Andrew M. McIntosh

Andrew M. McIntosh FRCPsych FRSE FMedSci
Born	(1971-01-21)January 21, 1971 Aberdeen
Alma mater	University of Aberdeen University of Edinburgh
Scientific career
Fields	Biological Psychiatry Genomics Data Science

Andrew M. McIntosh FRCPsych FRSE FMedSci is a UK academic psychiatrist. He is Professor of Biological Psychiatry at the University of Edinburgh, . The main focus of his research is using genomic and neuroimaging approaches to better understand the causes and causal consequences of Major Depressive Disorder.

Education

He completed his BSc and MBChB (medical qualification) at the University of Aberdeen and his psychiatric training in South East Scotland, and at the Royal Edinburgh Hospital, before gaining MRCPsych in 2000. He has an MPhil (Psychiatry) and MD (Psychiatry, 2004) from the University of Edinburgh and a MSc in Applied Statistics from Edinburgh Napier University. He has held an MRC Clinical Training, Health Foundation/Academy of Medical Sciences Clinician Scientist and Scottish Funding Council Senior Clinical Fellowships

Career

McIntosh is co-chair of the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. with Cathryn Lewis. He is Chief Scientist of the Health Data Research UK Mental Health Hub DATAMIND and chair of the Generation Scotland Mental Health Expert Working Group and was founding Chair of the MQ Mental Health Data Science Group. McIntosh is a Wellcome Trust Investigator and is an investigator on many studies of depression, including DepGenAfrica.

Selected publications

• Adams, M (2024). "Genome-wide study of major depression in 685,808 diverse individuals identifies 697 independent associations, infers causal neuronal subtypes and biological targets for novel pharmacotherapies". medRxiv. doi:10.1101/2024.04.29.24306535. PMC 11092713. PMID 38746223.

• Gao, C (2023). "Phenome-wide analyses identify an association between the parent-of-origin effects dependent methylome and the rate of aging in humans". Genome Biology. 24 (1): 117. doi:10.1186/s13059-023-02953-6. PMC 10184337. PMID 37189164.

• Hillary, RF (2023). "Blood-based epigenome-wide analyses of 19 common disease states: A longitudinal, population-based linked cohort study of 18,413 Scottish individuals". PLOS Medicine. 20 (7): e1004247. doi:10.1371/journal.pmed.1004247. PMC 10325072. PMID 37410739.

• Jiang, JC (2023). "Investigating the potential anti-depressive mechanisms of statins: a transcriptomic and Mendelian randomization analysis". Translational Psychiatry. 13 (1): 110. doi:10.1038/s41398-023-02403-8. PMC 10073189. PMID 37015906.

• Davyson, E (2023). "Metabolomic Investigation of Major Depressive Disorder Identifies a Potentially Causal Association With Polyunsaturated Fatty Acids". Biological Psychiatry. 94 (8): 630–639. doi:10.1016/j.biopsych.2023.01.027. PMC 10804990. PMID 36764567.

• Wigmore, EM (2020). "Genome-wide association study of antidepressant treatment resistance in a population-based cohort using health service prescription data and meta-analysis with GENDEP". The Pharmacogenomics Journal. 20 (2): 329–341. doi:10.1038/s41397-019-0067-3. PMC 7096334. PMID 30700811.

• Shen, X (2020). "A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank". Nature Communications. 11 (1): 2301. Bibcode:2020NatCo..11.2301S. doi:10.1038/s41467-020-16022-0. PMC 7210889. PMID 32385265.

• Howard, DM (2020). "Genetic stratification of depression in UK Biobank". Translational Psychiatry. 10 (1): 163. doi:10.1038/s41398-020-0848-0. PMC 7246256. PMID 32448866.

• McIntosh, AM (2019). "Uncovering the Genetic Architecture of Major Depression". Neuron. 102 (1): 91–103. doi:10.1016/j.neuron.2019.03.022. PMC 6482287. PMID 30946830.

• Howard, DM (2019). "Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions". Nature Neuroscience. 22 (3): 343–352. doi:10.1038/s41593-018-0326-7. PMC 6522363. PMID 30718901.

• Hill, WD (2019). "Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income". Nature Communications. 10 (1): 5741. Bibcode:2019NatCo..10.5741H. doi:10.1038/s41467-019-13585-5. PMC 6915786. PMID 31844048.

• Hafferty, JD (2019). "Pharmaco-epidemiology of antidepressant exposure in a UK cohort record-linkage study". Journal of Psychopharmacology. 33 (4): 482–493. doi:10.1177/0269881119827888. hdl:20.500.11820/8c7947a4-5c6f-498d-915d-e95bb2a4718d. PMID 30808242. S2CID 73492276.

• Howard, DM (2018). "Genome-wide association study of depression phenotypes in UK Biobank identifies variants in excitatory synaptic pathways". Nature Communications. 9 (1): 1470. Bibcode:2018NatCo...9.1470H. doi:10.1038/s41467-018-03819-3. PMC 5902628. PMID 29662059.