Behavior genetics is the study of the relationship between genetic variation and psychological traits. Turkheimer (2000) proposed “Three Laws of Behavior Genetics” based on empirical regularities observed in studies of twins and other kinships. On the basis of molecular studies that have measured DNA variation directly, we propose a Fourth Law of Behavior Genetics: “A typical human behavioral trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioral variability.” This law explains several consistent patterns in the results of gene-discovery studies, including the failure of candidate-gene studies to robustly replicate, the need for genome-wide association studies (and why such studies have a much stronger replication record), and the crucial importance of extremely large samples in these endeavors. We review the evidence in favor of the Fourth Law and discuss its implications for the design and interpretation of gene-behavior research.

Behavior genetics is the study of the manner in which genetic variation affects psychological phenotypes (traits), including cognitive abilities, personality, mental illness, and social attitudes. In a seminal article published in this journal, Turkheimer (2000) noted three robust empirical regularities that had by then emerged from the literature on behavior genetics. He dubbed these regularities the “Three Laws of Behavior Genetics.” They are:

1. All human behavioral traits are heritable. [That is, they are affected to some degree by genetic variation.]

2. The effect of being raised in the same family is smaller than the effect of genes.

3. A substantial portion of the variation in complex human behavioral traits is not accounted for by the effects of genes or families.

These observations surprised many outsiders to the field of behavior genetics at the time, yet they remain an accurate broad-brush summary of the empirical evidence 15 years later, as shown by a recent meta-analysis of virtually all twin studies ever conducted (Polderman et al., 2015). Indeed, they have attained the status of “null hypotheses”—the most reasonable a priori expectations to hold in the absence of contrary evidence (Turkheimer, Pettersson, & Horn, 2014).

The original Three Laws summarized results from studies of twins, adoptees, and other kinships. These research designs have many valuable uses, but they cannot discover particular genomic regions or specific variants that are causally responsible for downstream phenotypic variation. Since the completion of the Human Genome Project, numerous studies of behavioral traits have directly measured DNA variation among individuals in an attempt to take this logical next step. While there are many types of genetic variants, most studies have assayed single-nucleotide polymorphisms (SNPs), which are sites in the genome where single DNA base pairs carried by distinct individuals may differ.1 Virtually all SNPs have two different possible base pairs, called alleles. The less frequent allele in the population is called the minor allele of the SNP. If the frequency of a SNP’s minor allele in the population exceeds 1%, the SNP is called a common variant. Among individuals of European descent, there are approximately 8 million common variants in the human genome.

The results of studies searching for SNP-behavior associations have disappointed any hope that a small number of these common variants are responsible for a large percentage of cross-sectional trait variability. Instead, the evidence to date is consistent with what we propose as the Fourth Law of Behavior Genetics:

4. A typical human behavioral trait is associated with very many genetic variants, each of which accounts for a very small percentage of the behavioral variability.2

For purposes of the law, a “typical human behavioral trait” is one that is (a) commonly measured by psychometric methods, (b) a serious psychiatric disease, or (c) a social outcome, such as educational attainment, that is plausibly related to a person’s behavioral dispositions. As is customary in behavioral science, we use the word law to describe what we consider to be a very robust empirical regularity (not a universal, mechanistic truth). In the remainder of this article, we will summarize the mounting empirical evidence in favor of the Fourth Law, consider what gene-behavior research strategies are likely to be profitable in light of the Fourth Law, and briefly consider possible explanations for the Fourth Law.

The Importance of the Fourth Law We have recently highlighted two possible explanations for the Fourth Law (Chabris et al., 2013). First, causal chains from DNA variation to behavioral phenotypes are likely very long (longer than for physical traits—e.g., eye color), so the effect of any one variant on any one such trait is likely to be small. For example, a SNP may have a substantial effect on the concentration of an enzyme in cortical synapses, but that proximal biochemical phenotype is only one of many factors that explain why some people score higher than others on paper-and-pencil IQ tests, so the SNP has only a tiny effect on the distal phenotype of cognitive function. Second, when a population is already well adapted to its environment, mutations with large effects on a focal trait are likely to have deleterious side effects (Fisher, 1930). If the effect of a genetic variant is small enough, however, then its population frequency has some chance of drifting upward to a detectable level (Kimura, 1983).6 These forces could conspire to keep variants that have a large effect on a trait at negligible frequency while allowing some variants that have a small effect to become relatively common. Some support for this hypothesis comes from two observations: First, SNPs discovered in GWAS that have larger additive effects tend to have lower frequencies of the minor allele (Park et al., 2011), and second, variants with very large phenotypic effects, such as those causing mental retardation, are always very rare and thus contribute little to overall population variability, or they have their effects later in life (as in the case of, e.g, the well-documented relationship between variants of the apolipoprotein E, or APOE, gene and cognitive decline). These examples suggest that valuable evolutionary insights might flow from the detailed analysis of GWAS data (Turchin et al., 2012). In conclusion, we shall place the Fourth Law in the context of what has long been well understood about the relationship between genes and human behavior—namely, that it is mistaken to believe that there might be a gene “for” one complex trait or another (for an eloquent statement of this basic point, see Dawkins, 1979, p. 189). What the Fourth Law adds to this understanding is that most genetic variability in behavior between individuals is attributable to genetic differences that are each responsible for very small behavioral differences. The law we have proposed here provides a unified conceptual explanation for several consistent patterns in the results of the past two decades of gene-discovery studies, including the failure of candidate-gene studies to replicate, the need for GWAS (and why they actually do replicate), and the crucial importance of extremely large samples in these endeavors. We believe that compelling motives for pursuing gene-mapping studies of behavioral traits can be found in the promise of learning more about the evolutionary trajectory of the human species, the formulation of new biological hypotheses regarding cognition and neural function, and the value of polygenic scores in the social and medical sciences. The Fourth Law of Behavior Genetics provides fundamental guidance for how research in all of these areas can most efficiently progress.

Declaration of Conflicting Interests

The authors declared that they had no conflicts of interest with respect to their authorship or the publication of this article. Funding

This work was supported by the Pershing Square Fund for Research on the Foundations of Human Behavior, Ragnar Söderberg Foundation Grant E9/11, Swedish Research Council Grant 412-2013-1061, and National Institute on Aging Grants P01AG005842, P01AG005842-20S2, P30AG012810, R01AG021650, and T32AG000186-23. The content of this publication is solely the responsibility of the authors and does not necessarily represent the views of any of these funding organizations.

Notes 1.

There are other kinds of genetic variants besides SNPs, including insertions, deletions, and variable-length repeats of one or more base pairs or short sequences, but SNPs are the most abundant and readily assayed kind of variant. 2.

In a recent review of genetic research on intelligence, Plomin and Deary (2015) formulated a similar generalization: “A third law has emerged from molecular genetic research that attempts to identify specific genes responsible for widespread heritability, especially genome-wide association (GWA) studies of the past few years: The heritability of traits is caused by many genes of small effect” (pp. 98–99). Note that the parallel is between Plomin and Deary’s “third law” and what we are calling the “Fourth Law of Behavior Genetics.” 3.

This technique is also sometimes called genome-wide complex trait analysis, or GCTA (e.g., J. J. Lee & Chow, 2014). 4.

Additionally, when the results of GWAS and candidate-gene studies are compared directly, GWAS hits are usually more likely to replicate (as in, e.g., a study of SNPs reportedly associated with glioma: Walsh et al., 2013). 5.

Evidence also suggests that dominance effects (rather than additive effects, which are measured by GWAS) do not account for much heritability (Zhu et al., 2015). 6.

This explanation was first put forth by Lande (1983) to explain, for instance, why pesticide resistance is polygenic in some insect populations and dependent on variants of large effect in other populations that have been disturbed by humans.

Recommended Reading

Chabris, C. F., Hebert, B. M., Benjamin, D. J., Beauchamp, J., Cesarini, D., van der Loos, M., . . . Laibson, D. ( 2012 ). (See References). An empirical demonstration that genetic studies of general cognitive ability employing small sample sizes cannot be trusted to produce replicable results .

Google Scholar Cross-Disorder Group of the Psychiatric Genomics Consortium . ( 2013 ). (See References). An elegant application of the genomic-relatedness-matrix restricted maximum likelihood/genome-wide complex trait analysis method to understanding the genetic architecture of behavioral traits .

Google Scholar Lee, J. J. ( 2012 ). (See References). A target article on the notion of causality in the study of individual differences, accompanied by commentaries and an author response .

Google Scholar Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., . . . Koellinger, P. D. ( 2013 ). (See References). A study of over 125,000 individuals reporting three single-nucleotide polymorphisms associated with educational attainment, with supplemental online material that contains much valuable additional information .

Google Scholar Ripke, S., Neale, B. M., Corvin, A., Walters, J. R., Farh, K. -H., Holmans, P. A., . . . O’Donovan, M. C. ( 2014 ). (See References). A study reporting 108 single-nucleotide polymorphisms associated with schizophrenia and implicating neuronal calcium signaling and acquired immunity as important biological processes in the etiology of this disorder .

Google Scholar Visscher, P. M., Brown, M. A., McCarthy, M. I., Yang, J. ( 2012 ). (See References). An overview of what genome-wide association studies have discovered and a response to criticisms of this methodology .

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