Summary data and other resources
On this page you can access GWAS (Genome-Wide Association Study) and EWAS (Epigenome-Wide Association Study) summary data from our published research, find code for modelling LBC1936 longitudinal cognitive ability data and other resources.
GWAS and EWAS datasets (in alphabetical order, by first author)
Davies, G. et al. (2016) dataset supporting Genome-wide association study of cognitive functions and educational attainment in UK Biobank (N=112 151). Molecular Psychiatry.
Davies, G. et al. (2018) dataset (UK Biobank results) supporting Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications.
Davies, G. et al. (2018) dataset (Open dataset summary results) supporting Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function. Nature Communications.
Deary V. et al. (2018) dataset supporting Genetic contributions to self-reported tiredness. Molecular Psychiatry.
de la Fuente, J. et al. (2020) dataset supporting A general dimension of genetic sharing across diverse cognitive traits inferred from molecular data. Nature Human Behaviour
Hagenaars, S.P., et al. (2017) dataset supporting Genetic prediction of male pattern baldness. PLOS Genetics.
Harris, S.E. et al. (2017) dataset supporting Molecular genetic contributions to self-rated health. International Journal of Epidemiology.
Hill, W.D. et al. (2016) dataset supporting Molecular genetic contributions to social deprivation and household income in UK Biobank. Current Biology.
Hill, W.D. et al. (2019) dataset supporting Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income. Nature Communications.
Hill, W.D. et al. (2019) dataset supporting Genetic contributions to two special factors of neuroticism are associated with affluence, higher intelligence, better health, and longer life. Molecular Psychiatry.
Hillary, F.R. et al. (2019) GWAS dataset supporting Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936. Nature Communications.
Hillary, F.R. et al. (2019) EWAS dataset supporting Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936. Nature Communications.
Luciano, M. et al. (2018) dataset supporting Association analysis in over 329,000 individuals identifies 116 independent variants influencing neuroticism. Nature Genetics.
Luciano, M. et al. (2019) dataset supporting The influence of X chromosome variants on trait neuroticism. Molecular Psychiatry.
Marioni, R. et al. (2018) dataset supporting GWAS on family history of Alzheimer's disease. Translational Psychiatry.
Software described in Deary, I.J. et al. (2011): A free, easy-to-use, computer-based simple and four-choice reaction time programme: The Deary-Liewald reaction time task. Behavior Research Methods.
The LBC team have developed code to model cognitive ability level and change in the LBC1936. The text file versions of the code are available for R and Mplus and are free for researchers to use.
Code for Conte et al. (2022). Cognitive change before old age (11 to 70) predicts cognitive change during old age (70 to 82). Psychological Science.
Dr Susan Shenkin's step-by-step online guide for systematic reviews and meta-analyses [presentation]