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Record W4411267152 · doi:10.1101/2025.06.11.25329301

The Ethnic/Racial Variations of Intracerebral Hemorrhage Genetics (ERICH-GENE) Study Protocol

2025· preprint· en· W4411267152 on OpenAlexaff
Guido J. Falcone, Stacey Q Wolfe, Marialuisa Zedde, Rosario Pascarella, Jordi Jiménez‐Conde, Marta Vallverdú Prats, Joan Jiménez-Baladó, Alessandro Pezzini, Sandra Rossi, Rustam Al‐Shahi Salman, Neshika Samarasekera, Ramin Zand, J. Li, Christina Jern, Daniel Strbian, Liisa Tomppo, Hanne Sallinen, Mar Hernández Guillamon, Magdy Selim, Mayowa Owolabi, Rufus Akinyemi, Gregory Fakunle, Tsong-Hai Lee, David J. Werring, Isabel C. Hostettler, Henry Houlden, Pankaj Sharma, Isaac John, Gie Ken‐Dror, Wendy Jenkins, Kevin N. Sheth, Lauren Sansing, Dharambir K. Sanghera, Evgeny G. Sidorov, Israel Fernández‐Cadenas, Jara Cárcel‐Márquez, Ching‐Jen Chen, Andrea Becerril‐Gaitan, Keon‐Joo Lee, Hee‐Joon Bae, Martin Dichgans, Rainer Malik, Stéphanie Debette, Aniket Mishra, Guillaume Paré, Michael Chong, Zhengming Chen, Robin Walters, Sudha Seshadri, Myriam Fornage, Lee Gilkerson, Vivek Khandwala, Thomas Maloney, Stacie L Demel, Livia Parodi, Paul Nyquist, Wendy Ziai, Bradford B. Worrall, Vagal M Achala, Carl D. Langefeld, Jonathan Rosand, Christopher D. Anderson, Daniel Woo

Bibliographic record

VenuemedRxiv · 2025
Typepreprint
Languageen
FieldMedicine
TopicIntracerebral and Subarachnoid Hemorrhage Research
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersNational Institutes of Health
KeywordsIntracerebral hemorrhageEthnic groupProtocol (science)GeneticsEvolutionary biologyBiologyMedicineSociologyAnthropologyInternal medicinePathologySubarachnoid hemorrhageAlternative medicine

Abstract

fetched live from OpenAlex

Background: Spontaneous, non-traumatic intracranial hemorrhage (ICH) is highly heritable disease. However, the identification of the genetic risk factors driving this high genetic predisposition has been limited by small sample sizes and underrepresentation of non-European populations. The ERICH-GENE study will gather and harmonize clinical, neuroimaging and genomic data on the largest and more diverse collection of ICH cases assembled to date. Methods: ERICH-GENE is an NIH-funded, multi-center, international, genetic and neuroimaging study that aims to achieve the necessary sample size and diversity required to accurately describe the genetic architecture and trans-ethnic variation of ICH. ERICH-GENE will collect and harmonize clinical, neuroimaging and genomic data at least 10,000 multi-ethnic ICH cases. These data will be aggregated with 20,000 existing ICH cases and 600,000 ICH-free controls available through completed studies by the International Stroke Genetics Consortium. To ensure validity, data will undergo extensive harmonization, including expert review of neuroimages to ensure spontaneous etiology and hemorrhage location. We will conduct genome-wide association studies of risk, severity and outcome of ICH, testing for effect modification by race/ethnicity, sex and hemorrhage location. We will also conduct pathway, polygenic risk score and Mendelian randomization analyses. Results: This study will include whole genome sequencing data from 10,850 spontaneous ICH samples, including clinical and radiographic phenotypic data to ensure reliability of true non-traumatic, non-lesional ICH and lobar vs nonlobar location. Of these, 1,497 have already been genotyped using genome-wide arrays, 3,753 have undergone whole genome sequencing, and 5,600 will undergo genome-wide genotyping through ERICH-GENE. There are currently 42 contributing sites exceeding study milestone enrollments. 16,175 radiographic studies from 4,974 patients have been uploaded for harmonization to date, including 26% lobar and 64% nonlobar hemorrhages. Neuroimaging assessment will also include grading for white matter hyperintensities, cerebral atrophy, and presence and severity of IVH. Nearly 6,000 ICH cases will complete genotyping by August 2025. Data/material transfer agreements for summary statistics as well as additional samples are on target to meet the study's objectives. Conclusion: ERICH-GENE is the largest trans-ethnic genetic study of ICH conducted to date. Combining a diverse patient population with expert adjudication of neuroimaging data, ERICH-GENE will identify genetic risk loci that drive the high heritability observed for this disease and make a significant contribution to the understanding of the trans-ethnic variation of its genetic architecture.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.052
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.018
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0020.002
Science and technology studies0.0040.001
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0520.019

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 head0.053
GPT teacher head0.388
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2025
Admission routes1
Has abstractyes

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