Posts Tagged ‘abcd’

Rare genetic variants confer a high risk of ADHD and implicate neuronal biology | Nature

November 23, 2025

https://www.nature.com/articles/s41586-025-09702-8

Demontis, D., Duan, J., Hsu, Y. H., Pintacuda, G., Grove, J., Nielsen, T. T., Thirstrup, J., Martorana, M., Botts, T., Satterstrom, F. K., Bybjerg-Grauholm, J., Tsai, J. H. Y., Glerup, S., Hoogman, M., Buitelaar, J., Klein, M., Ziegler, G. C., Jacob, C., Grimm, O., . . . Børglum, A. D. (2025). Rare genetic variants confer a high risk of ADHD and implicate neuronal biology. Nature.
https://doi.org/10.1038/s41586-025-09702-8

QT:{{”
Common genetic variants associated with the disorder have been identified12,13, but the role of rare variants in ADHD is mostly unknown. Here, by analysing rare coding variants in exome-sequencing data from 8,895 individuals with ADHD and 53,780 control individuals, we identify three genes (MAP1A, ANO8 and ANK2; P < 3.07 × 10−6; odds ratios 5.55–15.13) that are implicated in ADHD.
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Eight Translational Biotech Projects Selected for 2025 Blavatnik Accelerator Awards | Yale Ventures

July 12, 2025

https://ventures.yale.edu/news/eight-translational-biotech-projects-selected-2025-blavatnik-accelerator-awards

QT:{{”
Mark Gerstein, PhD, Albert L Williams Professor of Biomedical Informatics and Professor of Molecular Biophysics & Biochemistry, of Computer Science, and of Statistics & Data Science

Project: WearGenix: Linking Wearables to Brain Health
Modality: Digital Platform | Therapeutic Area: Neuropsychiatry

Dr. Gerstein’s platform combines smartwatch data with genomic analysis to identify biomarkers for mental health and neurodegenerative diseases. The technology has been tested for ADHD and is expanding to other neurodegenerative conditions.
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FYI:”The US wants a wearable for all. Experts say it won’t fix the health crisis”

July 5, 2025

https://www.digitaltrends.com/mobile/the-us-wants-a-wearable-for-all-experts-say-it-wont-fix-the-health-crisis/

Blavatnik Awardees | Yale Ventures

June 29, 2025

https://ventures.yale.edu/programs/the-blavatnik-fund-for-innovation-at-yale/blavatnik-awardees
2025, weargenix

Sleep Data – National Sleep Research Resource – NSRR

May 24, 2025

https://sleepdata.org/

public data
from chat on Fri.

Wearables may offer clues to psychiatric diagnoses | Findings | Yale Alumni Magazine

March 1, 2025

https://www.yalealumnimagazine.com/articles/6008-wearables-may-offer-clues-to-psychiatric-diagnoses
QT:{{”
A study by a Yale-led research team has shown that more accurate diagnosis of attention-deficit/hyperactivity disorder (ADHD) and other mental health conditions may be on the horizon, thanks to wearable sensors such as smartwatches. The study was published in the journal Cell.

ADHD, characterized by difficulty focusing, restlessness, and impulsive behavior, is the most commonly diagnosed behavioral disorder in children; it can lead to disruptions in learning and daily functioning. Early, accurate diagnosis and intervention can be critical in minimizing its impact. The problem, notes co–senior author Mark Gerstein, the Albert L. Williams Professor of Biomedical Informatics, is that “ADHD has traditionally been diagnosed
symptomatically, and there is an element of subjectivity to
categorizing human behavior. We wanted to see if wearable devices could increase diagnostic precision.”

The study analyzed clinical, wearable, and genetic data from 11,878 US adolescents (ages 9–14) recruited by the NIH Adolescent Brain Cognitive Development Consortium project. Information collected included measurements of heart rate, calorie expenditure, activity intensity, steps, and sleep intensity.

The researchers determined that correctly processed smartwatch data could be used as a “digital phenotype.” (Phenotype is the observable expression—physical characteristics, behaviors—of someone’s genotype, which is each person’s unique DNA sequence and their environment.) “We found,” says Gerstein, “that from the sensor readings we could quite accurately determine if someone has ADHD.”

The researchers used the data to train AI models to identify two psychiatric disorders. They further determined which measurements were most useful in characterizing them. Heart rate was the most important predictor for ADHD, while sleep quality and stage were more useful for identifying anxiety. Based on the patterns across the wearable features, the researchers were able to pinpoint genes and genetic variants associated with ADHD.

Gerstein notes the significance of making the connection to genotype. “We found that the smartwatch measurements can better relate disorders to genetics than just correlating them directly with clinical diagnoses,” he says. “Finding more genetic variants and genes related to the disorders could uncover molecular mechanisms and give us pathways to new treatments.”
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Genome-wide association studies | Nature Reviews Methods Primers

January 25, 2025

g accounts for the cumulative effect of all other variants on the phenotype besides the effect of the specific variant being tested (SNP s).

Although theoretically we should consider the effect of g when testing for GWAS associations, in practice don’t think this happens in standard GWAS tools, such as PLINK and REGENIE (see below).

PLINK: https://www.cog-genomics.org/plink/2.0/assoc

REGENIE: https://www.nature.com/articles/s41588-021-00870-7#Sec10

https://www.nature.com/articles/s43586-021-00056-9

Many mental-health conditions have bodily triggers

May 4, 2024

https://www.economist.com/science-and-technology/2024/04/24/many-mental-health-conditions-have-bodily-triggers

interesting pop-sci article on why it’s necessary to phenotype better than DSM

Quantitative GWAS & QTL studies

February 25, 2023

Primer
Published: 25 January 2023

Molecular quantitative trait loci

François Aguet, Kaur Alasoo, Yang I. Li, Alexis Battle, Hae Kyung Im, Stephen B. Montgomery & Tuuli Lappalainen

Nature Reviews Methods Primers volume 3, Article number: 4 (2023)

Related to the discussion about quantitative GWAS and QTL, this primer review (and in particular box 1) is helpful in clarifying the (non) difference between the two types of studies:

https://www.nature.com/articles/s43586-022-00188-6

The statistics behind quantitative GWAS and QTL studies is the same, the only main difference might be the multiple testing correction procedure.
In general, it’s more a nomenclature distinction, as the term “QTL” is often used specifically for molecular traits.