HMG Advance Access published May 13, 2014 Genome-wide interaction studies reveal sex-specific asthma risk alleles Rachel A. Myers1,*, Nicole M. Scott1,17, W. James Gauderman2, Weiliang Qiu3, Rasika A. Mathias4, Isabelle Romieu5, Albert M. Levin6, Maria Pino-Yanes7,8, Penelope E. Graves9, Albino Barraza Villarreal10, Terri H. Beaty4, Vincent J. Carey3, Damien C. Croteau- Efrain Navarro-Olivos10, Badri Padhukasahasram12, Muhammad T. Salam2, Dara G. Torgerson7, David J. Van den Berg2, Hita Vora2, Eugene R. Bleecker13, Deborah A. Meyers13, L. Keoki Williams12,14, Fernando D. Martinez9, Esteban G. Burchard7, Kathleen C. Barnes6, on behalf of GRAAD, Frank D. Gilliland2, Scott T. Weiss3, Stephanie J. London15, Benjamin A. Raby3, Carole Ober1,†, Dan L. Nicolae1,16,†,* 1 Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA 2 Department of Preventative Medicine, University of Southern California, Los Angles, CA, 90089, USA 3 Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA 4 Departments of Medicine and Epidemiology, Johns Hopkins University, Baltimore, MD, 21205, USA 5 International Agency for Research on Cancer, Lyon, France 6 Department of Public Health Sciences, Henry Ford Health System, Detroit, MI, 48202, USA 7 Departments of Medicine and Bioengineering & Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94143, USA 8 CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain Published by Oxford University Press . This work is written by a US Government employee s and is in the public domain in the US. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Chonka3, Blanca del Rio Navarro11, Christopher Edlund2, Leticia Hernandez-Cadena10, 9 BIO5 Institute, Arizona Respiratory Care Center, University of Arizona, Tucson, AZ, 85721, USA 10 National Institute of Public Health of Mexico, Cuernavaca, Morelos, Mexico 11 Hospital Infantil de Mexico Federico Gómez, Mexico City, Mexico 12 Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, 13 Center for Genomics and Personalized Medicine, Wake Forest School of Medicine, Winston- Salem, NC, 27157, USA 14 Department of Internal Medicine, Henry Ford Health System, Detroit, MI, 48202, USA 15 National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, 27709, USA 16 * Departments of Statistics and Medicine, University of Chicago, Chicago, IL, 60637, USA To whom correspondence should be addressed: R.A.M.: Department of Human Genetics, University of Chicago, 920 East 58th St., CLSC 431F, Chicago, IL 60637, USA; phone: 773-7025898; fax 773-834-0505; email: ram330@gmail.com. D.L.N.: Department of Medicine, Section of Genetic Medicine, University of Chicago, 900 East 57th St., KCBD 3220, Chicago, IL 60637, USA; phone: 773-702-4837; fax: 773-702-9810; nicolae@galton.uchicago.edu. † These authors contributed equally to this work 17 Present address: Department of Ecology and Evolution, University of Chicago, Chicago, IL, 60637, USA Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 MI, 48202, USA Abstract Asthma is a complex disease with sex-specific differences in prevalence. Candidate gene studies have suggested that genotype-by-sex interaction effects on asthma risk exist, but this has not yet been explored at a genome-wide level. We aimed to identify sex-specific asthma risk alleles by performing a genome-wide scan for genotype-by-sex interactions in the ethnically specific genome-wide association studies (GWAS) in 2653 male asthma cases, 2566 female asthma cases, and 3830 non-asthma controls from European American, African American, African Caribbean, and Latino populations. Association tests were conducted in each study sample and the results were combined in ancestry-specific and cross-ancestry meta-analyses. Six sex-specific asthma risk loci had p-values <1x10-6, of which two were male-specific and four were female-specific; all were ancestry-specific. The most significant sex-specific association in European Americans was at the IRF1 locus on 5q31.1. We also identify a Latino female-specific association in RAP1GAP2. Both of these loci included single nucleotide polymorphisms (SNPs) that are known expression quantitative trait loci (eQTLs) and have been associated with asthma in independent studies. The IRF1 locus is a strong candidate region for male-specific asthma susceptibility due to the association and validation we demonstrate here, the known role of IRF1 in asthma-relevant immune pathways, and prior reports of sex-specific differences in interferon responses. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 diverse participants in the EVE Asthma Genetics Consortium. We performed male- and female- Introduction Many diverse human phenotypes are sexually dimorphic, including anatomical and morphological traits (e.g. height (1, 2), fat distribution (3)), immune response (4), risk for complex diseases (e.g. asthma (5), autoimmune disease (6, 7), psychiatric disorders (8) heart disease (9)), and gene expression traits (10). While X chromosome genes contribute to sex-specific phenotypic effects in humans (11, 12). Because the sequence of the autosomal genome does not differ between males and females, it is likely that variation affecting the gene expression plays an important role in determining sex-specific phenotypes. Such variation influencing phenotypes differently in males and females would not necessarily be discovered in genome-wide association studies (GWAS), where typically sex effects are regressed out in the analyses. Direct studies of genotype-by-sex interaction effects on common phenotypes and complex diseases have been limited, although some notable examples have emerged from studies of morphological traits (e.g. height, weight, body mass index, fat distribution (3, 13), bone mineral density (14)) and complex diseases (e.g. coronary artery disease and Crohn’s disease (15)). Thus, much of the regulatory variation influencing sexually dimorphic traits in humans remains largely unknown. The objective of our study was to characterize the sex-specific genetic architecture of asthma. Asthma is a common, heterogeneous disease affecting nearly 300 million people worldwide (16). Asthma prevalence shows an intriguing sex-specific architecture that varies by age: males are more likely to develop asthma during early childhood and females are more likely to develop asthma around the time of and following puberty (5). As a result, more boys have asthma pre-puberty and more women have persistent asthma throughout adult life. Furthermore, Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 phenotypic differences between males and females, variation in autosomal genes can also have sex-specific differences in developmental patterns of immune responses associated with the development of childhood-onset asthma have been reported (4, 17, 18). For example, serum IgE levels are higher in boys than girls (17); among children who wheeze, boys have higher cytokine responses than do girls in the first three years of life (18). In addition, variation in the IFNG gene have shown genotype-by-sex interaction effects on both IFN- response to LPS (endotoxin) in revealed genotype-by-sex interaction effects on asthma risk or asthma severity, including variation at the TSLP (20), ADRB2 (21), and KCNMB1 (22) loci. Suggestive evidence for genotype-by-sex interaction near the GRIA2 and TNFRSF11B loci for asthma risk by age 6 was found using a 2-step genome-wide scanning method (23). To date, no genome-wide study of asthma has reported statistically significant evidence for genotype-by-sex interactions (www.genome.gov/26525384). Here, we report the first meta-analysis of genome-wide genotype-by-sex interactions in asthma in ethnically diverse subjects from the EVE Consortium (24-26). We conducted metaanalyses of genome-wide genotype-by-sex associations in 2,653 male cases, 2,566 female asthma cases, and 3,830 controls from diverse North American populations and we identified variation associated with both sex-specific risks for asthma and sex-biased expression of nearby genes. Results Power analysis To investigate whether risk-alleles interact with sex on asthma risk we first evaluate the power to detect genotype-by-sex interactions in the EVE samples. We begin by considering three Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 the first year of life and asthma risk at age 6 (19). Other candidate gene studies have also general additive models of genotype-by-sex interactions; male-driven, where males have increased risk compared to females, female-driven, where females have increased risk compared to males, and flip-flop, where a given allele increases risk in one sex and is protective in the other sex. Given that the prevalence of asthma differs by sex, we hypothesize that asthma riskalleles follow either of the first two models and are sex-specific, i.e. the risk allele is at higher sex (the pooled controls). To test this hypothesis, we constructed the cases vs. pooled controls association test contrasting the cases of one sex to the combination of cases of the other sex and all controls. This design can be applied to both case-control and trio-design unlike other commonly used methods for testing genotype-by-environment interactions. We first explored the power of the cases vs. pooled controls approach to detect a male-specific association in each of the three ancestry groups and the combined sample in the EVE Consortium (Figure S1). Because the numbers of male and female cases in our study was similar, the power to detect male-specific associations should be similar to the power to detect female-specific associations. In each of the ancestry-specific analyses, we have >80% power to detect an Odds Ratio (OR) ≥ 1.75 for SNPs with minor allele frequency ≥ 0.1. In the combined sample, we have >80% power to detect an OR ≥ 1.5 with minor allele frequency ≥ 0.1 (Figure S1D). Next, we compared the power of the cases vs. pooled controls approach to two other commonly used methods for detecting additive genotype-by-sex interactions: 1) a case-only test directly comparing allele frequencies between male and female asthma cases and 2) including a genotype-by-sex interaction term in a logistic regression model to detect a male-specific effect with OR = 1.5. The cases vs. pooled controls approach is optimal for detecting male-specific associations because the approach included the controls, which are excluded in a case-only test, Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 frequency in the cases of one sex compared to all of the controls as well as the cases of the other and also included trio-design studies, to which logistic regression including the genotype-by-sex interaction term cannot be applied (Figure S2A). Finally, we compared the power of the cases vs. pooled controls approach and the two other interaction tests previously studied to detect associations under three different models; 1) a flip-flop effect (male OR = 1.5, female OR = 0.75), 2) an asthma main effect (OR = 1.5), and 3) a combination of an asthma main (OR = 1.5) The cases vs. pooled controls approach had nearly ideal power to detect flip-flop effects, small but non-zero power to detect main effects, and the highest power to detect the combination of main and sex-specific effects (Figure S2B-D). The cases vs. pooled controls approach is flexible and can be applied to both case-control and trio study designs. Additionally, it is powerful for detecting sex-specific as well as other genotype-by-sex interaction models, thus validating our decision to use the cases vs. pooled controls approach. Genotype-by-sex interaction analysis In each of the 11 EVE consortium samples (Table 1), we conducted two genome-wide genotype-by-sex interaction studies using the cases vs. pooled controls approach. The results of these sample-specific analyses were combined and meta-analyzed in each of three ancestry groups (European American: Nmale cases = 1052, Nfemale cases = 1019, Ncontrols = 1535; African American/African Caribbean: Nmale cases = 675, Nfemale cases = 837, Ncontrols = 1503; and Latino: Nmale cases = 926, Nfemale cases = 710, Ncontrols = 792) and in the combined sample (Nmale cases = 2653, Nfemale cases = 2566, Ncontrols = 3830), for a total of eight meta-analyses. Each meta-analysis has approximately 2.1 million imputed SNPs, which corresponds to a genome-wide significance threshold of 2.3x10-8. The Q-Q plots of the distributions of p-values in each meta-analysis are shown in Figure S3 with the inflation factor lambda noted in each figure. In all eight analyses, Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 and a male-specific effect (OR = 1.5, the sex effect is in the same direction as the main effect). the p-values closely follow the null (uniform) distribution, indicating there was little to no inflation. Although none of the associations reached the threshold for genome-wide significance, we observed six independent loci with at least one association p-value < 1x10-6 (Table 3, Figures 1, S4, and S5). Two of the six associations were male-specific (on chromosomes 5q31.1 and The most significant associations in European Americans were among males with SNPs in and near the interferon regulatory factor 1 (IRF1) gene on chromosome 5q31.1. The association signal at the IRF1 locus is supported by rs2549003 and 19 additional SNPs with p < 1x10-5; the direction of association is the same in each of the four European American samples (Figure 1AB). The risk allele frequency (RAF) at this marker was higher in male cases compared both to female cases and to male and female controls (Figure 1C). The most significant association in the African American/African Caribbean sample was also male-specific, with an intergenic SNP, rs17642749, on chromosome 10q26.11 between the genes EMX2 (338 kb, encodes empty spiracles homeobox 2) and RAB11FIP2 (117 kb, encodes Rab11 family-interacting protein 2, Figure S4). At this locus, the association was in the African Caribbean sample; this SNP was excluded in the other African American samples where the MAF of the associated SNP was less than 0.05. In the African Caribbean sample, the RAF was higher in male cases compared both to female cases and to male and female controls. Four associations had p < 1x10-6 in the female-specific analyses, one in the African American/African Caribbean sample (Figure S5A-C) and three in the Latino sample (Figure S5D-L). All four of these associations showed increased RAFs among female cases compared to both male cases and all controls, and the same direction of effect was seen in all samples within Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 10q26.1) and four were female-specific (on chromosomes 2q23.3, 2q34, 6q27, and 17p13.3). each group. The African American/African Caribbean association is with rs1012307, a SNP located in an uncharacterized gene, which may be a large intergenic non-coding RNA (AK057517), on chromosome 2q23.3 (Figure S5A). This association is supported by 16 additional SNPs with p < 10-5 (Figure S5B). The most significant association in the Latino females is with rs2675724, located on chromosome 6p27, 127 kb from C6orf118; 15 additional rs4673659, an intronic SNP in the ERBB4 gene encoding a cell surface receptor tyrosine-protein kinase. (Figure S5D-F). This association is supported by two additional SNPs with p < 1x10-5. Finally, the association with rs9895098 on 17p13.3 was supported by one additional SNP with p < 10-5. SNP rs9895098 is in the 3’ UTR of the RAP1GAP2 gene, which encodes a GTPase activating protein regulating dense granule secretion in platelets. Validation of associated SNPs in a published GWAS of asthma We assume some of the sex-specific associations may also be detected as attenuated sexaveraged main effects in large GWAS of asthma. We therefore mined the results of the largest and independent asthma study, the GABRIEL Consortium meta-analysis of asthma in European subjects (27), to validate the six most significant male- and female-specific associations. Two sex-specific associations in our study had SNPs in perfect linkage disequilibrium (LD; measured in the 1000 Genomes Project CEU samples) with p < 0.05 in the GABRIEL study: the European American male-specific association with rs2549003 near IRF1 (5q31.1, p = 0.00863) and the Latino female-specific association with rs9895098 in RAP1GAP2 (17p13.3, p = 0.0464). The remaining sex-specific associations either had a p > 0.05 (rs1012307, rs4673659, and rs2675725) or were not well tagged in the GABRIEL study (rs17642749). Associations between a microsatellite maker in the IRF1 gene on chromosome 5q31.1 and asthma have also been Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 SNPs have p < 1x10-5 in this region (Figure S5G-I). The association at the 2q34 locus is with reported in candidate gene studies in Taiwanese (28) and Japanese (29) populations. Furthermore, in a sex-stratified linkage analysis of asthma-related phenotypes in a European cohort, a male-specific linkage peak was detected on 5q31.1, including IRF1, for both lung function (FEV1/Height2) and allergen polysensitization (30). These results validated two of the six associations as asthma candidate loci. Our ability to validate sex-specific associations in the populations are part of EVE and already included in this study. Nonetheless, validating two sexspecific associations in published GWAS provides additional independent evidence that these two loci contribute to asthma susceptibility. Role of the associated SNPs in gene regulation We hypothesized that SNPs with sex-specific effects on asthma risk do so by modulating gene expression. To test this hypothesis, we first used results of seven published expression quantitative trait locus (eQTL) mapping studies in four different tissues or cell types to determine if the six asthma-associated SNPs in our study, or SNPs in strong LD with asthma-associated SNPs, are also associated with transcript abundance of nearby genes (i.e., cis eQTL). The same two SNPs validated in the GABRIEL study (rs2549003 on 5q31.1 and rs9895098 on 17p13.3) were also in high LD (r2 > 0.80) with reported cis eQTLs (Table 4); SNPs at the other four loci were not associated with transcript abundance in any of the published studies. SNP rs2549003 on 5q31.1 is in perfect LD with a cis eQTL for a specific IRF1 transcript (ENST00000245414) in LCLs from the European samples making up the 1000 Genomes Project (31). Additionally, rs2549003 was reported as a cis eQTL and was in LD with six SNPs that were reported as cis eQTLs for four of the eight genes at this locus, with the regulated gene varying by cell type and sample. Two SNPs (rs2070727 and rs9282761; both in perfect LD with rs2549003, r2=1.0) are Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Latinos and African Americans is limited because nearly all GWAS of asthma in these eQTLs for IRF1 in LCLs from asthma probands and their siblings (32) and in sputum samples from subjects with chronic obstructive pulmonary disease (COPD)(33). A third SNP in perfect LD with rs2549003 (rs2548999) and rs2549003 are eQTLs for RAD50 in lung tissues (34) and in LCLs from healthy female twins (35) respectively, two SNPs in near perfect LD with rs2549003 (rs13165038 and rs10035166; r2=0.97 and 0.81, respectively) are eQTLs for SLC22A4 and eQTL for SLC22A5 in LCLs from healthy female twins (35). None of these SNPs were reported as eQTLs for other genes at this region (C5orf56, IL5, IL13, IL4), but it is not possible to determine if these genes were detected as expressed in the all of the published studies. One other SNP at a sex-specific asthma-associated locus (rs9895098) was in LD with a reported eQTL (rs4077990) on 17p13.3. This SNP is a cis eQTL for the RAP1GAP2 gene in lung tissue (34). Thus, SNPs at two loci associated with sex-specific risks for asthma in our study and that show modest association with asthma as a main effect in the GABRIEL study are also in strong LD with functional regulatory variants in relevant tissues and cell types. However, the available data do not allow us to evaluate whether the possible eQTLs themselves have different effects in males and females. To further characterize sex effects on gene expression, we tested for differential expression by sex for genes listed in Table 4 in whole blood from EVE participants (Table 2). Three genes on 5q31.1 (SLC22A5, SLC22A4, IRF1) were detected as expressed in whole blood and one of them, SCL22A4, was expressed at significantly higher levels in females compared to males in three different samples (p-value for male-female difference = 2.5 x10-10, 4.0 x 10-9, 7.7 x10-8 in CAMP, CHS, and MCCAS respectively, Figure 2). Sex was not associated with expression of IRF1 in whole blood, suggesting the associated SNPs in IRF1 may be due to their Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 SLC22A5 in monocytes in two different studies (36, 37), and finally rs2548997 (r2 = 1.0) is an sex-specific regulatory effects on expression of neighboring genes or of IRF1 in cell types not considered here (e.g. airway cells). RAP1GAP2 was also detected as expressed in whole blood, but expression of this gene did not differ between males and females. Discussion asthma, a common disease with significant sex-specific architecture (5, 17-22). We observed several intriguing biological candidate loci among the most significant associations, two of which were further supported as asthma risk loci at reduced levels of statistical significance, consistent with a true sex difference, and by results from published GWAS of asthma. These results are further supported by eQTL studies. The validated associations are for SNPs near the IRF1 gene on 5q31.1 in European American males, and SNPs in the 3’UTR of the RAP1GAP2 gene in Latino females. RAP1GAP2 (Rap1 GTP-ase activating protein 2) is expressed in the lung (34), where it is involved in regulating the secretion of dense granules from platelets at sites of endothelial damage (38, 39). Allergen exposure can result in recruitment of platelets to the airways (40), suggesting RAP1GAP2 might be specifically involved in response to allergens. The most significant sex-specific association with asthma in the European American sample was with SNPs at the IRF1 locus. IRF1 encodes a transcription factor activating the transcription of the genes encoding IFN-α, , and , cytokines all of which have been implicated in asthma pathogenesis (18). Male-specific linkage results for asthma-related phenotypes (allergen polysensitization and lung function) have been reported at this locus (30). Furthermore, related genes, IRF5 and IRF7, were identified as hubs in a network module of gene expression responses to viral-induced exacerbation in nasal lavage samples from children with asthma (41). Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 We report the first genome-wide meta-analysis of genotype-by-sex interaction study of Another gene in the IRF1 activation pathway (INFG) showed sex-specific patterns of association with asthma and with early life wheezing illness in a candidate gene study (19), and IFNresponses show significant sex differences in early childhood, with increased response in boys compared to girls (18). Our study extends the sex-specific trends of genes in the IFN- pathway to include a key member of this important immune response, possibly providing a mechanism for SNPs at the IRF1 locus were cis eQTLs in previous studies of sputum from COPD patients and in LCLs from children with asthma and their siblings (32, 33). Thus, it is possible sex-specific effects on expression of IRF1 or eQTL-by-sex interaction effects are present in airway cells, or variation at this locus has regulatory effects on 5q31.1 genes not well interrogated on the arrays. For example, in data from the 1000 Genomes Project, a SNP (rs2070724) in an IRF1 splice acceptor site is in perfect LD (r2 = 1.00) with rs2549003 and both the splice variant and the associated SNPs are in perfect LD with a cis eQTL (rs2548997) for a specific IRF1 transcript isoform. Thus, the splice variant may affect mRNA splicing and cause sex-biased expression of alternative transcripts, a hypothesis that cannot be explored in the available data sets. Moreover, we observed sex-biased expression of another gene at this locus, SLC22A4, in whole blood samples, with increased expression in females. SLC22A4 encodes OCTN1, an organic cation transporter involved in eliminating environmental toxins and drugs, including the anticholinergic bronchodilator ipratropium, which is used to treat obstructive lung disease (42). Alternatively, sex-specific effects of this locus on asthma risk may be due to the regulation of expression of other genes, such as IL4, IL5, or IL13, which were either not detected as expressed or not assayed in whole blood RNA so could not be directly interrogated for sex effects. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 these earlier observations. Interestingly, the six most significant SNPs show population-specific patterns of association, even in instances where the MAF of the associated SNP is common in all populations. For example, the SNPs at the IRF1 locus are common in all ancestries (MAFs 0.32 – 0.44), yet the association is only observed in European Americans. Similarly, the associated SNP, rs9895098, on 17p13.3 has MAFs ranging from 0.065 to 0.28 in the different samples yet association on 2q23.3 in African Americans; where the MAF is < 0.05 in European Americans and Latinos but higher (0.15 – 0.18) in African Americans. These results exemplify the population-specific nature of asthma associations, which may not be surprising given the striking differences in asthma prevalence across populations with different ancestries. The cases vs. pooled controls approach used in this study was designed to detect maleand female-driven genotype-by-sex interactions in both case-control and trio-based study designs. This approach had optimal power to detect sex-specific associations, although other types of interactions could also be detected with good power (e.g. combinations of sex and main effects, and flip-flop interactions in which an allele increases risk in one sex and decreases risk in the other sex). In contrast, the cases vs. pooled controls approach that we used here has low, but non-zero, power to detect main effects on asthma risk (Figure S2C). As a result, some SNPs with true main effects could be identified as having sex-specific associations in our analysis. However, because we do not observe among our most significant results the associations reported in our previous meta-analysis of asthma GWAS that included these same subjects (e.g. ORMDL3, TSLP) (24), we think it is most likely that our results represent genotype-by-sex interactions and not main effects on asthma risk. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 the association signal is present in only the Latinos. The exception to this is the female-specific A limiting factor in this study is power to detect genotype-by-sex interactions. Previous meta-analyses report ORs for asthma main effects on the order of 1.1 - 1.3 (24, 27), and one asthma candidate gene study reported sex-specific odds ratios comparable in magnitude to the asthma main effects, ranging from 1.11 to 1.25 (20). Similarly, the GIANT Consortium metaanalysis of genotype-by-sex interaction effects on anthropomorphic traits revealed sex-specific expectation for sex-specific effect sizes is OR ≤ 1.3 and that our study is underpowered (< 10% to detect an OR = 1.25). Furthermore, sample and phenotype heterogeneity within and between studies can decrease the estimate of the effect sizes. A large-scale, international collaboration would be needed to obtain the sample sizes required to reliably detect modest sex-specific effects. This study highlights the advantages and complexity of jointly considering the effects of both genotype and environment (i.e., sex in this example) on asthma susceptibility. By testing for genotype-by-sex interactions, we identified sex-specific associations with asthma for SNPs in IRF1 and RAP1GAP2, which are supported by two independent lines of evidence: GWAS of asthma and studies of gene regulation. These SNPs (or SNPs in strong LD with them) showed only nominally significant, not genome-wide significant, associations with asthma in an independent GWAS and these SNPs also showed evidence for a role in gene regulation in eQTL mapping studies. Thus, our study identified two candidate loci with sex-specific associations with asthma risk and implicated regulatory variation in these effects. Overall, these observations contribute toward our understanding of the sex-specific architecture of this very common and complex disease. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 effects in this same range as the main effects (3, 13). Together, these studies suggest the Subjects and Methods The EVE Consortium The EVE Consortium is comprised of ten centers, which contributed genotype and phenotype data from 11 studies. Sample ascertainment schemes, sample characteristics, and information on genotyping platforms and quality control checks for each study have been asthma case-parent trios were recruited from locations in the U.S., Mexico and Barbados, representing European American, African American, African Caribbean, and Latino populations (Table 1). The participating studies used Affymetrix and Illumina genotyping platforms. Therefore, to facilitate meta-analysis of all samples, we used MaCH (43) to impute all variants reported in the HapMap Phase 2, release 21, reference panel. Statistical Analysis For each of the 11 samples, two genome-wide genotype-by-sex interaction studies (one for each sex) were performed to detect sex-specific associations. In the male-specific analysis, male cases were compared to a set of pooled controls composed of male controls, female cases, and female controls. Similarly, in the female-specific association test, female cases were compared to female controls grouped with male cases and controls. The cases vs. pooled controls approach was adapted to the trio-design studies by contrasting male and female affected probands, a case-only analysis. For both the case-control and trio studies, logistic regression was used to test for association between imputed allelic dosages and the sex-specific asthma status (e.g. male cases = 1, all other samples = 0); including either principal components or global ancestry estimates as population structure covariates. To account for relatedness in the extended families from Barbados, the MQLS test (44) was used as the association test. SNPs with either Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 previously described (24). Briefly, 3,585 asthma cases, 3,830 non-asthma controls, and 1,634 low minor allele frequency (< 0.05) or low imputation accuracy (R2 < 0.3) were excluded from all analyses. Meta-analyses were performed in the European Americans (Nmale cases = 1052, Nfemale cases = 1019, Ncontrols = 1535), African Americans/ African Caribbean (Nmale cases = 675, Nfemale cases = 837, Ncontrols = 1503), Latinos (Nmale cases = 926, Nfemale cases = 710, Ncontrols = 792), and the 3830), as in our earlier studies (24, 25). Test statistics from the sample-specific logistic regressions were combined using the weighted sums of z-score method. These weights (w) accounted for sample size (N), and the proportions of cases (v) in each study, as well as allele frequency (p) and the imputation accuracy (R2) of each SNP within each study ( w  R 2 2 p(1 p)v(1 v)N )(45). Significance was ascertained using standard normal approximations. The combined effect sizes were calculated as a linear combination of log odds ratios (OR) with weights proportional to the standard errors of each log OR. All statistical analyses were completed using R (www.r-project.org) and the R package meta was used to calculate the combined log OR. Power Analysis The power of the cases vs. pooled controls approach was determined in each of the four samples (European American, African American/African Caribbean, Latino, and combined). We limited our analysis to identifying male-specific effects since the number of males and female cases is similar, and subsequently, the power to detect male and female-specific effects should be similar. Phenotypes and genotypes were simulated under four additive models: A) a malespecific effect, B) a flip-flop effect (the same allele increases risk in males and decreases risk in females), C) a main effect, and D) a combination of main and male-specific effects. We Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 combined samples (i.e. across all ancestries, Nmale cases = 2653, Nfemale cases = 2566, Ncontrols = considered effect sizes ranging from an OR= 1 (no effect) to 2.0 (strong effect). Additionally, we assessed the power of three different genotype-by-sex interaction tests; 1) the cases vs. pooled controls association test contrasting male cases to the pool of male controls and all female, 2) the case-only test comparing male to female cases, and 3) the genotype-by-sex interaction term in a logistic model (case-control samples only), in the European Americans. For both power studies, threshold was 5x10-8 and 100,000 simulations were completed for each model. All power simulations and analyses were completed using R. Gene Expression and eQTL Analysis Gene expression data was available for whole blood samples from a subset of the EVE subjects, including 478 European American, African American, and Latino asthmatic children from the Childhood Asthma Management Program (CAMP), 123 Latino asthmatic children from Mexico City Childhood Asthma Study (MCCAS), and 191 Latino and European American cases and controls from the Children’s Health Study (CHS, Table 2). Gene expression was measured using the Illumina HumanHT-12v4 Expression BeadChip at the Channing Division of Network Medicine, using similar methods to those described previously (46). Samples with sex mismatches or low median pairwise rank correlation (r < 0.80) were removed. Probes were removed if they that had poor mapping quality, mapped to the X or Y chromosome, contained a SNP with MAF >0.01 (per the 1000 Genomes Project) in the probe sequence, or were detected in fewer than 20% of all subjects. Expression data were adjusted for background noise, log2 transformed and quantile normalized using the R Bioconductor package lumi (47). ComBat (48) was used to remove batch effects and surrogate variable analysis (SVA)(49) was used to detect Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 the minor allele frequencies (MAF) ranged from 0.05 to 0.5. The genome-wide significance and control for expression heterogeneity. Linear regression was used to test for differential expression by sex, adjusted for ancestry and asthma status. Information on eQTLs was extracted from the eQTL browser (eqtl.uchicago.edu, specifically eQTLs in monocytes (36)) and reports from studies of a variety of cell types including lung tissues (34), B-cell and monocyte samples (37), sputum samples from patients probands with asthma and their siblings (32), LCLs from 462 samples in the 1000 Genomes Project (31), LCLs from healthy female twins in the MuTHER resource (35), and CD4+ lymphocytes from 200 cases of asthma (46). Acknowledgements We thank Jose Rodriguez Santana, William Rodriguez Cintron, Rocio Chapela, Jean Ford, Shannon Thyne, Pedro C. Avila, Juan Jose Sienra Monge, Meher Boorgula, Chris Cheadle, and Celeste S. Eng for data collection, management, and analysis. We acknowledge support from J. Kiley, S. Banks-Schlegel, and W. Gan at the National Heart, Lung, and Blood Institute; all of the patients and families for their participation in these studies; and the numerous health care providers and community clinics for their support. This work was supported by grants from the Office of the Director, National Institutes of Health to C.O. and D.L.N.; the National Heart, Lung and Blood Institute [HL101651 to C.O. and D.L.N., HL085197 to C.O., HL087699 to K.C.B., HL075419, HL65899, HL083069, and HL066289 to S.T.W, HL101543 to B.A.R. and S.T.W., HL115606 and HL087680 to W.J.G., HL61768 and HL76647 to F.D.G., HL079055 to L.K.W., HL088133, HL078885, HL004464, and HL104608 to E.G.B.]; the National Institute of Allergy and Infection Diseases [AI079139, Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 with chronic obstructive pulmonary disease (COPD)(33), lymphoblastoid cell lines (LCLs) from AI061774 to L.K.W., AI095230 to C.O., AI077439 to E.G.B.]; the National Institute of Environmental Health Sciences [ES022719 to W.J.G., ES011627 to F.D.G., ES007048 and ES009581 to F.D.G. and W.J.G., ES015794 to E.G.B.]; the National Institute on Minority Health and Health Disparities [MD006902 to E.G.B.] the National Institute of General Medical Sciences [GM007546 to E.G.B]; Fundación Ramón Areces [M.P.Y.]; American Asthma Foundation [F.D.G.]; RWJF Amos Medical Faculty Development Award [E.G.B.]; the Sandler Foundation [E.G.B.]; and the Mary Beryl Patch Turnbull Scholar Program [K.C.B.]. This research was supported in part by the Intramural Research Program of the National Institutes of Health, National Institute of Environmental Health Sciences. Conflicts of Interest Kathleen Barnes is a consultant for Sanofi-Aventis and Merck, a consultant/Board Member for Genentech, and an Investigator for Sirius Genomics, Inc. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 [L.K.W. and E.G.B.]; the Fund for Henry Ford Hospital [L.K.W.]; Hastings Foundation References 1 Weiss, L.A., Pan, L., Abney, M. and Ober, C. (2006) The sex-specific genetic architecture of quantitative traits in humans. Nat. Genet., 38, 218-222. 2 Gray, J.P. and Wolfe, L.D. (1980) Height and sexual dimorphism of stature among human societies. Am. J. Phys. Anthropol., 53, 441-456. Heid, I.M., Jackson, A.U., Randall, J.C., Winkler, T.W., Qi, L., Steinthorsdottir, V., Thorleifsson, G., Zillikens, M.C., Speliotes, E.K., M Auml Gi, R. et al. (2010) Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet., 42, 949-960. 4 Grossman, C. (1989) Possible underlying mechanisms of sexual dimorphism in the immune response, fact and hypothesis. J. Steroid Biochem., 34, 241-251. 5 Osman, M. (2003) Therapeutic implications of sex differences in asthma and atopy. Arch. Dis. Child., 88, 587-590. 6 Moroni, L., Bianchi, I. and Lleo, A. (2012) Geoepidemiology, gender and autoimmune disease. Autoimmun. Rev., 11, A386-392. 7 McCombe, P.A., Greer, J.M. and Mackay, I.R. (2009) Sexual dimorphism in autoimmune disease. Curr. Mol. Med., 9, 1058-1079. 8 Costello, E.J., Mustillo, S., Erkanli, A., Keeler, G. and Angold, A. (2003) Prevalence and development of psychiatric disorders in childhood and adolescence. Arch. Gen. Psychiatry, 60, 837-844. 9 Choi, B.G. and McLaughlin, M.A. (2007) Why men's hearts break: cardiovascular effects of sex steroids. Endocrinol. Metab. Clin. North Am., 36, 365-377. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 3 10 Rinn, J.L. and Snyder, M. (2005) Sexual dimorphism in mammalian gene expression. Trends Genet., 21, 298-305. 11 Ober, C., Loisel, D.A. and Gilad, Y. (2008) Sex-specific genetic architecture of human disease. Nat. Rev. Genet., 9, 911-922. 12 Carrel, L. and Willard, H.F. (2005) X-inactivation profile reveals extensive variability in 13 Randall, J.C., Winkler, T.W., Kutalik, Z., Berndt, S.I., Jackson, A.U., Monda, K.L., Kilpelainen, T.O., Esko, T., Magi, R., Li, S. et al. (2013) Sex-stratified genome-wide association studies including 270,000 individuals show sexual dimorphism in genetic loci for anthropometric traits. PLoS Genet., 9, e1003500. 14 Liu, C.T., Estrada, K., Yerges-Armstrong, L.M., Amin, N., Evangelou, E., Li, G., Minster, R.L., Carless, M.A., Kammerer, C.M., Oei, L. et al. (2012) Assessment of gene-by-sex interaction effect on bone mineral density. J. Bone Miner. Res., 27, 2051-2064. 15 Liu, L.Y., Schaub, M.A., Sirota, M. and Butte, A.J. (2012) Sex differences in disease risk from reported genome-wide association study findings. Hum. Genet., 131, 353-364. 16 Masoli, M., Fabian, D., Holt, S., Beasley, R. and Program, G.I.f.A.G. (2004) The global burden of asthma: executive summary of the GINA Dissemination Committee report. Allergy, 59, 469-478. 17 Kulig, M., Tacke, U., Forster, J., Edenharter, G., Bergmann, R., Lau, S., Wahn, V., Zepp, F. and Wahn, U. (1999) Serum IgE levels during the first 6 years of life. J. Pediatr., 134, 453458. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 X-linked gene expression in females. Nature, 434, 400-404. 18 Uekert, S.J., Akan, G., Evans, M.D., Li, Z., Roberg, K., Tisler, C., Dasilva, D., Anderson, E., Gangnon, R., Allen, D.B. et al. (2006) Sex-related differences in immune development and the expression of atopy in early childhood. J. Allergy Clin. Immunol., 118, 1375-1381. 19 Loisel, D.A., Tan, Z., Tisler, C.J., Evans, M.D., Gangnon, R.E., Jackson, D.J., Gern, J.E., Lemanske, R.F., Jr. and Ober, C. (2011) IFNG genotype and sex interact to influence the risk of 20 Hunninghake, G.M., Soto-Quirós, M.E., Avila, L., Kim, H.P., Lasky-Su, J., Rafaels, N., Ruczinski, I., Beaty, T.H., Mathias, R.A., Barnes, K.C. et al. (2010) TSLP polymorphisms are associated with asthma in a sex-specific fashion. Allergy, 65, 1566-1575. 21 Santillan, A.A., Camargo, C.A., Jr., Ramirez-Rivera, A., Delgado-Enciso, I., Rojas- Martinez, A., Cantu-Diaz, F. and Barrera-Saldana, H.A. (2003) Association between beta2adrenoceptor polymorphisms and asthma diagnosis among Mexican adults. J. Allergy Clin. Immunol., 112, 1095-1100. 22 Seibold, M.A., Wang, B., Eng, C., Kumar, G., Beckman, K.B., Sen, S., Choudhry, S., Meade, K., Lenoir, M., Watson, H.G. et al. (2008) An african-specific functional polymorphism in KCNMB1 shows sex-specific association with asthma severity. Hum. Mol. Genet., 17, 26812690. 23 Gauderman, W.J., Zhang, P., Morrison, J.L. and Lewinger, J.P. (2013) Finding novel genes by testing G x E interactions in a genome-wide association study. Genet. Epidemiol., 37, 603-613. 24 Torgerson, D.G., Ampleford, E.J., Chiu, G.Y., Gauderman, W.J., Gignoux, C.R., Graves, P.E., Himes, B.E., Levin, A.M., Mathias, R.A., Hancock, D.B. et al. (2011) Meta-analysis of Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 childhood asthma. J. Allergy Clin. Immunol., 128, 524-531. genome-wide association studies of asthma in ethnically diverse North American populations. Nat. Genet., 43, 887-892. 25 Myers, R.A., Himes, B.E., Gignoux, C.R., Yang, J.J., Gauderman, W.J., Rebordosa, C., Xie, J., Torgerson, D.G., Levin, A.M., Baurley, J. et al. (2012) Further replication studies of the EVE Consortium meta-analysis identifies 2 asthma risk loci in European Americans. J. Allergy 26 Levin, A.M., Mathias, R.A., Huang, L., Roth, L.A., Daley, D., Myers, R.A., Himes, B.E., Romieu, I., Yang, M., Eng, C. et al. (2013) A meta-analysis of genome-wide association studies for serum total IgE in diverse study populations. J. Allergy Clin. Immunol., 131, 1176-1184. 27 Moffatt, M.F., Gut, I.G., Demenais, F., Strachan, D.P., Bouzigon, E., Heath, S., von Mutius, E., Farrall, M., Lathrop, M., Cookson, W.O.C.M. et al. (2010) A large-scale, consortium-based genomewide association study of asthma. N. Engl. J. Med., 363, 1211-1221. 28 Wang, T.N., Chu, Y.T., Chen, W.Y., Feng, W.W., Shih, N.H., Hsiang, C.H. and Ko, Y.C. (2006) Association of interferon-gamma and interferon regulatory factor 1 polymorphisms with asthma in a family-based association study in Taiwan. Clin. Exp. Allergy, 36, 1147-1152. 29 Nakao, F., Ihara, K., Kusuhara, K., Sasaki, Y., Kinukawa, N., Takabayashi, A., Nishima, S. and Hara, T. (2001) Association of IFN-gamma and IFN regulatory factor 1 polymorphisms with childhood atopic asthma. J. Allergy Clin. Immunol., 107, 499-504. 30 Aschard, H., Bouzigon, E., Corda, E., Ulgen, A., Dizier, M.H., Gormand, F., Lathrop, M., Kauffmann, F. and Demenais, F. (2009) Sex-specific effect of IL9 polymorphisms on lung function and polysensitization. Genes Immun., 10, 559-565. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Clin. Immunol., 130, 1294-1301. 31 Lappalainen, T., Sammeth, M., Friedlander, M.R., t Hoen, P.A., Monlong, J., Rivas, M.A., Gonzalez-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P.G. et al. (2013) Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 501, 506-511. 32 Dixon, A.L., Liang, L., Moffatt, M.F., Chen, W., Heath, S., Wong, K.C., Taylor, J., Burnett, E., Gut, I., Farrall, M. et al. (2007) A genome-wide association study of global gene 33 Qiu, W., Cho, M.H., Riley, J.H., Anderson, W.H., Singh, D., Bakke, P., Gulsvik, A., Litonjua, A.A., Lomas, D.A., Crapo, J.D. et al. (2011) Genetics of sputum gene expression in chronic obstructive pulmonary disease. PLoS ONE, 6, e24395. 34 Hao, K., Bosse, Y., Nickle, D.C., Pare, P.D., Postma, D.S., Laviolette, M., Sandford, A., Hackett, T.L., Daley, D., Hogg, J.C. et al. (2012) Lung eQTLs to help reveal the molecular underpinnings of asthma. PLoS Genet., 8, e1003029. 35 Grundberg, E., Meduri, E., Sandling, J.K., Hedman, A.K., Keildson, S., Buil, A., Busche, S., Yuan, W., Nisbet, J., Sekowska, M. et al. (2013) Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am. J. Hum. Genet., 93, 876-890. 36 Zeller, T., Wild, P., Szymczak, S., Rotival, M., Schillert, A., Castagne, R., Maouche, S., Germain, M., Lackner, K., Rossmann, H. et al. (2010) Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. PLoS ONE, 5, e10693. 37 Fairfax, B.P., Makino, S., Radhakrishnan, J., Plant, K., Leslie, S., Dilthey, A., Ellis, P., Langford, C., Vannberg, F.O. and Knight, J.C. (2012) Genetics of gene expression in primary immune cells identifies cell type-specific master regulators and roles of HLA alleles. Nat. Genet., 44, 502-510. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 expression. Nat. Genet., 39, 1202-1207. 38 Schultess, J., Danielewski, O. and Smolenski, A.P. (2005) Rap1GAP2 is a new GTPase- activating protein of Rap1 expressed in human platelets. Blood, 105, 3185-3192. 39 Neumuller, O., Hoffmeister, M., Babica, J., Prelle, C., Gegenbauer, K. and Smolenski, A.P. (2009) Synaptotagmin-like protein 1 interacts with the GTPase-activating protein Rap1GAP2 and regulates dense granule secretion in platelets. Blood, 114, 1396-1404. Pitchford, S.C., Momi, S., Baglioni, S., Casali, L., Giannini, S., Rossi, R., Page, C.P. and Gresele, P. (2008) Allergen induces the migration of platelets to lung tissue in allergic asthma. Am. J. Respir. Crit. Care Med., 177, 604-612. 41 Bosco, A., Ehteshami, S., Panyala, S. and Martinez, F.D. (2012) Interferon regulatory factor 7 is a major hub connecting interferon-mediated responses in virus-induced asthma exacerbations in vivo. J. Allergy Clin. Immunol., 129, 88-94. 42 Nakamura, T., Nakanishi, T., Haruta, T., Shirasaka, Y., Keogh, J.P. and Tamai, I. (2010) Transport of ipratropium, an anti-chronic obstructive pulmonary disease drug, is mediated by organic cation/carnitine transporters in human bronchial epithelial cells: implications for carriermediated pulmonary absorption. Mol. Pharm., 7, 187-195. 43 Li, Y., Willer, C.J., Ding, J., Scheet, P. and Abecasis, G.R. (2010) MaCH: Using Sequence and Genotype Data to Estimate Haplotypes and Unobserved Genotypes. Genet. Epidemiol., 34, 816-834. 44 Thornton, T. and McPeek, M.S. (2007) Case-control association testing with related individuals: a more powerful quasi-likelihood score test. Am. J. Hum. Genet., 81, 321-337. 45 Zaitlen, N. and Eskin, E. (2010) Imputation aware meta-analysis of genome-wide association studies. Genet. Epidemiol., 34, 537-542. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 40 46 Murphy, A., Chu, J.H., Xu, M., Carey, V.J., Lazarus, R., Liu, A., Szefler, S.J., Strunk, R., Demuth, K., Castro, M. et al. (2010) Mapping of numerous disease-associated expression polymorphisms in primary peripheral blood CD4+ lymphocytes. Hum. Mol. Genet., 19, 47454757. 47 Du, P., Kibbe, W.A. and Lin, S.M. (2008) lumi: a pipeline for processing Illumina 48 Johnson, W.E., Li, C. and Rabinovic, A. (2007) Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8, 118-127. 49 Leek, J.T. and Storey, J.D. (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet., 3, 1724-1735. 50 Saxena, R., Voight, B.F., Lyssenko, V., Burtt, N.P., de Bakker, P.I., Chen, H., Roix, J.J., Kathiresan, S., Hirschhorn, J.N., Daly, M.J. et al. (2007) Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science, 316, 1331-1336. 51 Johnson, A.D., Handsaker, R.E., Pulit, S.L., Nizzari, M.M., O'Donnell, C.J. and de Bakker, P.I.W. (2008) SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics, 24, 2938-2939. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 microarray. Bioinformatics, 24, 1547-1548. Figure Legends Figure 1. The male-specific asthma association on 5q31.1 in European Americans. A) The –log10(p-value) is shown on the y-axis and position along chromosome 5q31.1 is shown on the x-axis. SNPs are colored according to LD with the most significant SNP, rs2549003. This figure was generated using SNAP (50, 51). B) The log odds ratio and 95% confidence interval standard error (larger boxes correspond to smaller error). C) The association model with risk allele frequency (RAF) for rs2549003 plotted separately in cases and controls and in males (blue) and females (red); the vertical bars represent the 95% confidence interval of the risk allele frequency. Figure 2. SLC22A4 gene expression by sex. Gene expression of SLC22A4, in units of log2 (intensity), plotted by sex (F = female, M = male) in whole blood samples from A) CAMP, B) CHS, and C) MCCAS. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 for each of the EVE samples and in the combined sample. The black boxes are scaled by the Tables Table 1. Summary of the samples in EVE Consortium. Study No. Male Cases No. Female Cases No. Male Controls No. Female Controls European American CAMP* 244 143 NA NA CARE* CHS CAG / CSGA / SARP European American Total 148 348 69 295 NA 498 NA 461 312 512 211 365 1052 1019 709 826 BAGS 188 194 219 242 GRAAD 207 240 195 264 SAPPHIRE CAG / CSGA / SARP African American/African Caribbean Total CHS GALA* MCCAS* Latino Total 60 90 0 133 220 313 163 287 675 837 577 926 339 298 289 926 267 240 203 710 410 NA NA 410 382 NA NA 382 Total 2653 2566 1696 2134 African Caribbean African American Latino Combined Sample *Asthma proband–parent trios Studies: BAGS – Barbados Asthma Genetics Study, CAMP – Childhood Asthma Management Program, CARE – Childhood Asthma Research and Education, CHS – Children’s Health Study, CAG - Chicago Asthma Genetics Study, CSGA – Collaborative Studies on the Genetics of Asthma, SARP – Study of Asthma Phenotypes and Pharmacogenetics, GRAAD – Genomic Research on Asthma in the African Diaspora (and Barbados), SAPPHIRE – Study of Asthma Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Ancestry Phenotypes and Pharmacogenomic Interactions by Race-Ethnicity, GALA – Genetics of Asthma in Latino Americans, MCCAS – Mexico City Childhood Asthma Study Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Table 2. Summary of samples with microarray expression data in whole blood RNA Total (M:F) CAMP 292:186 CHS 79:122 MCCAS 69:54 Study European American (M:F) 206:128 30:29 NA African American (M:F) 40:32 NA NA Latino (M:F) 33:14 25:66 69:54 Other* (M:F) 13:12 24:27 NA CAMP – Childhood Asthma Management Program. CHS – Childhood Health Study, MCCAS – Mexico City Childhood Asthma Study *Self reported ancestries that were not one of the three North American populations, were mixed ancestry, or were missing. Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Table 3. SNPs with sex-specific association p-values < 1.0x10-6 Eth. Sex SNP Location Alleles (risk/alt) RAF* Nearest Gene (distance/location within gene) p-value OR** (95% CI) EA M rs2549003 5q31.1 G/A 0.39 IRF1 (2.8 kb) 8.90x10-7 1.33 (1.18, 1.49) AA M rs17642749 10q26.11 G/T 0.086 RAB11FIP2 (117 kb) 3.57x10-7 2.97 (1.90, 4.63) AA F rs1012307 2q23.3 C/A 0.20 AK057517 (intronic) 7.50x10-7 1.44 (1.25, 1.67) Latino F rs4673659 2q34 C/A 0.72 ERBB4 (intronic) 9.29x10-7 1.42 (1.23, 1.64) A/C 0.62 -7 1.44 (1.26, 1.66) Latino F rs2675724 6q27 C6orf118 (127 kb) 1.58x10 0.10 Latino F rs9895098 17p13.3 C/T RAP1GAP2 (3’ UTR) 2.79x10 2.31 (1.90, 2.8) SNPs are ordered by sex, ancestry, and genomic location. Allele frequency differences between male controls and female controls -7 were non-significant (p-value > 0.05) for all six SNPs. *Risk allele frequency in the sex-specific cases **OR from the sex-specific cases vs pooled controls Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Table 4. Literature based eQTLs in LD with sex-specific asthma-associated SNPs eQTL Locus Sex-specific asthmaassociated SNP LD between associated SNP and eQTL (r2) Regulated Gene eQTL p-value rs2549009 5q31.1 rs2549003 1.0 IRF1 6.24 x 10-10 rs2070727 5q31.1 rs2549003 1.0 IRF1 7.46 x 10-5 rs2548999* 5q31.1 rs2549003 1.0 RAD50 3.50 x 10-5 rs9282761* 5q31.1 rs2549003 1.0 IRF1 7.30 x 10-9 rs2548997* 5q31.1 rs2549003 1.0 SLC22A5 5.15 x 10-8 rs2549003* 5q31.1 rs2549003 NA RAD50 1.3 x 10-5 rs13165038* 5q31.1 rs2549003 0.97 SLC22A4 1.86 x 10-6 Sample Description LCLs from the 1000 Genomes Project European populations (31) Sputum from patients with COPD (33) Lung tissue collected from patients during lung resectional surgery (34) LCLs from asthmatic children and their siblings (32) LCLs from healthy female twins in the MuTHER resource (35) LCLs from healthy female twins in the MuTHER resource (35) Monocytes from volunteers (37) Monocytes from subjects in the Gutenberg Heart Study (36) Lung tissue collected from -10 rs4077990 17p13.3 rs9895098 1.0 patients during lung resectional RAP1GAP2 4.99 x 10 surgery (34) *There exists a SNP with a smaller eQTL p-value for the same gene: top eQTLs for RAD50 in a) lung tissue is rs11242103 (r2 = 0.21, rs10035166* 5q31.1 rs2549003 0.81 SLC22A5 1.37 x 10-17 p = 2.77x10-17) and b) LCLs from MuTHER resource is rs11950562 (r2 = 0.18, p = 2.11x10-23); top eQTL for IRF1 in LCLs from asthmatic children and their siblings is rs2070729 (r2 = 0.74, p = 4.9x10-10); top eQTLs for SLC22A5 in a) LCLs from the MuTHER Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 resource is rs17772583 (r2 = 0.12, p = 1.19 x10-35) and b) monocytes from the GHS is rs2631360 (r2 = 0.18, p = 6.6x10-85); the top eQTL for SLC22A4 in monocytes from healthy volunteers is rs274560 (r2 = 0.27 p=3.19x10-13) Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Abbreviations SNP – Single nucleotide polymorphism eQTL – expression quantitative trait loci GWAS – genome-wide association study OR – odds ratio MAF – minor allele frequency UTR – untranslated region LCL – lymphoblastoid cell line Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 RAF – risk allele frequency Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014 Downloaded from http://hmg.oxfordjournals.org/ at Henry Ford Hospital - Sladen Library on May 17, 2014