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. Author manuscript; available in PMC: 2013 Jul 8.
Published in final edited form as: Hum Genet. 2012 Nov 29;132(3):323–336. doi: 10.1007/s00439-012-1246-3

Gene-environment interactions and obesity traits among postmenopausal African-American and Hispanic women in the Women’s Health Initiative SHARe Study

Digna R Velez Edwards 1, Adam C Naj 2, Keri Monda 3, Kari E North 3, Marian Neuhouser 4, Oyunbileg Magvanjav 1, Ibukun Kusimo 1, Mara Z Vitolins 5, JoAnn E Manson 6, Mary Jo O’Sullivan 7, Evadnie Rampersaud 8, Todd L Edwards 1
PMCID: PMC3704217  NIHMSID: NIHMS476796  PMID: 23192594

Abstract

Genome-wide association studies of obesity measures have identified associations with single nucleotide polymorphisms (SNPs). However, no large-scale evaluation of gene-environment interactions has been performed. We conducted a search of gene-environment (G×E) interactions in post-menopausal African-American and Hispanic women from the Women’s Health Initiative SNP Health Association Resource GWAS study. Single SNP linear regression on body mass index (BMI) and waist-to-hip circumference ratio (WHR) adjusted for multidimensional-scaling-derived axes of ancestry and age was run in race-stratified data with 871,512 SNPs available from African-Americans (N=8,203) and 786,776 SNPs from Hispanics (N=3,484). Tests of G×E interaction at all SNPs for recreational physical activity (met-hrs/wk), dietary energy intake (kcal/day), alcohol intake (categorical), cigarette smoking years, and cigarette smoking (ever vs. never) were run in African-Americans and Hispanics adjusted for ancestry and age at interview, followed by meta-analysis of G×E interaction terms. The strongest evidence for concordant G×E interactions in African-Americans and Hispanics was for smoking and marker rs10133840 (Q statistic P=0.70, beta=−0.01, P=3.81×10−7) with BMI as the outcome. The strongest evidence for G×E interaction within a cohort was in African-Americans with WHR as outcome for dietary energy intake and rs9557704 (SNP×kcal =−0.04, P=2.17×10−7). No results exceeded the Bonferroni–corrected statistical significance threshold.

Keywords: BMI, WHR, genetic epidemiology, disparity, obesity, GWAS

INTRODUCTION

Globally, the prevalence of obese (body mass index (BMI)≥30) and overweight (BMI≥25) adults (persons with age≥15y) has increased to approximately 1.6 billion individuals worldwide according to WHO estimates (2009). The public health impact of obesity is substantial, since the condition is associated with increased risks for several common co-morbidities, including type 2 diabetes, cardiovascular disease, dyslipidemia, hypertension, sleep apnea, and several forms of cancer including postmenopausal breast cancer (Field et al., 2001; Must et al., 1999). As a result, the obesity epidemic has become major a global public health problem and economic burden.

Multiple lines of evidence indicate that a large proportion of obesity risk is mediated by genetic factors, with studies estimating that 40–90% of human population variation of BMI is due to genetic risk factors (Hjelmborg et al., 2008; Maes et al., 1997; Wardle et al., 2008). Over the last few years, several genome wide association studies (GWAS) have succeeded in identifying novel reproducible associations in candidate genes, although these appear to explain only a small proportion of obesity risk. Several of these genes are expressed or are known to function in the central nervous system (CNS) (Frayling et al., 2007; Loos et al., 2008; Meyre et al., 2009; Speliotes et al., 2010; Thorleifsson et al., 2009; Willer et al., 2009) supporting their role in regulating food intake and metabolism.

In the US, the obesity epidemic disproportionately affects ethnic minorities, including African-Americans (Ogden et al., 2006). Hispanic and African-American (non-Hispanic) adults in the U.S. are overweight and obese more often than European-American (non-Hispanic) adults (Flegal et al., 2010). For example, about half of non-Hispanic African-American and Hispanic women in their forties and fifties are obese, whereas only 36 percent of same-age European American women are obese (Flegal et al., 2010). The causes of the excess are poorly understood, but may be related to behavioral, environmental and genetic risk factors (Ogden et al., 2006). Although understanding the way in which genotype and environmental/behavioral risk factors interact may offer insights into modifiable behavioral changes that could reduce obesity risk, few studies have been performed to evaluate those phenomena (Hetherington and Cecil, 2010).

In the present study, we examined G×E interactions between single nucleotide polymorphisms (SNPs) genotyped in multi-ethnic GWAS that included African-American and Hispanic postmenopausal women in the Women’s Health Initiative (WHI)-Observational Study and the WHI-Clinical Trial. Our primary outcomes of interest were BMI and waist-to-hip ratio (WHR) and we focused on environmental/behavioral risk factors known to be associated with body weight, including recreational physical activity levels, total dietary energy intake, cigarette smoking, and alcohol intake.

MATERIALS AND METHODS

Study Population

The data used in this study were obtained from African-American and Hispanic women genotyped as part of the WHI-Observational Study (WHI-OS) and randomized clinical trial (WHI-CT) SNP Health Associated Resource (SHARe) GWAS of minority women. The WHI-OS is a prospective study that recruited 93,676 postmenopausal women ages 50–79 across 40 clinical centers in the United States between October 1, 1993 and December 31, 1998 who were not participating in the WHI-CT (1998;2009; Curb et al., 2003; Hays et al., 2003; Langer et al., 2003). The original protocol followed women until 2005; the women were then invited to participate in the WHI-OS Extension Study for continued follow-up through 2010. Women were excluded from participating in the WHI-OS if they were currently participating in an existing randomized trial, had medical complications that predicted survival at less than 3 years, or conditions that prevented study participation or adherence (1998;2009; Curb et al., 2003; Hays et al., 2003; Langer et al., 2003). The WHI-CT enrolled 68,132 postmenopausal women between 50–79 years of age from 1993–1998 and grouped women into trials focused on three prevention strategies: hormone therapy, dietary modification, and calcium/vitamin D (Carty et al., 2011; Manson et al., 2007; Neuhouser et al., 2009). Within the cohort of women that include the WHI-OS and WHI-CT we conducted a retrospective case-control study of African-American and Hispanic women who provided blood samples and were subsequently genotyped as part of the WHI-OS SHARe GWAS for minority women.

Information regarding demographics, clinical, behavioral characteristics, medical history, lifestyle/behavioral factors, and details of physical activity, among other risk factors, were obtained by standardized self-administered questionnaires at baseline. Measurements for weight, height, waist, and hip were ascertained during a baseline physical examination conducted by WHI. Protocols and ascertainment for WHI have been previously described (1998;2009; Curb et al., 2003; Hays et al., 2003; Langer et al., 2003). Variables examined in this study include: age at baseline interview (years [y]), BMI (kg/m2), WHR measurements, recreational physical activity (met-hrs/week), total dietary energy intake (kcal/day), cigarette smoking (y), cigarette smoking (ever vs. never), and alcohol intake (categorical, nondrinkers [referent], past drinkers, <1 drink/month, <1 drink/week, 1 to 7 drinks/week, ≥7 drinks/week).

The primary outcomes for this study were BMI and WHR, as measured in baseline interviews. Among the 12,008 women who were genotyped as part of the WHI SHARe GWAS dataset 8,400 African-American women and 3,587 Hispanic women had either BMI or measurements of WHR from the baseline interview.

Genotyping

The Affymetrix Human SNP Array 6.0 (Affymetrix®, Inc Santa Clara, CA) was used for genome wide SNP genotyping. Genomic DNA was quantitated via an ND-8000 spectrophotometer and DNA quality was evaluated via gel electrophoresis. The genomic DNA samples were processed according to standard Affymetrix procedures for processing of the assay. The data were processed for genotype calling using the Affymetrix® Genotypic Console software using the Birdseed calling algorithm version 2.0 (Affymetrix®, Inc., Santa Clara, CA) (Korn et al., 2008).

Quality Control

Data on 909,622 SNPs and 12,008 individuals were available prior to implementation of quality control. No individuals were removed after excluding individuals with low genotyping efficiency (<95%). 251 individuals were removed after kinship estimates identified related individuals using identity-by-descent sharing from a random selection of 100,000 autosomal SNPs. When a pair of related individuals was identified, only one member (parent or sibling) of the family was included, with priority given to parent over offspring. 11,757 unrelated women remained in the final dataset, and among these 11,687 had baseline measures of BMI.

All SNPs were tested for deviation from Hardy-Weinberg equilibrium (HWE) using PLINK software, stratified by race (Purcell et al., 2007). We excluded SNPs with HWE P≤10−6, dropping 17,562 SNPs in African-Americans and 8,571 SNPs in Hispanics. Markers with low genotyping efficiency (<95%) were also excluded, 6,020 SNPs in African-Americans and 6,967 SNPs in Hispanics. SNPs with a MAF<0.01 were also excluded, dropping 14,528 SNPs in African-Americans and 47,845 SNPs in Hispanics. Finally, those SNPs that did not map to a chromosomal position were excluded. This resulted in the removal of 62 SNPs in African-American and 57 SNPs in Hispanic women. Only SNPs with a MAF≥0.05 were considered to maintain stability of effect estimates and statistical validity for hypothesis tests at interaction terms, resulting in the removal of 84,674 SNPs in African-Americans and 130,820 SNPs in Hispanics and a total of 786,776 SNPs in African-Americans and 706,791 SNPs in Hispanics available for analysis. Quality control procedures are presented in Supplemental Figure 1.

In order to assess population stratification among African-American and Hispanic samples multi-dimensional scaling (MDS) was employed using PLINK software (Purcell et al., 2007) to estimate continuous axes of ancestry. The top two MDS components were extracted among African-American and Hispanic groups individually and used as covariates in the statistical models to test for association (Supplemental Figure 2).

Quantitative and ordinal variables were examined for normality, skewness, and kurtosis by performing the Shapiro-Wilk test of normality, visual inspection of normal quantile and histogram plots, and kurtosis and skewness summary statistics available from analysis in the Stata statistical software package, version 11 (StataCorp, College Station, TX, USA). Quantile-quantile (QQ) plots for each gene-environment interaction were examined and are provided in Supplemental Figures 3–4.

Statistical Analyses

The associations between each genetic marker and BMI, and WHR adjusted for BMI were assessed using linear regression stratified by race while adjusting for age and MDS-derived axes of ancestry using PLINK (Purcell et al., 2007). Demographic variables were analyzed with two-sample Wilcoxon rank-sum tests to compare between racial groups when variables were continuous, and Chi-squared tests were used for binary variables. Analyses of demographic data and transformations were conducted using Stata 11.

The relationship between BMI and WHR and environmental risk factors related to obesity were investigated separately for BMI and WHR as outcomes. Each factor was regressed separately on BMI and WHR adjusted for BMI in a linear regression model while adjusting for age. A robust measure of standard error was used in these regression analyses. BMI and WHR were not normally distributed; therefore we performed a Box-Cox transformation for both BMI and WHR. The normality of regression residuals was verified to evaluate the modeling approach. Histograms of the BMI and WHR distributions are provided in Supplemental Figure 5A and 5B and Supplemental Figure 6A and 6B. Dietary energy was also non-normally distributed and had a highly right-skewed distribution; after evaluating transformations, a log transformation was used.

For the G×E interaction investigation of BMI and WHR adjusted for BMI, we also included adjustments for MDS-derived axes of ancestry, age at interview, and terms for the environmental variable and additively encoded SNP genotypes in linear regression models. We followed up G×E interaction analyses with random-effects meta-analysis between African-American and Hispanic results using PLINK (Purcell et al., 2007), because underlying differences in population histories, correlations among SNPs, and modifiers may lead to heterogeneity in interaction effects. All P-values are two-sided.

The most significant gene-environment interactions in AAAfrican-American and Hispanic subjects with BMI as outcome were also evaluated using logistic regression subdividing BMI into strata (BMI<25.0 [referent], 25.0–29.9, 30.0–34.9, and ≥35.0 kg/m2) and evaluating each category vs. BMI<25.0 kg/m2 as the outcome to estimate the change in risk of overweight, obesity, and severe obesity, respectively. These models were adjusted for age at interview and ancestry.

RESULTS

Demographic data

This study included 8,203 African-American and 3,484 Hispanic female participants (Table 1). African-Americans had a higher BMI overall (mean=31.0 kg/m2±6.4) and proportion of obese (BMI≥30.0 kg/m2) individuals (50.3%) compared to Hispanics (mean BMI=28.9±5.6 kg/m2, 35.9% obese) (P<10−4). However, WHR was higher overall among Hispanics (mean=0.82±0.07) than African-Americans (mean=0.79±0.59) (P<10−4). African-Americans reported approximately 14.5% more individuals who had ever smoked cigarettes when compared to Hispanics (37.6% “ever” smokers) (P<10−4). African-Americans also reported more years of cigarette smoking (mean = 1.93 years) when compared to Hispanics (mean=1.14y) (P<10−4). Hispanics reported higher mean recreational physical activity hrs/week overall, total dietary energy intake, and alcohol intake (servings [12 ounces]/week).

TABLE 1.

Summary of demographic data

Variable African-American (N=8,203) Hispanic (N=3,484) P-Value
N Mean(S.D)/% N Mean(S.D)/%
Age at Interview (years) 8,203 61.6 (7.0) 3,484 60.3 (6.7) <10−4
BMI (kg/m2) 8,203 31.0 (6.4) 3,484 28.9 (5.6) <10−4
 <25 1,329 16.2% 880 25.3% <10−4
 ≥25–<30 2,745 33.5% 1355 38.9%
 ≥30–<35 2,192 26.7% 808 23.2%
 ≥35 1,937 23.6% 441 12.7%
Waist-Hip Ratio 8,203 0.79 (0.59) 3,468 0.82 (0.07) <10−4
Weight (kg) 8,203 81.9 (17.3) 3,484 71.6 (14.4) <10−4
Diabetes 8,190 3,483 <10−4
 No 7,047 85.9% 3,192 91.7%
 Yes 1,143 13.9% 291 8.4%
Cigarette Smoking 8,111 3,447 <10−4
 Never 3,885 47.9% 2,150 62.4%
 Ever 4,226 52.1% 1,297 37.6%
Cigarette Smoking (years) 7,942 1.93 (2.20) 3,373 1.14 (1.79) <10−4
Recreational Physical Activity (met–hrs/week) 8,021 9.7 (12.7) 3,312 10.8 (13.8) 0.0003
Total Dietary Energy Intake (kcal/day) 8,190 1,599.6 (950.6) 3,478 1,656.8 (972.2) 0.001
Alcohol (servings (12 oz. [ounces] /week) 8,180 1.10(4.4) 3,467 1.27(3.8) <10−4
Alcohol intake 8,190 3,428 <10−4
 Non drinker 1,296 16.0% 636 18.6%
 Past drinker 2,692 33.3% 736 21.5%
 < 1 drink/month 1,093 13.5% 461 13.5%
 < 1drink/week 1,535 19.0% 778 22.7%
 1 to 7 drinks/week 1,130 14.0% 643 18.8%
 ≥ 7 drinks/week 346 4.3% 174 5.1%
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Environmental/behavioral risk factors and BMI and WHR

Table 2 summarizes the association between BMI and WHR and other candidate environmental/behavioral risk factors. We examined age at interview, years of cigarette smoking, “ever” vs. “never” cigarette smoking, recreational physical activity, total dietary energy intake, and alcohol intake (categorically coded with nondrinker as referent). We observed statistically significant associations with BMI decreasing and WHR increasing with age at interview. BMI and WHR were inversely associated with recreational physical activity and alcohol intake in African-Americans and Hispanics. Increasing dietary energy intake significantly increased both WHR and BMI in African-Americans and Hispanics. We also observed decreases of BMI with years of cigarette smoking among African-Americans, but not Hispanics where they were weakly positively correlated, but without statistically significant association (Beta=0.001, 95% CI [−0.02–0.02], P=0.953). We also observed significant increases in WHR with years of smoking in African-Americans and Hispanics. All evaluated factors except recreational physical activity and alcohol intake increased either BMI or WHR with greater exposure.

TABLE 2.

Association between BMI or WHR adjusted for BMI and environmental/behavioral risk factors adjusted for age at interview and ancestry

Variable African-American (N=8,203)
P Hispanic (N=3,484)
P
Beta 95% CI Beta 95% CI
BMI Outcome
Age at Interview (years) −0.09 −0.11 −0.07 <10−10 −0.01 −0.02, −0.005 2.31×10−4
Cigarette Smoking (years) −0.10 −0.17– −0.03 0.008 0.001 −0.02, 0.02 0.953
Cigarette Smoking (ever/never) −0.09 0.34–0.21 0.537 0.05 −0.04, 0.13 0.278
Recreational Physical Activity (met–hrs/week) −.08 −0.09– −0.07 <10−10 −0.01 −0.02, −0.01 <10−10
Total Dietary Energy Intake (kcal/day) 0.03 0.03–0.04 1.00×10−10 0.008 0.006, 0.01 <10−10
Alcohol Intake (categorical) −0.59 −0.70– −0.49 <10−10 −0.10 −0.13, −0.08 <10−10
WHR Outcome
Age at Interview (years) 0.11 0.08–0.14 <10−10 0.15 0.11, 0.20 <10−10
Cigarette Smoking (years) 0.39 0.30–0.48 <10−10 0.36 0.19, 0.53 3.99×10−5
Cigarette Smoking (ever/never) 1.31 0.92–1.70 <10−10 0.96 0.34, 1.58 0.002
Recreational Physical Activity (met–hrs/week) −0.08 −0.10– −0.07 <10−10 −0.07 −0.10, −0.05 <10−10
Total Dietary Energy Intake (kcal/day) 0.03 0.02–0.03 1.20×10−9 0.02 0.005, 0.03 0.005
Alcohol Intake (categorical) −0.39 −0.53– −0.26 1.05×10−8 −0.65 −0.84, −0.46 1.00×10−10
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divided variables by 100 to simplify presentation

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Total dietary energy intake was log transformed

Gene-environment interactions

Potential interactions between each additively modeled genetic marker and continuous candidate obesity risk factors were examined within each racial group. The most statistically significant result from each set of tests for the transformed outcomes is provided in Table 3 for African-Americans and Table 4 for Hispanics. In order to facilitate interpretation of p values untransformed Betas are provided in Supplemental Tables 1 and 2. Summaries of single locus tests of association for SNPs highlighted for G×E interactions are provided in Supplemental Tables 3–4. We also present estimates of risk modification for other featured models of GxE interactions in Supplemental Table 5.

TABLE 3.

Summary of strongest gene x environment associations with transformed BMI and WHR adjusted for BMI among African-Americans adjusted for age at interview and ancestry.

Nearby Gene Chr. Interaction Position (bp) MA MAF BetaSNP (95% CI) BetaEnv (95% CI) BetaIntrxn (95% CI) P-Value Intrxn
BMI Outcome
LOC645687||PELI2 14 rs7350721-Smoking(years) 55403266 C 0.09 −0.01 (−0.02, −0.01) −0.15 (−0.22, −0.08) 0.41 (0.25, 0.58 5.97×10−7
LOC347097||PIP5K1B 9 rs7864204-Recreational Physical Activity 70418999 A 0.44 0.005 (0.002, 0.01) −0.04 (−0.06, −0.02) −0.04 (−0.06, −0.02) 8.70×10−7
CNTNAP2 7 rs4549702-Dietary Energy 147531949 G 0.40 −0.03 (−0.04, −0.01) 0.02 (0.01, 0.03) 0.02 (0.01, 0.03) 1.41×10−6
LOC100128373||DIO3OS 14 rs8008758-Alcohol Intake 100759798 A 0.25 −0.01 (−0.02, −0.01) −0.70 (−0.83, −0.57) 0.39 (0.23, 0.55) 2.32×10−6
LOC645687||PELI2 14 rs7350721-Smoking(ever/never) 55403266 C 0.09 −0.01 (−0.02, −0.01) −0.08 (−0.11, −0.06) 0.02 (0.01, 0.02) 3.90×10−6
WHR Outcome
ITGBL1 13 rs9557704-Dietary Energy 101025496 A 0.13 0.05 (0.03, 0.07) 0.02 (0.02, 0.03) −0.04 (−0.06, −0.03) 2.15×10−7
MGMT 10 rs11016883-Smoking(ever/never) 131390930 C 0.25 −0.01 (−0.01, −0.003) 0.15 (0.12, 0.17) 0.01 (0.01, 0.02) 8.51×10−7
ZNF385D||LOC728516 3 rs1388551-Recreational Physical Activity 22267575 A 0.17 0.01 (0.001, 0.01) −0.03 (−0.05, −0.02) −0.07 (−0.10, −0.04) 1.87×10−6
LOC642340 2 rs16827293-Smoking(years) 150217578 A 0.12 0.01 (0.002, 0.01) 0.52 (0.43, 0.62) −0.44 (−0.63, −0.25 5.23×10−6
SEPT11 4 rs17002342-Alcohol Intake 78174229 T 0.13 −0.02 (−0.03, −0.01) −0.30 (−0.45, −0.16) 0.61 (0.34, 0.88) 9.22×10−6
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BetaSNP-Beta coefficient for the SNP in the model; BetaEnv-Beta coefficient for the lifestyle factor included in the interaction; BetaIntrxn-Beta coefficient for the interaction term in the model; CI-Confidence interval; Intrxn-interaction

TABLE 4.

Summary of strongest gene x environment associations with transformed BMI and WHR adjusted for BMI among Hispanics adjusted for age at interview and ancestry

Nearby Gene Chr. Interaction Position (bp) MA MAF BetaSNP (95% CI) BetaEnv (95% CI) BetaIntrxn (95% CI) P-Value Intrxn
BMI Outcome
RPL15P2||DICER1 14 rs10133840-Dietary Energy 94480282 C 0.14 0.01 (0.005, 0.01) 0.01 (0.007, 0.01) −0.01 (−0.01, −0.005) 1.28×10−6
SLC39A11 17 rs4969049-Smoking(ever/never) 68512535 C 0.17 −0.001 (−0.002, −0.0004) −0.001 (−0.002, 0.0001) 0.004 (0.002, 0.01) 1.35×10−6
CNTNAP2 7 rs4549702-Recreational Physical Activity 147531949 G 0.34 0.001 (0.0003, 0.002) −0.01 (−0.01, −0.003) −0.01 (−0.02, −0.01) 1.37×10−6
LOC100130240||KCNE2 21 rs1013063-Smoking (years) 34620260 T 0.38 0.0004 (−0.0003, 0.001) 0.07 (0.03, 0.10) −0.08 (−0.11, −0.04) 4.56×10−6
LRP8 1 rs2788032-Alcohol Intake 53550219 C 0.08 −0.004 (−0.01, −0.002) −0.12 (−0.15, −0.09) 0.15 (0.09, 0.22) 5.68×10−6
WHR Outcome
MOSPD2||LOC100128521 X rs5980075-Dietary Energy 14860452 T 0.47 −0.04 (−0.06, −0.03) −0.04 (−0.06, −0.02) 0.04 (0.03, 0.06) 5.09×10−7
LOC152225||ZPLD1 3 rs10212363-Alcohol Intake 103331251 A 0.22 −0.02 (−0.03, −0.01) −0.64 (−0.86, −0.42) 0.73 (0.43, 1.02) 1.58×10−6
CDK4PS||LOC727839 1 rs11184708-Smoking(ever/never) 106231810 T 0.13 −0.01 (−0.02, −0.004) 0.002 (−0.004, 0.01) 0.03 (0.02, 0.04) 2.11×10−6
BCAM||PVRL2 19 rs1871045-Smoking (years) 50018608 T 0.39 −0.003 (−0.01, 0.002) 0.002 (−0.22, 0.23) 0.52 (0.30, 0.74) 3.95×10−6
LOC100132060||TBR1 2 rs1064576-Recreational Physical Activity 161976354 A 0.06 0.02 (0.01, 0.03) −0.02 (−0.04, −0.001) −0.15 (−0.21, −0.08) 3.04×10−5
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BetaSNP-Beta coefficient for the SNP in the model; BetaEnv-Beta coefficient for the lifestyle factor included in the interaction; BetaIntrxn-Beta coefficient for the interaction term in the model; CI-Confidence interval; Intrxn-interaction

BMI Outcome

The strongest gene by environment interaction with BMI as outcome among African-Americans was for the interaction between rs7350721 and cigarette smoking (years) (Betainteraction=0.41, 95% CI [0.25,0.58]; P=5.97×10−7). This SNP was not located within a gene but was between LOC645687 and the gene pellino homolog 2 (PELI2). This SNP also had the strongest gene– “ever” vs. “never” cigarette smoking interaction with BMI (Betainteraction=0.02, 95% CI [0.01, 0.02]; P=3.90×10−6). Among the strongest gene by environment interactions among African-Americans with BMI as outcome, only one interaction involved a SNP located within a gene-rs4549702. The gene was contactin associated protein-like 2 (CNTNAP2) and interacted with dietary energy intake (Betainteraction =0.02, 95% CI [0.01,0.03]; P=1.41×10−6).

The strongest association with BMI among Hispanics was for an interaction between rs10133840 and dietary energy intake (Betainteraction=−0.01, 95% CI [−0.01, −0.005]; P=1.28×10−6) (Table 4), a SNP located between the ribosomal protein L15 pseudogene 2 (RPL15P2) gene and LOC730118 on chromosome 14q32.13. Although variation in CNTPAP2 did not interact with dietary energy intake for BMI, the same SNP that interacted among African-Americans (rs4549702) modified the effect of recreational physical activity on BMI among Hispanics (Betainteraction=−0.01, 95% CI [−0.02, −0.01]; P=1.37×10−6).

Among the strongest G×E interactions with BMI in each racial group, we examined effect modification with BMI coded categorically (BMI<25 [referent] versus BMI=25.0–29.9, 30.0–34.9, and ≥35) and analyzed with logistic regression. Examining the interactions between rs7350721 and years of cigarette smoking among African-Americans and between rs10133840 and dietary energy intake among Hispanics, showed that effect modification was stronger among subjects with higher BMI (>30) for both interactions (Table 5). The results were not consistent across racial groups.

TABLE 5.

Summary of strongest associated gene x environment associations (TABLE 3 and 4) by BMI strata

Population Interaction BMI Category N ORSNP (95% CI) OREnv (95% CI) ORIntrxn (95% CI) P-Value Intrxn
African-Americans rs7350721- Smoking(years) <25 1,329 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) -
≥25–<30 2,745 0.89 (0.73, 1.09) 0.97 (0.94, 1.01) 1.02 (0.96, 1.10) 0.487
≥30–<35 2,192 0.74 (0.59, 0.92) 0.95 (0.92, 0.99) 1.12 (1.04, 1.21) 0.002
≥35 1,937 0.65 (0.50, 0.83) 0.95 (0.92, 0.99) 1.17 (1.07, 1.27) 2.03×10−4
Hispanics rs7350721- Smoking(years) <25 880 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) -
≥25–<30 1,355 0.96 (0.82, 1.12) 1.02 (0.96, 1.09) 1.02 (0.95, 1.10) 0.502
≥30–<35 808 1.07 (0.89, 1.28) 1.03 (0.96, 1.11) 1.02 (0.93, 1.11) 0.726
=35 441 1.12 (0.91, 1.38) 1.01 (0.92, 1.38) 0.96 (0.86, 1.07) 0.498
African-Americans rs10133840- Dietary Energy <25 1,329 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) -
≥25–<30 2,745 2.40 (1.11, 5.20) 1.55 (1.11, 5.20) 0.48 (0.25, 0.93) 0.028
≥30–<35 2,192 1.23 (0.55, 2.71) 2.43 (1.74, 3.37) 0.84 (0.43, 1.64) 0.607
≥35 1,937 2.24 (0.92, 5.47) 4.76 (3.38, 69.72) 0.50 (0.24, 1.05) 0.066
Hispanics rs10133840- Dietary Energy <25 880 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) -
≥25–<30 1,355 4.63 (1.90, 11.26) 3.93 (2.50, 6.19) 0.28 (0.13, 0.61) 0.001
≥30–<35 808 6.36 (2.39, 16.93) 7.73 (4.50, 13.26) 0.20 (0.09, 0.46) 1.74×10−4
≥35 441 8.29 (2.25, 30.50) 19.94 (10.30, 38.6) 0.14 (0.05, 0.44) 0.001
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ORSNP-Odds ratio for SNP association in the model; OREnv-Odds ratio for lifestyle factor in the model; ORIntrxn-Odds ratio for interaction term in the model; CI-Confidence interval; Intrxn-interaction

WHR Outcome

The strongest G×E interaction for WHR among African-Americans was between SNP rs9557704 in the integrin, beta-like 1 (ITGBL1) gene and dietary energy intake (Betainteraction=−0.04, 95% CI [−0.06, −0.03]; P=2.17×10−7). Also notable is an association observed between SNP rs11016883 in the methylguanine-DNA methyltransferase (MGMT) gene and cigarette smoking (ever/never) (Betainteraction=0.01, 95% CI [0.007,0.02]; P=8.51×10−7).

The strongest G×E interaction among Hispanics examining WHR as outcome was between the SNP rs5980075, located between LOC100128521 and motile sperm domain-containing protein 2 isoform 1 (MOSPD2) gene, and dietary energy intake (Betainteraction=0.04, 95% CI [0.03,0.06]; P=5.09×10−7).

DISCUSSION

Evaluating the WHI GWAS data for G×E interactions affecting both BMI and WHR in two distinct ethnic cohorts of women, we observed several novel interactions. While strong interactions were detected, these interactions did not replicate across cohorts. Possible contributors to the heterogeneity between the two ethnic groups examined include differences in patterns of environmental exposures and demographic histories. Furthermore, since these analyses were conducted using common variants from GWAS, differences in linkage disequilibrium and the potential for latent functional rare variants to be private across racial groups may lead to failure to formally replicate. In this study, all tested environmental/behavioral factors differed between African-Americans and Hispanic subjects. Hispanic subjects had significantly lower BMI and higher WHR than African-Americans. Hispanic subjects were younger, had higher physical activity, dietary energy intake, and alcohol consumption than African-Americans, although they smoked less, and had a smaller proportion of subjects with T2D.

We detected associations between most lifestyle factors and BMI and/or WHR. We observed an inverse association between years of smoking and BMI in African-Americans, and a positive association between both years of smoking and “ever”/“never” smoking and WHR in both ethnicities, which is consistent with previous findings (Chouraki et al., 2008; Simon et al., 1997). There were significant positive associations with BMI and WHR across populations for dietary energy intake and negative associations with BMI and WHR for physical activity and alcohol consumption. Age was negatively associated with BMI but positively associated with WHR in both groups, which is consistent with previous reports that show increasing WHR with age even in the absence of weight gain, and decreasing BMI with age.(Stevens et al., 2010)

The SNP rs10133840 interacted with dietary energy intake in Hispanics and in the meta-analysis. This SNP lies in an intergenic region of chromosome 14q32, bounded by the ribosomal protein L15 pseudogene and dicer 1, ribonuclease type III (DICER1) gene. Ribosomal protein L15 pseudogene encodes an untranscribed ribosomal protein; however, DICER1 is an important mediator of vertebrate development (Murchison and Hannon, 2004) and is essential for life (Bernstein et al., 2003; Wienholds et al., 2003). DICER1 is a member of the ribonuclease III family and is involved in the processing of microRNAs, which modulate gene expression after transcription (Macrae et al., 2006). Additionally, variation nearby DICER1 has been associated with the response of blood lipid levels to statin therapy (Barber et al., 2010a). Potential implications for a role of variation in DICER1 on BMI also include dysregulation of metabolism due to association of DICER1 variants and risk of multinodular thyroid goiters (Rio et al., 2011). These connections to thyroid dysfunction and lipid homeostasis suggest that this gene plays a role in metabolism and energy balance. Our findings support this model for DICER1, and suggest that these effects may also be mediated by dietary energy intake.

SNPs rs141320 and rs2144134, located in the gene serine palmitoyltransferase, long-chain base subunit 3 (SPTLC3), interacted with physical activity and alcohol consumption, respectively, with BMI in the meta-analysis. Notably, these interactions were in opposite directions where the interaction with physical activity increased BMI, and the interaction with alcohol decreased BMI. SPTLC3 encodes an isoform of the third subunit of serine palmitoyltranferase, which catalyzes the rate-limiting step of sphingolipid synthesis. Sphingolipids are important components of cellular plasma membranes, and are involved in cell proliferation, differentiation, senescence, apoptosis, and inflammation, and are particularly enriched in nervous system tissues (Hannun and Obeid, 2002). The sphingolipid synthesis genes are conserved back to yeast and are also essential for embryonic development (Hojjati et al., 2005; Ikushiro et al., 2001; Ikushiro et al., 2003). This gene has also been associated with levels of sphingolipids and bipolar disorder in previous GWAS studies (Alliey-Rodriguez et al., 2011; Hicks et al., 2009).

The SNP rs4549702 in the gene CNTNAP2 interacted with physical activity to decrease BMI in Hispanics, and interacted with dietary energy intake to increase BMI in African-Americans. CNTNAP2 is a member of the neurexin superfamily, a group of proteins that mediate cell-cell interactions in the nervous system. Rare recessive genetic variation in CNTNAP2 has been associated with loss-of-function mutations in epilepsy, intellectual disability, and autism spectrum disorders, accompanied by a range of neuropathologic findings (Strauss et al., 2006). Additional findings suggest associations with specific language impairment, as well as rare mutations in Tourette syndrome, syndromic intellectual disability, and schizophrenia (State MW, 2010). Further support for a connection between CNTNAP2 and autism was also provided by a whole-exome resequencing study (O’Roak et al., 2011). Previous reports have suggested that autistic patients have increased risk for obesity, even in childhood (Curtin et al., 2010; Tyler et al., 2011), and several mutations have been observed that seem to confer risk of both obesity and various developmental delays (Bochukova et al., 2010; Dykens et al., 2011; Shinawi et al., 2011; Walters et al., 2010). CNTNAP2 was also associated with schizophrenia and bipolar in a recent GWAS (Wang et al., 2010), as well as bone mass and geometry (Kiel et al., 2007). The apparent biological relationship between obesity, developmental delay and neurological dysregulation is consistent with a role for CNTNAP2 in obesity susceptibility, although this association seems to be modified by environmental factors related to energy balance.

The SNP rs1013063 near the gene potassium channel, voltage gated, ISK-related subfamily, member 2 (KCNE2) interacted with years of smoking to decrease BMI. KCNE2 has been associated with lung function (Artigas et al., 2011), height (Lango et al., 2010), and early-onset myocardial infarction (Kathiresan et al., 2009). Studies in humans and mice have also demonstrated that KCNE2 gene products form a thyroid stimulating hormone-stimulated potassium channel that is required for thyroid hormone biosynthesis (Roepke et al., 2009). KCNE2 is important for controlling metabolism and energy balance, and smoking or secondary effects on lung function may perturb this regulatory system.

Several other associated SNPs lie nearby or within genes with previously described behavioral, metabolism, or body composition phenotypes. The SNP rs9557704, located in the ITGBL1 gene interacted with dietary energy intake to increase WHR in African-Americans. Homozygous deletion of the ITGBL1 gene was observed in a pediatric case with growth hormone deficiency (Cody et al., 2010). The SNP rs7864204, near the gene phosphatidylinositol-4-phosphate 5-kinase, type I, beta (PIP5K1B) interacted with recreational physical activity in African-Americans. Variation in PIP5K1B has been associated with chronic kidney disease risk in European ancestry individuals (Kottgen et al., 2010). The SNP rs8008758 interacted with alcohol to increase BMI in African-Americans and lies in a paternally imprinted region containing the gene deiodinase, iodothyrodine, type III (DIO3), which catalyzes the inactivation of thyroid hormone, and has also been associated with type I diabetes risk (Wallace et al., 2010). The SNP rs17002342 in the gene septin 11 (SEPT11) interacted with alcohol intake to increase WHR in African-Americans. Variation in SEPT11 has been associated with schizophrenia and bipolar disorder by a comparative protein expression study in hippocampus samples (Focking et al., 2011). The SNP rs11876941 in the gene deleted in colorectal carcinoma (DCC) interacted with history of smoking to increase BMI in the meta-analysis, and DCC has been associated with risk of alcoholism (Heath et al., 2011). The SNP rs4877280 near the gene solute carrier family 28, member 3 (SLC28A3) interacted with dietary energy intake to increase WHR in the meta-analysis, and has been associated with lipid-lowering response to statins (Barber et al., 2010b). The SNP rs1871045 near the gene poliovirus receptor related 2 (PVRL2) interacted with years of smoking in Hispanics to increase WHR. Variation in PVRL2 has been associated with Alzheimer’s disease risk and age of onset in studies of European and African-Americans (Abraham et al., 2008; Kamboh et al., 2011; Logue et al., 2011; Naj et al., 2010), and has also been associated with high-density lipoprotein cholesterol levels in East Asians (Kim et al., 2011). Also the SNP rs10212363 near the gene zona pellucida-like domain containing 1 (ZPLD1) interacted with alcohol intake to increase WHR in Hispanics. Variation in ZPLD1 has been nominally associated with type 2 diabetes in Southeast Asians (Sim et al., 2011).

Although not all interactions had clear relationship with the genes and/or SNPs involved in the interactions based on what is known about these genes and/or SNPs in the literature, agnostic studies like this may provide novel insights into the biology of complex traits. Similarly, some of these models may only seem implausible because little is known about the biology of the implicated genes in the context of lifestyle. We did not obtain conclusive statistical evidence for these models from this study, and so these results will provide support to future investigations for the candidacy of these genes as obesity risk loci. Many of the SNPs discussed here are plausible candidates for roles in behavior, body size, shape, and composition. Genes implicated in interactions here are involved in metabolic processes, such as endocrine diseases like diabetes, lipid metabolism and transport or bone development and maintenance, or are known neurological factors with effects on development and psychological well-being. While none of the results presented here survive correction for multiple comparisons, they suggest that genes with known effects on metabolism and behavior are likely to modify the roles of important environmental exposures on obesity phenotypes. Accounting for effect modification by behavioral and cultural factors may be essential for discovering the genetic determinants of human obesity, as these phenomena may collectively explain more variance for these traits than direct effects by these genes. These findings merit further replication in necessary in order to validate these interactions.

Supplementary Material

Figure
Table

TABLE 6.

Summary of strongest gene x environment associations with BMI and WHR adjusted for BMI for random-effects meta-analysis of African-American and Hispanic gene x environment interactions

Nearby Gene Chr. Interaction Position (bp) MA Beta(R) Q I2 P-Value (R)
BMI Outcome
RPL15P2||DICER1 14 rs10133840-Dietary Energy 94480282 C −0.01 0.70 0.00 3.81×10−7
SGK1||LOC442261 6 rs1763500-Smoking (years) 67343218 C 0.26 0.46 0.00 1.71×10−6
PA2G4P2||SPTLC3 20 rs1413020-Recreational Physical Activity 12678042 G 0.02 0.33 0.00 3.04×10−6
SPTLC3||LOC100130692 20 rs2144134-Alcohol Intake 13096152 G −0.09 0.87 0.00 3.68×10−6
DCC 18 rs11876941-Smoking (ever/never) 49160029 A −0.003 0.90 0.00 4.72×10−6
WHR Outcome
LOC359819||EEF1B3 5 rs10067755-Smoking (years) 134602211 C −0.12 0.45 0.00 1.64×10−6
POU3F1||LOC400750 1 rs11802770-Smoking (ever/never) 38494250 T 0.01 0.57 0.00 2.90×10−6
DNAJC10||FRZB 2 rs10931041-Dietary Energy 183393807 A −0.02 0.79 0.00 9.02×10−6
SLC28A3||NTRK2 9 rs4877280-Recreational Physical Activity 86376225 T 0.04 0.85 0.00 8.52×10−6
MAML2 11 rs11021499-Alcohol Intake 95631777 A −0.39 0.48 0.00 1.86×10−6
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Acknowledgments

This work was funded by Vanderbilt Clinical and Translational Research Scholar award (5KL2RR024975) to TLE. We would also like to acknowledge the Women’s Health Initiative Presentations and Publications committee for helpful comments during the preparation of this manuscript. Additional funds were provided by the Building Interdisciplinary Research Careers in Women’s Health career development program (K12HD4383) to DRVE.

Footnotes

The authors have no conflicts of interest to disclose.

Reference List

  1. Design of the Women’s Health Initiative clinical trial and observational study. The Women’s Health Initiative Study Group. Control Clin Trials. 1998;19:61–109. doi: 10.1016/s0197-2456(97)00078-0. [DOI] [PubMed] [Google Scholar]
  2. WHO Global Database on Body Mass Index (BMI) 2009. [Google Scholar]
  3. Abraham R, Moskvina V, Sims R, Hollingworth P, Morgan A, Georgieva L, Dowzell K, Cichon S, Hillmer AM, O’Donovan MC, Williams J, Owen MJ, Kirov G. A genome-wide association study for late-onset Alzheimer’s disease using DNA pooling. BMC Med Genomics. 2008;1:44. doi: 10.1186/1755-8794-1-44. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alliey-Rodriguez N, Zhang D, Badner JA, Lahey BB, Zhang X, Dinwiddie S, Romanos B, Plenys N, Liu C, Gershon ES. Genome-wide association study of personality traits in bipolar patients. Psychiatr Genet. 2011;21:190–194. doi: 10.1097/YPG.0b013e3283457a31. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Artigas MS, Loth DW, Wain LV, Gharib SA, Obeidat M, Tang W, Zhai G, Zhao JH, Smith AV, Huffman JE, Albrecht E, Jackson CM, Evans DM, Cadby G, Fornage M, Manichaikul A, Lopez LM, Johnson T, Aldrich MC, Aspelund T, Barroso I, Campbell H, Cassano PA, Couper DJ, Eiriksdottir G, Franceschini N, Garcia M, Gieger C, Gislason GK, Grkovic I, Hammond CJ, Hancock DB, Harris TB, Ramasamy A, Heckbert SR, Heliovaara M, Homuth G, Hysi PG, James AL, Jankovic S, Joubert BR, Karrasch S, Klopp N, Koch B, Kritchevsky SB, Launer LJ, Liu Y, Loehr LR, Lohman K, Loos RJ, Lumley T, Al Balushi KA, Ang WQ, Barr RG, Beilby J, Blakey JD, Boban M, Boraska V, Brisman J, Britton JR, Brusselle GG, Cooper C, Curjuric I, Dahgam S, Deary IJ, Ebrahim S, Eijgelsheim M, Francks C, Gaysina D, Granell R, Gu X, Hankinson JL, Hardy R, Harris SE, Henderson J, Henry A, Hingorani AD, Hofman A, Holt PG, Hui J, Hunter ML, Imboden M, Jameson KA, Kerr SM, Kolcic I, Kronenberg F, Liu JZ, Marchini J, McKeever T, Morris AD, Olin AC, Porteous DJ, Postma DS, Rich SS, Ring SM, Rivadeneira F, Rochat T, Sayer AA, Sayers I, Sly PD, Smith GD, Sood A, Starr JM, Uitterlinden AG, Vonk JM, Wannamethee SG, Whincup PH, Wijmenga C, Williams OD, Wong A, Mangino M, Marciante KD, McArdle WL, Meibohm B, Morrison AC, North KE, Omenaas E, Palmer LJ, Pietilainen KH, Pin I, Pola Sbreve EO, Pouta A, Psaty BM, Hartikainen AL, Rantanen T, Ripatti S, Rotter JI, Rudan I, Rudnicka AR, Schulz H, Shin SY, Spector TD, Surakka I, Vitart V, Volzke H, Wareham NJ, Warrington NM, Wichmann HE, Wild SH, Wilk JB, Wjst M, Wright AF, Zgaga L, Zemunik T, Pennell CE, Nyberg F, Kuh D, Holloway JW, Boezen HM, Lawlor DA, Morris RW, Probst-Hensch N, Kaprio J, Wilson JF, Hayward C, Kahonen M, Heinrich J, Musk AW, Jarvis DL, Glaser S, Jarvelin MR, Ch Stricker BH, Elliott P, O’Connor GT, Strachan DP, London SJ, Hall IP, Gudnason V, Tobin MD. Genome-wide association and large-scale follow up identifies 16 new loci influencing lung function. Nat Genet. 2011 doi: 10.1038/ng.941. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Barber MJ, Mangravite LM, Hyde CL, Chasman DI, Smith JD, McCarty CA, Li X, Wilke RA, Rieder MJ, Williams PT, Ridker PM, Chatterjee A, Rotter JI, Nickerson DA, Stephens M, Krauss RM. Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS One. 2010a;5:e9763. doi: 10.1371/journal.pone.0009763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Barber MJ, Mangravite LM, Hyde CL, Chasman DI, Smith JD, McCarty CA, Li X, Wilke RA, Rieder MJ, Williams PT, Ridker PM, Chatterjee A, Rotter JI, Nickerson DA, Stephens M, Krauss RM. Genome-wide association of lipid-lowering response to statins in combined study populations. PLoS One. 2010b;5:e9763. doi: 10.1371/journal.pone.0009763. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bernstein E, Kim SY, Carmell MA, Murchison EP, Alcorn H, Li MZ, Mills AA, Elledge SJ, Anderson KV, Hannon GJ. Dicer is essential for mouse development. Nat Genet. 2003;35:215–217. doi: 10.1038/ng1253. [DOI] [PubMed] [Google Scholar]
  9. Bochukova EG, Huang N, Keogh J, Henning E, Purmann C, Blaszczyk K, Saeed S, Hamilton-Shield J, Clayton-Smith J, O’Rahilly S, Hurles ME, Farooqi IS. Large, rare chromosomal deletions associated with severe early-onset obesity. Nature. 2010;463:666–670. doi: 10.1038/nature08689. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Carty CL, Kooperberg C, Neuhouser ML, Tinker L, Howard B, Wactawski-Wende J, Beresford SA, Snetselaar L, Vitolins M, Allison M, Budrys N, Prentice R, Peters U. Low-fat dietary pattern and change in body-composition traits in the Women’s Health Initiative Dietary Modification Trial. Am J Clin Nutr. 2011;93:516–524. doi: 10.3945/ajcn.110.006395. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Chouraki V, Wagner A, Ferrieres J, Kee F, Bingham A, Haas B, Ruidavets JB, Evans A, Ducimetiere P, Amouyel P, Dallongeville J. Smoking habits, waist circumference and coronary artery disease risk relationship: the PRIME study. Eur J Cardiovasc Prev Rehabil. 2008;15:625–630. doi: 10.1097/HJR.0b013e32830fe479. [DOI] [PubMed] [Google Scholar]
  12. Cody JD, Heard P, Hale D. Identification of two novel chromosome regions associated with isolated growth hormone deficiency. J Pediatr Endocrinol Metab. 2010;23:1159–1164. doi: 10.1515/jpem.2010.181. [DOI] [PubMed] [Google Scholar]
  13. Curb JD, McTiernan A, Heckbert SR, Kooperberg C, Stanford J, Nevitt M, Johnson KC, Proulx-Burns L, Pastore L, Criqui M, Daugherty S. Outcomes ascertainment and adjudication methods in the Women’s Health Initiative. Ann Epidemiol. 2003;13:S122–S128. doi: 10.1016/s1047-2797(03)00048-6. [DOI] [PubMed] [Google Scholar]
  14. Curtin C, Anderson SE, Must A, Bandini L. The prevalence of obesity in children with autism: a secondary data analysis using nationally representative data from the National Survey of Children’s Health. BMC Pediatr. 2010;10:11. doi: 10.1186/1471-2431-10-11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dykens EM, Lee E, Roof E. Prader-Willi syndrome and autism spectrum disorders: an evolving story. J Neurodev Disord. 2011;3:225–237. doi: 10.1007/s11689-011-9092-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Field AE, Coakley EH, Must A, Spadano JL, Laird N, Dietz WH, Rimm E, Colditz GA. Impact of overweight on the risk of developing common chronic diseases during a 10-year period. Arch Intern Med. 2001;161:1581–1586. doi: 10.1001/archinte.161.13.1581. [DOI] [PubMed] [Google Scholar]
  17. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303:235–241. doi: 10.1001/jama.2009.2014. [DOI] [PubMed] [Google Scholar]
  18. Focking M, Dicker P, English JA, Schubert KO, Dunn MJ, Cotter DR. Common proteomic changes in the hippocampus in schizophrenia and bipolar disorder and particular evidence for involvement of cornu ammonis regions 2 and 3. Arch Gen Psychiatry. 2011;68:477–488. doi: 10.1001/archgenpsychiatry.2011.43. [DOI] [PubMed] [Google Scholar]
  19. Frayling TM, Timpson NJ, Weedon MN, Zeggini E, Freathy RM, Lindgren CM, Perry JR, Elliott KS, Lango H, Rayner NW, Shields B, Harries LW, Barrett JC, Ellard S, Groves CJ, Knight B, Patch AM, Ness AR, Ebrahim S, Lawlor DA, Ring SM, Ben-Shlomo Y, Jarvelin MR, Sovio U, Bennett AJ, Melzer D, Ferrucci L, Loos RJ, Barroso I, Wareham NJ, Karpe F, Owen KR, Cardon LR, Walker M, Hitman GA, Palmer CN, Doney AS, Morris AD, Smith GD, Hattersley AT, McCarthy MI. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316:889–894. doi: 10.1126/science.1141634. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hannun YA, Obeid LM. The Ceramide-centric universe of lipid-mediated cell regulation: stress encounters of the lipid kind. J Biol Chem. 2002;277:25847–25850. doi: 10.1074/jbc.R200008200. [DOI] [PubMed] [Google Scholar]
  21. Hays J, Hunt JR, Hubbell FA, Anderson GL, Limacher M, Allen C, Rossouw JE. The Women’s Health Initiative recruitment methods and results. Ann Epidemiol. 2003;13:S18–S77. doi: 10.1016/s1047-2797(03)00042-5. [DOI] [PubMed] [Google Scholar]
  22. Heath AC, Whitfield JB, Martin NG, Pergadia ML, Goate AM, Lind PA, McEvoy BP, Schrage AJ, Grant JD, Chou YL, Zhu R, Henders AK, Medland SE, Gordon SD, Nelson EC, Agrawal A, Nyholt DR, Bucholz KK, Madden PA, Montgomery GW. A quantitative-trait genomewide association study of alcoholism risk in the community: findings and implications. Biol Psychiatry. 2011;70:513–518. doi: 10.1016/j.biopsych.2011.02.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hetherington MM, Cecil JE. Gene-environment interactions in obesity. Forum Nutr. 2010;63:195–203. doi: 10.1159/000264407. [DOI] [PubMed] [Google Scholar]
  24. Hicks AA, Pramstaller PP, Johansson A, Vitart V, Rudan I, Ugocsai P, Aulchenko Y, Franklin CS, Liebisch G, Erdmann J, Jonasson I, Zorkoltseva IV, Pattaro C, Hayward C, Isaacs A, Hengstenberg C, Campbell S, Gnewuch C, Janssens AC, Kirichenko AV, Konig IR, Marroni F, Polasek O, Demirkan A, Kolcic I, Schwienbacher C, Igl W, Biloglav Z, Witteman JC, Pichler I, Zaboli G, Axenovich TI, Peters A, Schreiber S, Wichmann HE, Schunkert H, Hastie N, Oostra BA, Wild SH, Meitinger T, Gyllensten U, Van Duijn CM, Wilson JF, Wright A, Schmitz G, Campbell H. Genetic determinants of circulating sphingolipid concentrations in European populations. PLoS Genet. 2009;5:e1000672. doi: 10.1371/journal.pgen.1000672. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hjelmborg JB, Fagnani C, Silventoinen K, McGue M, Korkeila M, Christensen K, Rissanen A, Kaprio J. Genetic influences on growth traits of BMI: a longitudinal study of adult twins. Obesity (Silver Spring) 2008;16:847–852. doi: 10.1038/oby.2007.135. [DOI] [PubMed] [Google Scholar]
  26. Hojjati MR, Li Z, Jiang XC. Serine palmitoyl-CoA transferase (SPT) deficiency and sphingolipid levels in mice. Biochim Biophys Acta. 2005;1737:44–51. doi: 10.1016/j.bbalip.2005.08.006. [DOI] [PubMed] [Google Scholar]
  27. Ikushiro H, Hayashi H, Kagamiyama H. A water-soluble homodimeric serine palmitoyltransferase from Sphingomonas paucimobilis EY2395T strain. Purification, characterization, cloning, and overproduction. J Biol Chem. 2001;276:18249–18256. doi: 10.1074/jbc.M101550200. [DOI] [PubMed] [Google Scholar]
  28. Ikushiro H, Hayashi H, Kagamiyama H. Bacterial serine palmitoyltransferase: a water-soluble homodimeric prototype of the eukaryotic enzyme. Biochim Biophys Acta. 2003;1647:116–120. doi: 10.1016/s1570-9639(03)00074-8. [DOI] [PubMed] [Google Scholar]
  29. Kamboh MI, Barmada MM, Demirci FY, Minster RL, Carrasquillo MM, Pankratz VS, Younkin SG, Saykin AJ, Sweet RA, Feingold E, DeKosky ST, Lopez OL. Genome-wide association analysis of age-at-onset in Alzheimer’s disease. Mol Psychiatry. 2011 doi: 10.1038/mp.2011.135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kathiresan S, Voight BF, Purcell S, Musunuru K, Ardissino D, Mannucci PM, Anand S, Engert JC, Samani NJ, Schunkert H, Erdmann J, Reilly MP, Rader DJ, Morgan T, Spertus JA, Stoll M, Girelli D, McKeown PP, Patterson CC, Siscovick DS, O’Donnell CJ, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Melander O, Altshuler D, Ardissino D, Merlini PA, Berzuini C, Bernardinelli L, Peyvandi F, Tubaro M, Celli P, Ferrario M, Fetiveau R, Marziliano N, Casari G, Galli M, Ribichini F, Rossi M, Bernardi F, Zonzin P, Piazza A, Mannucci PM, Schwartz SM, Siscovick DS, Yee J, Friedlander Y, Elosua R, Marrugat J, Lucas G, Subirana I, Sala J, Ramos R, Kathiresan S, Meigs JB, Williams G, Nathan DM, MacRae CA, O’Donnell CJ, Salomaa V, Havulinna AS, Peltonen L, Melander O, Berglund G, Voight BF, Kathiresan S, Hirschhorn JN, Asselta R, Duga S, Spreafico M, Musunuru K, Daly MJ, Purcell S, Voight BF, Purcell S, Nemesh J, Korn JM, McCarroll SA, Schwartz SM, Yee J, Kathiresan S, Lucas G, Subirana I, Elosua R, Surti A, Guiducci C, Gianniny L, Mirel D, Parkin M, Burtt N, Gabriel SB, Samani NJ, Thompson JR, Braund PS, Wright BJ, Balmforth AJ, Ball SG, Hall AS, Schunkert H, Erdmann J, Linsel-Nitschke P, Lieb W, Ziegler A, Konig I, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Schunkert H, Samani NJ, Erdmann J, Ouwehand W, Hengstenberg C, Deloukas P, Scholz M, Cambien F, Reilly MP, Li M, Chen Z, Wilensky R, Matthai W, Qasim A, Hakonarson HH, Devaney J, Burnett MS, Pichard AD, Kent KM, Satler L, Lindsay JM, Waksman R, Knouff CW, Waterworth DM, Walker MC, Mooser V, Epstein SE, Rader DJ, Scheffold T, Berger K, Stoll M, Huge A, Girelli D, Martinelli N, Olivieri O, Corrocher R, Morgan T, Spertus JA, McKeown P, Patterson CC, Schunkert H, Erdmann E, Linsel-Nitschke P, Lieb W, Ziegler A, Konig IR, Hengstenberg C, Fischer M, Stark K, Grosshennig A, Preuss M, Wichmann HE, Schreiber S, Holm H, Thorleifsson G, Thorsteinsdottir U, Stefansson K, Engert JC, Do R, Xie C, Anand S, Kathiresan S, Ardissino D, Mannucci PM, Siscovick D, O’Donnell CJ, Samani NJ, Melander O, Elosua R, Peltonen L, Salomaa V, Schwartz SM, Altshuler D. Genome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants. Nat Genet. 2009;41:334–341. doi: 10.1038/ng.327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kiel DP, Demissie S, Dupuis J, Lunetta KL, Murabito JM, Karasik D. Genome-wide association with bone mass and geometry in the Framingham Heart Study. BMC Med Genet. 2007;8(Suppl 1):S14. doi: 10.1186/1471-2350-8-S1-S14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kim YJ, Go MJ, Hu C, Hong CB, Kim YK, Lee JY, Hwang JY, Oh JH, Kim DJ, Kim NH, Kim S, Hong EJ, Kim JH, Min H, Kim Y, Zhang R, Jia W, Okada Y, Takahashi A, Kubo M, Tanaka T, Kamatani N, Matsuda K, Park T, Oh B, Kimm K, Kang D, Shin C, Cho NH, Kim HL, Han BG, Lee JY, Cho YS. Large-scale genome-wide association studies in East Asians identify new genetic loci influencing metabolic traits. Nat Genet. 2011;43:990–995. doi: 10.1038/ng.939. [DOI] [PubMed] [Google Scholar]
  33. Korn JM, Kuruvilla FG, McCarroll SA, Wysoker A, Nemesh J, Cawley S, Hubbell E, Veitch J, Collins PJ, Darvishi K, Lee C, Nizzari MM, Gabriel SB, Purcell S, Daly MJ, Altshuler D. Integrated genotype calling and association analysis of SNPs, common copy number polymorphisms and rare CNVs 1. Nat Genet. 2008;40:1253–1260. doi: 10.1038/ng.237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kottgen A, Pattaro C, Boger CA, Fuchsberger C, Olden M, Glazer NL, Parsa A, Gao X, Yang Q, Smith AV, O’Connell JR, Li M, Schmidt H, Tanaka T, Isaacs A, Ketkar S, Hwang SJ, Johnson AD, Dehghan A, Teumer A, Pare G, Atkinson EJ, Zeller T, Lohman K, Cornelis MC, Probst-Hensch NM, Kronenberg F, Tonjes A, Hayward C, Aspelund T, Eiriksdottir G, Launer LJ, Harris TB, Rampersaud E, Mitchell BD, Arking DE, Boerwinkle E, Struchalin M, Cavalieri M, Singleton A, Giallauria F, Metter J, de Boer IH, Haritunians T, Lumley T, Siscovick D, Psaty BM, Zillikens MC, Oostra BA, Feitosa M, Province M, de AM, Turner ST, Schillert A, Ziegler A, Wild PS, Schnabel RB, Wilde S, Munzel TF, Leak TS, Illig T, Klopp N, Meisinger C, Wichmann HE, Koenig W, Zgaga L, Zemunik T, Kolcic I, Minelli C, Hu FB, Johansson A, Igl W, Zaboli G, Wild SH, Wright AF, Campbell H, Ellinghaus D, Schreiber S, Aulchenko YS, Felix JF, Rivadeneira F, Uitterlinden AG, Hofman A, Imboden M, Nitsch D, Brandstatter A, Kollerits B, Kedenko L, Magi R, Stumvoll M, Kovacs P, Boban M, Campbell S, Endlich K, Volzke H, Kroemer HK, Nauck M, Volker U, Polasek O, Vitart V, Badola S, Parker AN, Ridker PM, Kardia SL, Blankenberg S, Liu Y, Curhan GC, Franke A, Rochat T, Paulweber B, Prokopenko I, Wang W, Gudnason V, Shuldiner AR, Coresh J, Schmidt R, Ferrucci L, Shlipak MG, Van Duijn CM, Borecki I, Kramer BK, Rudan I, Gyllensten U, Wilson JF, Witteman JC, Pramstaller PP, Rettig R, Hastie N, Chasman DI, Kao WH, Heid IM, Fox CS. New loci associated with kidney function and chronic kidney disease. Nat Genet. 2010;42:376–384. doi: 10.1038/ng.568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Langer RD, White E, Lewis CE, Kotchen JM, Hendrix SL, Trevisan M. The Women’s Health Initiative Observational Study: baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol. 2003;13:S107–S121. doi: 10.1016/s1047-2797(03)00047-4. [DOI] [PubMed] [Google Scholar]
  36. Lango AH, Estrada K, Lettre G, Berndt SI, Weedon MN, Rivadeneira F, Willer CJ, Jackson AU, Vedantam S, Raychaudhuri S, Ferreira T, Wood AR, Weyant RJ, Segre AV, Speliotes EK, Wheeler E, Soranzo N, Park JH, Yang J, Gudbjartsson D, Heard-Costa NL, Randall JC, Qi L, Vernon SA, Magi R, Pastinen T, Liang L, Heid IM, Luan J, Thorleifsson G, Winkler TW, Goddard ME, Sin LK, Palmer C, Workalemahu T, Aulchenko YS, Johansson A, Zillikens MC, Feitosa MF, Esko T, Johnson T, Ketkar S, Kraft P, Mangino M, Prokopenko I, Absher D, Albrecht E, Ernst F, Glazer NL, Hayward C, Hottenga JJ, Jacobs KB, Knowles JW, Kutalik Z, Monda KL, Polasek O, Preuss M, Rayner NW, Robertson NR, Steinthorsdottir V, Tyrer JP, Voight BF, Wiklund F, Xu J, Zhao JH, Nyholt DR, Pellikka N, Perola M, Perry JR, Surakka I, Tammesoo ML, Altmaier EL, Amin N, Aspelund T, Bhangale T, Boucher G, Chasman DI, Chen C, Coin L, Cooper MN, Dixon AL, Gibson Q, Grundberg E, Hao K, Juhani JM, Kaplan LM, Kettunen J, Konig IR, Kwan T, Lawrence RW, Levinson DF, Lorentzon M, McKnight B, Morris AP, Muller M, Suh NJ, Purcell S, Rafelt S, Salem RM, Salvi E, Sanna S, Shi J, Sovio U, Thompson JR, Turchin MC, Vandenput L, Verlaan DJ, Vitart V, White CC, Ziegler A, Almgren P, Balmforth AJ, Campbell H, Citterio L, De GA, Dominiczak A, Duan J, Elliott P, Elosua R, Eriksson JG, Freimer NB, Geus EJ, Glorioso N, Haiqing S, Hartikainen AL, Havulinna AS, Hicks AA, Hui J, Igl W, Illig T, Jula A, Kajantie E, Kilpelainen TO, Koiranen M, Kolcic I, Koskinen S, Kovacs P, Laitinen J, Liu J, Lokki ML, Marusic A, Maschio A, Meitinger T, Mulas A, Pare G, Parker AN, Peden JF, Petersmann A, Pichler I, Pietilainen KH, Pouta A, Ridderstrale M, Rotter JI, Sambrook JG, Sanders AR, Schmidt CO, Sinisalo J, Smit JH, Stringham HM, Bragi WG, Widen E, Wild SH, Willemsen G, Zagato L, Zgaga L, Zitting P, Alavere H, Farrall M, McArdle WL, Nelis M, Peters MJ, Ripatti S, van Meurs JB, Aben KK, Ardlie KG, Beckmann JS, Beilby JP, Bergman RN, Bergmann S, Collins FS, Cusi D, den HM, Eiriksdottir G, Gejman PV, Hall AS, Hamsten A, Huikuri HV, Iribarren C, Kahonen M, Kaprio J, Kathiresan S, Kiemeney L, Kocher T, Launer LJ, Lehtimaki T, Melander O, Mosley TH, Jr, Musk AW, Nieminen MS, O’Donnell CJ, Ohlsson C, Oostra B, Palmer LJ, Raitakari O, Ridker PM, Rioux JD, Rissanen A, Rivolta C, Schunkert H, Shuldiner AR, Siscovick DS, Stumvoll M, Tonjes A, Tuomilehto J, van Ommen GJ, Viikari J, Heath AC, Martin NG, Montgomery GW, Province MA, Kayser M, Arnold AM, Atwood LD, Boerwinkle E, Chanock SJ, Deloukas P, Gieger C, Gronberg H, Hall P, Hattersley AT, Hengstenberg C, Hoffman W, Lathrop GM, Salomaa V, Schreiber S, Uda M, Waterworth D, Wright AF, Assimes TL, Barroso I, Hofman A, Mohlke KL, Boomsma DI, Caulfield MJ, Cupples LA, Erdmann J, Fox CS, Gudnason V, Gyllensten U, Harris TB, Hayes RB, Jarvelin MR, Mooser V, Munroe PB, Ouwehand WH. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature. 2010;467:832–838. doi: 10.1038/nature09410. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Logue MW, Schu M, Vardarajan BN, Buros J, Green RC, Go RC, Griffith P, Obisesan TO, Shatz R, Borenstein A, Cupples LA, Lunetta KL, Fallin MD, Baldwin CT, Farrer LA. A comprehensive genetic association study of Alzheimer disease in African Americans. Arch Neurol. 2011;68:1569–1579. doi: 10.1001/archneurol.2011.646. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Loos RJ, Lindgren CM, Li S, Wheeler E, Zhao JH, Prokopenko I, Inouye M, Freathy RM, Attwood AP, Beckmann JS, Berndt SI, Jacobs KB, Chanock SJ, Hayes RB, Bergmann S, Bennett AJ, Bingham SA, Bochud M, Brown M, Cauchi S, Connell JM, Cooper C, Smith GD, Day I, Dina C, De S, Dermitzakis ET, Doney AS, Elliott KS, Elliott P, Evans DM, Sadaf FI, Froguel P, Ghori J, Groves CJ, Gwilliam R, Hadley D, Hall AS, Hattersley AT, Hebebrand J, Heid IM, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Herrera B, Hinney A, Hunt SE, Jarvelin MR, Johnson T, Jolley JD, Karpe F, Keniry A, Khaw KT, Luben RN, Mangino M, Marchini J, McArdle WL, McGinnis R, Meyre D, Munroe PB, Morris AD, Ness AR, Neville MJ, Nica AC, Ong KK, O’Rahilly S, Owen KR, Palmer CN, Papadakis K, Potter S, Pouta A, Qi L, Randall JC, Rayner NW, Ring SM, Sandhu MS, Scherag A, Sims MA, Song K, Soranzo N, Speliotes EK, Syddall HE, Teichmann SA, Timpson NJ, Tobias JH, Uda M, Vogel CI, Wallace C, Waterworth DM, Weedon MN, Willer CJ, Wraight, Yuan X, Zeggini E, Hirschhorn JN, Strachan DP, Ouwehand WH, Caulfield MJ, Samani NJ, Frayling TM, Vollenweider P, Waeber G, Mooser V, Deloukas P, McCarthy MI, Wareham NJ, Barroso I, Jacobs KB, Chanock SJ, Hayes RB, Lamina C, Gieger C, Illig T, Meitinger T, Wichmann HE, Kraft P, Hankinson SE, Hunter DJ, Hu FB, Lyon HN, Voight BF, Ridderstrale M, Groop L, Scheet P, Sanna S, Abecasis GR, Albai G, Nagaraja R, Schlessinger D, Jackson AU, Tuomilehto J, Collins FS, Boehnke M, Mohlke KL. Common variants near MC4R are associated with fat mass, weight and risk of obesity. Nat Genet. 2008;40:768–775. doi: 10.1038/ng.140. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Macrae IJ, Zhou K, Li F, Repic A, Brooks AN, Cande WZ, Adams PD, Doudna JA. Structural basis for double-stranded RNA processing by Dicer. Science. 2006;311:195–198. doi: 10.1126/science.1121638. [DOI] [PubMed] [Google Scholar]
  40. Maes HH, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27:325–351. doi: 10.1023/a:1025635913927. [DOI] [PubMed] [Google Scholar]
  41. Manson JE, Allison MA, Rossouw JE, Carr JJ, Langer RD, Hsia J, Kuller LH, Cochrane BB, Hunt JR, Ludlam SE, Pettinger MB, Gass M, Margolis KL, Nathan L, Ockene JK, Prentice RL, Robbins J, Stefanick ML. Estrogen therapy and coronary-artery calcification. N Engl J Med. 2007;356:2591–2602. doi: 10.1056/NEJMoa071513. [DOI] [PubMed] [Google Scholar]
  42. Meyre D, Delplanque J, Chevre JC, Lecoeur C, Lobbens S, Gallina S, Durand E, Vatin V, Degraeve F, Proenca C, Gaget S, Korner A, Kovacs P, Kiess W, Tichet J, Marre M, Hartikainen AL, Horber F, Potoczna N, Hercberg S, Levy-Marchal C, Pattou F, Heude B, Tauber M, McCarthy MI, Blakemore AI, Montpetit A, Polychronakos C, Weill J, Coin LJ, Asher J, Elliott P, Jarvelin MR, Visvikis-Siest S, Balkau B, Sladek R, Balding D, Walley A, Dina C, Froguel P. Genome-wide association study for early-onset and morbid adult obesity identifies three new risk loci in European populations. Nat Genet. 2009;41:157–159. doi: 10.1038/ng.301. [DOI] [PubMed] [Google Scholar]
  43. Murchison EP, Hannon GJ. miRNAs on the move: miRNA biogenesis and the RNAi machinery. Curr Opin Cell Biol. 2004;16:223–229. doi: 10.1016/j.ceb.2004.04.003. [DOI] [PubMed] [Google Scholar]
  44. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH. The disease burden associated with overweight and obesity. JAMA. 1999;282:1523–1529. doi: 10.1001/jama.282.16.1523. [DOI] [PubMed] [Google Scholar]
  45. Naj AC, Beecham GW, Martin ER, Gallins PJ, Powell EH, Konidari I, Whitehead PL, Cai G, Haroutunian V, Scott WK, Vance JM, Slifer MA, Gwirtsman HE, Gilbert JR, Haines JL, Buxbaum JD, Pericak-Vance MA. Dementia revealed: novel chromosome 6 locus for late-onset Alzheimer disease provides genetic evidence for folate-pathway abnormalities. PLoS Genet. 2010:6. doi: 10.1371/journal.pgen.1001130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Neuhouser ML, Wassertheil-Smoller S, Thomson C, Aragaki A, Anderson GL, Manson JE, Patterson RE, Rohan TE, van HL, Shikany JM, Thomas A, LaCroix A, Prentice RL. Multivitamin use and risk of cancer and cardiovascular disease in the Women’s Health Initiative cohorts. Arch Intern Med. 2009;169:294–304. doi: 10.1001/archinternmed.2008.540. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. O’Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, Karakoc E, Mackenzie AP, Ng SB, Baker C, Rieder MJ, Nickerson DA, Bernier R, Fisher SE, Shendure J, Eichler EE. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet. 2011;43:585–589. doi: 10.1038/ng.835. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295:1549–1555. doi: 10.1001/jama.295.13.1549. [DOI] [PubMed] [Google Scholar]
  49. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Rio FT, Bahubeshi A, Kanellopoulou C, Hamel N, Niedziela M, Sabbaghian N, Pouchet C, Gilbert L, O’Brien PK, Serfas K, Broderick P, Houlston RS, Lesueur F, Bonora E, Muljo S, Schimke RN, Bouron-Dal SD, Arseneau J, Schultz KA, Priest JR, Nguyen VH, Harach HR, Livingston DM, Foulkes WD, Tischkowitz M. DICER1 mutations in familial multinodular goiter with and without ovarian Sertoli-Leydig cell tumors. JAMA. 2011;305:68–77. doi: 10.1001/jama.2010.1910. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Roepke TK, King EC, Reyna-Neyra A, Paroder M, Purtell K, Koba W, Fine E, Lerner DJ, Carrasco N, Abbott GW. Kcne2 deletion uncovers its crucial role in thyroid hormone biosynthesis. Nat Med. 2009;15:1186–1194. doi: 10.1038/nm.2029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Shinawi M, Sahoo T, Maranda B, Skinner SA, Skinner C, Chinault C, Zascavage R, Peters SU, Patel A, Stevenson RE, Beaudet AL. 11p14.1 microdeletions associated with ADHD, autism, developmental delay, and obesity. Am J Med Genet A. 2011;155A:1272–1280. doi: 10.1002/ajmg.a.33878. [DOI] [PubMed] [Google Scholar]
  53. Sim X, Ong RT, Suo C, Tay WT, Liu J, Ng DP, Boehnke M, Chia KS, Wong TY, Seielstad M, Teo YY, Tai ES. Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia. PLoS Genet. 2011;7:e1001363. doi: 10.1371/journal.pgen.1001363. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Simon JA, Seeley DG, Lipschutz RC, Vittinghoff E, Browner WS. The relation of smoking to waist-to-hip ratio and diabetes mellitus among elderly women. Prev Med. 1997;26:639–644. doi: 10.1006/pmed.1997.0230. [DOI] [PubMed] [Google Scholar]
  55. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Allen HL, Lindgren CM, Luan J, Magi R, Randall JC, Vedantam S, Winkler TW, Qi L, Workalemahu T, Heid IM, Steinthorsdottir V, Stringham HM, Weedon MN, Wheeler E, Wood AR, Ferreira T, Weyant RJ, Segre AV, Estrada K, Liang L, Nemesh J, Park JH, Gustafsson S, Kilpelainen TO, Yang J, Bouatia-Naji N, Esko T, Feitosa MF, Kutalik Z, Mangino M, Raychaudhuri S, Scherag A, Smith AV, Welch R, Zhao JH, Aben KK, Absher DM, Amin N, Dixon AL, Fisher E, Glazer NL, Goddard ME, Heard-Costa NL, Hoesel V, Hottenga JJ, Johansson A, Johnson T, Ketkar S, Lamina C, Li S, Moffatt MF, Myers RH, Narisu N, Perry JR, Peters MJ, Preuss M, Ripatti S, Rivadeneira F, Sandholt C, Scott LJ, Timpson NJ, Tyrer JP, van WS, Watanabe RM, White CC, Wiklund F, Barlassina C, Chasman DI, Cooper MN, Jansson JO, Lawrence RW, Pellikka N, Prokopenko I, Shi J, Thiering E, Alavere H, Alibrandi MT, Almgren P, Arnold AM, Aspelund T, Atwood LD, Balkau B, Balmforth AJ, Bennett AJ, Ben-Shlomo Y, Bergman RN, Bergmann S, Biebermann H, Blakemore AI, Boes T, Bonnycastle LL, Bornstein SR, Brown MJ, Buchanan TA, Busonero F, Campbell H, Cappuccio FP, Cavalcanti-Proenca C, Chen YD, Chen CM, Chines PS, Clarke R, Coin L, Connell J, Day IN, Heijer M, Duan J, Ebrahim S, Elliott P, Elosua R, Eiriksdottir G, Erdos MR, Eriksson JG, Facheris MF, Felix SB, Fischer-Posovszky P, Folsom AR, Friedrich N, Freimer NB, Fu M, Gaget S, Gejman PV, Geus EJ, Gieger C, Gjesing AP, Goel A, Goyette P, Grallert H, Grassler J, Greenawalt DM, Groves CJ, Gudnason V, Guiducci C, Hartikainen AL, Hassanali N, Hall AS, Havulinna AS, Hayward C, Heath AC, Hengstenberg C, Hicks AA, Hinney A, Hofman A, Homuth G, Hui J, Igl W, Iribarren C, Isomaa B, Jacobs KB, Jarick I, Jewell E, John U, Jorgensen T, Jousilahti P, Jula A, Kaakinen M, Kajantie E, Kaplan LM, Kathiresan S, Kettunen J, Kinnunen L, Knowles JW, Kolcic I, Konig IR, Koskinen S, Kovacs P, Kuusisto J, Kraft P, Kvaloy K, Laitinen J, Lantieri O, Lanzani C, Launer LJ, Lecoeur C, Lehtimaki T, Lettre G, Liu J, Lokki ML, Lorentzon M, Luben RN, Ludwig B, Manunta P, Marek D, Marre M, Martin NG, McArdle WL, McCarthy A, McKnight B, Meitinger T, Melander O, Meyre D, Midthjell K, Montgomery GW, Morken MA, Morris AP, Mulic R, Ngwa JS, Nelis M, Neville MJ, Nyholt DR, O’Donnell CJ, O’Rahilly S, Ong KK, Oostra B, Pare G, Parker AN, Perola M, Pichler I, Pietilainen KH, Platou CG, Polasek O, Pouta A, Rafelt S, Raitakari O, Rayner NW, Ridderstrale M, Rief W, Ruokonen A, Robertson NR, Rzehak P, Salomaa V, Sanders AR, Sandhu MS, Sanna S, Saramies J, Savolainen MJ, Scherag S, Schipf S, Schreiber S, Schunkert H, Silander K, Sinisalo J, Siscovick DS, Smit JH, Soranzo N, Sovio U, Stephens J, Surakka I, Swift AJ, Tammesoo ML, Tardif JC, Teder-Laving M, Teslovich TM, Thompson JR, Thomson B, Tonjes A, Tuomi T, van Meurs JB, van Ommen GJ. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42:937–948. doi: 10.1038/ng.686. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. State MW. The genetics of child psychiatric disorders: focus on autism and Tourette syndrome. Neuron. 2010;68:254–269. doi: 10.1016/j.neuron.2010.10.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Stevens J, Katz EG, Huxley RR. Associations between gender, age and waist circumference. Eur J Clin Nutr. 2010;64:6–15. doi: 10.1038/ejcn.2009.101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Strauss KA, Puffenberger EG, Huentelman MJ, Gottlieb S, Dobrin SE, Parod JM, Stephan DA, Morton DH. Recessive symptomatic focal epilepsy and mutant contactin-associated protein-like 2. N Engl J Med. 2006;354:1370–1377. doi: 10.1056/NEJMoa052773. [DOI] [PubMed] [Google Scholar]
  59. Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I, Jonsdottir T, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Jonsson F, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Lauritzen T, Aben KK, Verbeek AL, Roeleveld N, Kampman E, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Becker DM, Gulcher J, Kiemeney LA, Pedersen O, Kong A, Thorsteinsdottir U, Stefansson K. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nat Genet. 2009;41:18–24. doi: 10.1038/ng.274. [DOI] [PubMed] [Google Scholar]
  60. Tyler CV, Schramm SC, Karafa M, Tang AS, Jain AK. Chronic disease risks in young adults with autism spectrum disorder: forewarned is forearmed. Am J Intellect Dev Disabil. 2011;116:371–380. doi: 10.1352/1944-7558-116.5.371. [DOI] [PubMed] [Google Scholar]
  61. Wallace C, Smyth DJ, Maisuria-Armer M, Walker NM, Todd JA, Clayton DG. The imprinted DLK1-MEG3 gene region on chromosome 14q32.2 alters susceptibility to type 1 diabetes. Nat Genet. 2010;42:68–71. doi: 10.1038/ng.493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Walters RG, Jacquemont S, Valsesia A, de Smith AJ, Martinet D, Andersson J, Falchi M, Chen F, Andrieux J, Lobbens S, Delobel B, Stutzmann F, El-Sayed Moustafa JS, Chevre JC, Lecoeur C, Vatin V, Bouquillon S, Buxton JL, Boute O, Holder-Espinasse M, Cuisset JM, Lemaitre MP, Ambresin AE, Brioschi A, Gaillard M, Giusti V, Fellmann F, Ferrarini A, Hadjikhani N, Campion D, Guilmatre A, Goldenberg A, Calmels N, Mandel JL, Le CC, David A, Isidor B, Cordier MP, Dupuis-Girod S, Labalme A, Sanlaville D, Beri-Dexheimer M, Jonveaux P, Leheup B, Ounap K, Bochukova EG, Henning E, Keogh J, Ellis RJ, Macdermot KD, van Haelst MM, Vincent-Delorme C, Plessis G, Touraine R, Philippe A, Malan V, Mathieu-Dramard M, Chiesa J, Blaumeiser B, Kooy RF, Caiazzo R, Pigeyre M, Balkau B, Sladek R, Bergmann S, Mooser V, Waterworth D, Reymond A, Vollenweider P, Waeber G, Kurg A, Palta P, Esko T, Metspalu A, Nelis M, Elliott P, Hartikainen AL, McCarthy MI, Peltonen L, Carlsson L, Jacobson P, Sjostrom L, Huang N, Hurles ME, O’Rahilly S, Farooqi IS, Mannik K, Jarvelin MR, Pattou F, Meyre D, Walley AJ, Coin LJ, Blakemore AI, Froguel P, Beckmann JS. A new highly penetrant form of obesity due to deletions on chromosome 16p11.2. Nature. 2010;463:671–675. doi: 10.1038/nature08727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Wang KS, Liu XF, Aragam N. A genome-wide meta-analysis identifies novel loci associated with schizophrenia and bipolar disorder. Schizophr Res. 2010;124:192–199. doi: 10.1016/j.schres.2010.09.002. [DOI] [PubMed] [Google Scholar]
  64. Wardle J, Carnell S, Haworth CM, Plomin R. Evidence for a strong genetic influence on childhood adiposity despite the force of the obesogenic environment. Am J Clin Nutr. 2008;87:398–404. doi: 10.1093/ajcn/87.2.398. [DOI] [PubMed] [Google Scholar]
  65. Wienholds E, Koudijs MJ, van Eeden FJ, Cuppen E, Plasterk RH. The microRNAproducing enzyme Dicer1 is essential for zebrafish development. Nat Genet. 2003;35:217–218. doi: 10.1038/ng1251. [DOI] [PubMed] [Google Scholar]
  66. Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, McCarroll SA, Papadakis K, Qi L, Randall JC, Roccasecca RM, Sanna S, Scheet P, Weedon MN, Wheeler E, Zhao JH, Jacobs LC, Prokopenko I, Soranzo N, Tanaka T, Timpson NJ, Almgren P, Bennett A, Bergman RN, Bingham SA, Bonnycastle LL, Brown M, Burtt NP, Chines P, Coin L, Collins FS, Connell JM, Cooper C, Smith GD, Dennison EM, Deodhar P, Elliott P, Erdos MR, Estrada K, Evans DM, Gianniny L, Gieger C, Gillson CJ, Guiducci C, Hackett R, Hadley D, Hall AS, Havulinna AS, Hebebrand J, Hofman A, Isomaa B, Jacobs KB, Johnson T, Jousilahti P, Jovanovic Z, Khaw KT, Kraft P, Kuokkanen M, Kuusisto J, Laitinen J, Lakatta EG, Luan J, Luben RN, Mangino M, McArdle WL, Meitinger T, Mulas A, Munroe PB, Narisu N, Ness AR, Northstone K, O’Rahilly S, Purmann C, Rees MG, Ridderstrale M, Ring SM, Rivadeneira F, Ruokonen A, Sandhu MS, Saramies J, Scott LJ, Scuteri A, Silander K, Sims MA, Song K, Stephens J, Stevens S, Stringham HM, Tung YC, Valle TT, Van Duijn CM, Vimaleswaran KS, Vollenweider P, Waeber G, Wallace C, Watanabe RM, Waterworth DM, Watkins N, Witteman JC, Zeggini E, Zhai G, Zillikens MC, Altshuler D, Caulfield MJ, Chanock SJ, Farooqi IS, Ferrucci L, Guralnik JM, Hattersley AT, Hu FB, Jarvelin MR, Laakso M, Mooser V, Ong KK, Ouwehand WH, Salomaa V, Samani NJ, Spector TD, Tuomi T, Tuomilehto J, Uda M, Uitterlinden AG, Wareham NJ, Deloukas P, Frayling TM, Groop LC, Hayes RB, Hunter DJ, Mohlke KL, Peltonen L, Schlessinger D, Strachan DP, Wichmann HE, McCarthy MI, Boehnke M, Barroso I, Abecasis GR, Hirschhorn JN. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat Genet. 2009;41:25–34. doi: 10.1038/ng.287. [DOI] [PMC free article] [PubMed] [Google Scholar]

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Figure
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Table
NIHMS476796-supplement-Table.docx (57.4KB, docx)

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