1982; Hopper and Mathews 1983; Whitfield et al. 2007) have considered the question of familial similarity, and most have only examined Pb. We recently reported a twin study that showed significant genetic effects on blood Pb concentration (Whitfield et al. 2007). However, in the literature, we found no similar data for the other toxic elements, apart from one study that examined both Pb and Cd (Bjorkman et al. 2000). Current analytical methods allow the simultaneous measurement of multiple ele- ments in biological samples, including both the potentially toxic ones already mentioned and essential elements including copper (Cu), selenium (Se), and zinc (Zn). These are not only necessary for function of some enzymes, but they potentially interact with the absorp- tion, distribution, or effects of the toxic ele- ments (Peraza et al. 1998). We have now examined data on As, Cd, Hg, and also the essential elements Cu, Se, and Zn in blood samples from twin subjects previously studied for Pb. Our primary aim was to evaluate the contributions of genetic and environmental factors to interindividual differences. We have also assessed the con- tributions of physiological or demographic characteristics to variation, estimated corre- lations between concentrations of these ele- ments, and performed linkage analysis using data from dizygotic (DZ) twin pairs. We aimed to determine which characteristics affect the uptake or distribution of these potentially toxic elements; whether elements other than Pb show evidence for genetic effects; and to what degree the correlations between concen- trations of these elements are due to environ- mental effects (such as simultaneous exposure to more than one potentially toxic element in adverse environments) or to genetic effects (such as variation in uptake or distribution kinetics affecting multiple elements through common biological mechanisms); and to iden- tify gene loci that may affect other elements, as we previously have for Pb. Materials and Methods Participants. Participants were twins enrolled in the Australian Twin Registry and born between 1903 and 1964. The subjects and procedures were the same as those described previously for blood Pb (Whitfield et al. 2007). Briefly, participants completed a postal ques- tionnaire in 1989, completed a telephone interview in 1993–1994, and provided a blood sample in 1993–1996 (Whitfield et al. 1998). We determined zygosity from responses to questions about physical similarity and the inability of others to tell them apart, supple- mented by blood group information and, for those DZ pairs participating in linkage proj- ects (Beekman et al. 2003), genome-wide microsatellite genotyping. Details are given in the Supplemental Material (doi:10.1289/ ehp.0901541). Participants gave written informed consent, and the studies were approved by the appropriate ethics committees. In 1993–1996, blood was collected from 1,134 men and 2,241 women. At the same visit, each participant’s height and weight were measured and body mass index (BMI) was cal- culated (weight in kilograms divided by height in meters squared). Information on alcohol intake, smoking, years of education, and social class was obtained by self-report questionnaires, as described in the Supplemental Material (doi:10.1289/ehp.0901541). Participants’ addresses were categorized into urban, sub- urban, or rural zones using their postcodes at the time of blood collection and a database on the geographic location of Australian post- codes. Information on subjects is summarized in Supplemental Material, Table 1. Laboratory procedures. Because plasma and buffy from the original blood samples were used for other purposes, we used erythrocytes for elemental analysis. Before analysis, the erythrocytes were thawed at room temperature and diluted 1:20 in ammonia/ EDTA solution containing rhodium as an internal standard. As, Cd, Cu, Hg, Pb, Se, and Zn concentra- tions were measured by inductively coupled plasma mass spectrometry on a Perkin-Elmer Elan 5000 mass spectrometer (PerkinElmer Inc, Wellesley, MA, USA) or a Varian UltraMass (Varian Inc., Palo Alto, CA, USA). Hemoglobin concentration was then measured on the diluted samples using the cyanmethe- moglobin method. Quality control methods and results are discussed in the Supplemental Material (doi:10.1289/ehp.0901541). Data analysis. We measured erythrocyte element concentrations for 2,926 individuals. We accounted for error variation inherent in measurement of element concentrations in the samples, with both quality control and twin samples contributing to the maximum- likelihood estimation of and adjustment for day effects [see Supplemental Material (doi:10.1289/ehp.0901541) for additional details]. We performed model fitting to test for effects of genetic and environmental sources of variation and multivariate analysis of cova- riate effects using Mx (Neale 1999). Data from 2,832 people were analyzed using Mx: 428 monozygotic (MZ) female, 165 MZ male, 218 DZ female, 90 DZ male, and 222 DZ opposite-sex pairs; and 586 individuals (356 female and 230 male) whose co-twin did not participate. The latter contribute to esti- mates of means and variances but not directly to co-twin correlations. Effects of covariates. We adjusted for sam- ple hemoglobin concentration, effects of day- to-day analytical variation, sex, and age. At each step, the improvement in goodness-of-fit Environmental Health Perspectives • volume 118 | number 6 | June 2010 Genetic effects on trace elements was assessed by the likelihood ratio chi-square test as each variable was added. We then tested for the effects of personal, geographic, and social characteristics expected to affect the results: sex, age, alcohol intake, smoking status, residential location, social class, edu- cational level, and BMI. Univariate analysis using SPSS (SPSS Inc., Chicago, IL, USA) was supplemented with Mx analysis to assess the independent effects of these covariates and to take account of twin relatedness. The base- line model contained all the covariates; a series of submodels were then fitted in which one of the covariates was removed and the change in goodness-of-fit calculated to determine the significance of their independent contribu- tion. See Supplemental Material (doi:10.1289/ ehp.0901541) for additional details. Genetic and environmental sources of variation. We saved residuals from the model containing all the covariates and used them to fit models of genetic and environ- mental sources of variation and covariation to the within- and between-pair covariances by zygosity group, as previously described (Whitfield et al. 2007), and also for the link- age analysis. For consistency (and because we particularly wished to distinguish between shared environmental and genetic effects), we initially tested models with additive genetic (A), nonshared environmental (E), and shared environmental (C) sources of variation; these ACE models were then compared with mod- els containing only A and E, to determine whether shared environmental sources of vari- ation could be excluded. Linkage analysis. We performed linkage scans for erythrocyte element concentrations on 501 DZ twin pairs with phenotypic data and linkage marker data. DNA was extracted from blood or buccal swabs according to standard procedures (Miller et al. 1988). Genotyping data were collated from genome- scans previously performed for other proj- ects. Errors were resolved and Mendelian segregation inconsistencies were identified and removed as described in Supplemental Material (doi:10.1289/ehp.0901541). Of the 501 available twin pairs, 487 (97%) were genotyped at 300–1,717 markers (mean, 743) and 14 (3%) at < 300 markers. Sibling identity by descent was estimated as described in Supplemental Material (doi:10.1289/ ehp.0901541). Empirical genomew‑ide thresholds. For the Pb-linkage peak, we estimated the probabil- ity of the observed logarithm of odds (LOD) score occurring by chance using 1,000 gene- dropping simulations as described by Abecasis et al. (2004). See Supplemental Material (doi:10.1289/ehp.0901541) for additional details. In summary, we measured element con- centrations (As, Cd, Cu, Hg, Pb, Se, Zn) in 777