S to the 0.55 quantile of all pairs. Adjusted GAM (Lower Left) includes control for same birth region (census division). Adjusted EAM (Lower Right) includes a control for kinship between the pairs.of GAM. Together, these results answer our first two questions. Namely, GAM exists in this sample but it is substantially smaller in magnitude than EAM. Next, we investigated whether GAM and EAM have a specific common explanation through a small set of SNPs related to educational attainment. We tested this hypothesis by examining proxies for the SNPs that reached genome-wide significance in a recent genome-wide association study (GWAS) on educational attainment (11). In particular, we conducted a 2 test using the sum of the risk alleles for the target SNPs for husbands and wives. The original SNPs were GSK2256098 cancer rs9320913, rs11584700, and rs4851266. We identified proxies using SNP Annotation and Proxy Search (14) that were correlated with the original SNP at no less than 0.8. The P values for the three tests were all above 0.35. Hence, we found little evidence that there was assortative mating based on these SNPs. We conducted a replication analysis using data from the second SB 203580MedChemExpress SB 203580 generation of the Framingham Heart Study (15). It is important to note that the participants of this study are a group of predominantly white respondents from a geographically constrained area. In this secondary data set, we estimated GAM to be 0.025 (95 CI: 0.005, 0.046) based on 685 spousal pairs. Although we replicate the rejection of the null hypothesis of zero GAM in a second sample, we also note the decline in the magnitude of GAM compared with the estimate from HRS. Our estimated EAM in the Framingham sample was similar to the result from the HRS sample, 0.121 (95 CI: 0.102, 0.141).Impact of Population Stratification on GAM. The existence of population stratification, small differences in allele frequencies that may exist across socially defined racial and ethnic groups, complicatesDomingue et al.many genetic analyses. In this section, we consider the extent to which population stratification may be present in our sample and how it may influence our measure of GAM. To characterize genetic divisions among the sample of non-Hispanic whites, we computed principal components (PCs) (SI Text, section S2) based on the complete set of SNPs. These methods consider the correlation between all of the SNPs within a population and identify factors that account for the greatest amount of common genetic variance. These factors align strongly with self-reported race and ethnicity and provide continuous measures of ancestry that are important controls for population stratification. There is substantial variability in the first PC only. Although we do not have information on ethnicity aside from Hispanicity, the PCs are largely unassociated with birth region (as a proxy for ethnic mixture). Differences in PCs may be capturing the genetic similarity (unrelated to population stratification) that we hope to investigate in our GAM analysis. As it is unclear if these PCs are confounding our estimate of GAM or are themselves an interesting component of GAM, we do not focus on estimates that control for these differences. We instead consider three alternative methods of adjusting for these differences in population stratification (estimates based on direct controls for PCs are shown in SI Text, section S2). First, we use a subsample of our respondents with less variability in the first, and subseq.S to the 0.55 quantile of all pairs. Adjusted GAM (Lower Left) includes control for same birth region (census division). Adjusted EAM (Lower Right) includes a control for kinship between the pairs.of GAM. Together, these results answer our first two questions. Namely, GAM exists in this sample but it is substantially smaller in magnitude than EAM. Next, we investigated whether GAM and EAM have a specific common explanation through a small set of SNPs related to educational attainment. We tested this hypothesis by examining proxies for the SNPs that reached genome-wide significance in a recent genome-wide association study (GWAS) on educational attainment (11). In particular, we conducted a 2 test using the sum of the risk alleles for the target SNPs for husbands and wives. The original SNPs were rs9320913, rs11584700, and rs4851266. We identified proxies using SNP Annotation and Proxy Search (14) that were correlated with the original SNP at no less than 0.8. The P values for the three tests were all above 0.35. Hence, we found little evidence that there was assortative mating based on these SNPs. We conducted a replication analysis using data from the second generation of the Framingham Heart Study (15). It is important to note that the participants of this study are a group of predominantly white respondents from a geographically constrained area. In this secondary data set, we estimated GAM to be 0.025 (95 CI: 0.005, 0.046) based on 685 spousal pairs. Although we replicate the rejection of the null hypothesis of zero GAM in a second sample, we also note the decline in the magnitude of GAM compared with the estimate from HRS. Our estimated EAM in the Framingham sample was similar to the result from the HRS sample, 0.121 (95 CI: 0.102, 0.141).Impact of Population Stratification on GAM. The existence of population stratification, small differences in allele frequencies that may exist across socially defined racial and ethnic groups, complicatesDomingue et al.many genetic analyses. In this section, we consider the extent to which population stratification may be present in our sample and how it may influence our measure of GAM. To characterize genetic divisions among the sample of non-Hispanic whites, we computed principal components (PCs) (SI Text, section S2) based on the complete set of SNPs. These methods consider the correlation between all of the SNPs within a population and identify factors that account for the greatest amount of common genetic variance. These factors align strongly with self-reported race and ethnicity and provide continuous measures of ancestry that are important controls for population stratification. There is substantial variability in the first PC only. Although we do not have information on ethnicity aside from Hispanicity, the PCs are largely unassociated with birth region (as a proxy for ethnic mixture). Differences in PCs may be capturing the genetic similarity (unrelated to population stratification) that we hope to investigate in our GAM analysis. As it is unclear if these PCs are confounding our estimate of GAM or are themselves an interesting component of GAM, we do not focus on estimates that control for these differences. We instead consider three alternative methods of adjusting for these differences in population stratification (estimates based on direct controls for PCs are shown in SI Text, section S2). First, we use a subsample of our respondents with less variability in the first, and subseq.