The promise and challenges of incorporating genetic data into longitudinal social science surveys and research.
In this paper, I argue that social science and genomics can be integrated; however, the way this marriage is currently occurring rests on spurious methods and assumptions and, as a result, will yield few lasting insights. However, recent advances in both econometrics and in developmental genomics provide scientists with a novel opportunity to understand how genes and environment interact to produce social outcomes. Key to any causal inference about the interplay between genes and social environment is that either genotype be exogenously manipulated (i.e. through sibling fixed effects) while environmental conditions are held constant, and/or that environmental variation is exogenous in nature, i.e. experimental or arising from a natural experiment of sorts. Further, initial allele selection should be motivated by findings from genetic experiments in model animal studies linked to orthologous human genes. Likewise, genetic associations found in human population studies should then be tested through knock-out and over-expression studies in model organisms.
Conley
Dalton
D
New York University, New York, New York 10003, USA. conley@nyu.edu
D003625:Data Collection / Q000379:methods; D005819:Genetic Markers; D020022:Genetic Predisposition to Disease; D030661:Genetic Privacy; D015894:Genome, Human; D005838:Genotype; D006801:Humans; D008137:Longitudinal Studies; D056186:Metagenomics / Q000379:methods*; D012931:Social Environment; D012942:Social Sciences / Q000379:methods*; D014481:United States
D005819:Genetic Markers