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Review
. 2012 Nov;24(4):1195-214.
doi: 10.1017/S0954579412000648.

Confluence of Genes, Environment, Development, and Behavior in a Post Genome-Wide Association Study World

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Free PMC article
Review

Confluence of Genes, Environment, Development, and Behavior in a Post Genome-Wide Association Study World

Scott I Vrieze et al. Dev Psychopathol. .
Free PMC article

Abstract

This article serves to outline a research paradigm to investigate main effects and interactions of genes, environment, and development on behavior and psychiatric illness. We provide a historical context for candidate gene studies and genome-wide association studies, including benefits, limitations, and expected payoffs. Using substance use and abuse as our driving example, we then turn to the importance of etiological psychological theory in guiding genetic, environmental, and developmental research, as well as the utility of refined phenotypic measures, such as endophenotypes, in the pursuit of etiological understanding and focused tests of genetic and environmental associations. Phenotypic measurement has received considerable attention in the history of psychology and is informed by psychometrics, whereas the environment remains relatively poorly measured and is often confounded with genetic effects (i.e., gene-environment correlation). Genetically informed designs, which are no longer limited to twin and adoption studies thanks to ever-cheaper genotyping, are required to understand environmental influences. Finally, we outline the vast amount of individual difference in structural genomic variation, most of which remains to be leveraged in genetic association tests. Although the genetic data can be massive and burdensome (tens of millions of variants per person), we argue that improved understanding of genomic structure and function will provide investigators with new tools to test specific a priori hypotheses derived from etiological psychological theory, much like current candidate gene research but with less confusion and more payoff than candidate gene research has to date.

Figures

Figure 1
Figure 1
Common Forms of DNA Variation. Humans have two chromosomes, one inherited from their father (paternal) and one from their mother (maternal). Since the same bases always pair together in a single chromosomal strand (A with T; C with G), giving both pairs for each strand is redundant, and DNA sequences are therefore represented by two rows of bases (the “Simplified Representation” in the figure). The “CA” SNP represents the only difference between the maternal and paternal autosomal segments. Catalogued in this figure is the Single Nucleotide Polymorphism (SNP) as well as several common types of structural variation, including Indels, Block Substitutions, inversions, VNTRs, and CNVs. Note that these particular variants are illustrative, and that variation is not necessarily within-person. For example, this individual is heterozygous for the SNP, but other individuals may be homozygous CC or homozygous AA. The same is true for structural variants. In the Indel example, this individual has a GAT insertion on the paternal chromosome and no such insertion on the maternal chromosome. Another individual may have GAT insertions on both chromosomes; yet another individual may lack the GAT insertion altogether.
Figure 2
Figure 2
Sample sizes and number of SNPs required to explain significant proportions of variance in anthropometric traits. These data are based on visual inspection of graphs provided in (Allen, et al., 2010) and (Speliotes, et al., 2010); see the original sources for full details. Note the wide difference in trend for height (estimates based on sample of ~185,000 subjects) and for BMI (estimates based on sample of ~235,000 subjects). Height is much more promising, in that it will take ~500,000 samples to obtain enough genome-wide significant SNPs to account for 15% of the variance in height. For BMI, on the other hand, it is projected that ~700,000 samples are required to account for only 5% of the variance in height. Cause of the discrepancy in genetic architecture of these traits is unknown. These values differ slightly from what is described in the text because extrapolating beyond currently available sample sizes required sample splitting and replication procedures in the original studies in order to unbiasedly estimate effect sizes.
Figure 3
Figure 3
Association between GxE interaction effects and G main effects. Graphs are organized into rows. The left-hand graph in each row is an example GxE interaction; on the right is the corresponding main effect (assuming the environmental exposure was 50:50 in this sample). The GxE effect observed in row (a), similar to that found in (Caspi, et al., 2003), also shows a main effect when environment is ignored. The effect in row (a) would also show unequal variances across genotypes due to the increasing mean separation between environments as we move from the AA genotype to the BB genotype. The effect in row (b) shows an interaction but no main effect. Row (b) would show unequal variances across genotypes. Row (c), although similar to row (b) would show an interaction, a main effect, and unequal variances. Row (d) is unique in that the effect there would show an interaction, but would demonstrate neither a main effect nor unequal variances.

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