Trzaskowski, Maciej

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

eQTLGen, Wray, N. R., 23andMe, the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Ripke, S., Mattheisen, M., Trzaskowski, M., Byrne, E. M., Abdellaoui, A., Adams, M. J., et al. (2018) · Nature Genetics

Genetic Associations and EpidemiologyTreatment of Major Depression

Cited by 3,005

Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

Howard, D. M., Adams, M. J., Clarke, T.-K., Hafferty, J. D., Gibson, J., Shirali, M., Coleman, J. R. I., Hagenaars, S. P., Ward, J., Wigmore, E. M., et al. (2019) · Nature Neuroscience

Genetic Associations and EpidemiologyNeurotransmitter Receptor Influence on Behavior

Cited by 2,565

Genome-wide association study identifies 30 loci associated with bipolar disorder

eQTLGen Consortium, Stahl, E. A., BIOS Consortium, the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., et al. (2019) · Nature Genetics

Bipolar Disorder and TreatmentGenetic Associations and Epidemiology

Cited by 1,519

Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses

Study, L. C., Okbay, A., Baselmans, B. M. L., De Neve, J.-E., Turley, P., Nivard, M. G., Fontana, M. A., Meddens, S. F. W., Linnér, R. K., Rietveld, C. A., et al. (2016) · Nature Genetics

Genetic Associations and EpidemiologyNeurotransmitter Receptor Influence on Behavior

Cited by 1,013

Causal associations between risk factors and common diseases inferred from GWAS summary data

Zhu, Z., Zheng, Z., Zhang, F., Wu, Y., Trzaskowski, M., Maier, R., Robinson, M. R., McGrath, J. J., Visscher, P. M., Wray, N. R., et al. (2018) · Nature Communications

Genetic Associations and EpidemiologyGenetics and Physical Performance

Cited by 951

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences

23and Me Research Team, Karlsson Linnér, R., eQTLgen Consortium, International Cannabis Consortium, Social Science Genetic Association Consortium, Biroli, P., Kong, E., Meddens, S. F. W., Wedow, R., Fontana, M. A., et al. (2019) · Nature Genetics

Genetic Associations and EpidemiologyNeurotransmitter Receptor Influence on Behavior

Cited by 756

Socioeconomic Status (SES) and Children's Intelligence (IQ): In a UK-Representative Sample SES Moderates the Environmental, Not Genetic, Effect on IQ

Hanscombe, K. B., Trzaskowski, M., Haworth, C. M. A., Davis, O. S. P., Dale, P. S., & Plomin, R. (2012) · PLoS ONE

Cognitive Abilities and TestingIntergenerational and Educational Inequality Studies

Cited by 343

GWAS of Suicide Attempt in Psychiatric Disorders and Association With Major Depression Polygenic Risk Scores

Mullins, N., Bigdeli, T. B., Børglum, A. D., Coleman, J. R. I., Demontis, D., Mehta, D., Power, R. A., Ripke, S., Stahl, E. A., Starnawska, A., et al. (2019) · American Journal of Psychiatry

Suicide and Self-Harm StudiesGenetic Associations and Epidemiology

Cited by 270

Common genetic variants associated with cognitive performance identified using the proxy-phenotype method

Rietveld, C. A., Esko, T., Davies, G., Pers, T. H., Turley, P., Benyamin, B., Chabris, C. F., Emilsson, V., Johnson, A. D., Lee, J. J., et al. (2014) · Proceedings of the National Academy of Sciences

Genetic Associations and EpidemiologyCognitive Abilities and Testing

Cited by 258

GWAS meta-analysis of suicide attempt: identification of 12 genome-wide significant loci and implication of genetic risks for specific health factors

Docherty, A. R., Mullins, N., Ashley-Koch, A. E., Qin, X., Coleman, J. R. I., Shabalin, A., Kang, J., Murnyak, B., Wendt, F., Adams, M., et al. (2023) · American Journal of Psychiatry

Genetic Associations and EpidemiologySuicide and Self-Harm Studies

Cited by 160

Imprint of assortative mating on the human genome

Yengo, L., Robinson, M. R., Keller, M. C., Kemper, K. E., Yang, Y., Trzaskowski, M., Gratten, J., Turley, P., Cesarini, D., Benjamin, D. J., et al. (2018) · Nature Human Behaviour

Genetic Associations and EpidemiologyGenetic and phenotypic traits in livestock

Cited by 148

Integrated analysis of environmental and genetic influences on cord blood DNA methylation in new-borns

Czamara, D., Eraslan, G., Page, C. M., Lahti, J., Lahti-Pulkkinen, M., Hämäläinen, E., Kajantie, E., Laivuori, H., Villa, P. M., Reynolds, R. M., et al. (2019) · Nature Communications

Epigenetics and DNA MethylationBirth, Development, and Health

Cited by 143

Discontinuity in the genetic and environmental causes of the intellectual disability spectrum

Reichenberg, A., Cederlöf, M., McMillan, A., Trzaskowski, M., Kapra, O., Fruchter, E., Ginat, K., Davidson, M., Weiser, M., Larsson, H., et al. (2016) · Proceedings of the National Academy of Sciences

Down syndrome and intellectual disability researchCognitive Abilities and Testing

Cited by 115

Nature, nurture, and expertise: response to ericsson.

Plomin, R., Shakeshaft, N. G., McMillan, A., & Trzaskowski, M. (2014) · Intelligence

Cognitive Abilities and TestingReading and Literacy Development

Cited by 13

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

MacDonald, S., Foley, H., Yap, M., Johnston, R. L., Steven, K., Koufariotis, L. T., Sharma, S., Wood, S., Addala, V., Pearson, J. V., et al. (2022) · Scientific Reports

Adversarial Robustness in Machine LearningMachine Learning in Healthcare

Cited by 12

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

MacDonald, S., Foley, H., Yap, M., Johnston, R., Steven, K., Koufariotis, L., Sharma, S., Wood, S., Addala, V., Pearson, J., et al. (2022) · Scientific Reports

Adversarial Robustness in Machine LearningMachine Learning in Healthcare

Cited by 12

Generalising uncertainty improves accuracy and safety of deep learning analytics applied to oncology

MacDonald, S., Foley, H., Yap, M., Johnston, R. L., Steven, K., Koufariotis, L. T., Sharma, S., Wood, S., Addala, V., Pearson, J. V., et al. (2023) · Scientific Reports

Adversarial Robustness in Machine LearningMachine Learning in Healthcare

Cited by 12