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Record W4391772211 · doi:10.1101/2024.02.12.24302043

Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction

2024· preprint· en· W4391772211 on OpenAlexaff
Kristia Yiangou, Nasim Mavaddat, Joe Dennis, Maria Zanti, Qin Wang, Manjeet K. Bolla, Mustapha Abubakar, Thomas U. Ahearn, Irene L. Andrulis, Hoda Anton‐Culver, Natalia Antonenkova, Volker Arndt, Kristan J. Aronson, Annelie Augustinsson, Adinda Baten, Sabine Behrens, Marina Bermisheva, Amy Berrington de González, Katarzyna Białkowska, Nicholas Boddicker, Clara Bodelón, Natalia Bogdanova, Stig E. Bojesen, Kristen D. Brantley, Hiltrud Brauch, Hermann Brenner, Nicola J. Camp, Federico Canzian, Jose E. Castelao, Melissa H. Cessna, Jenny Chang‐Claude, Georgia Chenevix‐Trench, Wendy K. Chung, Sarah V. Colonna, Fergus J. Couch, Angela Cox, Simon S. Cross, Kamila Czene, Mary B. Daly, Peter Devilee, Thilo Dörk, Alison M. Dunning, A. Heather Eliassen, Christoph Engel, Mikael Eriksson, D. Gareth Evans, Peter A. Fasching, Olivia Fletcher, Henrik Flyger, Lin Fritschi, Manuela Gago-Domínguez, Aleksandra Gentry‐Maharaj, Anna González‐Neira, Pascal Guénel, Eric Hahnen, Christopher A. Haiman, Ute Hamann, Jaana M. Hartikainen, Vikki Ho, James M. Hodge, Antoinette Hollestelle, Ellen Honisch, Maartje J. Hooning, Reiner Hoppe, John L. Hopper, Sacha J. Howell, Anthony Howell, Simona Jakovchevska, Anna Jakubowska, Helena Jernström, Nichola Johnson, Rudolf Kaaks, Э. К. Хуснутдинова, Cari M. Kitahara, Stella Koutros, Vessela N. Kristensen, James V. Lacey, Diether Lambrechts, Flavio Lejbkowicz, Annika Lindblom, Michael Lush, Arto Mannermaa, Dimitrios Mavroudis, Usha Menon, Rachel A. Murphy, Heli Nevanlinna, Nadia Obi, Kenneth Offit, Tjoung‐Won Park‐Simon, Alpa V. Patel, Cheng Peng, Paolo Peterlongo, Guillermo Pita, Dijana Plaseska‐Karanfilska, Katri Pylkäs, Paolo Radice, Muhammad Usman Rashid, Gad Rennert, Eleanor Roberts, Juan Rodríguez, Atocha Romero, Efraim H. Rosenberg, Emmanouil Saloustros, Dale P. Sandler, Elinor J. Sawyer, Rita K. Schmutzler, Christopher G. Scott, Xiao‐Ou Shu, Melissa C. Southey, Jennifer Stone, Jack A. Taylor, Lauren R. Teras, Irma van de Beek, Walter C. Willett, Robert Winqvist, Wei Zheng, Celine M. Vachon, Marjanka K. Schmidt, Per Hall, Robert J. MacInnis, Roger L. Milne, Paul D.P. Pharoah, Jacques Simard, Antonis C. Antoniou, Douglas F. Easton, Kyriaki Michailidou

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsUniversity of British ColumbiaCentre hospitalier universitaire de QuébecBC Cancer AgencyQueen's UniversityUniversité LavalUniversité de MontréalMount Sinai HospitalLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
FundersMedical Research Council
KeywordsPolygenic risk scoreBreast cancerBiobankDemographyDistribution (mathematics)Risk assessmentGeographyCancerGenotypeMedicineBiologyInternal medicineGeneticsSingle-nucleotide polymorphism

Abstract

fetched live from OpenAlex

Abstract The 313-variant polygenic risk score (PRS 313 ) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS 313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS 313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS 313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS 313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.325
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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