Genetics without genes
A couple of weeks ago, Razib Kahn wrote a post in which he argued that “you don’t need to know the exact gene of major effect to conclude that a trait is genetic.” Where a lot of research is invested in finding the specific genes behind traits, and with a media hungry for these kinds of stories, many people have forgotten how much can be understood without knowledge of the specific genes. As Razib points out, much of genetics predates molecular biology and the discovery of the structure of DNA.
From the perspective of integrating evolutionary biology and economics, a few of the papers I have posted about this year illustrate Razib’s point. Ashraf and Galor’s soon to be published paper on genetic diversity and economic development uses DNA-level data. While there is a statistically significant relationship between genetic diversity and economic development, few people seem convinced that there is a causative relationship between the two. How does the genetic diversity manifest itself into the economic outcomes? In developing the evidence for that causative relationship, it is not clear where you would start. Cross-species analysis might yield some insight on the cooperative effects of diversity, but how would you show the positive effects on innovation of higher levels of diversity? Linking the molecular biology to economic outcomes is difficult.
We’re also seeing an increasing number of genoeconomics papers on the genetic basis for economic traits, such as time and risk preference, that use molecular data. Unfortunately, many studies find spurious relationships and the low size of the effect of most alleles has resulted in this work being of limited use in economic analysis.
However, this is not the point to give up on molecular biology as a tool for analysing economic traits and outcomes. The genoeconomics enterprise is in its early days and may bear fruit. And in the interim, we already have a lot of information that can already be used. Take the estimates of heritability of economic preferences we have from twin studies. We cannot pinpoint specific genes to account for even a small fraction of the heritability, but those estimates can still be useful for increasing our understanding of the role of genetics in economic outcomes, and may even be useful in policy development.
My last post on Gregory Clark and Neil Cummins’s use of surnames to track social mobility across the generations is another case in point. Combining data about life outcomes across the generations could yield insight into the genetic factors underlying the transmission of socioeconomic status, adding to that already obtained through twin studies and shorter term analysis of intergenerational transmission. The data lend itself to quantitative genetic analysis. And in this analysis, there is not a gene in sight.