Fri. Apr 19th, 2024

,214 SNPs. We also performed replication analysis on data from the Framingham Heart Study (15) (a description of these data can be found in SI Text, section S6). Measuring Genetic Similarity. Quantifying GAM in the population relies on a valid and reliable measure of genetic relatedness between all individuals in the study. Genetic relatedness is a basic biological concept that undergirds quantitative genetic analyses (1). The bulk of this research relied on unmeasured genetic similarity among different types of relatives (e.g., NS-018 manufacturer siblings, twins, cousins, etc.) and recently this same conceptual approach used genome-wide data from related (26) and unrelated (27) individuals. These methods are similar in that they take advantage of naturally 3′-Methylquercetin web occurring variability in the degree to which two individuals’ genomes are more or less similar compared with others in the population. It is precisely this variability between unrelated individuals that we use here. There are a number of methods for estimating genetic similarity based on measured genotype but the properties of these various estimates differ. We experimented with a measure that is based on the assumption of a common allele frequency across a sample (28) but this measure was found to be highly sensitive to population stratification (details are shown in SI Text, section S7). Therefore, we use a measure of kinship that has been shown to be more robust to population stratification than previous estimates of genetic similarity across the genome (29). This procedure produces a matrix that describes the genetic similarity for all pairs of individuals in our sample. Measuring GAM. The traditional approach to measuring EAM is to analyze the correlation of spousal educational attainment. It is important to note that this approach is possible because each spouse has a level of education. In contrast,measures of genetic relatedness exist at the pair level because relatedness measures a quantity with respect to a specific alter, rather than an absolute level (e.g., years of completed schooling). Hence, a spousal pair would have only a single measure of genetic relatedness versus two measures of education, one for each spouse. The correlation approach is thus not a viable option for measuring GAM. We have instead chosen to concentrate on differences in the distributions of genetic relatedness between married and unmarried pairs of respondents. Although this approach is unique, we studied its behavior via a simulation study (SI Text, section S5), which demonstrated that the method is able to distinguish assortative mating from random mating in samples of this size. Characterizing the presence and magnitude of genetic homogamy via a comparison of distributions is challenging because it requires a relevant comparison group. One approach would be to consider, for a focal individual, only those individuals with whom the individual is likely to marry given certain characteristics (e.g., age). Results based on such an approach would perhaps be unpersuasive given their potential sensitivity to the formation of the group of potential spouses for a person. To avoid this dilemma, we test GAM against the null hypothesis of random mating. As such, we make only minimal assumptions about the possible range of mates by restricting our comparisons of interest only to cross-sex, same-race individuals. We impose these sex and race restrictions due to limitations in existing data and methods. With respect to sex, we do.,214 SNPs. We also performed replication analysis on data from the Framingham Heart Study (15) (a description of these data can be found in SI Text, section S6). Measuring Genetic Similarity. Quantifying GAM in the population relies on a valid and reliable measure of genetic relatedness between all individuals in the study. Genetic relatedness is a basic biological concept that undergirds quantitative genetic analyses (1). The bulk of this research relied on unmeasured genetic similarity among different types of relatives (e.g., siblings, twins, cousins, etc.) and recently this same conceptual approach used genome-wide data from related (26) and unrelated (27) individuals. These methods are similar in that they take advantage of naturally occurring variability in the degree to which two individuals’ genomes are more or less similar compared with others in the population. It is precisely this variability between unrelated individuals that we use here. There are a number of methods for estimating genetic similarity based on measured genotype but the properties of these various estimates differ. We experimented with a measure that is based on the assumption of a common allele frequency across a sample (28) but this measure was found to be highly sensitive to population stratification (details are shown in SI Text, section S7). Therefore, we use a measure of kinship that has been shown to be more robust to population stratification than previous estimates of genetic similarity across the genome (29). This procedure produces a matrix that describes the genetic similarity for all pairs of individuals in our sample. Measuring GAM. The traditional approach to measuring EAM is to analyze the correlation of spousal educational attainment. It is important to note that this approach is possible because each spouse has a level of education. In contrast,measures of genetic relatedness exist at the pair level because relatedness measures a quantity with respect to a specific alter, rather than an absolute level (e.g., years of completed schooling). Hence, a spousal pair would have only a single measure of genetic relatedness versus two measures of education, one for each spouse. The correlation approach is thus not a viable option for measuring GAM. We have instead chosen to concentrate on differences in the distributions of genetic relatedness between married and unmarried pairs of respondents. Although this approach is unique, we studied its behavior via a simulation study (SI Text, section S5), which demonstrated that the method is able to distinguish assortative mating from random mating in samples of this size. Characterizing the presence and magnitude of genetic homogamy via a comparison of distributions is challenging because it requires a relevant comparison group. One approach would be to consider, for a focal individual, only those individuals with whom the individual is likely to marry given certain characteristics (e.g., age). Results based on such an approach would perhaps be unpersuasive given their potential sensitivity to the formation of the group of potential spouses for a person. To avoid this dilemma, we test GAM against the null hypothesis of random mating. As such, we make only minimal assumptions about the possible range of mates by restricting our comparisons of interest only to cross-sex, same-race individuals. We impose these sex and race restrictions due to limitations in existing data and methods. With respect to sex, we do.