Whether a person develops a particular disease can depend on both genetic and environmental factors. Many studies have found that people of different races and ethnicities have different likelihoods of acquiring certain diseases. Race and ethnicity are social constructs; that is, they are not necessarily defined biologically. However, shared ancestry will produce genetic links between members of a group. In addition, members of an ethnic group often share a culture or environment that may influence their risk of disease. For example, the ‘Mediterranean diet’ inspired by the dietary habits of Southern Italians has been shown to reduce the risk of heart disease, diabetes and cancer.

The addition of chemical groups – such as methyl groups – to DNA strands can affect the activity of nearby genes. Methylation is controlled by both genetic and environmental factors, and altered patterns of DNA methylation are seen in some diseases. It is therefore an ideal biological process to study to determine how race/ethnicity and ancestry contribute to a person’s susceptibility to disease.

Galanter et al. have now studied the patterns of methylation found in the blood of 573 people from diverse Latino ethnic sub-groups. The different groups displayed significantly different patterns of methylation at hundreds of locations across the genome. Genetic ancestry explained approximately 75% of the variation in methylation between the sub-groups. In addition, the methylation patterns at DNA locations known to be affected by environmental exposures – for example, by exposure to tobacco while in the womb – were disproportionately likely to be methylated differently in different sub-groups.

Now that more is known about the relative effects of race/ethnicity and genetic ancestry on methylation, the next step is to apply this knowledge to disease processes. This will help us to better understand the source of health disparities across different groups of people.

Race, ethnicity, and genetic ancestry have had a complex and often controversial history within biomedical research and clinical practice (Risch et al., 2002; Cooper et al., 2003; Yudell et al., 2016; Burchard et al., 2003; Phimister, 2003). For example, race- and ethnicity-specific clinical reference standards are based on population-based sampling on a given physical trait such as pulmonary function (Hankinson et al., 1999; Quanjer et al., 2012). However, because race and ethnicity are social constructs and poor markers for genetic diversity, they fail to capture the heterogeneity present within racial/ethnic groups and in admixed populations (Borrell, 2005). To account for these heterogeneities and to avoid social and political controversies, the genetics community has grouped individuals by genetic ancestry instead of race and ethnicity (Yudell et al., 2016). Indeed, recent work from our group and others have demonstrated that genetic ancestry improves diagnostic precision compared to racial/ethnic categorizations for specific medical conditions and clinical decisions (Kumar et al., 2010; Udler et al., 2015; Nalls et al., 2008).

However, racial and ethnic categories also reflect the shared experiences and exposures to known risk factors for disease, such as air pollution and tobacco smoke, poverty, and inadequate access to medical services, which have all contributed to worse disease outcomes in certain populations (Nguyen et al., 2014; Evans and Kantrowitz, 2002). Thus, it is unclear whether defining groups through genetic ancestry can capture these shared exposures. In this work we seek to explore the contributions of genetically defined ancestry and social, cultural and environmental factors to understanding differential methylation between ethnic groups.

Epigenetic modification of the genome through methylation plays a key role in the regulation of diverse cellular processes (Smith and Meissner, 2013). Changes in DNA methylation patterns have been associated with complex diseases, including various cancers (Kulis and Esteller, 2010), cardiovascular disease (Udali et al., 2013; Kato et al., 2015), obesity (Bell et al., 2010), diabetes (Chambers et al., 2015), autoimmune and inflammatory diseases (Liu et al., 2013), and neurodegenerative diseases (Lardenoije et al., 2015). Epigenetic changes are thought to reflect influences of both genetic (Bell et al., 2011) and environmental factors (Feil and Fraga, 2011), and have been shown to vary between racial groups (Barfield et al., 2014). The discovery of methylation quantitative trait loci (meQTL’s) across populations by Bell et al. established the influence of genetic factors on methylation levels in a variety of tissue types (Bell et al., 2011), with meQTL’s explaining between 22% and 63% of the variance in methylation levels. Multiple environmental factors have also been shown to affect methylation levels, including endocrine disruptors, tobacco smoke (Joubert et al., 2012, 2016), polycyclic aromatic hydrocarbons, infectious pathogens, particulate matter, diesel exhaust particles (Jiang et al., 2014), allergens, heavy metals, and other indoor and outdoor pollutants (Ho et al., 2012). Psychosocial factors, including measures of traumatic experiences (Chen et al., 2013; Ressler et al., 2011; van der Knaap et al., 2014), socioeconomic status (Lam et al., 2012; Borghol et al., 2012), and general perceived stress (Vidal et al., 2014), also affect methylation levels. Since both genetic and environmental exposures affect methylation, this represents an ideal phenotype to explore the contributions of these two factors on differential methylation between ethnic groups.

In this work, we leveraged genome-wide methylation data in 573 Latino children of diverse Latino sub-ethnicities enrolled in the Genes-Environment and Admixture in Latino Americans (GALA II) study (Oh et al., 2012) whose genetic ancestry had been determined from dense genotyping arrays. This allowed us to explore the extent to which the differences in methylation between Latino sub-groups could be explained by their shared genetic ancestry. We found that many of the methylation differences associated with ethnicity could be explained by shared genetic ancestry. However, even after adjusting for ancestry, significant differences in methylation remained between the groups at multiple loci, reflecting social and environmental influences upon methylation.

Our findings have important implications for both the use of ancestry to capture biological changes and of race/ethnicity to account for social and environmental exposures. Epigenome-wide association studies in diverse populations may be susceptible to confounding due to environmental exposures in addition to confounding due to population stratification (Michels et al., 2013). The findings also have implications for the common practice of considering individuals of Latino descent, regardless of origin as a single ethnic group.

The study included 573 participants, the majority of whom self-identified as being either of Puerto Rican (n = 220) or Mexican origin (n = 276). Table 1 displays baseline characteristics of the GALA II study participants with methylation data included in this study, stratified by ethnic subgroups (Puerto Rican, Mexican, Other Latino, and Mixed Latinos who had grandparents of more than one national origin). Figure 1 shows the distribution of African, European, and Native American ancestry among the 524 participants with genomic ancestry estimates.

Figure 1

Download asset Open asset

Methylation data used in this study has been previously made publicly available at the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77716 (Rahmani et al., 2016). Genotyping data has been deposited in dbGaP; link will be activated when the data becomes publicly available (Burchard, http://www.ncbi.nlm.nih.gov/gap/?term=phs001180).

Global patterns of methylation

Differences in ethnicity and ancestry resulted in discernible patterns in the global methylation profile as demonstrated in a multidimensional scaling analysis (Figure 2A). As expected (Houseman et al., 2012; Lam etal., 2012), the first few principal coordinates are strongly correlated to imputed cell composition (Figure 2B–C). There are also significant associations of self-identified sub-ethnicity with PC2 (p-ANOVA = 0.003), PC3 (p-ANOVA = 0.004), PC6 (p-ANOVA = 0.0001), PC7 (p-ANOVA = 0.0003) (Figure 3A), and PC8 (p-ANOVA = 0.0003), after adjusting for age, sex, disease status, cell components, and technical laboratory factors (plate and position). Genetic ancestry was associated with PC3 (p=0.002), PC7 (p=0.0004) (Figure 3B) and PC8 (p=0.001) in a two degree of freedom ANOVA test, adjusting for age, sex, disease status, cell components, technical factors, and ethnicity. Supplementary file 1A summarizes the results of the simple correlation analysis of methylation with ethnicity and ancestry, as well as the adjusted nested ANOVA models described above and the mediation results described below.

Figure 2

Download asset Open asset

Figure 3

Download asset Open asset

A mediation analysis (Tingley et al., 2014) revealed that the associations between ethnicity and PCs 3, 7, and eight were significantly mediated by Native American ancestry, which explained ~100% (95% CI: 37–100%, p=0.01) of PC3, 83% (95% CI 37–100%, p<0.001) of PC7 and 66% (95% CI: 25% to 100%, p<0.001) of PC8. Inclusion of Native American ancestry in the regression model of PCs 3, 7, and eight caused the ethnicity associations to be non-significant. However, the associations of ethnicity with PCs 2 and 6 were not explained by Native American, African or European ancestry (mediation p>0.05), suggesting that the ethnic differences in these principal components are associated with global methylation patterns not captured by the shared genetic ancestry of each ethnic group. When genetic ancestry was regressed on the methylation data with the principal coordinates recalculated using the residuals of the regression between methylation and ancestry, there was an association between ethnicity and PC6 (p-ANOVA = 0.003). However, there was no association with any of the other principal coordinates. These observations suggest that while shared genetic ancestry can explain over 50% of the association between ethnicity and global methylation patterns in three PC’s, other non-genetic factors, such as environmental and social exposure differences associated with ethnicity influence methylation and are not captured by measures of genetic ancestry in two others.

Epigenome-wide association of self-identified ethnicity

An epigenome-wide association study of self-identified ethnicity (see Materials and methods for details of ascertainment of ethnicity) and methylation identified a significant difference in methylation M-values between ethnic groups at 916 CpG sites at a Bonferroni-corrected significance level of less than 1.6 × 10⁻⁷ (Figure 4A and Supplementary file 1B). The most significant association with ethnicity occurred at cg12321355 in the ABO blood group gene (ABO) on chromosome 3 (p-ANOVA 6.7 × 10⁻²²) (Figure 4B). A two degree of freedom ANOVA test for genomic ancestry was also significantly associated with methylation level at this site (p=2.3×10⁻⁵) (Figure 4C), and when the analysis was stratified by ethnic sub-group, showed an association in both Puerto Ricans and Mexicans (p=0.001 for Puerto Ricans, p=0.003 for Mexicans). Although adjusting for genomic ancestry attenuated the effect of ethnicity, a significant association between ethnicity and methylation remained (p=0.04). Recruitment site, an environmental exposure proxy, was not significantly associated with methylation at this locus (p=0.5), suggesting that environmental differences associated with ethnicity beyond geography and ancestry are driving the association.

Figure 4

Download asset Open asset

To determine the contribution of shared genetic ancestry and other factors associated with ethnicity, we repeated the analysis adjusting for ancestry. A significant association remained in 314 of the 834 (37.8%, p=1.7×10⁻¹⁸³ for enrichment) CpG sites associated with ethnicity (Figure 5A and Supplementary file 1B) (82 sites were excluded because they demonstrated unstable coefficient estimates and inflated standard errors due to strong correlations between ethnicity and ancestry, especially Native American ancestry [see Figure 1]).

Table 2 and Figure 5b show the proportion of variance explained by ethnicity, genomic ancestry, and their joint effect in the 916 CpG’s associated with ethnicity, as well as the 314 CpG’s that remained associated with ethnicity after adjustment for ancestry and the 520 CpG’s whose association with ethnicity was no longer significant when ancestry terms were introduced into the model. Even after adjusting for genomic ancestry, ethnicity explained 1.7% (IQR 0.785% to 3.0%) but as much as 13.4% of the variance in methylation across these loci. Genomic ancestry explained a median of 4.2% (IQR 1.8% to 8.3%) of the variance in methylation at all loci associated with ethnicity and accounts for a median of 75.7% (IQR 45.8% to 92%) of the total variance in methylation explained jointly by ethnicity and ancestry (median of 6.8%, IQR 4.5% to 10.0%) (Figure 5B).

Figure 5

Download asset Open asset

Table 2

Component	All CpG’s associated with ethnicity (n = 916)	CpG’s associated with ethnicity after adjusting for ancestry (n = 314)	CpG’s whose association with ethnicity is explained by ancestry (n = 520)
Joint	6.8% (4.5% to 10%)	6.2% (4.4% to 8.8%)	7.8% (5.3% to 11.1%)
Ethnicity	1.7% (0.78% to 3.0%)	3.5% (2.2% to 5.1%)	<1%
Ancestry	4.2% (1.8% to 8.3%)	1.8% (0.8% to 4.0%)	6.6% (4.0% to 10.2%)

Ethnicity and ancestry jointly explained as much as 38.5% of the variance in methylation in one CpG (cg0966827) and there were 17 CpG’s where ethnicity and ancestry jointly explain more 25% of the variance. Among the 314 CpG’s that remained associated with ethnicity after adjustment for ancestry, ethnicity accounted for a larger share of the joint variance than genomic ancestry (3.5%, IQR 2.2% to 5.1% versus 1.8%, IQR 0.8% to 4.0%). We saw a moderate amount of correlation between the 314 methylation sites associated with ethnicity after adjusting for ancestry (median R² of 0.044, IQR 0.01 to 0.13).

Sensitivity tests for departures from linearity, fine scale population substructure and the exclusion of the 16 participants who self-identified as ‘Mixed Latino’ sub-ethnicity, did not meaningfully affect our results (See Supplementary file 1B–F). To rule out any residual confounding due to recruitment sites, we conducted an additional analysis on the effect of recruitment site on methylation both for the overall study and for the Mexican participants (the largest study population in this analysis). We observed no significant independent effect of recruitment site suggesting that confounding due to recruitment region was limited, at least within the United States.

To explore the effect of departures from a linear association between ancestry and methylation, we incorporated both higher order polynomials and cubic splines of ancestry into our models. We observed a significant departure from linearity (p<0.05) in only 26 (for splines) and 25 (for polynomials) of the 314 CpG’s where an association between ethnicity and methylation remained after adjusting for ancestry; however, the association between ethnicity and methylation remained even after adjusting for non-linearity at all sites (Supplementary file 1C,D).

Environmental differences between geographic locations or recruitment sites are a potential non-genetic explanation for ethnic differences in methylation. We investigated the independent effect of recruitment site on methylation by analyzing the associations between recruitment site and individual methylation loci after adjusting for ethnicity. We did not find any loci significantly associated with recruitment site at a significance threshold of 1.6×10⁻⁷. We then performed an analysis to assess the effect of recruitment sites on methylation stratified by ethnicity. We did not find any loci significantly associated with recruitment site and methylation among Mexican participants. We were underpowered to perform a similar analysis for Puerto Ricans because there were only 27 Puerto Rican participants recruited outside of Puerto Rico. To ensure that the absence of association in Mexicans was not due to the loss of power from the smaller sample size, we repeated our analysis of the association between ethnicity and ancestry randomly down-sampling to 276 participants to match the sample size in the analysis of geography in Mexicans. While down-sampling the study to this degree resulted in a loss of power, 128 methylation sites were still associated with ancestry. We conclude that recruitment site was unlikely to be a significant confounder of our associations between ethnicity and methylation and was not a significant independent predictor of methylation.

While most population substructure in Latinos would be expected to arise from differences in continental ancestry (Galanter, 2012; Bryc et al., 2010), there is evidence of finer scale (sub-continental) ancestry in Latino populations (Moreno-Estrada et al., 2014). We tested for the effect of fine scale substructure by calculating principal components for all participants with genotyping data using Eigensoft (Patterson et al., 2006). We found significant associations between principal components 3–10 (PC’s 1 and 2 were almost perfectly collinear with ancestry, with an adjusted R² > 0.998 for all three ancestry proportions, and were therefore excluded) and ethnicity. We therefore added these 8 PC’s to models of ethnicity and methylation, and found an association between these genetic PC’s and methylation in 63/314 CpG’s that had remained associated with ethnicity after adjusting for ancestry. Adjusting for higher order substructure in these CpG’s explained the association between ethnicity and methylation in 51 additional loci. This left 263 loci associated with ethnicity after adjustment for ancestry where there was either no association between PC’s 3–10 and methylation or the inclusion of these PC’s did not affect the association between ethnicity and methylation. (Supplementary file 1E)

At these 314 loci, the median total variance accounted for by ethnicity, ancestry, and fine-scale substructure was 10.4% (IQR 6.6% to 16.1%), of which ethnicity explained a median of 1.7% (IQR 0.8% to 3.8%), ancestry explained a median of 2.9% (IQR 1.0 to 4.6%) and fine scale substructure explained a median of 3.4% (IQR 2.0% to 4.2%). Among the 263 CpG’s whose association with ethnicity could not be explained by fine-scale substructure, ethnicity explained a median of 1.9% (IQR 1.0% to 4.0%; max 26.7%), ancestry explained 2.8% (IQR 1.0% to 6.2%), and fine scale ancestry explained 3.2% (IQR 1.9% to 4.7%).

As only 16 participants self-identified as ‘Mixed Latino’, we performed a sensitivity analysis to test the effect of excluding these participants from the analysis and only examining Puerto Ricans, Mexicans, and ‘Other Latinos’. We found that excluding self-identified ‘Mixed Latino’ participants from the analysis did not significantly alter the results in most cases (Supplementary file 1F). Of the 916 CpG’s associated with ethnicity at a genome-wide scale (p<1.6×10–7) in models including individuals self-identified as ‘Mixed Ethnicity’, 894 (97.5%) were still significant at a genome-wide scale when ‘Mixed Latinos’ were excluded. All but two of the CpG’s that did not meet genome-wide significance were significant when correcting for 916 tests (p<5×10–5). In addition, an additional 290 CpG loci that did not meet genome-wide significance in the original analysis were significant at a genome-wide scale when self-identified ‘Mixed Latinos’ were excluded. While these loci did not meet genome-wide significance in the original analysis that included Mixed Latinos, they all had p-values lower than 2 × 10⁻⁶. Thus we conclude that a sensitivity test excluding individuals of mixed Latino ethnicity did not significantly alter the conclusions.

We conclude that shared genetic ancestry explains much but not all of the association between ethnicity and methylation. Other, non-genetic factors associated with ethnicity likely explain the ethnicity-associated methylation changes that cannot be accounted for by genomic ancestry alone.

Ethnic differences in environmentally-associated methylation sites

Methylation at CpG loci that had previously been reported to be associated with environmental exposures whose exposure prevalence differs between ethnic groups were tested for association with ethnicity in this study. A recent meta-analysis of maternal smoking during pregnancy, an exposure that varies significantly by ethnicity (Oh et al., 2012), identified associations with methylation at over 6000 CpG loci (Joubert et al., 2016). We found 1341 of 4404 that passed QC in our own study (30.4%) were nominally associated with ethnicity (p<0.05), which represented a highly significant (p<2×10⁻¹⁶) enrichment. Using a Bonferroni correction for the 4404 loci tested, 126 maternal-smoking related loci were associated with ethnicity (p<1.1×10⁻⁵), and 27 loci were among the 916 CpG’s reported above as associated with ethnicity (Supplementary file 1G). Of these, 14 were among the 314 CpG’s whose association with ethnicity could not be explained by ancestry and 12 were among the 263 CpG’s whose association with ethnicity could not be explained by ancestry or fine-scale substructure. We also examined methylation loci from an earlier study of maternal smoking in Norwegian newborns (Joubert et al., 2012) as well as studies of diesel exhaust particles (Jiang et al., 2014) and exposure to violence (Chen et al., 2013). These results are supportive of our hypothesis that environmental exposures may be responsible for the observed differences in methylation between ethnic groups and are presented in Supplementary file 1H.

In an earlier study of maternal smoking in Norwegian newborns (Joubert et al., 2012) that identified 26 loci associated with maternal smoking during pregnancy, 19 passed quality control (QC) in our own analysis, and the association between methylation and ethnicity was found to be nominally significant (p<0.05)at 6 (31.6%) CpG loci. Adjusting for 19 tests (p<0.0026), cg23067299 in the aryl hydrocarbon receptor repressor (AHRR) gene on chromosome five remained statistically significant (Supplementary file 1H). These results suggest that ethnic differences in methylation at loci known to be responsive to tobacco smoke exposure in utero may be explained in part by ethnic-specific differences in the prevalence of maternal smoking during pregnancy.

We also found that CpG loci previously reported to be associated with diesel-exhaust particle (DEP) exposure (Jiang et al., 2014) were significantly enriched among the set of loci whose methylation levels varied between ethnic groups. Specifically, of the 101 CpG sites that were significantly associated with exposure to DEP and passed QC in our dataset, 31 were nominally associated with ethnicity (p<0.05), and five were associated with ethnicity after adjusting for 101 comparisons (p<0.005). Finally, we found that methylation levels at cg11218385 in the pituitary adenylate cyclase-activating polypeptide type I receptor gene (ADCYAP1R1), which had been associated with exposure to violence in Puerto Ricans (Chen et al., 2013) and with heavy trauma exposure in adults (Ressler et al., 2011), was significantly associated with ethnicity (p=0.02).

We also found 194 loci with a significant association between global genetic ancestry and methylation levels (after adjusting for ethnicity) at a Bonferroni corrected association p-value of less than 1.6 × 10⁻⁷ (Figure 6 and Supplementary file 1I), including 48 that were associated with ethnicity in our earlier analysis. Of these significant associations, 55 were driven primarily by differences in African ancestry, 94 by differences in Native American ancestry, and 45 by differences in European ancestry. The most significant association between methylation and ancestry occurred at cg04922029 in the Duffy antigen receptor chemokine gene (DARC) on chromosome 1 (ANOVA p-value 3.1 × 10⁻²⁴) (Figure 6B). This finding was driven by a strong association between methylation level and global African ancestry; each 25 percentage point increase in African ancestry was associated with an increase in M-value of 0.98, which corresponds to an almost doubling in the ratio of methylated to unmethylated DNA at the site (95% CI 0.72 to 1.06 per 25% increase in African ancestry, p=1.1×10⁻²¹). There was no significant heterogeneity in the association between genetic ancestry and methylation between Puerto Ricans and Mexicans (p-het = 0.5). Mexicans have a mean unadjusted methylation M-value 0.48 units lower than Puerto Ricans (95% CI 0.35 to 0.62 units, p=1.1×10⁻¹¹). However, adjusting for African ancestry accounts for the differences in methylation level between the two sub-groups (p-adjusted = 0.4), demonstrating that ethnic differences in methylation at this site are due to differences in African ancestry.

Figure 6

Download asset Open asset

The distribution of methylation M-values at cg04922029 is tri-modal, raising the possibility that a SNP whose allele frequency differs between African and non-African populations may be driving the association. We therefore looked at the association between methylation at cg0422029 and ancestry at that locus. We found almost perfect correlation between methylation and African ancestry at the locus (p=6×10⁻¹⁶²) (Figure 7A). Each African haplotype at the CpG site was associated with an increase in methylation M-value of 2.7, corresponding to a 6.5-fold increase in the ratio of methylated to unmethylated DNA per African haplotype at that locus. We then looked for SNPs within 10,000 base pairs of the CpG site that explained the admixture mapping association. We found that methylation at cg04922029 was significantly correlated with SNP rs2814778 (Figure 7B), the Duffy null mutation, 212 base pairs away; each copy of the C allele was associated with an increase in M-value of 1.5, or a 2.9-fold increase in the ratio of methylated to unmethylated DNA (p=3.8×10⁻⁹⁰) (Figure 7C).

Figure 7

Download asset Open asset

When we examined the effect of local ancestry at the other 194 CpG’s we find that a substantial proportion of the effect of global ancestry on local methylation levels is due to local ancestry acting in –cis. Among the 194 CpG sites associated with global ancestry, local ancestry at the CpG site explained a median of 10.4% (IQR 3.0% to 19.4%) of the variance in methylation, accounting for a median of 52.8% (IQR 20.3% to 84.9%) of the total variance explained jointly by local and global ancestry (Figure 8).

Figure 8

Download asset Open asset

In a diverse population of Latinos, we have shown that a substantial number of loci are differentially methylated between ethnic sub-groups. While genomic ancestry can explain the association between ethnicity and methylation at 66% of the 916 loci associated with ethnicity, factors other than shared ancestry that correlate with ethnicity, such as social, economic, cultural and environmental exposures account for the association between ethnicity and methylation at 34% (314/916) of loci.

We conclude that systematic environmental differences between ethnic subgroups likely play an important role in shaping the methylome for both individuals and populations. Loci previously associated with diverse environmental exposures such as in utero exposure to tobacco smoke (Joubert et al., 2012, 2016), as well as diesel exhaust particles (Jiang et al., 2014) and psychosocial stress (Chen et al., 2013) were enriched in our set of loci where methylation was associated with ethnicity. Twenty-seven of the loci associated with maternal smoking during pregnancy in a large consortium meta-analysis (Joubert et al., 2016) were differentially methylated between Latino sub-groups at a genome-wide significance threshold of 1.6 × 10⁻⁷. Interestingly, this included both loci whose association persisted after adjustment for ancestry and fine-scale population substructure and are thus presumed to be due to environmental differences between ethnic groups and loci in which the association between ethnicity and methylation could be fully explained by genetically defined ancestry.

There are a number of plausible reasons for overlap between CpG’s associated with ancestry and those associated with environmental exposure. It is possible that this represents a gene-environment interaction, and that individuals with certain genetic backgrounds are more susceptible to the effects of environmental exposures such as in utero tobacco smoke than those of other genetic backgrounds. It has been previously reported that Hispanic smokers with high Native American ancestry had reduced risk of methylation across 12 genes, suggesting an ancestry by smoking interaction (Leng et al., 2013). Because the majority of studies that comprised the consortium that identified differentially methylated regions enrolled participants of European descent, such interactions might not have been evident in their study.

It is also possible that environmental exposures correlate with ancestry and that participants with certain ancestral backgrounds may have been more exposed to in utero tobacco smoke than those of other backgrounds. Several studies have shown correlations between genetic ancestry and environmental exposures, including socioeconomic status (Florez et al., 2011), overweight and obesity (Ziv et al., 2006), and birth site and country of residence (González Burchard et al., 2005).

Though our analysis of global ancestry showed that a majority of the variance explained jointly by local and global ancestry can be traced to specific loci in the genome acting in –cis, a substantial proportion cannot. Some of the residual association between global ancestry and methylation may be due to genetic effects acting in –trans; however, the possibility that some of it may be due to environmental exposures correlating with global ancestry cannot be excluded. Thus, it is plausible that genomic ancestry is acting as a proxy for both genetic and environmental effects in our study. If this is the case, our study likely underestimates the degree to which environmental factors explain differential methylation between ethnic groups.

Finally, it is possible that our analysis identified DMRs that are independently modifiable by both genetic and environmental exposures. Thus, regions of the genome that are differentially methylated due to genetic polymorphisms may also be more susceptible to differential methylation due to environmental exposures.

Thus, inclusion of relevant social and environmental exposures in studies of methylation may help elucidate racial/ethnic disparities in disease prevalence, health outcomes and therapeutic response. However, in many cases, a detailed environmental exposure history is unknown, unmeasurable or poorly quantifiable, and race/ethnicity may be a useful, albeit imperfect proxy. However, if a comprehensive catalog of the effects of exposures can be compiled, it may be possible to use genome-wide methylation analysis as a biomarker of exposure long after the exposure has passed and can no longer be measured.

Our comprehensive analysis of high-density methyl- and genotyping from genomic DNA allowed us to investigate the genetic control of methylation in great detail and without the potential destabilizing effects of EBV transformation and culture in cell lines (Grafodatskaya et al., 2010). The strongest patterns of methylation are associated with cell composition in whole blood (Lam et al., 2012). However, the specific type of Latino ethnic-subgroups (Puerto Rican, Mexican, other, or mixed) is also associated with principal coordinates of genome-wide methylation.

Our approach has some potential limitations. It is possible that fine-scale population structure (sub-continental ancestry) within European, African, and Native American populations may contribute to ethnic differences in methylation, as we had previously reported in the case of lung function (Moreno-Estrada et al., 2014). However, despite the presence of additional substructure among the GALA II participants, PC’s 3–10 explained the association between ethnicity and ancestry at only 51 loci. PCs from chip-based genotypes will not capture all forms of genetic variation. Clusters of ethnicity specific rare variants of large effect or strong ethnicity-specific selective sweeps in the last 8–12 generations (Galanter et al., 2012) could also give rise to methylation differences, but these are inconsistent with existing rare variant and selection analyses (Hernandez et al., 2011; Tang et al., 2007). Our models of genetic ancestry assumed a linear effect of ancestry on methylation, whereas a nonlinear association or other model misspecification could have led to incomplete adjustment for genetic ancestry, and thus, led to a residual association between ethnicity and methylation. However, when we added second and third order polynomials or cubic splines to our models, we found evidence for a nonlinear association between ancestry and methylation at only 25 and 26 loci, respectively, and it did not affect the association between ethnicity and methylation. Although it is impossible to account for all types of non-linearity and non-additivity (such as gene by gene or gene by environment interaction), our analysis suggests that non-linear effects are unlikely to be significant. Since our study was geographically diverse, recruiting participants at five recruitment sites in the United States and Puerto Rico, it is possible that systematic differences associated with site of recruitment might have influenced observed methylation differences between ethnic groups. However, when we included recruitment site as a covariate, we found no significant effect on methylation independent of ethnicity.

The presence of a strong association between genetic ancestry and methylation raises the possibility that epigenetic studies can be confounded by population stratification, similar to genetic association studies, and that adjustment for either genetic ancestry or selected principal components is warranted. This possibility was first demonstrated in a previous analysis of the association between self-described race and methylation (Barfield et al., 2014). However, the study only evaluated two distinct racial groups (African Americans and Whites), while the present study demonstrates the possibility of population stratification in an admixed and heterogeneous population with participants from diverse Latino national origins. The tendency to consider Latinos as a homogenous or monolithic ethnic group makes any analysis of this population particularly challenging. Our finding of loci whose methylation patterns differed between Latino ethnic subgroups, even after adjusting for genetic ancestry, suggests that any analysis of these populations in disease-association studies without adjusting for ethnic heterogeneity is likely to result in spurious associations even after controlling for genomic ancestry. However, the methylation loci identified in this study, as well as studies of environmental exposures, could be particularly interesting loci for the study of biomedical outcomes, particularly those with disparate prevalence between racial/ethnic groups, such as asthma (Barr et al., 2016). If methylation loci associated with ethnicity or ancestry were shown to be associated with a biomedical outcome, it could help explain racial/ethnic disparities in disease.

In summary, this study provides a framework for understanding how genetic, social and environmental factors can contribute to systematic differences in methylation patterns between ethnic subgroups, even between presumably closely related populations such as Puerto Ricans and Mexicans. Methylation QTL’s whose allele frequency varies by ancestry lead to an association between local ancestry and methylation level. This, in turn, leads to systematic variation in methylation patterns by ancestry, which then contributes to ethnic differences in genome-wide patterns of methylation. However, although genetic ancestry has been used to adjust for confounding in genetic studies, and can account for much of the ethnic differences in methylation in this study, ethnic identity is associated with methylation beyond the effects of shared genetic ancestry. This is likely due to social and environmental effects captured by ethnicity. Indeed, we find that CpG sites known to be influenced by social and environmental exposures are also differentially methylated between ethnic subgroups. These findings called attention to a more complete understanding of the effect of social and environmental variables on methylation in the context of race and ethnicity to fully understanding this complex process.

Our findings have important implications for the independent and joint effects of race, ethnicity, and genetic ancestry in biomedical research and clinical practice, especially in studies conducted in diverse or admixed populations. Our conclusions may be generalizable to any population that is racially mixed such as those from South Africa, India, and Brazil, though we would encourage further study in diverse populations, and likely has implications for all studies of diverse populations. As the National Institutes of Health (NIH) embarks on a precision medicine initiative, this research underscores the importance of including diverse populations and studying factors capturing the influence of social, cultural, and environmental factors, in addition to genetic ones, upon disparities in disease and drug response.

1. 1. Burchard EG

   (2016) Differential DNA methylation in Latino population

   Publicly available at the NCBI Gene Expression Omnibus (accession no. GSE77716).

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77716
2. 1. Burchard EG

   (2016) Genes-Environment and Ancestry in Latino Americans

   Publicly available at NCBI dbGaP (accession no. phs001180.v1.p1).

   https://www.ncbi.nlm.nih.gov/gap/?term=phs001180

1. 1. Alexander DH
   2. Novembre J
   3. Lange K

   (2009) Fast model-based estimation of ancestry in unrelated individuals

   Genome Research 19:1655–1664.

   https://doi.org/10.1101/gr.094052.109

   • PubMed
   • Google Scholar
2. 1. Baran Y
   2. Pasaniuc B
   3. Sankararaman S
   4. Torgerson DG
   5. Gignoux C
   6. Eng C
   7. Rodriguez-Cintron W
   8. Chapela R
   9. Ford JG
   10. Avila PC
   11. Rodriguez-Santana J
   12. Burchard EG
   13. Halperin E

   (2012) Fast and accurate inference of local ancestry in Latino populations

   Bioinformatics 28:1359–1367.

   https://doi.org/10.1093/bioinformatics/bts144

   • PubMed
   • Google Scholar
3. 1. Barfield RT
   2. Almli LM
   3. Kilaru V
   4. Smith AK
   5. Mercer KB
   6. Duncan R
   7. Klengel T
   8. Mehta D
   9. Binder EB
   10. Epstein MP
   11. Ressler KJ
   12. Conneely KN

   (2014) Accounting for population stratification in DNA methylation studies

   Genetic Epidemiology 38:231–241.

   https://doi.org/10.1002/gepi.21789

   • PubMed
   • Google Scholar
4. 1. Barr RG
   2. Avilés-Santa L
   3. Davis SM
   4. Aldrich TK
   5. Gonzalez F
   6. Henderson AG
   7. Kaplan RC
   8. LaVange L
   9. Liu K
   10. Loredo JS
   11. Mendes ES
   12. Ni A
   13. Ries A
   14. Salathe M
   15. Smith LJ

   (2016) Pulmonary disease and age at immigration among hispanics. results from the hispanic community health study study of latinos

   American Journal of Respiratory and Critical Care Medicine 193:386–395.

   https://doi.org/10.1164/rccm.201506-1211OC

   • PubMed
   • Google Scholar
5. 1. Bell CG
   2. Finer S
   3. Lindgren CM
   4. Wilson GA
   5. Rakyan VK
   6. Teschendorff AE
   7. Akan P
   8. Stupka E
   9. Down TA
   10. Prokopenko I
   11. Morison IM
   12. Mill J
   13. Pidsley R
   14. Deloukas P
   15. Frayling TM
   16. Hattersley AT
   17. McCarthy MI
   18. Beck S
   19. Hitman GA
   20. International Type 2 Diabetes 1q Consortium

   (2010) Integrated genetic and epigenetic analysis identifies haplotype-specific methylation in the FTO type 2 diabetes and obesity susceptibility locus

   PloS One 5:e14040.

   https://doi.org/10.1371/journal.pone.0014040

   • PubMed
   • Google Scholar
6. 1. Bell JT
   2. Pai AA
   3. Pickrell JK
   4. Gaffney DJ
   5. Pique-Regi R
   6. Degner JF
   7. Gilad Y
   8. Pritchard JK

   (2011) DNA methylation patterns associate with genetic and gene expression variation in HapMap cell lines

   Genome Biology 12:R10.

   https://doi.org/10.1186/gb-2011-12-1-r10

   • PubMed
   • Google Scholar
7. 1. Borghol N
   2. Suderman M
   3. McArdle W
   4. Racine A
   5. Hallett M
   6. Pembrey M
   7. Hertzman C
   8. Power C
   9. Szyf M

   (2012) Associations with early-life socio-economic position in adult DNA methylation

   International Journal of Epidemiology 41:62–74.

   https://doi.org/10.1093/ije/dyr147

   • PubMed
   • Google Scholar
8. 1. Borrell LN

   (2005) Racial identity among Hispanics: implications for health and well-being

   American Journal of Public Health 95:379–381.

   https://doi.org/10.2105/AJPH.2004.058172

   • PubMed
   • Google Scholar
9. 1. Bryc K
   2. Velez C
   3. Karafet T
   4. Moreno-Estrada A
   5. Reynolds A
   6. Auton A
   7. Hammer M
   8. Bustamante CD
   9. Ostrer H

   (2010) Colloquium paper: genome-wide patterns of population structure and admixture among Hispanic/Latino populations

   PNAS 107 Suppl 2:8954–8961.

   https://doi.org/10.1073/pnas.0914618107

   • PubMed
   • Google Scholar
10. 1. Burchard EG
    2. Ziv E
    3. Coyle N
    4. Gomez SL
    5. Tang H
    6. Karter AJ
    7. Mountain JL
    8. Pérez-Stable EJ
    9. Sheppard D
    10. Risch N

    (2003) The importance of race and ethnic background in biomedical research and clinical practice

    New England Journal of Medicine 348:1170–1175.

    https://doi.org/10.1056/NEJMsb025007

    • PubMed
    • Google Scholar
11. 1. Chambers JC
    2. Loh M
    3. Lehne B
    4. Drong A
    5. Kriebel J
    6. Motta V
    7. Wahl S
    8. Elliott HR
    9. Rota F
    10. Scott WR
    11. Zhang W
    12. Tan ST
    13. Campanella G
    14. Chadeau-Hyam M
    15. Yengo L
    16. Richmond RC
    17. Adamowicz-Brice M
    18. Afzal U
    19. Bozaoglu K
    20. Mok ZY
    21. Ng HK
    22. Pattou F
    23. Prokisch H
    24. Rozario MA
    25. Tarantini L
    26. Abbott J
    27. Ala-Korpela M
    28. Albetti B
    29. Ammerpohl O
    30. Bertazzi PA
    31. Blancher C
    32. Caiazzo R
    33. Danesh J
    34. Gaunt TR
    35. de Lusignan S
    36. Gieger C
    37. Illig T
    38. Jha S
    39. Jones S
    40. Jowett J
    41. Kangas AJ
    42. Kasturiratne A
    43. Kato N
    44. Kotea N
    45. Kowlessur S
    46. Pitkäniemi J
    47. Punjabi P
    48. Saleheen D
    49. Schafmayer C
    50. Soininen P
    51. Tai ES
    52. Thorand B
    53. Tuomilehto J
    54. Wickremasinghe AR
    55. Kyrtopoulos SA
    56. Aitman TJ
    57. Herder C
    58. Hampe J
    59. Cauchi S
    60. Relton CL
    61. Froguel P
    62. Soong R
    63. Vineis P
    64. Jarvelin MR
    65. Scott J
    66. Grallert H
    67. Bollati V
    68. Elliott P
    69. McCarthy MI
    70. Kooner JS

    (2015) Epigenome-wide association of DNA methylation markers in peripheral blood from Indian Asians and Europeans with incident type 2 diabetes: a nested case-control study

    The Lancet Diabetes & Endocrinology 3:526–534.

    https://doi.org/10.1016/S2213-8587(15)00127-8

    • PubMed
    • Google Scholar
12. 1. Chen W
    2. Boutaoui N
    3. Brehm JM
    4. Han YY
    5. Schmitz C
    6. Cressley A
    7. Acosta-Pérez E
    8. Alvarez M
    9. Colón-Semidey A
    10. Baccarelli AA
    11. Weeks DE
    12. Kolls JK
    13. Canino G
    14. Celedón JC

    (2013) ADCYAP1R1 and asthma in Puerto Rican children

    American Journal of Respiratory and Critical Care Medicine 187:584–588.

    https://doi.org/10.1164/rccm.201210-1789OC

    • PubMed
    • Google Scholar
13. 1. Cooper RS
    2. Kaufman JS
    3. Ward R

    (2003) Race and genomics

    New England Journal of Medicine 348:1166–1170.

    https://doi.org/10.1056/NEJMsb022863

    • PubMed
    • Google Scholar
14. 1. Du P
    2. Zhang X
    3. Huang CC
    4. Jafari N
    5. Kibbe WA
    6. Hou L
    7. Lin SM

    (2010) Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis

    BMC Bioinformatics 11:587.

    https://doi.org/10.1186/1471-2105-11-587

    • PubMed
    • Google Scholar
15. 1. Evans GW
    2. Kantrowitz E

    (2002) Socioeconomic status and health: the potential role of environmental risk exposure

    Annual Review of Public Health 23:303–331.

    https://doi.org/10.1146/annurev.publhealth.23.112001.112349

    • PubMed
    • Google Scholar
16. 1. Feil R
    2. Fraga MF

    (2012) Epigenetics and the environment: emerging patterns and implications

    Nature Reviews Genetics 13:97–109.

    https://doi.org/10.1038/nrg3142

    • PubMed
    • Google Scholar
17. 1. Florez JC
    2. Price AL
    3. Campbell D
    4. Riba L
    5. Parra MV
    6. Yu F
    7. Duque C
    8. Saxena R
    9. Gallego N
    10. Tello-Ruiz M
    11. Franco L
    12. Rodríguez-Torres M
    13. Villegas A
    14. Bedoya G
    15. Aguilar-Salinas CA
    16. Tusié-Luna MT
    17. Ruiz-Linares A

    (2011) Strong Association of Socioeconomic Status and Genetic Ancestry in Latinos: Implications for Admixture Studies of Type 2 Diabetes

    Racial Identities, Genetic Ancestry, and Health in South America pp. 137–153.

    https://doi.org/10.1057/9781137001702_7

    • Google Scholar
18. 1. Galanter JM
    2. Fernandez-Lopez JC
    3. Gignoux CR
    4. Barnholtz-Sloan J
    5. Fernandez-Rozadilla C
    6. Via M
    7. Hidalgo-Miranda A
    8. Contreras AV
    9. Figueroa LU
    10. Raska P
    11. Jimenez-Sanchez G
    12. Zolezzi IS
    13. Torres M
    14. Ponte CR
    15. Ruiz Y
    16. Salas A
    17. Nguyen E
    18. Eng C
    19. Borjas L
    20. Zabala W
    21. Barreto G
    22. González FR
    23. Ibarra A
    24. Taboada P
    25. Porras L
    26. Moreno F
    27. Bigham A
    28. Gutierrez G
    29. Brutsaert T
    30. León-Velarde F
    31. Moore LG
    32. Vargas E
    33. Cruz M
    34. Escobedo J
    35. Rodriguez-Santana J
    36. Rodriguez-Cintrón W
    37. Chapela R
    38. Ford JG
    39. Bustamante C
    40. Seminara D
    41. Shriver M
    42. Ziv E
    43. Burchard EG
    44. Haile R
    45. Parra E
    46. Carracedo A
    47. Silva Zolezzi I
    48. Rondón González F
    49. Gonzalez Burchard E
    50. LACE Consortium

    (2012) Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas

    PLoS Genetics 8:e1002554.

    https://doi.org/10.1371/journal.pgen.1002554

    • PubMed
    • Google Scholar
19. 1. Galanter JM
    2. Gignoux CR
    3. Torgerson DG
    4. Roth LA
    5. Eng C
    6. Oh SS
    7. Nguyen EA
    8. Drake KA
    9. Huntsman S
    10. Hu D
    11. Sen S
    12. Davis A
    13. Farber HJ
    14. Avila PC
    15. Brigino-Buenaventura E
    16. LeNoir MA
    17. Meade K
    18. Serebrisky D
    19. Borrell LN
    20. Rodríguez-Cintrón W
    21. Estrada AM
    22. Mendoza KS
    23. Winkler CA
    24. Klitz W
    25. Romieu I
    26. London SJ
    27. Gilliland F
    28. Martinez F
    29. Bustamante C
    30. Williams LK
    31. Kumar R
    32. Rodríguez-Santana JR
    33. Burchard EG

    (2014) Genome-wide association study and admixture mapping identify different asthma-associated loci in Latinos: the Genes-environments & Admixture in Latino Americans study

    Journal of Allergy and Clinical Immunology 134:295–305.

    https://doi.org/10.1016/j.jaci.2013.08.055

    • PubMed
    • Google Scholar
20. 1. González Burchard E
    2. Borrell LN
    3. Choudhry S
    4. Naqvi M
    5. Tsai HJ
    6. Rodriguez-Santana JR
    7. Chapela R
    8. Rogers SD
    9. Mei R
    10. Rodriguez-Cintron W
    11. Arena JF
    12. Kittles R
    13. Perez-Stable EJ
    14. Ziv E
    15. Risch N

    (2005) Latino populations: a unique opportunity for the study of race, genetics, and social environment in epidemiological research

    American Journal of Public Health 95:2161–2168.

    https://doi.org/10.2105/AJPH.2005.068668

    • PubMed
    • Google Scholar
21. 1. Grafodatskaya D
    2. Choufani S
    3. Ferreira JC
    4. Butcher DT
    5. Lou Y
    6. Zhao C
    7. Scherer SW
    8. Weksberg R

    (2010) EBV transformation and cell culturing destabilizes DNA methylation in human lymphoblastoid cell lines

    Genomics 95:73–83.

    https://doi.org/10.1016/j.ygeno.2009.12.001

    • PubMed
    • Google Scholar
22. 1. Hankinson JL
    2. Odencrantz JR
    3. Fedan KB

    (1999) Spirometric reference values from a sample of the general U.S. population

    American Journal of Respiratory and Critical Care Medicine 159:179–187.

    https://doi.org/10.1164/ajrccm.159.1.9712108

    • PubMed
    • Google Scholar
23. 1. Hernandez RD
    2. Kelley JL
    3. Elyashiv E
    4. Melton SC
    5. Auton A
    6. McVean G
    7. Sella G
    8. Przeworski M
    9. 1000 Genomes Project

    (2011) Classic selective sweeps were rare in recent human evolution

    Science 331:920–924.

    https://doi.org/10.1126/science.1198878

    • PubMed
    • Google Scholar
24. 1. Ho SM
    2. Johnson A
    3. Tarapore P
    4. Janakiram V
    5. Zhang X
    6. Leung YK

    (2012) Environmental epigenetics and its implication on disease risk and health outcomes

    ILAR Journal 53:289–305.

    https://doi.org/10.1093/ilar.53.3-4.289

    • PubMed
    • Google Scholar
25. 1. Hoffmann TJ
    2. Zhan Y
    3. Kvale MN
    4. Hesselson SE
    5. Gollub J
    6. Iribarren C
    7. Lu Y
    8. Mei G
    9. Purdy MM
    10. Quesenberry C
    11. Rowell S
    12. Shapero MH
    13. Smethurst D
    14. Somkin CP
    15. Van den Eeden SK
    16. Walter L
    17. Webster T
    18. Whitmer RA
    19. Finn A
    20. Schaefer C
    21. Kwok PY
    22. Risch N

    (2011) Design and coverage of high throughput genotyping arrays optimized for individuals of East Asian, African American, and Latino race/ethnicity using imputation and a novel hybrid SNP selection algorithm

    Genomics 98:422–430.

    https://doi.org/10.1016/j.ygeno.2011.08.007

    • PubMed
    • Google Scholar
26. 1. Houseman EA
    2. Accomando WP
    3. Koestler DC
    4. Christensen BC
    5. Marsit CJ
    6. Nelson HH
    7. Wiencke JK
    8. Kelsey KT

    (2012) DNA methylation arrays as surrogate measures of cell mixture distribution

    BMC Bioinformatics 13:86.

    https://doi.org/10.1186/1471-2105-13-86

    • PubMed
    • Google Scholar
27. 1. Jiang R
    2. Jones MJ
    3. Sava F
    4. Kobor MS
    5. Carlsten C

    (2014) Short-term diesel exhaust inhalation in a controlled human crossover study is associated with changes in DNA methylation of circulating mononuclear cells in asthmatics

    Particle and Fibre Toxicology 11:71.

    https://doi.org/10.1186/s12989-014-0071-3

    • PubMed
    • Google Scholar
28. 1. Joubert BR
    2. Felix JF
    3. Yousefi P
    4. Bakulski KM
    5. Just AC
    6. Breton C
    7. Reese SE
    8. Markunas CA
    9. Richmond RC
    10. Xu CJ
    11. Küpers LK
    12. Oh SS
    13. Hoyo C
    14. Gruzieva O
    15. Söderhäll C
    16. Salas LA
    17. Baïz N
    18. Zhang H
    19. Lepeule J
    20. Ruiz C
    21. Ligthart S
    22. Wang T
    23. Taylor JA
    24. Duijts L
    25. Sharp GC
    26. Jankipersadsing SA
    27. Nilsen RM
    28. Vaez A
    29. Fallin MD
    30. Hu D
    31. Litonjua AA
    32. Fuemmeler BF
    33. Huen K
    34. Kere J
    35. Kull I
    36. Munthe-Kaas MC
    37. Gehring U
    38. Bustamante M
    39. Saurel-Coubizolles MJ
    40. Quraishi BM
    41. Ren J
    42. Tost J
    43. Gonzalez JR
    44. Peters MJ
    45. Håberg SE
    46. Xu Z
    47. van Meurs JB
    48. Gaunt TR
    49. Kerkhof M
    50. Corpeleijn E
    51. Feinberg AP
    52. Eng C
    53. Baccarelli AA
    54. Benjamin Neelon SE
    55. Bradman A
    56. Merid SK
    57. Bergström A
    58. Herceg Z
    59. Hernandez-Vargas H
    60. Brunekreef B
    61. Pinart M
    62. Heude B
    63. Ewart S
    64. Yao J
    65. Lemonnier N
    66. Franco OH
    67. Wu MC
    68. Hofman A
    69. McArdle W
    70. Van der Vlies P
    71. Falahi F
    72. Gillman MW
    73. Barcellos LF
    74. Kumar A
    75. Wickman M
    76. Guerra S
    77. Charles MA
    78. Holloway J
    79. Auffray C
    80. Tiemeier HW
    81. Smith GD
    82. Postma D
    83. Hivert MF
    84. Eskenazi B
    85. Vrijheid M
    86. Arshad H
    87. Antó JM
    88. Dehghan A
    89. Karmaus W
    90. Annesi-Maesano I
    91. Sunyer J
    92. Ghantous A
    93. Pershagen G
    94. Holland N
    95. Murphy SK
    96. DeMeo DL
    97. Burchard EG
    98. Ladd-Acosta C
    99. Snieder H
    100. Nystad W
    101. Koppelman GH
    102. Relton CL
    103. Jaddoe VW
    104. Wilcox A
    105. Melén E
    106. London SJ

    (2016) DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis

    The American Journal of Human Genetics 98:680–696.

    https://doi.org/10.1016/j.ajhg.2016.02.019

    • PubMed
    • Google Scholar
29. 1. Joubert BR
    2. Håberg SE
    3. Nilsen RM
    4. Wang X
    5. Vollset SE
    6. Murphy SK
    7. Huang Z
    8. Hoyo C
    9. Midttun Ø
    10. Cupul-Uicab LA
    11. Ueland PM
    12. Wu MC
    13. Nystad W
    14. Bell DA
    15. Peddada SD
    16. London SJ

    (2012) 450K epigenome-wide scan identifies differential DNA methylation in newborns related to maternal smoking during pregnancy

    Environmental Health Perspectives 120:1425–1431.

    https://doi.org/10.1289/ehp.1205412

    • PubMed
    • Google Scholar
30. 1. Kato N
    2. Loh M
    3. Takeuchi F
    4. Verweij N
    5. Wang X
    6. Zhang W
    7. Kelly TN
    8. Saleheen D
    9. Lehne B
    10. Mateo Leach I
    11. Drong AW
    12. Abbott J
    13. Wahl S
    14. Tan ST
    15. Scott WR
    16. Campanella G
    17. Chadeau-Hyam M
    18. Afzal U
    19. Ahluwalia TS
    20. Bonder MJ
    21. Chen P
    22. Dehghan A
    23. Edwards TL
    24. Esko T
    25. Go MJ
    26. Harris SE
    27. Hartiala J
    28. Kasela S
    29. Kasturiratne A
    30. Khor CC
    31. Kleber ME
    32. Li H
    33. Mok ZY
    34. Nakatochi M
    35. Sapari NS
    36. Saxena R
    37. Stewart AF
    38. Stolk L
    39. Tabara Y
    40. Teh AL
    41. Wu Y
    42. Wu JY
    43. Zhang Y
    44. Aits I
    45. Da Silva Couto Alves A
    46. Das S
    47. Dorajoo R
    48. Hopewell JC
    49. Kim YK
    50. Koivula RW
    51. Luan J
    52. Lyytikäinen LP
    53. Nguyen QN
    54. Pereira MA
    55. Postmus I
    56. Raitakari OT
    57. Bryan MS
    58. Scott RA
    59. Sorice R
    60. Tragante V
    61. Traglia M
    62. White J
    63. Yamamoto K
    64. Zhang Y
    65. Adair LS
    66. Ahmed A
    67. Akiyama K
    68. Asif R
    69. Aung T
    70. Barroso I
    71. Bjonnes A
    72. Braun TR
    73. Cai H
    74. Chang LC
    75. Chen CH
    76. Cheng CY
    77. Chong YS
    78. Collins R
    79. Courtney R
    80. Davies G
    81. Delgado G
    82. Do LD
    83. Doevendans PA
    84. Gansevoort RT
    85. Gao YT
    86. Grammer TB
    87. Grarup N
    88. Grewal J
    89. Gu D
    90. Wander GS
    91. Hartikainen AL
    92. Hazen SL
    93. He J
    94. Heng CK
    95. Hixson JE
    96. Hofman A
    97. Hsu C
    98. Huang W
    99. Husemoen LL
    100. Hwang JY
    101. Ichihara S
    102. Igase M
    103. Isono M
    104. Justesen JM
    105. Katsuya T
    106. Kibriya MG
    107. Kim YJ
    108. Kishimoto M
    109. Koh WP
    110. Kohara K
    111. Kumari M
    112. Kwek K
    113. Lee NR
    114. Lee J
    115. Liao J
    116. Lieb W
    117. Liewald DC
    118. Matsubara T
    119. Matsushita Y
    120. Meitinger T
    121. Mihailov E
    122. Milani L
    123. Mills R
    124. Mononen N
    125. Müller-Nurasyid M
    126. Nabika T
    127. Nakashima E
    128. Ng HK
    129. Nikus K
    130. Nutile T
    131. Ohkubo T
    132. Ohnaka K
    133. Parish S
    134. Paternoster L
    135. Peng H
    136. Peters A
    137. Pham ST
    138. Pinidiyapathirage MJ
    139. Rahman M
    140. Rakugi H
    141. Rolandsson O
    142. Rozario MA
    143. Ruggiero D
    144. Sala CF
    145. Sarju R
    146. Shimokawa K
    147. Snieder H
    148. Sparsø T
    149. Spiering W
    150. Starr JM
    151. Stott DJ
    152. Stram DO
    153. Sugiyama T
    154. Szymczak S
    155. Tang WH
    156. Tong L
    157. Trompet S
    158. Turjanmaa V
    159. Ueshima H
    160. Uitterlinden AG
    161. Umemura S
    162. Vaarasmaki M
    163. van Dam RM
    164. van Gilst WH
    165. van Veldhuisen DJ
    166. Viikari JS
    167. Waldenberger M
    168. Wang Y
    169. Wang A
    170. Wilson R
    171. Wong TY
    172. Xiang YB
    173. Yamaguchi S
    174. Ye X
    175. Young RD
    176. Young TL
    177. Yuan JM
    178. Zhou X
    179. Asselbergs FW
    180. Ciullo M
    181. Clarke R
    182. Deloukas P
    183. Franke A
    184. Franks PW
    185. Franks S
    186. Friedlander Y
    187. Gross MD
    188. Guo Z
    189. Hansen T
    190. Jarvelin MR
    191. Jørgensen T
    192. Jukema JW
    193. Kähönen M
    194. Kajio H
    195. Kivimaki M
    196. Lee JY
    197. Lehtimäki T
    198. Linneberg A
    199. Miki T
    200. Pedersen O
    201. Samani NJ
    202. Sørensen TI
    203. Takayanagi R
    204. Toniolo D
    205. Ahsan H
    206. Allayee H
    207. Chen YT
    208. Danesh J
    209. Deary IJ
    210. Franco OH
    211. Franke L
    212. Heijman BT
    213. Holbrook JD
    214. Isaacs A
    215. Kim BJ
    216. Lin X
    217. Liu J
    218. März W
    219. Metspalu A
    220. Mohlke KL
    221. Sanghera DK
    222. Shu XO
    223. van Meurs JB
    224. Vithana E
    225. Wickremasinghe AR
    226. Wijmenga C
    227. Wolffenbuttel BH
    228. Yokota M
    229. Zheng W
    230. Zhu D
    231. Vineis P
    232. Kyrtopoulos SA
    233. Kleinjans JC
    234. McCarthy MI
    235. Soong R
    236. Gieger C
    237. Scott J
    238. Teo YY
    239. He J
    240. Elliott P
    241. Tai ES
    242. van der Harst P
    243. Kooner JS
    244. Chambers JC
    245. BIOS-consortium
    246. CARDIo GRAMplusCD
    247. LifeLines Cohort Study
    248. InterAct Consortium

    (2015) Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation

    Nature Genetics 47:1282–1293.

    https://doi.org/10.1038/ng.3405

    • PubMed
    • Google Scholar
31. 1. Kulis M
    2. Esteller M

    (2010) DNA methylation and cancer

    Advances in Genetics 70:27–56.

    https://doi.org/10.1016/B978-0-12-380866-0.60002-2

    • PubMed
    • Google Scholar
32. 1. Kumar R
    2. Seibold MA
    3. Aldrich MC
    4. Williams LK
    5. Reiner AP
    6. Colangelo L
    7. Galanter J
    8. Gignoux C
    9. Hu D
    10. Sen S
    11. Choudhry S
    12. Peterson EL
    13. Rodriguez-Santana J
    14. Rodriguez-Cintron W
    15. Nalls MA
    16. Leak TS
    17. O'Meara E
    18. Meibohm B
    19. Kritchevsky SB
    20. Li R
    21. Harris TB
    22. Nickerson DA
    23. Fornage M
    24. Enright P
    25. Ziv E
    26. Smith LJ
    27. Liu K
    28. Burchard EG

    (2010) Genetic ancestry in lung-function predictions

    New England Journal of Medicine 363:321–330.

    https://doi.org/10.1056/NEJMoa0907897

    • PubMed
    • Google Scholar
33. 1. Lam LL
    2. Emberly E
    3. Fraser HB
    4. Neumann SM
    5. Chen E
    6. Miller GE
    7. Kobor MS

    (2012) Factors underlying variable DNA methylation in a human community cohort

    PNAS 109 Suppl 2:17253–17260.

    https://doi.org/10.1073/pnas.1121249109

    • PubMed
    • Google Scholar
34. 1. Lardenoije R
    2. Iatrou A
    3. Kenis G
    4. Kompotis K
    5. Steinbusch HW
    6. Mastroeni D
    7. Coleman P
    8. Lemere CA
    9. Hof PR
    10. van den Hove DL
    11. Rutten BP

    (2015) The epigenetics of aging and neurodegeneration

    Progress in Neurobiology 131:21–64.

    https://doi.org/10.1016/j.pneurobio.2015.05.002

    • PubMed
    • Google Scholar
35. 1. Leng S
    2. Liu Y
    3. Thomas CL
    4. Gauderman WJ
    5. Picchi MA
    6. Bruse SE
    7. Zhang X
    8. Flores KG
    9. Van Den Berg D
    10. Stidley CA
    11. Gilliland FD
    12. Belinsky SA

    (2013) Native American ancestry affects the risk for gene methylation in the lungs of Hispanic smokers from New Mexico

    American Journal of Respiratory and Critical Care Medicine 188:1110–1116.

    https://doi.org/10.1164/rccm.201305-0925OC

    • PubMed
    • Google Scholar
36. 1. Liu Y
    2. Aryee MJ
    3. Padyukov L
    4. Fallin MD
    5. Hesselberg E
    6. Runarsson A
    7. Reinius L
    8. Acevedo N
    9. Taub M
    10. Ronninger M
    11. Shchetynsky K
    12. Scheynius A
    13. Kere J
    14. Alfredsson L
    15. Klareskog L
    16. Ekström TJ
    17. Feinberg AP

    (2013) Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis

    Nature Biotechnology 31:142–147.

    https://doi.org/10.1038/nbt.2487

    • PubMed
    • Google Scholar
37. 1. Michels KB
    2. Binder AM
    3. Dedeurwaerder S
    4. Epstein CB
    5. Greally JM
    6. Gut I
    7. Houseman EA
    8. Izzi B
    9. Kelsey KT
    10. Meissner A
    11. Milosavljevic A
    12. Siegmund KD
    13. Bock C
    14. Irizarry RA

    (2013) Recommendations for the design and analysis of epigenome-wide association studies

    Nature Methods 10:949–955.

    https://doi.org/10.1038/nmeth.2632

    • PubMed
    • Google Scholar
38. 1. Moreno-Estrada A
    2. Gignoux CR
    3. Fernández-López JC
    4. Zakharia F
    5. Sikora M
    6. Contreras AV
    7. Acuña-Alonzo V
    8. Sandoval K
    9. Eng C
    10. Romero-Hidalgo S
    11. Ortiz-Tello P
    12. Robles V
    13. Kenny EE
    14. Nuño-Arana I
    15. Barquera-Lozano R
    16. Macín-Pérez G
    17. Granados-Arriola J
    18. Huntsman S
    19. Galanter JM
    20. Via M
    21. Ford JG
    22. Chapela R
    23. Rodriguez-Cintron W
    24. Rodríguez-Santana JR
    25. Romieu I
    26. Sienra-Monge JJ
    27. del Rio Navarro B
    28. London SJ
    29. Ruiz-Linares A
    30. Garcia-Herrera R
    31. Estrada K
    32. Hidalgo-Miranda A
    33. Jimenez-Sanchez G
    34. Carnevale A
    35. Soberón X
    36. Canizales-Quinteros S
    37. Rangel-Villalobos H
    38. Silva-Zolezzi I
    39. Burchard EG
    40. Bustamante CD

    (2014) Human genetics. The genetics of Mexico recapitulates Native American substructure and affects biomedical traits

    Science 344:1280–1285.

    https://doi.org/10.1126/science.1251688

    • PubMed
    • Google Scholar
39. 1. Nalls MA
    2. Wilson JG
    3. Patterson NJ
    4. Tandon A
    5. Zmuda JM
    6. Huntsman S
    7. Garcia M
    8. Hu D
    9. Li R
    10. Beamer BA
    11. Patel KV
    12. Akylbekova EL
    13. Files JC
    14. Hardy CL
    15. Buxbaum SG
    16. Taylor HA
    17. Reich D
    18. Harris TB
    19. Ziv E

    (2008) Admixture mapping of white cell count: genetic locus responsible for lower white blood cell count in the Health ABC and Jackson Heart studies

    The American Journal of Human Genetics 82:81–87.

    https://doi.org/10.1016/j.ajhg.2007.09.003

    • PubMed
    • Google Scholar
40. 1. Nguyen AB
    2. Moser R
    3. Chou WY

    (2014) Race and health profiles in the United States: an examination of the social gradient through the 2009 CHIS adult survey

    Public Health 128:1076–1086.

    https://doi.org/10.1016/j.puhe.2014.10.003

    • PubMed
    • Google Scholar
41. 1. Nishimura KK
    2. Galanter JM
    3. Roth LA
    4. Oh SS
    5. Thakur N
    6. Nguyen EA
    7. Thyne S
    8. Farber HJ
    9. Serebrisky D
    10. Kumar R
    11. Brigino-Buenaventura E
    12. Davis A
    13. LeNoir MA
    14. Meade K
    15. Rodriguez-Cintron W
    16. Avila PC
    17. Borrell LN
    18. Bibbins-Domingo K
    19. Rodriguez-Santana JR
    20. Sen Ś
    21. Lurmann F
    22. Balmes JR
    23. Burchard EG

    (2013) Early-life air pollution and asthma risk in minority children. The GALA II and SAGE II studies

    American Journal of Respiratory and Critical Care Medicine 188:309–318.

    https://doi.org/10.1164/rccm.201302-0264OC

    • PubMed
    • Google Scholar
42. 1. Oh SS
    2. Tcheurekdjian H
    3. Roth LA
    4. Nguyen EA
    5. Sen S
    6. Galanter JM
    7. Davis A
    8. Farber HJ
    9. Gilliland FD
    10. Kumar R
    11. Avila PC
    12. Brigino-Buenaventura E
    13. Chapela R
    14. Ford JG
    15. LeNoir MA
    16. Lurmann F
    17. Meade K
    18. Serebrisky D
    19. Thyne S
    20. Rodriguez-Cintron W
    21. Rodriguez-Santana JR
    22. Williams LK
    23. Borrell LN
    24. Burchard EG

    (2012) Effect of secondhand smoke on asthma control among black and Latino children

    Journal of Allergy and Clinical Immunology 129:1478–1483.

    https://doi.org/10.1016/j.jaci.2012.03.017

    • PubMed
    • Google Scholar
43. 1. Patterson N
    2. Price AL
    3. Reich D

    (2006) Population structure and eigenanalysis

    PLoS Genetics 2:e190.

    https://doi.org/10.1371/journal.pgen.0020190

    • PubMed
    • Google Scholar
44. 1. Phimister EG

    (2003) Medicine and the racial divide

    New England Journal of Medicine 348:1081–1082.

    https://doi.org/10.1056/NEJMp030028

    • PubMed
    • Google Scholar
45. 1. Quanjer PH
    2. Stanojevic S
    3. Cole TJ
    4. Baur X
    5. Hall GL
    6. Culver BH
    7. Enright PL
    8. Hankinson JL
    9. Ip MS
    10. Zheng J
    11. Stocks J
    12. ERS Global Lung Function Initiative

    (2012) Multi-ethnic reference values for spirometry for the 3-95-yr age range: the global lung function 2012 equations

    European Respiratory Journal 40:1324–1343.

    https://doi.org/10.1183/09031936.00080312

    • PubMed
    • Google Scholar
46. Website

    1. R Core Team

    (2016) R: a language and environment for statistical computing

    http://www.R-project.org/
47. 1. Rahmani E
    2. Zaitlen N
    3. Baran Y
    4. Eng C
    5. Hu D
    6. Galanter J
    7. Oh S
    8. Burchard EG
    9. Eskin E
    10. Zou J
    11. Halperin E

    (2016) Sparse PCA corrects for cell type heterogeneity in epigenome-wide association studies

    Nature Methods 13:443–445.

    https://doi.org/10.1038/nmeth.3809

    • PubMed
    • Google Scholar
48. 1. Ressler KJ
    2. Mercer KB
    3. Bradley B
    4. Jovanovic T
    5. Mahan A
    6. Kerley K
    7. Norrholm SD
    8. Kilaru V
    9. Smith AK
    10. Myers AJ
    11. Ramirez M
    12. Engel A
    13. Hammack SE
    14. Toufexis D
    15. Braas KM
    16. Binder EB
    17. May V

    (2011) Post-traumatic stress disorder is associated with PACAP and the PAC1 receptor

    Nature 470:492–497.

    https://doi.org/10.1038/nature09856

    • PubMed
    • Google Scholar
49. 1. Risch N
    2. Burchard E
    3. Ziv E
    4. Tang H

    (2002)

    Categorization of humans in biomedical research: genes, race and disease

    Genome Biology 3:comment2007.

    • PubMed
    • Google Scholar
50. 1. Rosenberg NA
    2. Huang L
    3. Jewett EM
    4. Szpiech ZA
    5. Jankovic I
    6. Boehnke M

    (2010) Genome-wide association studies in diverse populations

    Nature Reviews Genetics 11:356–366.

    https://doi.org/10.1038/nrg2760

    • PubMed
    • Google Scholar
51. 1. Smith ZD
    2. Meissner A

    (2013) DNA methylation: roles in mammalian development

    Nature Reviews Genetics 14:204–220.

    https://doi.org/10.1038/nrg3354

    • PubMed
    • Google Scholar
52. 1. Tang H
    2. Choudhry S
    3. Mei R
    4. Morgan M
    5. Rodriguez-Cintron W
    6. Burchard EG
    7. Risch NJ

    (2007) Recent genetic selection in the ancestral admixture of Puerto Ricans

    The American Journal of Human Genetics 81:626–633.

    https://doi.org/10.1086/520769

    • PubMed
    • Google Scholar
53. 1. Thakur N
    2. Oh SS
    3. Nguyen EA
    4. Martin M
    5. Roth LA
    6. Galanter J
    7. Gignoux CR
    8. Eng C
    9. Davis A
    10. Meade K
    11. LeNoir MA
    12. Avila PC
    13. Farber HJ
    14. Serebrisky D
    15. Brigino-Buenaventura E
    16. Rodriguez-Cintron W
    17. Kumar R
    18. Williams LK
    19. Bibbins-Domingo K
    20. Thyne S
    21. Sen S
    22. Rodriguez-Santana JR
    23. Borrell LN
    24. Burchard EG

    (2013) Socioeconomic status and childhood asthma in urban minority youths. The GALA II and SAGE II studies

    American Journal of Respiratory and Critical Care Medicine 188:1202–1209.

    https://doi.org/10.1164/rccm.201306-1016OC

    • PubMed
    • Google Scholar
54. 1. Tingley D
    2. Yamamoto T
    3. Hirose K
    4. Keele L
    5. Imai K

    (2014) mediation: R package for causal mediation analysis

    Journal of Statistical Software 59:.

    https://doi.org/10.18637/jss.v059.i05

    • Google Scholar
55. 1. Udali S
    2. Guarini P
    3. Moruzzi S
    4. Choi SW
    5. Friso S

    (2013) Cardiovascular epigenetics: from DNA methylation to microRNAs

    Molecular Aspects of Medicine 34:883–901.

    https://doi.org/10.1016/j.mam.2012.08.001

    • PubMed
    • Google Scholar
56. 1. Udler MS
    2. Nadkarni GN
    3. Belbin G
    4. Lotay V
    5. Wyatt C
    6. Gottesman O
    7. Bottinger EP
    8. Kenny EE
    9. Peter I

    (2015) Effect of Genetic African Ancestry on eGFR and Kidney Disease

    Journal of the American Society of Nephrology 26:1682–1692.

    https://doi.org/10.1681/ASN.2014050474

    • PubMed
    • Google Scholar
57. 1. van der Knaap LJ
    2. Riese H
    3. Hudziak JJ
    4. Verbiest MM
    5. Verhulst FC
    6. Oldehinkel AJ
    7. van Oort FV

    (2014) Glucocorticoid receptor gene (NR3C1) methylation following stressful events between birth and adolescence. The TRAILS study

    Translational Psychiatry 4:e381.

    https://doi.org/10.1038/tp.2014.22

    • PubMed
    • Google Scholar
58. 1. Vidal AC
    2. Benjamin Neelon SE
    3. Liu Y
    4. Tuli AM
    5. Fuemmeler BF
    6. Hoyo C
    7. Murtha AP
    8. Huang Z
    9. Schildkraut J
    10. Overcash F
    11. Kurtzberg J
    12. Jirtle RL
    13. Iversen ES
    14. Murphy SK

    (2014) Maternal stress, preterm birth, and DNA methylation at imprint regulatory sequences in humans

    Genetics & Epigenetics 6:37–44.

    https://doi.org/10.4137/GEG.S18067

    • PubMed
    • Google Scholar
59. 1. Yudell M
    2. Roberts D
    3. DeSalle R
    4. Tishkoff S

    (2016) Science and society. Taking race out of human genetics

    Science 351:564–565.

    https://doi.org/10.1126/science.aac4951

    • PubMed
    • Google Scholar
60. 1. Ziv E
    2. John EM
    3. Choudhry S
    4. Kho J
    5. Lorizio W
    6. Perez-Stable EJ
    7. Burchard EG

    (2006) Genetic ancestry and risk factors for breast cancer among Latinas in the San Francisco Bay Area

    Cancer Epidemiology Biomarkers & Prevention 15:1878–1885.

    https://doi.org/10.1158/1055-9965.EPI-06-0092

    • PubMed
    • Google Scholar

Author details

1. Joshua M Galanter

   1. Department of Medicine, University of California, San Francisco, United States
   2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
   3. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Present address

   Genentech, South San Francisco, United States

   Contribution

   JMG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   galanter@protonmail.com

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-2561-6384

2. Christopher R Gignoux

   Department of Genetics, Stanford University, Stanford, United States

   Contribution

   CRG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-9728-6567

3. Sam S Oh

   1. Department of Medicine, University of California, San Francisco, United States
   2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States
   3. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, United States

   Contribution

   SSO, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-2815-6037

4. Dara Torgerson

   Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States

   Contribution

   DT, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Maria Pino-Yanes

   1. Hospital Universitario Nuestra Señora de Candelaria, Tenerife, Spain
   2. CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain

   Contribution

   MP-Y, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0003-0332-437X

6. Neeta Thakur

   Department of Medicine, University of California, San Francisco, United States

   Contribution

   NT, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0001-6126-6601

7. Celeste Eng

   Department of Medicine, University of California, San Francisco, United States

   Contribution

   CE, Acquisition of data

   Competing interests

   The authors declare that no competing interests exist.

8. Donglei Hu

   Department of Medicine, University of California, San Francisco, United States

   Contribution

   DH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

9. Scott Huntsman

   Department of Medicine, University of California, San Francisco, United States

   Contribution

   SH, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

10. Harold J Farber

    Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas

    Contribution

    HJF, Recruited participants, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Pedro C Avila

    Division of Allergy and Immunology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois

    Contribution

    PCA, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

12. Emerita Brigino-Buenaventura

    Kaiser Permanente-Vallejo Medical Center, Vallejo, United States

    Contribution

    EB-B, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

13. Michael A LeNoir

    Bay Area Pediatrics, Oakland, United States

    Contribution

    MAL, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

14. Kelly Meade

    Department of Pediatrics, Children’s Hospital and Research Center, Oakland, United States

    Contribution

    KM, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

15. Denise Serebrisky

    Jacobi Medical Center, Bronx, United States

    Contribution

    DS, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

16. William Rodríguez-Cintrón

    Veterans Caribbean Health System, San Juan, United States

    Contribution

    WR-C, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

17. Rajesh Kumar

    Division of Allergy and Immunology, The Ann and Robert H Lurie Children’s Hospital of Chicago, Chicago, United States

    Contribution

    RK, Recruited participants, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

18. Jose R Rodríguez-Santana

    Centro de Neumología Pediátrica, San Juan, United States

    Contribution

    JRR-S, Recruited participants, Acquisition of data

    Competing interests

    The authors declare that no competing interests exist.

19. Max A Seibold

    Center for Genes, Environment, and Health, Department of Pediatrics, National Jewish Health, Denver, United States

    Contribution

    MAS, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

20. Luisa N Borrell

    Graduate School of Public Health and Health Policy, City University of New York, New York, United States

    Contribution

    LNB, Recruited participants, Acquisition of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

21. Esteban G Burchard

    1. Department of Medicine, University of California, San Francisco, United States
    2. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, United States

    Contribution

    EGB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Contributed equally with

    Noah Zaitlen

    For correspondence

    esteban.burchard@ucsf.edu

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0001-7475-2035

22. Noah Zaitlen

    Department of Medicine, University of California, San Francisco, United States

    Contribution

    NZ, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Contributed equally with

    Esteban G Burchard

    For correspondence

    noah.zaitlen@ucsf.edu

    Competing interests

    The authors declare that no competing interests exist.

• 7,681

  views

• 1,256

  downloads

• 159

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.