E following section.Discussion Spouses are more genetically similar than two individuals chosen at random. As described in SI Text, section S5, our unadjusted GAM result of 0.045 suggests that a 1-SD increase in genetic similarity increases the probability of marriage by roughly 15 . This association is confounded, in part, by intraethnic marriage among whites but we continue to observe GAM even after a series of models designed to eliminate this source of assortative mating. That is, after replication with an independent dataset that is geographically homogeneous, restriction of our analyses to a genetically homogeneous subsample of respondents, adjustment of kinships for common birth region, and elimination from genetic data of SNPs that capture population structure, we obtain estimates of GAM between 0.02 and 0.03. The lack of additional ethnicity information in HRS makes it difficult to understand the quantity of GAM that is due to ethnic homogamy alone but the additional analyses suggest that ARQ-092 manufacturer preference for intraethnic marriage accounts for roughly one-half of observed GAM among non-Hispanic whites. It is worth noting that other phenomena could be related to both marriage preference and genetic architecture. Religion, for example, could be a source of GAM in this respect. Future research could consider the proportion of GAM that is due to such factors. Although GAM exists, an important finding in our analyses is that the magnitude of GAM is significantly smaller than the magnitude of EAM. Furthermore, similar genotype explains only a small fraction of EAM (less than 10 ). Our attempt to understand the amount of EAM that could be explained by GAM is based on the hypothesis that a fraction of phenotypic similarity is due to genetic similarity. In short, that GAM causes EAM. However, it is important for us to acknowledge that there are alternative explanations. Education could structure GAM through gene nvironment correlations (17). For example, previous research (18) suggests that genetic similarity among friends is higher in order LM22A-4 schools with higher levels of economic inequality, which emphasizes the need to consider structural differences in educational institutions as a precursor to genetic selection into friendships. Our results (in particular, the 42 decline in GAM after controlling for EAM) indicate that social institutions may segregate people on genotype (presumably unwittingly), which could be behind some of the GAM that we observe. We do not assess this hypothesis empirically but we encourage others to consider this possibility in future research. It is also important to note that both understandings (EAM causes GAM or GAM causes EAM) do not consider that this relationship is contingent upon the mean level of education among the pairs. For example, Eckland (19) hypothesizes that spousal correlations for intelligence are higher when the intelligence of either spouse is either exceptionally high or exceptionally low. This nonlinear relationship in conjunction with the strong correlation between intelligence and years of completed education suggests that the direction and magnitude of the GAM AM relationship may vary across the educational spectrum. Eckland (19) and others (20) have argued that7998 | www.pnas.org/cgi/doi/10.1073/pnas.assortative mating and the genetic influences on status-related outcomes may change over time. Higher levels of social inequality reduce the likelihood that otherwise small genetic factors will signif.E following section.Discussion Spouses are more genetically similar than two individuals chosen at random. As described in SI Text, section S5, our unadjusted GAM result of 0.045 suggests that a 1-SD increase in genetic similarity increases the probability of marriage by roughly 15 . This association is confounded, in part, by intraethnic marriage among whites but we continue to observe GAM even after a series of models designed to eliminate this source of assortative mating. That is, after replication with an independent dataset that is geographically homogeneous, restriction of our analyses to a genetically homogeneous subsample of respondents, adjustment of kinships for common birth region, and elimination from genetic data of SNPs that capture population structure, we obtain estimates of GAM between 0.02 and 0.03. The lack of additional ethnicity information in HRS makes it difficult to understand the quantity of GAM that is due to ethnic homogamy alone but the additional analyses suggest that preference for intraethnic marriage accounts for roughly one-half of observed GAM among non-Hispanic whites. It is worth noting that other phenomena could be related to both marriage preference and genetic architecture. Religion, for example, could be a source of GAM in this respect. Future research could consider the proportion of GAM that is due to such factors. Although GAM exists, an important finding in our analyses is that the magnitude of GAM is significantly smaller than the magnitude of EAM. Furthermore, similar genotype explains only a small fraction of EAM (less than 10 ). Our attempt to understand the amount of EAM that could be explained by GAM is based on the hypothesis that a fraction of phenotypic similarity is due to genetic similarity. In short, that GAM causes EAM. However, it is important for us to acknowledge that there are alternative explanations. Education could structure GAM through gene nvironment correlations (17). For example, previous research (18) suggests that genetic similarity among friends is higher in schools with higher levels of economic inequality, which emphasizes the need to consider structural differences in educational institutions as a precursor to genetic selection into friendships. Our results (in particular, the 42 decline in GAM after controlling for EAM) indicate that social institutions may segregate people on genotype (presumably unwittingly), which could be behind some of the GAM that we observe. We do not assess this hypothesis empirically but we encourage others to consider this possibility in future research. It is also important to note that both understandings (EAM causes GAM or GAM causes EAM) do not consider that this relationship is contingent upon the mean level of education among the pairs. For example, Eckland (19) hypothesizes that spousal correlations for intelligence are higher when the intelligence of either spouse is either exceptionally high or exceptionally low. This nonlinear relationship in conjunction with the strong correlation between intelligence and years of completed education suggests that the direction and magnitude of the GAM AM relationship may vary across the educational spectrum. Eckland (19) and others (20) have argued that7998 | www.pnas.org/cgi/doi/10.1073/pnas.assortative mating and the genetic influences on status-related outcomes may change over time. Higher levels of social inequality reduce the likelihood that otherwise small genetic factors will signif.