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Search results (568)

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“There’s no representation”: a qualitative study of attitudes and motivations towards genomic research participation among Australian South Asians

Nathan V. et al, (2026), European Journal of Human Genetics

Author Correction: Genome-wide fine-mapping improves identification of causal variants.

Wu Y. et al, (2026), Nat Genet

Extensive antagonistic variants across the human genome.

Bian B. et al, (2026), Nat Commun

Poor mood after oral contraceptive use is associated with increased vulnerability to peripartum depression, premenstrual dysphoric disorder, and higher genetic risk for depression.

Kiewa J. et al, (2026), Arch Womens Ment Health, 29

From GWAS to drug: A framework for drug candidate prioritisation using a gene expression signature matching approach

Chauquet S. et al, (2026)

Empirical evaluation of analytic validity of polygenic scores.

Lin T. et al, (2026), Genome Med

Rethinking covariate adjustment in psychiatric biomarker research: a framework applied to UK Biobank blood samples

Shin M. et al, (2026)

Cohort Profile: Investigating Antidepressant Response within Generation Scotland

Calnan M. et al, (2026), Wellcome Open Research, 11, 210 - 210

Genome-wide meta-analyses of non-response to antidepressants provide insights into underlying molecular genetics and suggest potential pharmacotherapies.

Koch E. et al, (2026), Mol Psychiatry, 31, 2005 - 2013

Psychiatric Nosology Should Include Genetic Syndromes.

Wray NR. and Owen MJ., (2026), Biol Psychiatry, 99, 508 - 510

Genome-wide fine-mapping improves identification of causal variants.

Wu Y. et al, (2026), Nat Genet, 58, 940 - 951

Progress in understanding the biological basis of polygenic disorders.

Wray NR. et al, (2026), Curr Opin Genet Dev, 97

Phenome-Wide Associations of Polygenic Scores for Schizophrenia and Major Depression in 100,000 Chinese Adults.

Wang B. et al, (2026), Biol Psychiatry Glob Open Sci, 6

Genetic and Phenotypic Associations With Sustained Antidepressant Use in Major Depressive Disorder.

Walker A. et al, (2026), JAMA Psychiatry, 83, 247 - 258

Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.

Jiang J-C. et al, (2026), Transl Psychiatry, 16

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