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Uent, PC(s) that presumably have less ethnic variability than the

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Uent, PC(s) that presumably have less ethnic variability than the full sample. This should in turn reduce the impact that ethnic intramarriage among whites would have on our estimates. We estimated GAM among only those respondents with PC 1 > -0.003 to be 0.021 (95 CI: 0.002, 0.041). Note that this is very similar to the value obtained from our estimate of GAM in the Framingham Heart Study (15), a geographically homogenous sample. A second approach for controlling the impact of population stratification is to control for birth region in our estimate of GAM because individuals from the same birth region are more likely to come from the same ethnic group than two individuals sampled from the entire nation (SI Text, section S3 and ref. 16). In Fig. 1, Lower Left, we present an adjusted GAM estimate produced by residualizing kinship using a linear model with a dummy variable indicating whether a pair was born in the same census division. Based on this approach, we estimated an adjusted GAM of 0.033 (95 CI: 0.013, 0.049). This change suggests that some of our the initial GAM estimate is due to the fact that people from the same geographic area are more likely to marry one another than people from different areas (65 of the spousal pairs are from the same birth region compared with just 13 of nonspousal pairs) and that these geographic areas may capture subtle allele frequency differences across the population. That said, there is evidence for residual GAM, even with geographic controls. We note that this source of GAM is often not adjusted for in many PD168393 site estimates of heritability or demographic models of spousal assortative mating that use national (or international) samples, even if the samples are of non-Hispanic whites. Finally, we also attempted to adjust for the influence of population stratification via direct manipulation of the genetic data. After computation of PCs, we identified SNPs that were most associated with the first five PCs (and thus potential ethnic markers) via GWAS. We then removed these SNPs from the genetic data and recalculated kinship (additional details on this process are in SI Text, section S4). Even after imposing extremely conservative restrictions that removed 70 of the SNPs (remaining SNPs were unrelated to any of the first five PCs), we estimated a GAM of 0.026 (95 CI: 0.005, 0.045). We discuss the relationship between the various estimates of GAM that control for population stratification in Discussion, but pause to note that several different approaches have converged on an estimate of residual GAM between 0.02 and 0.03.PNAS | June 3, 2014 | vol. 111 | no. 22 |1.0.0.0.0.0.0.0.0.0.0.1.SOCIAL SCIENCESRelationship Between GAM and EAM. To answer the third research question, we estimated EAM after first regressing out genetic similarity (based on the kinship estimates). Fig. 1, Lower Right describes the results of this analysis. As shown in this figure, adjusting for GAM reduced EAM to 0.115 (95 CI: 0.102, 0.133). Given that the kinship values used for this buy Sulfatinib analysis may be affected by population stratification, we view this as an upper bound. Hence, at most 10 of the variance in EAM is due to GAM. We also examine this relationship in reverse by computing a GAM coefficient based on the residualized kinship coefficients (kinship was regressed on the squared educational differences of a pair). This coefficient declined from 0.045 to 0.026, a reduction of 42 . We discuss our interpretation of this result in th.Uent, PC(s) that presumably have less ethnic variability than the full sample. This should in turn reduce the impact that ethnic intramarriage among whites would have on our estimates. We estimated GAM among only those respondents with PC 1 > -0.003 to be 0.021 (95 CI: 0.002, 0.041). Note that this is very similar to the value obtained from our estimate of GAM in the Framingham Heart Study (15), a geographically homogenous sample. A second approach for controlling the impact of population stratification is to control for birth region in our estimate of GAM because individuals from the same birth region are more likely to come from the same ethnic group than two individuals sampled from the entire nation (SI Text, section S3 and ref. 16). In Fig. 1, Lower Left, we present an adjusted GAM estimate produced by residualizing kinship using a linear model with a dummy variable indicating whether a pair was born in the same census division. Based on this approach, we estimated an adjusted GAM of 0.033 (95 CI: 0.013, 0.049). This change suggests that some of our the initial GAM estimate is due to the fact that people from the same geographic area are more likely to marry one another than people from different areas (65 of the spousal pairs are from the same birth region compared with just 13 of nonspousal pairs) and that these geographic areas may capture subtle allele frequency differences across the population. That said, there is evidence for residual GAM, even with geographic controls. We note that this source of GAM is often not adjusted for in many estimates of heritability or demographic models of spousal assortative mating that use national (or international) samples, even if the samples are of non-Hispanic whites. Finally, we also attempted to adjust for the influence of population stratification via direct manipulation of the genetic data. After computation of PCs, we identified SNPs that were most associated with the first five PCs (and thus potential ethnic markers) via GWAS. We then removed these SNPs from the genetic data and recalculated kinship (additional details on this process are in SI Text, section S4). Even after imposing extremely conservative restrictions that removed 70 of the SNPs (remaining SNPs were unrelated to any of the first five PCs), we estimated a GAM of 0.026 (95 CI: 0.005, 0.045). We discuss the relationship between the various estimates of GAM that control for population stratification in Discussion, but pause to note that several different approaches have converged on an estimate of residual GAM between 0.02 and 0.03.PNAS | June 3, 2014 | vol. 111 | no. 22 |1.0.0.0.0.0.0.0.0.0.0.1.SOCIAL SCIENCESRelationship Between GAM and EAM. To answer the third research question, we estimated EAM after first regressing out genetic similarity (based on the kinship estimates). Fig. 1, Lower Right describes the results of this analysis. As shown in this figure, adjusting for GAM reduced EAM to 0.115 (95 CI: 0.102, 0.133). Given that the kinship values used for this analysis may be affected by population stratification, we view this as an upper bound. Hence, at most 10 of the variance in EAM is due to GAM. We also examine this relationship in reverse by computing a GAM coefficient based on the residualized kinship coefficients (kinship was regressed on the squared educational differences of a pair). This coefficient declined from 0.045 to 0.026, a reduction of 42 . We discuss our interpretation of this result in th.

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