:
ama: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. Causal
inference for multiple risk factors and diseases from genomics data. bioRxiv.
2024. doi:10.1101/2023.12.06.570392
apa: Machnik, N. N., Mahmoudi, S. M., Borczyk, M., Krätschmer, I., Bauer, M. J.,
& Robinson, M. R. (2024). Causal inference for multiple risk factors and diseases
from genomics data. bioRxiv. https://doi.org/10.1101/2023.12.06.570392
chicago: Machnik, Nick N, Seyed Mahdi Mahmoudi, Malgorzata Borczyk, Ilse Krätschmer,
Markus J. Bauer, and Matthew Richard Robinson. “Causal Inference for Multiple
Risk Factors and Diseases from Genomics Data.” BioRxiv, 2024. https://doi.org/10.1101/2023.12.06.570392.
ieee: N. N. Machnik, S. M. Mahmoudi, M. Borczyk, I. Krätschmer, M. J. Bauer, and
M. R. Robinson, “Causal inference for multiple risk factors and diseases from
genomics data,” bioRxiv. 2024.
ista: Machnik NN, Mahmoudi SM, Borczyk M, Krätschmer I, Bauer MJ, Robinson MR. 2024.
Causal inference for multiple risk factors and diseases from genomics data. bioRxiv,
10.1101/2023.12.06.570392.
mla: Machnik, Nick N., et al. “Causal Inference for Multiple Risk Factors and Diseases
from Genomics Data.” BioRxiv, 2024, doi:10.1101/2023.12.06.570392.
short: N.N. Machnik, S.M. Mahmoudi, M. Borczyk, I. Krätschmer, M.J. Bauer, M.R.
Robinson, BioRxiv (2024).
corr_author: '1'
date_created: 2024-12-11T10:42:59Z
date_published: 2024-08-10T00:00:00Z
date_updated: 2026-07-08T22:30:27Z
day: '10'
department:
- _id: MaRo
doi: 10.1101/2023.12.06.570392
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc/4.0/
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2023.12.06.570392
month: '08'
oa: 1
oa_version: Preprint
project:
- _id: 9B8D11D6-BA93-11EA-9121-9846C619BF3A
grant_number: PCEGP3_181181
name: Improving estimation and prediction of common complex disease risk
- _id: bd936e6f-d553-11ed-ba76-a82299f63e8c
grant_number: '590359'
name: Advanced statistical modelling to facilitate more accurate characterisation
of disease phenotypes, improved genetic mapping, and effective therapeutic hypothesis
generation
publication: bioRxiv
publication_status: published
related_material:
record:
- id: '18642'
relation: dissertation_contains
status: public
status: public
title: Causal inference for multiple risk factors and diseases from genomics data
tmp:
image: /images/cc_by_nc.png
legal_code_url: https://creativecommons.org/licenses/by-nc/4.0/legalcode
name: Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
short: CC BY-NC (4.0)
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2024'
...
---
_id: '14753'
abstract:
- lang: eng
text: "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\r\n 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."
acknowledgement: "P.F. and C.L. acknowledge support from Fundação para a Ciência e
a Tecnologia, Portugal, under contract CERN/FIS-PAR/0010/2019.\r\nOpen Access funded
by SCOAP3."
article_number: '137871'
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Pietro
full_name: Faccioli, Pietro
last_name: Faccioli
- first_name: Ilse
full_name: Krätschmer, Ilse
id: 30d4014e-7753-11eb-b44b-db6d61112e73
last_name: Krätschmer
orcid: 0000-0002-5636-9259
- first_name: Carlos
full_name: Lourenço, Carlos
last_name: Lourenço
citation:
ama: 'Faccioli P, Krätschmer I, Lourenço C. Low-pT quarkonium polarization measurements:
Challenges and opportunities. Physics Letters B. 2023;840. doi:10.1016/j.physletb.2023.137871'
apa: 'Faccioli, P., Krätschmer, I., & Lourenço, C. (2023). Low-pT quarkonium
polarization measurements: Challenges and opportunities. Physics Letters B.
Elsevier. https://doi.org/10.1016/j.physletb.2023.137871'
chicago: 'Faccioli, Pietro, Ilse Krätschmer, and Carlos Lourenço. “Low-PT Quarkonium
Polarization Measurements: Challenges and Opportunities.” Physics Letters B.
Elsevier, 2023. https://doi.org/10.1016/j.physletb.2023.137871.'
ieee: 'P. Faccioli, I. Krätschmer, and C. Lourenço, “Low-pT quarkonium polarization
measurements: Challenges and opportunities,” Physics Letters B, vol. 840.
Elsevier, 2023.'
ista: 'Faccioli P, Krätschmer I, Lourenço C. 2023. Low-pT quarkonium polarization
measurements: Challenges and opportunities. Physics Letters B. 840, 137871.'
mla: 'Faccioli, Pietro, et al. “Low-PT Quarkonium Polarization Measurements: Challenges
and Opportunities.” Physics Letters B, vol. 840, 137871, Elsevier, 2023,
doi:10.1016/j.physletb.2023.137871.'
short: P. Faccioli, I. Krätschmer, C. Lourenço, Physics Letters B 840 (2023).
date_created: 2024-01-08T13:09:17Z
date_published: 2023-05-10T00:00:00Z
date_updated: 2025-09-09T14:13:46Z
day: '10'
ddc:
- '530'
department:
- _id: MaRo
doi: 10.1016/j.physletb.2023.137871
external_id:
isi:
- '000967947300001'
file:
- access_level: open_access
checksum: 02dec160dbc81d95985e755869d8afbf
content_type: application/pdf
creator: dernst
date_created: 2024-01-09T08:59:24Z
date_updated: 2024-01-09T08:59:24Z
file_id: '14762'
file_name: 2023_PhysicsLettersB_Faccioli.pdf
file_size: 855494
relation: main_file
success: 1
file_date_updated: 2024-01-09T08:59:24Z
has_accepted_license: '1'
intvolume: ' 840'
isi: 1
keyword:
- Nuclear and High Energy Physics
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
publication: Physics Letters B
publication_identifier:
eissn:
- 1873-2445
issn:
- 0370-2693
publication_status: published
publisher: Elsevier
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'Low-pT quarkonium polarization measurements: Challenges and opportunities'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 840
year: '2023'
...