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Ronald de Vlaming

dr.

 https://orcid.org/0000-0001-6416-6067
20152019
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Personal profile

Personal information

Ronald de Vlaming is a postdoc at the department of Economics of the VU. He is affiliated with the Erasmus University Rotterdam Institute for Behavior and Biology (EURIBEB), and member of the Social Science Genetic Association Consortium (SSGAC).

Research

Ronald's research focusses on the application of statistical methods, matrix algebra, and numerical methods to estimation problems that arise in the study of the genetic architecture of complex traits. His work has been published in the following peer-reviewed journals: PLOS Genetics, BioMed Research International, Nature Genetics, Nature Human Behaviour, and Nature. He has presented his work at meetings of the Behavior Genetics Association, the Conference on Computational Statistics, and the European Human Genetics Conference.

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Research Output 2015 2019

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

Social Science Genetic Association Consortium, 23and Me Research Team, eQTLGen Consortium & International Cannabis Consortium, 1 Feb 2019, In : Nature Genetics. 51, 2, p. 245-257 13 p.

Research output: Contribution to Journal › Article › Academic › peer-review

Genetic Loci
Genome-Wide Association Study
Single Nucleotide Polymorphism
Computational Biology
Synaptic Transmission

Genomic structural equation modelling provides insights into the multivariate genetic architecture of complex traits

Grotzinger, A. D., Rhemtulla, M., de Vlaming, R., Ritchie, S. J., Mallard, T. T., Hill, W. D., Ip, H. F., Marioni, R. E., McIntosh, A. M., Deary, I. J., Koellinger, P. D., Harden, K. P., Nivard, M. G. & Tucker-Drob, E. M., 1 Mar 2019, In : Nature Human Behaviour.

Research output: Contribution to Journal › Article › Academic › peer-review

Genome-Wide Association Study
Single Nucleotide Polymorphism
Phenotype
Genetic Models
Psychiatry

Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia

Bansal, V., Mitjans, M., Burik, C. A. P., Linnér, R. K., Okbay, A., Rietveld, C. A., Begemann, M., Bonn, S., Ripke, S., de Vlaming, R., Nivard, M. G., Ehrenreich, H. & Koellinger, P. D., 6 Aug 2018, In : Nature Communications. 9, 1, p. 1-12 12 p., 3078.

Research output: Contribution to Journal › Article › Academic › peer-review

Open Access
schizophrenia
Genetic Heterogeneity
genome
Genome-Wide Association Study
Schizophrenia

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle

Koellinger, P. D., Meddens, S. F. W., de Vlaming, R., Bowers, P., Burik, C. A. P., Karlsson Linner, R., Lee, C., Okbay, A., Turley, P., Rietveld, C. A., Fontana, M. A., Ghanbari, M., Imamuri, F., McMahon, G., van der Most, P. J., Voortman, T., Wade, K. H., Anderson, E. L., Braun, K. V., Emmett, P. M. & 24 othersEsko, T., Gonzalez, J. R., Kiefte-de Jong, J. C., Luan, J., Langenberg, C., Muka, T., Ring, S. M., Rivadeneira, F., Schoufour, J. D., Snieder, H., van Rooij, F. J. A., Wolffenbuttel, B. H. R., Smith, G. D., Franco, O. H., Forouhi, N. G., Arfan Ikram, M., Uitterlinden, A. G., van Vliet-Ostaptchouk, J. V., Wareham, N. J., Cesarini, D., Harden, K. P., Lee, J. J., Benjamin, D. J. & Chow, C. C., 2 Aug 2018, In : bioRxiv.

Research output: Contribution to Journal › Article › Academic

Life Style
Carbohydrates
Diet
Waist-Hip Ratio
Genome-Wide Association Study

Signatures of negative selection in the genetic architecture of human complex traits

Zeng, J., De Vlaming, R., Wu, Y., Robinson, M. R., Lloyd-Jones, L. R., Yengo, L., Yap, C. X., Xue, A., Sidorenko, J., McRae, A. F., Powell, J. E., Montgomery, G. W., Metspalu, A., Esko, T., Gibson, G., Wray, N. R., Visscher, P. M. & Yang, J., 16 Apr 2018, In : Nature Genetics. 50, 5, p. 746-753 8 p.

Research output: Contribution to Journal › Article › Academic › peer-review

Medical Genetics
Single Nucleotide Polymorphism
Gene Frequency
Genetic Selection
Linear Models