The use of heritability in policy development


Jason Collins


December 13, 2011

The  heritability straw man has copped another bashing, this time in the Journal of Economic Perspectives. In it, Charles Manski picks up an old line of argument by Goldberger from 1979 and argues that heritability research is uninformative for the analysis of policy.

Manski starts by arguing that heritability estimates are based on the assumption that there is no gene-environment correlation. Manski writes:

The assumption that g and e are uncorrelated is at odds with the reasonable conjecture that persons who inherit relatively strong genetic endowments tend to grow up in families with more favorable environments for child development.

Any review of discussions of heritability, whether in the peer-reviewed literature or the blogosphere, will show that his claim is generally false. The proviso that the heritability estimate is only relevant to the existing environment is usually threaded through any discussion of heritability.

It is true that gene-environment covariance can affect estimates of heritability. Yet this does not mean that existing estimates have no value, nor that there are not methods that seek to account for the covariance. For example, the use of comparisons between misdiagnosed identical twins and actual identical twins allows for bounded estimates of heritability to be developed (pdf).

Manski’s broader claim, adopted directly from Goldberger, is that even if you knew the heritability of a trait, it tells you nothing about social policy. Manski uses Goldberger’s eyeglasses example as an illustration:

Consider Goldberger’s use of distribution of eyeglasses as the intervention. For simplicity, suppose that nearsightedness derives entirely from the presence of a particular allele of a specific gene. Suppose that this gene is observable, taking the value g = 0 if a person has the allele for nearsightedness and g = 1 if he has the one that yields normal sight.

Let the outcome of interest be effective quality of sight, where “effective” means sight when augmented by eyeglasses, should they be available. A person has effective normal sight either if he has the allele for normal sight or if eyeglasses are available. A person is effectively nearsighted if that person has the allele for nearsightedness and eyeglasses are unavailable.

Now suppose that the entire population lacks eyeglasses. Then the heritability of effective quality of sight is one. What does this imply about the usefulness of distributing eyeglasses as a treatment for nearsightedness? Nothing, of course. The policy question of interest concerns effective quality of sight in a conjectured environment where eyeglasses are available. However, the available data only reveal what happens when eyeglasses are unavailable.

Manski and Goldberger may be correct that the heritability estimate is uninformative as to the efficacy of distributing eyeglasses, but it is useful in assessing other policy responses to the problem and the trade-offs between them. Is it possible to prevent the eyesight loss in the first place? Is that policy cheaper and more effective than eyeglasses? If the heritability estimate was zero, you would look to the environmental causes and ask whether the eyesight problem is more appropriately dealt with by addressing the cause rather than by distribution of eyeglasses.

There is no shortage of other areas where heritability estimates might add value. Heritability estimates can inform whether it is an effective use of resources to make sure that everyone has a university degree or is over six-foot tall. Is everyone putty in the hands of the policy maker, or are there some constraints? On a personal level, Bryan Caplan’s use of heritability in Selfish Reasons to Have More Kids is a useful input to his parenting strategy.

For me, the most salient example of the usefulness of heritability research comes from examination of the heritability of IQ among children. Among high socioeconomic status families, the heritability tends to be high. Among low socioeconomic status families, it is significantly lower. This suggests that there is significant room to improve the outcomes of the children at the bottom of the socioeconomic ladder in the early years of their life (assuming those changes have effects that persist into adulthood). Increasing heritability of IQ might be evidence that environmental disadvantages are being ameliorated and opportunity equalised.

The latter part of Manski’s paper turns to the use of genes as covariates in statistical regressions. Regression identifies statistical association and not causation, which appears to be an important point in attracting Manski to this use. Noting the wealth of data being created and the possibility of observing changes in the effect of genes as the environment changes, Manski considers that these regression exercises may assist in examining how genes and environment interact.

I don’t disagree with Manski, but at present, genome association studies have plenty of issues. First, there is the missing heritability problem. To date, the magnitude of the identified effect of genes on most traits accounts for a miniscule proportion of the trait’s heritability. This points to the important role played by heritability research to provide direction to research on genes as covariates. It also indicates that until these genes are found, heritability estimates will be more informative for social policy.

A second issue is that with 30,000 odd genes and the ability to test so many of them for correlation with traits, many are found to have a statistically significant relationship through chance. As blogged about recently by Razib, this is shown when people seek to replicate earlier results - such as when it was found that most reported genetic associations with general intelligence are probably false positives (pdf).

Finally, genome based research is now feeding back into estimates of heritability. From a recent paper:

We conducted a genome-wide analysis of 3511 unrelated adults with data on 549 692 single nucleotide polymorphisms (SNPs) and detailed phenotypes on cognitive traits. We estimate that 40% of the variation in crystallized-type intelligence and 51% of the variation in fluid-type intelligence between individuals is accounted for by linkage disequilibrium between genotyped common SNP markers and unknown causal variants. These estimates provide lower bounds for the narrow-sense heritability of the traits.

Despite all the critiques about methodology, most new studies confirm that the old “methodologically poor” heritability estimates were in the right ballpark. The problem is not that the estimates are not useful, but rather that they are not used.