Genetic Epidemiology

RESEARCH ARTICLE Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions

Jacob Basson,1 †∗ Yun Ju Sung,1 †∗ Lisa de las Fuentes,1,2 Karen Schwander,1 L. Adrienne Cupples,3,4 and Dabeeru C. Rao1 1

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri, United States of America; 2 Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, United States of America; 3 The Framingham Heart Study, Framingham, Massachusetts, United States of America; 4 Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

Received 7 January 2015; Revised 27 March 2015; accepted revised manuscript 1 April 2015. Published online 3 May 2015 in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/gepi.21904

ABSTRACT: Background: Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific variants. Interactions between genes and BP-associated factors may explain some “missing heritability.” Cigarette smoking increases BP after short-term exposure and decreases BP with longer exposure. Gene-smoking interactions have discovered novel BP loci, but the contribution of smoking status and intensity to gene discovery is unknown. Methods: We analyzed gene-smoking intensity interactions for association with systolic BP (SBP) in three subgroups from the Framingham Heart Study: current smokers only (N = 1,057), current and former smokers (“ever smokers,” N = 3,374), and all subjects (N = 6,710). We used three smoking intensity variables defined at cutoffs of 10, 15, and 20 cigarettes per day (CPD). We evaluated the 1 degree-of-freedom (df) interaction and 2df joint test using generalized estimating equations. Results: Analysis of current smokers using a CPD cutoff of 10 produced two loci associated with SBP. The rs9399633 minor allele was associated with increased SBP (5 mmHg) in heavy smokers (CPD > 10) but decreased SBP (7 mmHg) in light smokers (CPD ࣘ 10). The rs11717948 minor allele was associated with decreased SBP (8 mmHg) in light smokers but decreased SBP (2 mmHg) in heavy smokers. Across all nine analyses, 19 additional loci reached P < 1 × 10−6 . Discussion: Analysis of current smokers may have the highest power to detect gene-smoking interactions, despite the reduced sample size. Associations of loci near SASH1 and KLHL6/KLHL24 with SBP may be modulated by tobacco smoking. Genet Epidemiol 39:480–488, 2015. © 2015 Wiley Periodicals, Inc.

KEY WORDS: cardiovascular genetics; gene interactions; smoking; blood pressure

Introduction High blood pressure (BP) is a major risk factor for cardiovascular disease and was the single biggest health burden globally in 2010 [Lim et al., 2012]. Although the heritability of BP is estimated to be approximately 30%, the specific genes and variants responsible have proven elusive. Genome-wide association studies have identified dozens of variants associated with BP, but they collectively explain less than 3% of BP variability [Ehret, 2010]. Among the factors hypothesized to contribute to this “missing heritability” are interactions between genes and other clinical factors [Manolio et al., 2009] known to influence BP, including age [Shi et al., 2009], sex [RamirezLorca et al., 2007], BMI [Ramirez-Lorca et al., 2007], alcohol Supporting Information is available in the online issue at wileyonlinelibrary.com. †

Both these authors contributed equally.



Correspondence to: Jacob Basson, Division of Biostatistics, Washington University

School of Medicine, Campus Box 8067, 660 South Euclid, Saint Louis, MO 63110. E-mail: [email protected]; Yun Ju Sung, Division of Biostatistics, Washington University School of Medicine, Campus Box 8067, 660 South Euclid, Saint Louis, MO 63110. E-mail: [email protected]

consumption [Simino et al., 2013], and diet [Zhang et al., 2013]. Cigarette smoking is an environmental exposure that is an important risk factor for cardiovascular disease as well as a variety of other diseases including cancer and lung disease. Smoking has long been known to influence BP directly [Benowitz et al., 1984.] and is recently receiving attention for its role in modifying the influence of genetic variants on BP [Sung et al., 2015]. The effects of smoking on BP are complex; epidemiologic studies have shown acute increases in BP but decreased BP with longer tobacco exposure [Green et al., 1986]. Given the influence of tobacco smoking on BP, genesmoking interactions may enable the detection of novel BPassociated variants. However, the complexity of the relationship between smoking and BP raises questions regarding the methods for investigating gene-smoking interactions that are most likely to contribute to novel gene discovery. In this study, we examined gene-smoking interactions by models that categorized subjects by two different exposure metrics: smoking status, with subjects self-identifying as current smokers,  C 2015 WILEY PERIODICALS, INC.

Table 1.

Descriptive statistics of analysis cohorts

Sample size CPD10, % (n) CPD15, % (n) CPD20, % (n) Never Smokers, % (n) Age, years Male, % Taking Antihypertensive meds, % SBP, mmHg

All subjects

Ever smokers

Current smokers

6,710 32.7 (2,192) 27.8 (1,868) 10.6 (710) 49.7 (3,336) 49.1 ± 13.5 46.5 (3,123) 19.1 (1,283) 123 ± 19

3,374 65.0 (2,192) 55.4 (1,868) 21.0 (710) NA 51.6 ± 12.9 47.0 (1,586) 22.4 (757) 125 ± 20

1,057 65.1 (688) 51.4 (543) 15.6 (165) NA 46.0 ± 12.2 47.9 (506) 13.8 (146) 121 ± 18

Data presented as % (n) or means ± standard deviation.

former smokers, or never smokers; and smoking intensity, characterized as the reported number of cigarettes smoked per day (CPD). To investigate the influence these variables have on gene discovery, we performed a genome-wide analysis of gene-smoking interactions in three population subgroups (current smokers only, current and former smokers, and all subjects) defining dichotomous smoking intensity variables based on three different CPD cutoffs (>10, >15, and >20 CPD).

Methods Subjects The analysis used data from the Framingham SNP Health Association Resource (SHARe) from the database of Genotypes and Phenotypes (dbGaP; http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/ study.cgi?study_id=phs000342.v12.p9). The Framingham Heart Study is a longitudinal study comprising three Caucasian cohorts: the Original cohort, recruited in 1948; the Offspring cohort, recruited in 1971 comprising the offspring of the Original cohort and their spouses and children; and the Third-Generation cohort, recruited in 2002 and consisting of the biological and adopted offspring of the Offspring cohort. The current study analyzed data from the lone clinic visit of the Third Generation cohort as well as the Original cohort visit and Offspring cohort visit most closely matching the date of that visit. All subjects analyzed had nonmissing genotype and imputed genotype data and complete data for SBP, CPD, age, sex, and antihypertension medication use. Three sets of subjects were analyzed. The All Subjects sample (N = 6,710) consisted of all subjects with complete data regardless of smoking status. The Ever Smokers sample (N = 3,374) consisted of subjects who were current or former smokers but excluded those who had never smoked. Finally, the Current Smokers sample (N = 1,057) consisted only of current smokers. Descriptive statistics for these samples are shown in Table 1. Phenotypes and Genotypes The analysis phenotype was the average of three systolic BP (SBP) readings (one taken by a nurse/technician and two

taken by a physician). In subjects taking antihypertensive medication, the SBP value was adjusted by adding 15 mmHg. Three dichotomous smoking covariates were derived from the continuous CPD data using different CPD cutoffs: CPD10, CPD15, and CPD20. Subjects have a dichotomous covariate value of 1 if their CPD value is greater than the cutoff. For example, CPD10 has a value of 1 when CPD > 10. For both the All Subjects and Ever Smokers samples, dichotomous CPD covariates were derived in former smokers using the reported average CPD value during the time they were smoking. Genotyping was performed on the GeneChip Human Mapping 500K Array Set (Affymetrix, Santa Clara, CA) that assays 487,998 SNPs. A total of 2.5 million SNPs were imputed based on HapMap data using MACH [Li et al., 2010]. For all analyses, SNPs were excluded for Mendelian errors, HardyWeinberg disequilibrium (P ࣘ 10–6 ), or poor imputation quality (r2 < 0.3). In addition, only SNPs with minor allele frequency (MAF) ࣙ5% (calculated separately for each sample) were analyzed. The resulting SNP set included 2,144,020 SNPs in the All Subjects sample, 2,143,542 SNPs in the Ever Smokers sample, and 2,142,057 SNPs in the Current Smokers sample. Analysis The analyses were performed in a generalized estimating equations (GEE) framework using the R package geepack (available through CRAN). This approach uses robust standard errors that help reduce the inflation (and type I error) that can burden analysis of interactions [Voorman et al., 2011]. The model we analyzed (using CPD10 in this example) is y = β0 + β1 × Ag e + β2 × Sex + β3 × CPD10 +β4

× SNP + β5 × SNP × CPD10 + e

with each SNP coded using an additive model (0,1,2) and subjects clustered in families. Two tests were performed based on this model: a 1 degree-of-freedom (df) test of the SNP × CPD interactions (β5 ) and the 2df joint test of the SNP main effect and SNP × CPD interaction (β4 , β5 ), and a genomewide significance threshold of α = 5 × 10–8 was used.

Results A total of nine analyses were performed, one for each combination of sample (All Subjects, Ever Smokers, and Current Smokers) and CPD cutoff (CPD10, CPD15, CPD20). One of the goals of this investigation was to determine which combination of the sample and CPD cutoff is promising for discovery in large consortia. For each analysis, the genomic inflation factor (λ, the ratio of the observed median χ2 value to the expected median χ2 value) for the 1df and 2df tests is shown in Table 2 (main effect λ values are shown in supplementary Table S1). Nine QQ plots showing both the 1df and 2df tests are shown in Figure 1. The observed λ values are largely in line with the expectation for highly polygenic Genetic Epidemiology, Vol. 39, No. 6, 480–488, 2015

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Table 2. and 2df

Genomic inflation λ of the SNP × CPD interaction, 1df 1df λ

Sample All subjects Ever smokers Current smokers

2df λ

CPD10

CPD15

CPD20

CPD10

CPD15

CPD20

1.04 1.03 1.05

1.03 1.02 1.06

1.01 1.01 1.04

1.18 1.09 1.06

1.17 1.09 1.06

1.16 1.08 1.05

Table 2 shows the genomic inflation λ value for the 1df test for each combination of sample and CPD cutoff.

traits [Yang et al., 2011], although the 2df test shows evidence of moderate inflation, particularly in the All Subjects sample. This inflation is driven by the main effects (supplementary Table S1), while the corresponding 1df tests are well controlled (Table 2). Across all nine analyses, two loci contained SNPs that reached genome-wide significance using p-values adjusted for genomic inflation (α = 5 × 10–8 ; Table 3). Both loci were detected in the Current Smokers CPD10 analysis. For this analysis, both the 1df and 2df tests gave rise to a strong association signal (inflation-adjusted 1df: p = 1.0 × 10–8 ; 2df: p = 1.8×10-8 ) on chromosome 6 with multiple SNPs

Figure 1. QQ plots by sample and CPD cutoff. The figure shows the QQ plots for each of the nine analyses with the indicated combination of sample composition and CPD covariate. The 1df test is shown in red and 2df test is shown in blue. The genomic control lambda values for both tests are inset. The majority of analyses are well controlled, showing modest inflation. The primary signal of interest (–log(P) > 7) is located in the analysis of the Current Smokers group using CPD10 and comes from both 1df and 2df tests.

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Table 3.

Significant associations

Sample

CPD cutoff

SNP

Major/minor allele

CHR

BP

MAF

BETA SNP

BETA INT

P SNPGC

P INTGC

P 2dfGC

Gene

Distance (kb)

Current Current Current Current Current Current Current Current Current Current Current Current Current

10 10 10 10 10 10 10 10 10 10 10 10 10

rs9399633 rs1534774 rs9390526 rs1534775 rs1358689 rs9386204 rs1404488 rs1881827 rs9377097 rs1881826 rs11717948 rs9872196 rs11706582

C/T C/G T/C A/G T/C A/G G/A A/G A/G G/A G/A C/A T/C

6 6 6 6 6 6 6 6 6 6 3 3 3

148062948 148034416 148030680 148035906 148012824 148013180 148038927 148020538 148019979 148020622 183591640 183592150 183591975

0.146 0.121 0.121 0.121 0.121 0.121 0.121 0.121 0.121 0.121 0.0547 0.0546 0.0552

5.07 5.53 –5.5 5.53 –5.5 5.54 –5.5 5.53 5.53 –5.5 –9 –9.3 –9.2

–9.6 –9.8 9.78 –9.8 9.74 –9.7 9.73 –9.7 –9.7 9.66 7.04 7.42 7.24

1.42 × 10–6 4.06 × 10–7 4.07 × 10–7 4.10 × 10–7 4.07 × 10–7 4.06 × 10–7 4.17 × 10–7 4.06 × 10–7 4.06 × 10–7 4.07 × 10–7 1.47 × 10–8 1.37× 10–8 1.46 × 10–8

1.00 × 10–8 7.64 × 10–8 7.72 × 10–8 8.07 × 10–8 8.54 × 10–8 8.65 × 10–8 8.90 × 10–8 1.07 × 10–7 1.07 × 10–7 1.07 × 10–7 8.34 × 10–3 7.08× 10–3 8.34 × 10–3

1.83 × 10–8 2.94 × 10–8 2.96 × 10–8 3.06 × 10–8 3.16 × 10–8 3.19 × 10–8 3.30 × 10–8 3.66 × 10–8 3.66 × 10–8 3.66 × 10–8 5.03 × 10–8∗ 5.07× 10–8∗ 5.09 × 10–8∗

SASH1 SASH1 SASH1 SASH1 SASH1 SASH1 SASH1 SASH1 SASH1 SASH1 KLHL6 KLHL24 KLHL6 KLHL24 KLHL6 KLHL24

149 178 181 176 199 199 173 192 192 191 36 36 36

Figure 2. Mean SBP by SASH1 genotype and CPD10 status in Current Smokers. The figure shows the average SBP for groups of subjects defined by their genotype at rs9399633 and their CPD10 status. Subjects with CPD ࣘ 10 are shown in blue, and subjects with CPD > 10 are shown in red. Error bars indicate standard error for each mean SBP value. reaching genome-wide significance in a locus located 150 – 200 kb upstream of the nearest gene, SASH1. Among subjects who smoked ࣘ 10 CPD, each C allele at the most significant SNP in this locus (rs9399633, MAF = 14.6%, Table 3) was associated with an average SBP decrease of about 5 mmHg, while in subjects who smoke >10 CPD, each C

allele was associated with an average SBP increase of 5 mmHg (Fig. 2). A second genome-wide significant locus was detected with the 2df test (inflation-adjusted P = 5.0 × 10–8 ) on chromosome 3, with KLHL6 and KLHL24 located about 40 kb on either side of the associated SNPs. Among subjects who Genetic Epidemiology, Vol. 39, No. 6, 480–488, 2015

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Figure 3. Mean SBP by KLHL6/KLHL24 genotype and CPD10 status. The figure shows the average SBP for groups of subjects defined by their

genotype at rs11717948 and their CPD10 status. Subjects with CPD ࣘ 10 are shown in blue, and subjects with CPD > 10 are shown in red. Error bars indicate standard error for each mean SBP value.

smoked ࣘ10 CPD, each G allele at the most significant SNP in the locus (rs11717948, MAF = 5.4%, Table 3) was associated with an average SBP decrease of about 9 mmHg, while among subjects who smoked >10 CPD, G alleles were associated with an average SBP decrease of only about 2 mmHg (Fig. 3). These genome-wide significant association results are shown in Table 3. A total of 19 other loci across all analyses gave rise to suggestive associations (inflation-adjusted P < 10–6 ), with the majority (13) also identified from the analysis of the Current Smokers sample: one suggestive locus was detected using a CPD cutoff of 10, one locus was detected using a cutoff of 15, and 11 suggestive loci were detected using a cutoff of 20 (Table 4). Of the six suggestively associated loci detected in other samples, four were detected in the All Subjects sample (two from CPD15 and two from CPD20 analyses) and two were detected in the Ever Smokers sample (one from both CPD10 and CPD15 and one from CPD20 analyses, Table 4). Manhattan plots (using inflation-adjusted P-values) for the analysis of the Current Smokers sample with CPD10 are shown in Figure 4 (1df test) and Figure 5 (2df test). Manhattan plots for the remaining eight analyses are in the supplementary materials.

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Discussion We analyzed the interaction effects of smoking intensity (i.e., CPD) and genetic variants on SBP in three different smoking exposure cohorts (current smokers; current and former smokers; and current, former, and never smokers) using three different CPD interacting covariates (defined at CPD cutoffs of 10, 15, and 20). These nine analyses yielded two loci with genome-wide significance and 19 suggestive (inflation-adjusted P < 10–6 ) associations. Both of the significant loci were based on the analysis of the Current Smokers sample and a CPD cutoff of 10. This finding has important implications for the design of future studies analyzing the effects of smoking-SNP interactions on BP. The other two samples, All Subjects and Ever Smokers, are approximately 6.3 and 3.2 times larger than the Current Smokers sample, respectively (Table 1). The observation that they produced no genome-wide significant findings compared to the two significant findings from the Current Smokers sample (as well as far fewer suggestive associations) in spite of these much larger sample sizes suggests that the genetic modulation of smoking effects on BP diminishes after a subject stops smoking. As a consequence, analyses of current smokers may be

Table 4.

Suggestive associations

Sample

CPD cutoff

All All All All All All All Ever Ever Ever Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current Current

15 15 15 20 20 20 20 10 15 20 10 10 10 10 10 10 15 15 15 15 15 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20

SNP

Noncoded/ coded allele

CHR

Position

rs6916459 rs12662500 rs12469933 rs4761750 rs12372697 rs11107292 rs1229982 rs2355654 rs2355654 rs12699566 rs6934331 rs2328843 rs9322120 rs1973858 rs7746768 rs351326 rs6454802 rs12202119 rs11753332 rs12194007 rs12196749 rs9471517 rs9493000 rs7740929 rs9492992 rs12481734 rs17154431 rs901508 rs4594252 rs7973791 rs7959834 rs12228056 rs3798221 rs12033204 rs16991403 rs9899604

A/C G/C T/G G/C A/G A/G T/G C/T C/T T/C T/A C/T G/A T/C C/G A/C T/C T/A A/G T/G A/G C/T C/T G/A T/G C/G A/G T/C C/A C/A C/T T/C T/G A/C T/C C/T

6 6 2 12 12 12 4 14 14 7 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 21 5 5 16 12 12 12 6 1 20 17

54734919 54730930 139266368 93928698 93935745 93946957 99322775 49988369 49988369 13934836 148044773 148047081 148047385 148049408 148056613 93402467 90104480 90105471 90109434 90113440 90110096 12341521 131487451 131487482 131481287 23699432 126342485 2006893 55286934 91050948 91050279 91036196 160577116 212274784 1092170 71414692

MAF

Beta Main

Beta 1df Intxn

P SNP Main (genomic control)

0.104 0.104 0.102 0.0742 0.0742 0.0742 0.189 0.36 0.36 0.269 0.132 0.132 0.132 0.132 0.132 0.0806 0.392 0.392 0.392 0.392 0.442 0.163 0.0603 0.0514 0.0514 0.0967 0.0668 0.0943 0.102 0.0568 0.0568 0.0568 0.188 0.0555 0.0596 0.0549

–1.07 1.07 –4.46 –2.11 2.1 2.1 1.59 –3.95 –3.73 0.421 4.96 –4.96 4.96 4.95 –4.95 –4.31 1.83 1.82 –1.8 1.79 –1.64 3.62 –0.115 0.524 0.543 1.48 4.83 –3.64 –1.11 –5.53 5.53 –5.53 –2.32 –2.06 1.67 4.45

5.75 –5.74 4.28 12.7 –12.7 –12.8 3.38 1.54 1.42 6.75 –9.12 9.1 –9.1 –9.08 9.06 12.8 –7.71 –7.68 7.64 –7.6 7.32 –13.3 –13.5 11.4 11.4 –20.4 –15.9 16.1 15 17.3 –17.3 17.2 11.5 19.1 14.6 –17.3

7.91 × 10–2 7.92 × 10–2 2.58 × 10–7 2.42 × 10–2 2.43 × 10–2 2.48 × 10–2 4.06 × 10–4 9.59 × 10–6 6.68 × 10–6 5.60 × 10–1 5.50 × 10–6 5.54 × 10–6 5.54 × 10–6 5.63 × 10–6 5.71 × 10–6 5.95 × 10–3 9.57 × 10–2 9.65 × 10–2 9.81 × 10–2 9.93 × 10–2 1.35 × 10–1 2.33 × 10–4 9.40 × 10–1 7.40 × 10–1 7.30 × 10–1 5.39 × 10–1 9.17 × 10–3 2.95 × 10–2 3.02 × 10–1 2.84 × 10–3 2.84 × 10–3 2.85 × 10–3 2.37 × 10–2 3.35 × 10–1 3.74 × 10–1 7.31 × 10–3

prioritized for discovery of gene-smoking interactions on BP, even if that means a substantial reduction in sample size relative to a sample containing subjects who have never smoked or who have quit smoking. These data are less conclusive with respect to the CPD cutoff that optimizes gene discovery. Although the most significant results were obtained using a cutoff of 10 CPD, the majority of the results with genomic-controlled P < 10–6 were obtained using a cutoff of 20. The majority of these suggestive associations were detected in the Current Smokers sample, however, which contained only 165 subjects who smoked more than 20 CPD. Although the MAF cutoff of 5% helped ensure that at least 16 copies of the minor allele were present in the heavier smoking group, this value still allows for substantial instability in the regression estimates. As the majority of these suggestive associations were found for SNPs having MAF between 5% and 10%, these results should be treated with caution. Based on these data, we conclude that current smokers revealed the strongest signals for gene-smoking interaction on BP, including two genome-wide significant associations. The strongest result was for rs9399633 (inflation-adjusted 1df P = 1.00 × 10–8 , 2df P = 1.83 × 10–8 , Table 3), located about

P 1df Intxn (genomic control) 9.58 × 10–7 9.64 × 10–7 4.59 × 10–3 2.41 × 10–7 2.41 × 10–7 3.22 × 10–7 6.79 × 10–3 1.73 × 10–1 2.06 × 10–1 5.56 × 10–6 1.77 × 10–7 1.85 × 10–7 1.87 × 10–7 2.03 × 10–7 2.23 × 10–7 9.16 × 10–7 5.58 × 10–7 5.64 × 10–7 5.71 × 10–7 5.79 × 10–7 2.10 × 10–6 1.04 × 10–6 3.83 × 10–6 3.01 × 10–5 3.22 × 10–5 2.02 × 10–6 7.05 × 10–7 7.84 × 10–8 1.66 × 10–7 3.64 × 10–7 3.71 × 10–7 4.42 × 10–7 6.92 × 10–7 1.01 × 10–6 6.47 × 10–5 2.71 × 10–7

P 2df Intxn (genomic control) 2.38 × 10–5 2.39 × 10–5 3.82 × 10–7 6.60 × 10–6 6.61 × 10–6 8.14 × 10–6 4.00 × 10–7 2.56 × 10–7 5.16 × 10–7 9.31 × 10–7 2.60 × 10–7 2.69 × 10–7 2.71 × 10–7 2.88 × 10–7 3.09 × 10–7 6.78 × 10–6 1.25 × 10–7 1.25 × 10–7 1.25 × 10–7 1.26 × 10–7 5.25 × 10–7 8.32 × 10–7 3.45 × 10–7 6.69 × 10–7 7.34 × 10–7 7.11 × 10–7 4.78 × 10–6 3.84 × 10–7 7.71 × 10–7 2.56 × 10–6 2.61 × 10–6 3.07 × 10–6 4.93 × 10–6 8.87 × 10–7 1.00 × 10–6 2.00 × 10–6

Gene

Distance (kb)

FAM83B FAM83B NXPH2/ LRP1B CRADD CRADD CRADD ADH1B C14orf182 C14orf182 ETV1 SASH1 SASH1 SASH1 SASH1 SASH1 EPHA7 BACH2 BACH2 BACH2 BACH2 BACH2 ARG1/ MED23 ARG1/ MED23 ARG1/ MED23 ARG1/ MED23 D21S2088E GRAMD3 IRX4 IRX6 KERA KERA KERA LPA PPP2R5A PSMF1 SOX9

112 116 486 33 40 51 intron intron intron intron 167 165 165 163 156 intron intron intron intron intron intron 12342 131487 131487 131481 23699 126342 2007 55287 3’ UTR 91050 91036 intron 212275 1092 71415

150 kb upstream of SASH1 (SAM and SH3 domain containing 1). SASH1 is best known as a tumor suppressor gene for several types of cancer including colon [Nitsche et al., 2012], breast [Zeller et al., 2003], and melanoma [Lin et al., 2012]. It is also located in a susceptibility locus for lung cancer [Bailey-Wilson et al., 2004], and SASH1 overexpression reduces the viability, proliferation, and migration of lung cancer cells, whereas targeted silencing of SASH1 by RNA interference has opposing effects [Chen et al., [2012]. At least three studies that investigated the influence of tobacco exposure on gene expression in circulating blood cells found that SASH1 expression was significantly correlated with tobacco exposure (although the direction of the change varied across studies) [Beineke et al., 2012.; Charles et al., 2008; Verdugo et al., 2013]. This smoking-induced change in expression suggests a possible mechanism explaining the interaction between smoking and rs9399633. The effect of SASH1 on BP may be mediated through its expression in endothelial cells of microvascular beds, where it contributes to activation of the NF-κB complex and proinflammatory cytokines [Dauphinee et al., 2013]. Motivated by the co-morbidity of inflammatory processes and hypertension in metabolic syndrome, a recent analysis of SNP-SNP interactions among inflammation Genetic Epidemiology, Vol. 39, No. 6, 480–488, 2015

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Figure 4. Manhattan plot for Current Smokers sample, CPD10, 1df test. The figure shows the Manhattan plot for the analysis of the Current Smokers sample using the CPD10 interacting covariate. The –log(P) value adjusted for genomic inflation for the 1df test is shown. A significant locus is located on chromosome 6 (–log(P) = 8.0) 149 kb from SASH1. A suggestively associated locus is also located on chromosome 6 (–log(P) = 6.0) in an intron of EPHA7.

genes for association with BP suggested a BP-regulatory role for interactions among members and regulators of the NFκB complex [Basson et al., 2015]. Furthermore, a suggestive association between SASH1 and diabetic nephropathy, a risk factor for hypertension, has been identified in a genome-wide association study of African Americans [Mcdonough et al., 2011]. Thus, several lines of evidence support the biologic plausibility that SASH1 plays a role in the modulating effect of tobacco on BP. The other genome-wide significant association with SBP was located on chromosome 3, with the strongest result (rs11717948, inflation-adjusted 2df P = 5.03 × 10–8 , Table 3) located approximately 36 kb downstream of KLHL6 and 44 kb upstream of KLHL24. Although relatively little

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is known about these genes, they belong to the Kelch-like gene family that contains several evolutionarily conserved domains and several of whose members are associated with Mendelian diseases [Dhanoa et al., 2013]; notably, rare mutations in KLHL3 alter renal ion transport and cause familial hyperkalemic hypertension [Louis-Dit-Picard et al., 2012]. Expression of KLHL6 in CD133(+) has also been associated with coronary artery disease [Liu et al., 2011], and KLHL24 has been shown to regulate kainite receptors [Laezza et al., 2007], which are known to play a role in nicotine dependence [Kenny et al., 2003; Ma et al., 2010; Vink et al., 2009]. The 2df result at this locus is driven by the SNP main effect (inflation-adjusted main effect P = 1.47 × 10–8 , 1df P = 8.34 × 10–3 , Table 3).

Figure 5. Manhattan plot for Current Smokers sample, CPD10, 2df test. The figure shows the Manhattan plot for the analysis of the Current Smokers sample using the CPD10 interacting covariate. The –log(P) value adjusted for genomic inflation for the 2df test is shown. A significantly associated locus is located on chromosome 6 (–log(P) = 7.7) 149 kb from SASH1. A second significantly associated locus is located on chromosome 7 (–log(P) = 7.3) about 36 kb from the genes KLHL6 and KLHL24.

These results indicate that gene-smoking interactions can reveal novel BP loci and that a sample consisting exclusively of current smokers likely provides the greatest power to detect such interactions even when the sample size is considerably smaller than an alternative sample containing former smokers and those who have never smoked. In addition, these analyses implicate SASH1 as modulating the effect of smoking on BP.

and CPD were determined at the time of the visit without reference to the duration of smoking or smoking abstinence; therefore, subjects who recently quit were considered equal to nonsmokers and new smokers were considered equal to life-long smokers. The CPD values analyzed were based on self-reports that averaged changing CPD values over time and may not be precise. Finally, although these hypotheses will be investigated in a large CHARGE Consortium (devoted to gene-lifestyle interactions in cardiovascular traits), our findings have not been replicated in external samples.

Limitations This study has several limitations. It only analyzed data from one ethnic group, Caucasians, thus limiting generalizability to other racial and ethnic groups. Smoking status

Acknowledgments We greatly appreciate very helpful comments from Laura Beirut, M.D., both during the design of this investigation as well as on Genetic Epidemiology, Vol. 39, No. 6, 480–488, 2015

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an earlier draft of the manuscript. We thank all participants of the Framingham Heart Study for their dedication to cardiovascular health research. Our investigation was supported partly by grants R01 HL107552, R01 HL118305, and K25HL121091 from the National Heart, Lung, and Blood Institute (NHLBI). The Framingham Heart Study is conducted and supported by the NHLBI in collaboration with Boston University (Contract No. N01-HC-25195). Funding for SHARe Affymetrix genotyping was provided by NHLBI Contract N02-HL-64278. This manuscript was not prepared in collaboration with investigators of the Framingham Heart Study and does not necessarily reflect the opinions or views of the Framingham Heart Study, Boston University, or the NHLBI.

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Influence of Smoking Status and Intensity on Discovery of Blood Pressure Loci Through Gene-Smoking Interactions.

Genetic variation accounts for approximately 30% of blood pressure (BP) variability but most of that variability has not been attributed to specific v...
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