The social genome of friends and schoolmates in the National Longitudinal Study of Adolescent to Adult Health

Our study reported significant findings of a “social genome” that can be quantified and studied to understand human health and behavior. In a national sample of more than 5,000 American adolescents, we found evidence of social forces that act to make friends and schoolmates more genetically similar to one another compared with random pairs of unrelated individuals. This subtle genetic similarity was observed across the entire genome and at sets of genomic locations linked with specific traits—educational attainment and body mass index—a phenomenon we term “social–genetic correlation.” We also find evidence of a “social–genetic effect” such that the genetics of a person’s friends and schoolmates influenced their own education, even after accounting for the person’s own genetics

Wave III College Mobility Data Documentation

At Wave III of the Add Health survey, respondents were asked if they were currently enrolled in a postsecondary institution. Respondents who answered in the affirmative were then asked to report the institution in which they were currently enrolled. Using this information on current enrollment, data from the Mobility Report Card: The Role of Colleges in Intergenerational Mobility (Chetty 2017) were linked to the Add Health respondents. For variables C4CMR01-C4CMR11M, data came from the Preferred Estimates of Access and Mobility by College dataset (Chetty et al. 2017). These data were collected from a sample of college students who were born between 1980 and 1982 and who attended a college or university in the early 2000’s. These students were between the ages of 19 and 22 at the time of their entry into college. Further information on how the original researchers collected the data for these variables can be found here: http://www.equality-of-opportunity.org/data/college/Codebook%20MRC%20Table%201.pdf For variables C3FIN01-C3MAJ08, Chetty and colleagues drew these data from the Integrated Postsecondary Education Data System (IPEDS). Information for each of these variables were collected for the years 2000 and 2013 (unless otherwise stated). For all variables there were some instances where colleges were grouped together, for instance when multiple colleges made up a State University-System. For these colleges, data values for the variables are enrollment-weighted means of the underlying values for each of the colleges being grouped together. Though the variables available on the College Mobility data at Wave III are the same as those on the College Mobility data at Wave IV, the way in which respondents were asked to self-report college or university attendance was different between the two waves, and interpretation of these contextual data is slightly different as a result. At Wave III, respondents were asked to report if they were currently enrolled in a college of university, and information on the institution in which they were currently enrolled was collected. Information on institutions was collected regardless of the degree that the respondent was currently seeking. At Wave IV, respondents were asked to report the name of the college or university from which they received a degree. Additionally, this question was only asked if respondents reported receiving a bachelor’s degree. See “Documentation for College Mobility Data: Wave IV” (Gaydosh et al. 2019) for more information on linked college- and university-level data for this wave. In addition to the data available here, previously created contextual data on Wave III postsecondary institutions is also available. See “Wave III Education Data: Postsecondary Contextual Component Codebook” (Riegel-Crumb et al. 2008) for further information

Wave IV County Health and Mobility Data Documentation

The following is a list of data that were collected from secondary data sources and merged to Wave IV of Add Health. These variables are available at the county or state level. Data was matched to the county or state that the Add Health respondent was living in at the time of the Wave IV interview and data was matched to respondents so as to insure that these contextual variables correspond as closely as possible to the year in which the Add Health respondents were interviewed at Wave IV (2008)

Wave III Tobacco Tax Data Documentation

These data are meant to supplement the County Health and Mobility Data available for Waves I & IV1. Tobacco tax information is at the state level. Data were matched to the state that the Add Health respondent was living in at the time of the Wave III interview. Data were matched to respondents so as to ensure that these contextual variables correspond as closely as possible to the year in which the Add Health respondents were interviewed at Wave III (2001)

Wave II Tobacco Tax Data Documentation

These data are meant to supplement the County Health and Mobility Data available for Waves I & IV1. Tobacco tax information is at the state level. Data were matched to the state that the Add Health respondent was living in at the time of the Wave II interview. Data were matched to respondents so as to ensure that these contextual variables correspond as closely as possible to the year in which the Add Health respondents were interviewed at Wave II (1996)

Can Genetics Predict Response to Complex Behavioral Interventions? Evidence from a Genetic Analysis of the Fast Track Randomized Control Trial

Early interventions are a preferred method for addressing behavioral problems in high-risk children, but often have only modest effects. Identifying sources of variation in intervention effects can suggest means to improve efficiency. One potential source of such variation is the genome. We conducted a genetic analysis of the Fast Track randomized control trial, a 10-year-long intervention to prevent high-risk kindergarteners from developing adult externalizing problems including substance abuse and antisocial behavior. We tested whether variants of the glucocorticoid receptor gene NR3C1 were associated with differences in response to the Fast Track intervention. We found that in European-American children, a variant of NR3C1 identified by the single-nucleotide polymorphism rs10482672 was associated with increased risk for externalizing psychopathology in control group children and decreased risk for externalizing psychopathology in intervention group children. Variation in NR3C1 measured in this study was not associated with differential intervention response in African-American children. We discuss implications for efforts to prevent externalizing problems in high-risk children and for public policy in the genomic era

The Effect of the Earned Income Tax Credit on Physical and Mental health—Results from the Atlanta Paycheck Plus Experiment

Policy Points: The Paycheck Plus randomized controlled trial tested a fourfold increase in the Earned Income Tax Credit (EITC) for single adults without dependent children r over 3 years in New York and Atlanta. In New York, the intervention improved economic, mental, and physical health outcomes. In Atlanta, it had no economic benefit or impact on physical health r and may have worsened mental health. In Atlanta, tax filing and bonus receipt were lower than in the New York arm of the trial, which may explain the lack of economic benefits. Lower mental health scores in the treatment group were driven by disadvantaged men, and the study sample was in good mental health