GSCAN
Welcome to GSCAN (GWAS & Sequencing Consortium of Alcohol and Nicotine use), an international genetic association meta-analysis consortium. This brief page provides basic information about the study and links to data and publications.
GSCAN aggregates genetic association findings across scores of studies with millions of individuals, with a focus on understanding the etiology of alcohol and nicotine use and addiction.
We're hiring!
Like what you see? Want to get involved? We're always taking new PhD students and hiring postdoctoral researchers. Contact Scott if you are interested.
GSCAN GWAS Phase 2
In April 2018 we began working on a second phase of GSCAN, including many more studies of diverse ancestry.
Our internal document to track progress | is here.
Saunders et al. (2022)
Summary statistics from Saunders et al. (2022) "Genetic diversity fuels gene discovery for tobacco and alcohol use" publication details provided upon acceptance, are archived at the University of Minnesota library:
https://conservancy.umn.edu/handle/11299/241912
Please contact the dataset contact on that page if you notice any issues. We will readily fix them.
GSCAN GWAS Phase 1
Liu et al. (2019) Nature Genetics
Summary statistics from Liu et al. (2019) Association Studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use" published in Nature Genetics, are archived at the University of Minnesota library:
https://conservancy.umn.edu/handle/11299/201564
Please contact the dataset contact on that page if you notice any issues. We will readily fix them.
PheWeb
Summary statistics from Liu et al. (2019) Nature Genetics, are also displayed via our local PheWeb.
Documentation for internal users to add phenotypes is here: GSCAN PheWeb Documentation.
Internal historical notes
Forgive the organization of this page. It has been used historically both as an outward-facing page, but also for internal purposes. Below are items from the past decade of GSCAN.
- Phenotype workgroup: Laura Bierut, Marilyn Cornelis, Dave Hinds, Youna Hu, Jaakko Kaprio, Eric Jorgenson, Dajiang Liu, Matt McGue, Marcus Munafo, Gunter Schumann, Scott Vrieze, Luisa Zuccolo
- Analysis workgroup: Goncalo Abecasis, David Hinds, Youna Hu, Eric Jorgenson, Charles Kooperberg, Pete Kraft, Penelope Lind, Dajiang Liu, Nancy Saccone, Dan Stram, Scott Vrieze, Xiaowei Zhan
Phenotype definitions and analysis plan
The analysis plan and phenotypes are described in files linked below (makes it easier to keep track of versioning!). Coding of phenotypes is described in the aptly-named "phenotype definitions" file whereas the genome-wide analysis plan is in the all-too-aptly-named "analysis plan" document. Please note that the phenotype definitions document only contains information on how to code the eight smoking/drinking phenotypes. File formats for those phenotypes, which many will recognize as standard pedigree formats, are included in the analysis plan. Everything else should be fairly straightforward.
Click here to find the GSCAN GWAS analysis plan.
Click here to find the GSCAN GWAS phenotype definitions.
Coordination and organization
Progress, internal and external, are tracked in this Google Doc. More specific progress on internal studies is tracked here.
Study contact info is tracked in this Google Sheet.
Studies available in dbGaP, along with accession numbers, etc. are tracked in this Airtable.
File locations
Study data to which we have direct access are located either on twins or RC. Twins data are organized in the folder /net/twins/svrieze/everything-else/wp/GSCAN/GWAS. Within this folder those studies to which we have raw data access are in the folder CU_Boulder_samples (for lack of a better name!). Summary stats generated on these samples are organized within summary_stats_generated_internally. Summary stats generated by outside groups and submitted for meta-analysis are organized within summary_stats_generated_externally.
On RC the organization is similar. Everything is located within the folder /work/KellerLab/GSCAN/GWAS. Study data to which we have raw data access are in the folder individual_level_study_data. Summary stats generated on these samples are organized within summary_stats_generated_internally. Summary stats generated by outside groups and submitted for meta-analysis are organized within summary_stats_generated_externally.
GSCAN dbGaP & UK Biobank
Studies included from dbGaP, and the process by which phenotypes and genotypes were constructed and merged is outlined on the GSCAN dbGaP page.
Meetings
Regular conference calls are held and minutes are available here.
Other meeting materials from internal meetings are here:
Authorship guidelines
While authorship is decided on an individual basis for each GSCAN paper, typically, authorship is arranged in groups. We hope the GIANT investigators will forgive us for adopting and adapting their authorship guidelines.
Author Ordering
- A group of usually 6 or fewer junior investigators who strongly led the efforts, usually starred to denote equal contribution, followed by additional junior investigators who played key, central roles.
- In alphabetical order, junior investigators who had substantial individual contributions but not as much as those in Group 1. Typically, these might be lead analysts or other junior investigators who made a sizable contribution such as GWA analyses performed specifically for the paper.
- In alphabetical order, all investigators who do not belong to another group. In some cases, there may be an additional group of junior investigators who had notable individual contributions but not as much as those in Groups 1 or 2.
- In alphabetical order, senior investigators who participated strongly in GSCAN activities but did not strongly lead/oversee the writing and/or analysis for the paper. Typically, these might be leaders of key GSCAN activities.
- The senior investigators who strongly led/oversaw the writing and/or analysis of the paper, including a subset that are co-corresponding authors (usually 6 or fewer).
Proposal for number of authors
Given the author categories above, we expect each study to propose a reasonable number of authors, in line with the number of authors from other studies. In our experience, there are typically 3-5 "study-specific" authors. Study-specific authors include local analysts who generated summary stats as well as PIs or others who supervised the local study work. These authors are in addition to any individuals associated with those studies who contributed in significant ways to the central meta-analysis or writing. Particularly large or complex cohorts may have higher limits; if you believe your cohort fits this description, please discuss with [Scott Vrieze] or [Dajiang Liu]. Of course, in all cases, authors must meet the broadly accepted criteria for authorship.
GSCAN Sequencing
TOPMed
We hope to update this section with detailed descriptions of how we have conducted phenotype derivations for each TOPMed cohort to which we have access to raw data.
- For now, the R scripts to go from source phenotype file to eventual derived phenotype is located here:
/net/twins/svrieze/everything-else/wp/GSCAN/TOPMed/README
- We're tracking analyses in this Google doc
Phenotype definitions and analysis plan for external studies
Phenotype definitions and analysis plans for the TOPMed studies are contained in this document.
The list of dbGaP studies in TOPMed is in this Airtable.
GSCAN Exome Chip
Phenotype definitions and analysis plan
Exome chip analysis plan and phenotype definitions.
File Locations
Freeze 1. We concluded a pilot freeze of the exome chip project in 2015 and are writing up our results now. All of the summary statistics are on twins at /net/twins/svrieze/everything-else/wp/GSCAN/freeze1-25-Mar-2015.
Freeze 2. New studies that will be included in Freeze 2 are located on RC at /work/KellerLab/GSCAN/EXOME. Each folder in that directory is the name of a study and includes two subfolders, one for Phenotypes and one for Genotypes. Genotypes are split by chromosome to facilitate analyses.