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Record W2795608223 · doi:10.1101/289983

A novel, biologically-informed polygenic score reveals role of mesocorticolimbic insulin receptor gene network on impulsivity and addiction

2018· preprint· en· W2795608223 on OpenAlexaff
Kathryn McCracken, Shantala A. Hari Dass, Irina Pokhvisneva, Lawrence M. Chen, Elika Garg, Thao T. T. Nguyen, Moein Yaqubi, Lisa M. McEwen, Julie L. MacIsaac, Josie Diorio, Michael S. Kobor, Kieran J. O’Donnell, Michael J. Meaney, Patrícia Pelufo Silveira

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2018
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Associations and Epidemiology
Canadian institutionsBC Children's HospitalDouglas Mental Health University InstituteMcGill UniversityJohn Abbott College
Fundersnot available
KeywordsImpulsivityAddictionPsychologyPrefrontal cortexPsychopathologyNeurosciencePsychiatryClinical psychologyCognition

Abstract

fetched live from OpenAlex

Abstract Importance Activation of brain insulin receptors occurs on mesocorticolimbic regions, modulating reward sensitivity and inhibitory control. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related psychopathologies (attention-deficit hyperactivity disorder, addiction), an idea that agrees with the high comorbidity between insulin resistant states and psychiatric conditions. While genetic studies comprise an interesting tool to explore neurobiological mechanisms in community samples, the conventional genome-wide association studies and polygenic risk score methodologies completely ignore the fact that genes operate in networks, and code for precise biological functions in specific tissues. Objective We propose a novel, biologically informed genetic score reflecting the mesocorticolimbic insulin receptor-related gene network, and investigate if it predicts dopamine-related psychopathology (impulsivity and addiction) in community samples. Design Birth cohort (Maternal Adversity, Vulnerability and Neurodevelopment, MAVAN) and adult cohort (Study of Addiction, Genes and Environment, SAGE). Setting General community. Participants 212 4-year-old children (MAVAN), and 1626 adults (SAGE). Exposure The biologically informed, mesocorticolimbic specific, insulin receptor polygenic score was created based on levels of co-expression with the insulin receptor in striatum and prefrontal cortex, and calculated in the two samples using the genotype data (Psychip/Psycharray). Main outcome childhood impulsivity in the Information Sampling task, and risk for early addiction onset. Results The insulin receptor polygenic score showed improved prediction of childhood impulsivity in boys and risk for early addiction onset in males in comparison to conventional polygenic risk scores for attention-deficit hyperactivity disorder or addiction. Conclusions and relevance This novel genomic approach reveals insulin action as a relevant biological process involved in the risk for dopamine-related psychopathology. Key points Question Considering the modulation of mesocorticolimbic dopaminergic pathways by insulin through the action on its receptors (IR), we investigated if a novel, region specific polygenic score on the IR-related gene network (ePRS-IR) is associated with dopamine-related behaviors (impulsivity and addiction). Findings The ePRS-IR showed improved prediction of childhood impulsivity and risk for early addiction onset in comparison to conventional polygenic risk scores for ADHD or addiction. Meaning This novel genomic approach reveals insulin action as a biological process involved in the risk for dopamine-related psychopathology.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.231
Teacher spread0.218 · 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 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

Citations1
Published2018
Admission routes1
Has abstractyes

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