Data

Data Sharing

A major catalyst for the formation of SSGAC was the inability of scientists to replicate the results of existing genetic studies. Therefore, the SSGAC places high priority on enabling other researchers to replicate our findings and conduct follow-up research. To this end, we have adopted a data-sharing policy to provide a clear, transparent and administratively simple framework for the data-sharing of our meta-analysis results.

When you report results of research that utilizes SSGAC data in any way, it is our policy that you mention the SSGAC in your paper and cite the relevant publication of the original results. Please inform us about your intended use of SSGAC data by sending an email to (contact@ssgac.org). Doing so will help us to keep track of ongoing research initiatives and allow us to facilitate collaboration of researchers, whenever possible. If you would like additional results (e.g. a meta-analysis of a subset of cohorts included in the original paper), please submit a short, informal research proposal to us (contact@ssgac.org). 

 

Below, we release summary data from past studies of the SSGAC. To protect subject confidentiality, we are not releasing sample allele frequencies (see the "Read me" file for details).

Summary Statistics for Karlsson Linnér et al. (2019)

Karlsson Linnér et al. (2019). Genome-wide association analyses of risk tolerance and risky behaviors in over one million individuals identify hundreds of loci and shared genetic influences. Nature Genetics, 51, 245-257. doi: 10.1038/s41588-018-0309-3

 

       

Note: please refer to the README for a description of how the SNPs for the following files were selected.

  • Summary data file - RISK_GWAS_MA_UKB+23andMe.txt - The discovery GWAS meta-analysis of general risk tolerance, which includes the UKB and the 23andMe cohort. 

  • Summary data file - RISK_GWAS_MA_UKB+23andMe+replication.txt - The meta-analysis of the discovery and replication GWAS of general risk tolerance, which includes the UKB, the 23andMe cohort, and the 10 replication cohorts.

  • Summary data file - ADVENTUROUSNESS_GWAS.txt - The GWAS of adventurousness in the 23andMe cohort.

  • Summary data file - RISK_MTAG.txt - The phenotype-specific MTAG findings with respect to general risk tolerance.

 

Summary Statistics for Lee et al. (2018)

Lee et al. (2018). Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment. Nature Genetics, 50(8), 1112-1121. doi: 10.1038/s41588-018-0147-3

 

  • Summary data file - GWAS_EA_excl23andMe.txt - Educational attainment (EA) meta-analysis of all discovery cohorts except 23andMe.

  • Summary data file - GWAS_CP_all.txt - Cognitive performance (CP) GWAS meta-analysis of all discovery cohorts.

       

Note: please refer to the README for a description of how the SNPs for the following files were selected.

  • Summary data file - GWAS_EA.to10K.txt - Educational attainment meta-analysis of all discovery cohorts.

  • Summary data file - GWAS_CP.to10K.txt - Cognitive performance meta-analysis of all discovery cohorts.

  • Summary data file - GWAS_HM.to10K.txt - Highest-level math class completed GWAS in the 23andMe cohort.

  • Summary data file - GWAS_MA.to10K.txt - Self-reported math ability GWAS in the 23andMe cohort.

  • Summary data file - MTAG_EA.to10K.txt - Educational attainment results from MTAG on educational attainment, cognitive performance, highest-level math class completed, and self-reported math ability GWAS.

  • Summary data file - MTAG_CP.to10K.txt Cognitive performance results from MTAG on educational attainment, cognitive performance, highest-level math class completed, and self-reported math ability GWAS.

  • Summary data file - MTAG_HM.to10K.txt - Highest-level math class completed results from MTAG on educational attainment, cognitive performance, highest-level math class completed, and self-reported math ability GWAS.

  • Summary data file - MTAG_MA.to10K.txt - Self-reported math ability results from MTAG on educational attainment, cognitive performance, highest-level math class completed, and self-reported math ability GWAS.

  • Summary data file - COMBINED.to10K.txt - Combined results from files 2-9, with the corresponding columns of each result suffixed by analysis type and trait (e.g., "Beta_GWAS_HM").

  • Summary data file - CAVIARBF.to10K.txt - Results from CAVIARBF analysis for 10K SNPs.

 
 

Summary Statistics for Bansal et al. (2018)

Bansal et al. (2018). Genome-wide association study results for educational attainment aid in identifying genetic heterogeneity of schizophrenia. Nature Communications, 9(1), Article number: 3078. doi: 10.1038/s41467-018-05510-z

 

 

Summary Statistics for Turley et al. (2018)


Turley et al. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature Genetics, 50, 229-237. doi: 10.1038/s41588-017-0009-4

Summary Statistics for Karlsson Linnér et al. (2017)

Karlsson Linnér et al. (2017), An epigenome-wide association study meta-analysis of educational attainment. Molecular Psychiatry, 22, 1680-1690. doi: 10.1038/mp.2017.210

 
 

Summary Statistics for Barban et al. (2016)

Barban et al. (2016), Genome-wide analysis identifies 12 loci influencing human reproductive behavior. Nature Genetics, 48(12), 1462-1472. doi: 10.1038/ng.3698

Summary Statistics for Okbay et al. (2016)

Okbay et al. (2016), Genome-wide association study identifies 74 loci associated with educational attainment. Nature, 533, 539-542. doi: 10.1038/nature17671

 

 

Summary Statistics for Okbay et al. (2016)

Okbay et al. (2016), Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nature Genetics, 48(6), 624-633. doi: 10.1038/ng.3552.

 

Summary Statistics for Rietveld et al. (2013)

Rietveld et al. (2013), GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment. Science, 340(6139), 1467-1471. doi: 10.1126/science.1235488

 

Summary Statistics for Rietveld et al. (2014)

Rietveld et al. (2014), Common genetic variants associated with cognitive performance identified using the proxy-phenotype method. Proceedings of the National Academy of Sciences of the United States of America, 111(38), 13790-13794. doi: 10.1073/pnas.1404623111

 

  • Summary data file - MA_EA_1st_stage.txt.gz - First stage meta-analysis

  • Summary data file - MA_CF_2nd_stage.txt.gz - Second stage meta-analysis 

    • Note: Contains SNPs prioritized by the first stage meta-analysis. For the entire set of summary statistics from the CHIC study (Benyamin et al., 2014), see next item.

  • Summary data file - CHIC_Summary_Benyamin2014.txt.gz - Consortium meta-analysis summary statistics 

    • From Benyamin et al. (2014); for explanatory notes, see here.

 

Summary Statistics for van der Loos, M. et al. (2013)

van der Loos MJHM, Rietveld CA, Eklund N, Koellinger PD, Rivadeneira F, et al. (2013) The Molecular Genetic Architecture of Self-Employment. PLOS ONE 8(4): e60542. https://doi.org/10.1371/journal.pone.0060542

 

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Acknowledgments

 

For financial support, the SSGAC thanks the U.S. National Science Foundation, the U.S. National Institutes of Health (National Institute on Aging, and the Office for Behavioral and Social Science Research), the Open Philanthropy Project, the Ragnar Söderberg Foundation, the Swedish Research Council, The Jan Wallander and Tom Hedelius Foundation, the European Research Council, and the Pershing Square Fund of the Foundations of Human Behavior.

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