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Published in final edited form as: Addict Biol. 2015 Oct 12;22(2):535–549. doi: 10.1111/adb.12317

Genome-wide Association Study of Body Mass Index in Subjects with Alcohol Dependence

Renato Polimanti 1, Huiping Zhang 1, Andrew H Smith 2, Hongyu Zhao 3,4, Lindsay A Farrer 5, Henry R Kranzler 6, Joel Gelernter 1,4,7
PMCID: PMC5102811  NIHMSID: NIHMS731130  PMID: 26458734

Abstract

Outcomes related to disordered metabolism are common in alcohol dependence (AD). To investigate alterations in the regulation of body mass that occur in the context of AD, we performed a GWAS of BMI in African-Americans (AAs) and European-Americans (EAs) with AD. Subjects were recruited for genetic studies of alcohol or drug dependence, and evaluated using the Semi-Structured Assessment for Drug Dependence and Alcoholism. We investigated a total of 2,587 AAs and 2,959 EAs with DSM-IV AD diagnosis. In the stage-1 sample (N=4,137), we observed three genome-wide significant (GWS) SNP associations, rs200889048 (p=8.98*10−12) and rs12490016 (p=1.44*10−8) in EAs, and rs1630623 (p=5.14*10−9) in AAs and EAs meta-analyzed. In the stage-2 sample (N=1,409), we replicated 278, 253, and 168 of the stage-1 suggestive loci (p<5*10−4) in AAs, EAs, and AAs and EAs meta-analyzed, respectively. A meta-analysis of stage-1 and stage-2 samples (N=5,546) identified two additional GWS signals: rs28562191 in EAs (p=4.46*10−8) and rs56950471 in AAs (p=1.57*10−9). Three of the GWS loci identified (rs200889048, rs12490016, rs1630623) were not previously reported by GWAS of BMI in the general population and two of them raise interesting hypotheses: rs12490016 – a regulatory variant located within LINC00880, where there are other GWAS-identified variants associated with birth size, adiposity in newborns, and bulimia symptoms which also interact with social stress in relation to birth size; rs1630623 – a regulatory variant related to ALDH1A1, a gene involved in alcohol metabolism and adipocyte plasticity. These loci offer molecular insights regarding the regulatory mechanisms of body mass in the context of AD.

Keywords: alcohol addiction, metabolic processes, GWAS, complex traits, ancestry, genetics

Introduction

GWAS of BMI in the general population indicated that this trait was significantly associated with genes involved in different substance dependence (SD)-susceptible mechanisms, such as neural function and energy balance (Hebebrand et al., 2010; Speliotes et al., 2010). Furthermore, dysregulated brain reward pathways may contribute to both SD and “food addiction” (Berthoud et al., 2011), suggesting partially shared pathogenic mechanisms of these traits.

Alcohol abuse is the third leading cause of preventable death in the United States (Mokdad et al., 2004), and alcohol dependence (AD) is experienced by ~14% of alcohol users (Grant et al., 2001). Several GWAS of AD have been performed, detecting different risk loci (Bierut et al., 2010; Edenberg et al., 2010; Gelernter et al., 2014; Quillen et al., 2014; Zuo et al., 2012). The most strongly supported risk locus in European- and African-ancestry populations is the ADH cluster, but other loci also play important roles in AD risk. No previous GWAS of BMI in subjects identified as AD has been performed, but a genome-wide gene-environment interaction analysis failed to find significant loci that interacted with alcohol consumption in relation to BMI (Velez Edwards et al., 2013). A recent study offered evidence in support of the hypothesis that there are six types of obesity and one of them is related to heavy alcohol drinkers (Green et al., 2015), suggesting that subjects with AD can have specific pathogenic mechanisms that affect body mass regulation.

Previous studies have focused on the effects of AD on BMI. AD subjects with a low level of alcohol drinking showed normal metabolic control, with alcohol intake being compensated by a decrease in non-alcoholic nutrients; conversely, AD subjects with high alcohol intake showed a loss of metabolic control, where alcohol accelerated metabolism and decreased fat mass and leptin levels (de Timary et al., 2012). Neurobiological investigation of AD subjects has indicated that BMI – independent of age, alcohol consumption, and common comorbidities – is correlated to regional concentrations of N-acetyl-aspartate (a marker of neuronal viability), choline-containing compounds (a marker of membrane turnover), creatine and phosphocreatine (markers of high energy metabolism), and myoinositol (a putative marker of astrocytes) (Gazdzinski et al., 2010). Genetic studies of BMI in AD subjects have all been candidate gene analyses and have yielded limited data. A longitudinal study of the effect of AD familial risk on BMI developmental changes observed significant differences between males with high AD risk and those with low AD risk, and interaction of DRD2 and FTO gene variation with risk status and sex (Lichenstein et al., 2014). Previous candidate gene studies of FTO in relation to AD reported nominally significant associations (Sobczyk-Kopciol et al., 2011; Wang et al., 2013), but the results are in some cases not concordant and no GWAS has confirmed these findings. Finally, exon sequencing analysis of the POMC gene, which encodes melanocortin peptides that are linked to SD and obesity risk, indicated that variation at this locus can contribute to risk for both traits (Wang et al., 2012). However, the effects of AD on BMI are complex and not well understood.

In the present study, we used GWAS to investigate the genetics of BMI in AD subjects, to identify AD-specific mechanisms. We analyzed data from a total of 5,546 subjects (stage-1 N=4,137; stage-2 N=1,409) with DSM-IV diagnosis of lifetime AD (2,587 AAs and 2,959 EAs), combining our samples with the Study of Addiction: Genetics and Environment (SAGE), which is available through dbGaP (accession number phs000092.v1.p) (Bierut et al., 2010).

Methods and Materials

Subjects and Diagnostic Procedures

Our stage-1 sample combined two independent populations of subjects with DSM-IV diagnosis of lifetime AD that were both genotyped on ~1M-SNP microarrays, our sample (Yale-Penn, N = 3,017) (Gelernter et al., 2014) and the SAGE sample (N = 1,120) (Bierut et al., 2010). A total of 1,981 AAs and 2,156 EAs were included. For the stage-2 analysis, we recruited an additional 606 AAs and 803 EAs with AD using the same criteria as the initial Yale-Penn cohort; these samples were genotyped on a sparser array.

The study was approved by the institutional review board at each site and we obtained written informed consent from each participant. Yale-Penn subjects were evaluated using the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) to derive DSM-IV diagnoses of lifetime AD and other major psychiatric traits (Pierucci-Lagha et al., 2005), and SAGE subjects were evaluated using the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA) (Bucholz et al., 1994). BMI was calculated on the basis of the height and weight that each participant reported during the SSADDA or SSAGA interview via the formula for BMI using inches and pounds. As performed also by previous GWAS of BMI (Monda et al., 2013; Speliotes et al., 2010), we used BMI values calculated on self-reported height and weight since the putative error in these data would likely bias the results toward the null outcome. Detailed information about the sample is available in our previous GWAS of alcohol dependence in AAs and EAs (Gelernter et al., 2014). Table 1 reports the characteristics of the analyzed populations.

Table 1.

Sample characteristics.

Stage-1 cohort (N = 4,137) Stage-2 cohort(N = 1,409)
Yale-Penn (N = 3,017) SAGE (N = 1,120)
AA (N = 1,686) EA (N = 1,331) AA (N = 295) EA (N = 825) AA (N = 606) EA (N = 803)
age-years, mean±SD 41.7±8.3 38.8±10.1 40.5±7.4 39.8±9.5 41.7±10.4 39.2±12.3
Female (%) 707 (42) 495 (37) 126 (42) 311 (38) 193 (32) 260 (32)
BMI < 19 (%) 23 (1) 26 (2) 3 (1) 17 (2) 10 (2) 13 (2)
BMI 19–24.9 (%) 447 (27) 439 (33) 68 (23) 307 (37) 140 (23) 280 (35)
BMI 25–29.9 (%) 590 (35) 494 (37) 119 (40) 316 (38) 220 (36) 303 (38)
BMI 30–34.9 (%) 339 (20) 246 (19) 56 (19) 121 (15) 127 (21) 131 (16)
BMI > 35 (%) 287 (17) 126 (9) 49 (17) 64 (8) 109 (18) 76 (9)
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Genotyping and Imputation

Yale-Penn samples were genotyped on the Illumina HumanOmni1-Quad v1.0 microarray containing 988,306 autosomal SNPs, at the Center for Inherited Disease Research (CIDR) or the Yale Center for Genome Analysis. Genotypes were called using GenomeStudio software V2011.1 and genotyping module V1.8.4 (Illumina, San Diego, CA, USA). The SAGE samples were genotyped on the Illumina Human 1M array containing 1,069,796 total SNPs. The stage-2 cohort was genotyped using the Illumina HumanCoreExome array, which contains over 550,000 markers spilt between common and low-frequency variants. Principal component (PC) analysis was conducted in each sample (i.e., Yale-Penn, SAGE, and stage-2 cohort) using Eigensoft (Price et al., 2006) and SNPs that were common to the GWAS datasets and HapMap panel (after pruning the GWAS SNPs for linkage disequilibrium (LD) (r2) > 80%) to characterize the underlying genetic architecture of the samples. Detailed information about pre-imputation quality control is available in our published AD GWAS (Gelernter et al., 2014). Imputation was performed using Impute2 software and the 1,000 Genomes reference panel. After imputation, we excluded SNPs with a minor allele frequency < 5% and poor imputation quality (Certainty < 0.9, Info < 0.3). Considering the SNPs common to GWAS cohorts (i.e., Yale-Penn and SAGE), 8,353,798 variants in AAs and 5,990,735 variants in EAs were included in association analyses.

Data analysis methods

Association tests were performed using the R package GWAF to fit a generalized estimating equations (GEE) model to correct for correlations among related individuals (Chen and Yang, 2010). GEE model analysis was performed considering pedigree information after checking genetic relatedness (i.e., confirming the relatedness of samples and excluding cryptic relatedness). We tested the association of the imputed minor allele dosage with BMI considered as the phenotype, and using DSM-IV cocaine dependence (CD) diagnosis, DSM-IV opioid dependence (OD) diagnosis, DSM-IV nicotine dependence (ND) diagnosis, sex, age, and the first three ancestry PCs, as covariates. Analyses were performed separately within each dataset and ancestry group, and the results were combined by meta-analysis using the program METAL (Willer et al., 2010). To prevent bias due to population stratification, we analyzed the AA and EA samples separately, and within each ancestry group we considered the first three principal components to adjust the genetic analysis. A P-value of 5*10−8 was the threshold for genome-wide significance (GWS) in the GWAS. Negligible inflation of P values was observed in both AAs and EAs (Supplemental Figures S1 and S2). To annotate the functional effects of the identified variants, we used information available in the UCSC genome browser (Kent et al., 2002), HaploReg (Ward and Kellis, 2012), Variant Effect Predictor (VEP) (McLaren et al., 2010), GTEx project (GTEx Consortium, 2013), and rSNPbase (Guo et al., 2014). Considering the results of GWAS in AAs and EAs, we performed a gene-based association analysis in each ancestry group using VEGAS software (Liu et al., 2010). Reference panels to correct for LD patterns in EAs and AAs were HapMap CEU and HapMap YRI, respectively. In gene-based association analysis, we estimated false discovery rate using the R package qvalue (Dabney and Storey, 2010), and considered q values < 0.05 as significant. Considering the gene-based association analysis data, we performed a protein-protein interaction (PPI)-based association analysis using the R package dmGWAS (Jia et al., 2011). Specifically, we defined PPIs of all genes with gene-based association using the Protein Interaction Network Analysis platform (PINA) v2.0 (Cowley et al., 2012), and subsequently we used R package dmGWAS to identify PPI modules enriched with small p values. We used both available independent population samples (AAs and EAs) to search for PPI modules enriched for BMI-associated genes (the “dual-evaluation” strategy). We applied a dense module search in the EAs and follow-up analysis in AAs. The modules that remained significant after Bonferroni correction in AAs were considered to be relevant. Finally, we used DAVID 6.7 (Huang da et al., 2009) to perform functional annotation clustering, and generate a functional annotation chart using the results of the gene-based and PPI-based association analysis, respectively. High classification stringency and Bonferroni correction for multiple comparisons were considered in the DAVID analyses.

Results

Replication of loci previously associated with BMI in AAs and EAs

We evaluated whether previously identified BMI-associated loci could be replicated in our AA and EA GWAS cohorts (Supplemental Table 1 and Supplemental Table 2, respectively), considering the data provided by recent large GWAS of BMI in AAs and EAs (Monda et al., 2013; Speliotes et al., 2010). In AAs, the top reported BMI-associated variant, SEC16B rs543874, was replicated in our GWAS cohort (p = 0.027), as was another BMI-associated locus, ADCY3 rs7586879 (p =0.021). We replicated FTO rs17817964 (p = 0.034) in our AA replication cohort. In EAs, the top-two BMI associated loci (FTO rs1558902 and TMEM18 rs2867125) were both replicated in our GWAS cohort (p = 7.0 * 10−6 and p = 1.12*10−4, respectively), together with other BMI-associated loci (i.e., ETV5 rs9816226, p = 4.72*10−3; NRXN3 rs10150332, p = 0.031; and CADM2 rs13078807, p = 7.48*10−3). FTO rs1558902 was also replicated (p = 0.018), and RBJ rs713586 (p = 0.039), ETV5 rs9816226 (p = 0.021), NRXN3 rs10150332 (p = 4.04*10−3), and NUDT3 rs206936 (p = 0.028) in our EA replication cohort.

Novel findings from SNP-based association analysis

Table 2a reports the top 20 variants in the SNP-based association analysis of BMI in EAs with AD. Among them, rs200889048 and rs12490016 were GWS in meta-analysis of EAs (rs200889048: EA meta-analysis p = 8.98*10−12, Yale-Penn p = 2.14*10−4, and SAGE p = 2.52*10−10; rs12490016: EA meta-analysis p = 1.44*10−8, Yale-Penn p = 1.09*10−4, and SAGE p = 2.16*10−5). Specifically, the minor alleles of rs200889048 and rs12490016 are both associated with increased BMI in AD subjects. Fourteen of the top 20 variants in EAs with AD are located in the FTO gene, the top BMI-associated locus for European ancestry reported in the largest previous meta-analysis (Speliotes et al., 2010). Table 2b reports the top 20 variants observed in the SNP-based association analysis of AAs with AD, none of which reached GWS in the Yale-Penn sample, SAGE sample, or meta-analysis of these two samples. Finally, to identify loci in which there was evidence for association in both populations, we performed a meta-analysis of AA and EA GWAS samples (Table 2c). One variant, rs1630623, was GWS in trans-population GWAS: the minor allele is associated with increased BMI in both AAs and EAs (trans-population p = 5.14*10−9, EA p = 1.85*10−7, and AA p = 2.6610−3). We report regional Manhattan plots of the three GWS hits in supplemental Figure 3.

Table 2.

Top-20 variants associated with BMI in AD considering EA subjects (A), AA subjects (B), and trans-population analysis (C), respectively. For EAs and trans-population association analysis, we reported also the p value of GIANT consortium (Stage 1). GWS p values are highlighted in bold. Minor allele frequencies (MAF) are calculated for each variant as meta-analysis of the investigated samples

Table 2a – EA association analysis
rsId Chr Location MAF Gene P value (Yale-Penn) P value (SAGE) P value (meta-analysis) Direction P value (GIANT Stage 1)
rs6545444 2 55035355 0.068 EML6 4.13E-04 4.45E-04 7.53E-07 ++ 0.954
rs2116440 2 55037697 0.068 EML6 3.88E-04 4.34E-04 6.90E-07 ++ 0.99
rs200889048 3 74977426 0.063 NA 2.14E-04 2.52E-10 8.98E-12 ++ NA
rs12490016 3 156838931 0.064 LINC00880 1.09E-04 2.16E-05 1.44E-08 ++ NA
rs114337256 4 134164378 0.056 NA 2.33E-06 2.74E-02 3.88E-07 ++ NA
rs1630623 9 75340239 0.177 TMC1 8.66E-04 2.70E-05 1.85E-07 ++ NA
rs9937521 16 53799296 0.396 FTO 7.00E-06 6.01E-03 1.70E-07 −− NA
rs28562191 16 53799303 0.390 FTO 8.44E-06 5.25E-03 1.73E-07 −− NA
rs9937354 16 53799847 0.428 FTO 1.74E-05 9.88E-03 6.65E-07 −− NA
rs9928094 16 53799905 0.428 FTO 1.74E-05 9.88E-03 6.65E-07 −− 1.35E-57
rs9930397 16 53799985 0.428 FTO 1.74E-05 9.88E-03 6.65E-07 −− NA
rs9940278 16 53800200 0.428 FTO 1.74E-05 9.87E-03 6.65E-07 −− NA
rs9939973 16 53800568 0.428 FTO 1.74E-05 9.85E-03 6.63E-07 −− 9.68E-58
rs9940646 16 53800629 0.428 FTO 1.74E-05 9.85E-03 6.63E-07 −− 1.43E-56
rs9940128 16 53800754 0.427 FTO 1.73E-05 9.84E-03 6.58E-07 −− 1.39E-57
rs1421086 16 53801343 0.428 FTO 1.73E-05 9.80E-03 6.57E-07 −− NA
rs9923147 16 53801549 0.427 FTO 1.66E-05 9.75E-03 6.29E-07 −− 1.02E-57
rs1558901 16 53803187 0.429 FTO 1.43E-05 8.16E-03 4.52E-07 −− NA
rs11075985 16 53805207 0.429 FTO 1.46E-05 7.70E-03 4.32E-07 −− 1.60E-57
rs1121980 16 53809247 0.429 FTO 1.36E-05 8.22E-03 4.34E-07 −− 1.78E-57
Table 2b – AA association analysis
rsID Chr Location MAF Gene P value (Yale-Penn) P value (SAGE) P value (meta-analysis) Direction
rs2046823 3 56779011 0.498 ARHGEF3 1.51E-06 2.08E-01 8.53E-07 ++
rs2029466 3 56780003 0.438 ARHGEF3 1.00E-07 6.43E-01 3.52E-07 ++
rs3772218 3 56782813 0.497 ARHGEF3 4.66E-06 1.20E-01 1.40E-06 ++
rs35198830 3 1.74E+08 0.275 NLGN1 9.22E-06 1.27E-02 4.34E-07 ++
rs1436526 4 86977277 0.055 MAPK10 1.03E-05 2.33E-02 7.66E-07 ++
rs73834000 4 86981300 0.055 MAPK10 1.02E-05 2.33E-02 7.54E-07 ++
rs1561154 4 86982434 0.055 MAPK10 1.01E-05 2.33E-02 7.51E-07 ++
rs61454320 9 76530716 0.055 NA 5.51E-06 3.03E-02 4.94E-07 ++
rs73470398 11 31849472 0.147 NA 1.21E-06 6.71E-02 2.16E-07 −−
rs16922496 11 31856488 0.147 NA 1.32E-06 7.08E-02 2.47E-07 −−
rs56950471 11 1.15E+08 0.262 NA 1.14E-05 7.32E-03 3.69E-07 ++
rs74789538 15 36281417 0.086 NA 9.15E-07 1.89E-01 4.75E-07 ++
rs79037607 15 36283130 0.088 NA 1.07E-06 1.50E-01 4.29E-07 ++
rs113423262 15 36291751 0.086 NA 7.97E-07 1.53E-01 3.29E-07 ++
rs116546602 15 36293032 0.086 NA 8.13E-07 1.53E-01 3.38E-07 ++
rs1510391 15 36295848 0.086 NA 8.16E-07 1.61E-01 3.58E-07 ++
rs1584033 18 3240255 0.100 NA 8.45E-07 4.14E-01 1.18E-06 ++
rs1579766 18 3240288 0.099 NA 9.82E-07 4.13E-01 1.35E-06 ++
rs205881 20 486771 0.336 CSNK2A1 5.60E-06 4.19E-02 6.56E-07 −−
rs34379659 22 39575873 0.089 NA 6.00E-07 3.60E-01 7.12E-07 ++
Table 2c – trans-population association analysis
rsId Chr Location MAF (AA/EA) Gene P value (trans-population meta-analysis) Direction P value (EA meta-analysis) P value (AA meta-analysis) P value (GIANT Stage 1)
rs2715093 7 50733034 0.493/0.483 GRB10 1.63E-07 −−−− 6.22E-05 6.96E-04 NA
rs2589626 9 75319272 0.300/0.165 TMC1 1.43E-07 ++++ 4.26E-06 5.00E-03 9.12E-01
rs1630623 9 75340239 0.284/0.177 TMC1 5.14E-09 ++++ 1.85E-07 2.66E-03 NA
rs1444825 9 75345502 0.283/0.166 TMC1 5.73E-08 ++++ 3.40E-06 2.73E-03 8.50E-01
rs1838486 9 75345816 0.281/0.166 TMC1 6.70E-08 ++++ 3.41E-06 3.11E-03 8.49E-01
rs2589608 9 75345891 0.276/0.166 TMC1 2.18E-07 ++++ 3.74E-06 7.68E-03 NA
rs2487465 9 75346270 0.281/0.166 TMC1 6.70E-08 ++++ 3.40E-06 3.11E-03 8.48E-01
rs2487466 9 75346354 0.276/0.166 TMC1 2.17E-07 ++++ 3.73E-06 7.69E-03 NA
rs1444826 9 75346619 0.281/0.166 TMC1 6.70E-08 ++++ 3.40E-06 3.12E-03 8.39E-01
rs1444827 9 75346847 0.291/0.162 TMC1 1.05E-07 ++++ 9.20E-06 2.24E-03 8.28E-01
rs2589610 9 75347643 0.282/0.167 TMC1 5.43E-08 ++++ 2.34E-06 3.38E-03 7.64E-01
rs1663738 9 75347852 0.281/0.166 TMC1 6.84E-08 ++++ 3.41E-06 3.16E-03 7.50E-01
rs10655647 9 75348082 0.276/0.166 TMC1 1.87E-07 ++++ 3.09E-06 7.70E-03 NA
rs2589632 9 75349586 0.283/0.166 TMC1 1.39E-07 ++++ 3.39E-06 5.70E-03 5.92E-01
rs2793168 9 75350343 0.282/0.166 TMC1 7.11E-08 ++++ 3.42E-06 3.26E-03 6.40E-01
rs2793169 9 75354006 0.284/0.166 TMC1 1.36E-07 ++++ 3.58E-06 5.41E-03 NA
rs2793170 9 75355918 0.282/0.166 TMC1 8.48E-08 ++++ 3.59E-06 3.66E-03 NA
rs2793171 9 75357642 0.282/0.166 TMC1 8.07E-08 ++++ 3.19E-06 3.80E-03 NA
rs2793172 9 75357660 0.291/0.166 TMC1 9.65E-08 ++++ 3.20E-06 4.40E-03 6.65E-01
rs1361531 9 75360357 0.282/0.166 TMC1 1.01E-07 ++++ 3.27E-06 4.48E-03 6.46E-01
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Functional annotation of GWS variants

Our GWAS of BMI in AD subjects identified three GWS variants: two in EAs (rs200889048 and rs12490016), and one in the combined-population analysis (irs1630623). The top variant in EAs, rs200889048, is a 1-bp deletion located in an intergenic region. Considering the UCSF Brain DNA Methylation data (Maunakea et al., 2010) and information from HaploReg, we found that this variant is located in a methylated region, where different CpG sites are present, and affects 10 different regulatory motifs. The second GWS variant in EAs, rs12490016, is located in long noncoding RNA 880 (LINC00880). According to functional annotation by VEP and the information available from the UCSC Genome Browser, rs12490016 is located within a promoter flanking region (ENSR00001485403) near a CpG island (580 bp) and a K562 FAIRE peak (1,329 bp). rSNPbase classified rs12490016 as a regulatory SNP involved in distal regulation of several genes (TIPARP, TIPARP-AS1, CCNL1, SSR3, and LINC881). Additionally, recent GWAS identified the variants rs17451107, rs1482853, rs900400, and rs7624327 in the region of LINC00880 as associated with birth weight, adiposity in newborns, and bulimia (Boraska et al., 2012; Horikoshi et al., 2013; Urbanek et al., 2013). The trans-population GWS variant, rs1630623, is located in TMC1, a gene associated with deafness and hearing loss (Kurima et al., 2002). It is 61 bp from an H3K27me3 region, and was classified by rSNPbase as a regulatory SNP involved in RNA binding protein mediated regulation. It is 175 kb downstream of ALDH1A1, a gene involved in alcohol metabolism, and in the regulation of the metabolic responses to a high-fat diet (Kiefer et al., 2012; Lind et al., 2012). Considering GTEx project data, we find that rs1630623 genotypes affect ALDH1A1 gene expression significantly in whole blood (p = 0.04, N = 168)

Gene-based association analysis

In EAs, six genes (i.e., KRTAP4-1, KRTAP4-3, KRTAP4-4, KRTAP4-5, KRTAP4-2, and KRTAP9-2) showed significant associations with BMI in AD subjects (q < 0.05; Supplemental Table 3). However, these genes are clustered within 70 kb. Because the VEGAS software defines genes boundaries as ± 50 kb of the 5′ and 3′ UTRs, these observations are not independent. In AAs, gene-based association analysis did not reveal significant associations (Supplemental Table 4). To cluster genes on the basis of functional information, we used DAVID 6.7, considering nominally significant genes in AAs and EAs. After Bonferroni correction, there were three significant clusters observed in EAs, (Supplemental Table 5). However, two clusters are related to keratin and keratin-associated genes, located in a tight gene cluster on chromosome 17. Conversely, the top cluster is related to 36 Kruppel-associated (KRAB) proteins. Although some of these KRAB genes overlapped in the VEGAS analysis, 20 of them are completely independent. In contrast, no significant clusters were observed in AAs.

PPI-based association analysis

We used the results of GWAS in AAs and EAs to find PPI modules enriched for BMI-associated genes in AD. We used the dual evaluation approach of the R package dmGWAS, considering EAs as the discovery dataset and AAs as the evaluation dataset. In EAs, 12,125 PPI modules were identified. Considering the identified PPI modules in EAs, we then verified the enrichment of BMI-associated genes in AAs. One PPI module was significant after dual evaluation analysis (Figure 1). Eleven genes were included in this PPI network that was associated with BMI in AD. Performing a term enrichment analysis, we observed several terms that remained significant after Bonferroni correction (Table 3). The top enriched terms are related to BMI-associated loci and cellular metabolism regulations.

Figure 1.

Figure 1

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PPI modules constructed with dual evaluation of stage-1 EAs and AAs. The grey color gradient of a node is proportional to its p values.

Table 3.

Term enrichment Analysis of PPI network associated with BMI in AD. Fisher exact test p values adjusted for Bonferroni correction are reported.

Term Genes Adjusted P value
OMIN:Six new loci associated with body mass index highlight a neuronal influence on body weight regulation TMEM18, FTO 4.43E-04
OMIN:Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity TMEM18, FTO 1.24E-03
GO:0043086~negative regulation of catalytic activity GPS1, UBC, CDC20 4.43E-03
GO:0044092~negative regulation of molecular function GPS1, UBC, CDC20 7.56E-03
GO:0005882~intermediate filament KRTAP9-2, KRTAP9-3, KRT33B 8.10E-03
GO:0045111~intermediate filament cytoskeleton KRTAP9-2, KRTAP9-3, KRT33B 8.64E-03
GO:0031145~anaphase-promoting complex-dependent proteasomal ubiquitin-dependent protein catabolic process UBC, CDC20 1.24E-02
GO:0051436~negative regulation of ubiquitin-protein ligase activity during mitotic cell cycle UBC, CDC20 1.24E-02
GO:0051352~negative regulation of ligase activity UBC, CDC20 1.30E-02
GO:0051444~negative regulation of ubiquitin-protein ligase activity UBC, CDC20 1.30E-02
GO:0051437~positive regulation of ubiquitin-protein ligase activity during mitotic cell cycle UBC, CDC20 1.35E-02
GO:0051443~positive regulation of ubiquitin-protein ligase activity UBC, CDC20 1.40E-02
GO:0051439~regulation of ubiquitin-protein ligase activity during mitotic cell cycle UBC, CDC20 1.46E-02
GO:0051351~positive regulation of ligase activity UBC, CDC20 1.51E-02
GO:0031397~negative regulation of protein ubiquitination UBC, CDC20 1.51E-02
GO:0051438~regulation of ubiquitin-protein ligase activity UBC, CDC20 1.73E-02
GO:0051340~regulation of ligase activity UBC, CDC20 1.89E-02
GO:0031398~positive regulation of protein ubiquitination UBC, CDC20 2.05E-02
GO:0031396~regulation of protein ubiquitination UBC, CDC20 2.86E-02
GO:0043161~proteasomal ubiquitin-dependent protein catabolic process UBC, CDC20 2.97E-02
GO:0010498~proteasomal protein catabolic process UBC, CDC20 2.97E-02
GO:0031400~negative regulation of protein modification process UBC, CDC20 4.05E-02
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Analysis of stage-1 findings in the stage-2 cohorts

We evaluated the stage-1 findings (with p<5*10−4) in our independent stage-2 cohorts. Of these suggestive loci, 278, 253, and 168 of stage-1 suggestive loci replicated at nominal significance (P<0.05) and had effects that were directionally consistent with those in stage 1 (Supplemental Table 6, 7 and 8). A meta-analysis of stage-1 and stage-2 samples confirmed the GWS signal of rs200889048 in EAs (p=9.44*10−10) and a suggestive GWS association of rs1630623 (p = 9.73*10−8) in the AA-EA meta-analysis. Furthermore, this meta-analysis also identified two additional GWS variants: rs28562191 in EAs (p=4.46*10−8) and rs56950471 in AAs (p=1.57*10−9). Table 4 reports the relevant findings of the meta-analysis of stage-1 and stage-2 samples.

Table 4.

Meta-analysis of stage-1 and stage-2 samples of the GW significant variants in EAs, AAs and trans-population association analysis. GW significant results are highlighted in bold.

Ancestry rsId Stage-1 Stage-2 P value Meta-analysis P value Direction
P value (Yale-Penn) P-value (SAGE)
EA rs200889048 2.14E-04 2.52E-10 5.71E-01 9.44E-10 +++
rs12490016 1.09E-04 2.16E-05 8.79E-01 1.94E-06 ++−
rs28562191 8.44E-06 5.25E-03 5.24E-02 4.46E-08 −−−
AA rs56950471 1.13E-05 7.32E-03 1.03E-03 1.57E-09 −−−
AA-EA rs1630623 Stage-1 Stage-2 Meta-analysis P value Direction
EA P value AA P value EA P value AA P value
1.85E-07 2.66E-03 5.86E-01 8.12E-01 9.73E-08 ++++++
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Discussion

Our GWAS of BMI in subjects with DSM-IV diagnosis of lifetime AD identified novel significant risk variants, genes, PPI networks, and pathways. Most of these significant findings appear to be specifically related to AD, since they were not reported in previous GWAS of BMI in the general population in considerably larger samples. Therefore, the predisposition to body mass changes in AD subjects could be partially related to AD-associated genetic mechanisms, providing specific evidence that alcohol intake can modify biological mechanisms and affect the genetic predisposition to this phenotypic trait. We analyzed EA and AA subjects and performed a trans-population investigation in a multiple-stage analysis to detect ancestry-specific and trans-population risk alleles (Polimanti et al., 2015). The stage-1 analysis (N=4,137) identified three GWS variants. In the stage-2 (N=1,409), we replicated numerous suggestive findings of stage-1 and observed concordant direction for two of the stage-1 GWS findings (rs200889048 and rs1630623), and a meta-analysis of stage-1 and stage-2 samples (N=5,546) identified two additional GWS loci. The loci highlighted by the meta-analysis of stage-1 and stage-2 samples were previously indicated by GWAS of BMI in general population: rs28562191 is located in the FTO gene, which is the top locus associated with BMI in populations with European ancestry (Speliotes et al., 2010), and rs56950471 is located in chromosome 11q23.3 where multiple GWAS identified variants associated with lipid traits and BMI in different population groups (Kiel et al., 2007; Ko et al., 2014; Shin et al., 2014). Conversely, the stage-1 GWS findings were not previously identified by GWAS of BMI in general population, indicating potential AD-specific loci associated with BMI.

In stage-1 EAs, we found two GWS associations. The top variant, rs200889048, was GWS in the SAGE cohort (p = 2.53*10−10), a significant effect was observed in the Yale-Penn cohort (p = 2.14*10−4), and the variant was highly significant in the meta-analysis of stage-1 cohorts (p = 8.98*10−12). The meta-analysis of stage-1 and stage-2 cohorts also confirmed the GWS significant association of rs200889048 SNP with BMI in AD subjects (p=9.44*10−10). This variant is located in a nongenic region, flanked by CNTN3 (407 kb upstream) and MIR444-1 (286 kb upstream) loci. Although the UCSF Brain DNA Methylation data and HaploReg indicated that this variant is located in a highly methylated region and affects regulatory motifs, no further data seem to explain this genetic association. A number of databases are available for annotating gene function and regulation, but understanding the functional mechanism of GWAS-identified variants remains a key challenge.

The second GWS variant identified in stage-1 EAs, rs12490016, is located in LINC00880, flanked by the LEKR and CCNL1 genes (EA meta-analysis p = 1.44*10−8; SAGE p = 2.16*10−5; Yale-Penn p = 1.09*10−4). Previous GWAS have shown that other variants in LINC00800 region were significantly associated with birth weight and adiposity in newborns and bulimia symptoms (Boraska et al., 2012; Horikoshi et al., 2013; Urbanek et al., 2013). Furthermore, a post-GWAS analysis indicated significant interplay between variants located in this region and social stress in relation to birth size (Ali Khan et al., 2012). Previous studies hypothesized that weight and weight gain during pre-natal life and infancy play a role in determining adulthood obesity (Bjerregaard et al., 2014). To address this issue, recent investigations analyzed the relationship between birth size, childhood and adulthood obesity, and behavioral factors. Genetic risk scores based on obesity studies in adults were significantly associated with postnatal growth, newborn adiposity, and “large for gestational age birth” phenotype (Chawla et al., 2014; Elks et al., 2014). Also highly relevant is a prospective analysis of the Helsinki Birth Cohort Study (N = 12,594) that showed pre- and post-natal growth to be associated with the risk for alcohol use disorders (AUD) later in life (Lahti et al., 2014). Finally, bulimia and AUD frequently co-occur, and some studies indicated that bulimia may share genetic factors with obesity and AD (Gamero-Villarroel et al., 2014; Muller et al., 2012; Trace et al., 2013). On the basis of these reported findings, the association of rs12490016 with BMI in AD provide further insight into the complex interplays between pre-natal, childhood and adulthood events in determining body mass changes.

The trans-population stage-1 analysis identified another GWS variant, rs1630623 (trans-population p = 5.14*10−9; EA p = 1.8510−7; AA p = 2.6610−3). Although this variant is located in TMC1, a gene associated with deafness and hearing loss (Kurima et al., 2002), it is 175 kb downstream from ALDH1A1 and its genotype is significantly associated with ALDH1A1 gene expression. Beyond the association evidence and support for a functional effect of the associated SNP, ALDH1A1 is an intriguing functional candidate as a BMI-associated gene in AD. It encodes aldehyde dehydrogenase family 1 member A1, an alcohol-metabolism enzyme, and it is expressed predominately in white adipose tissue (Kiefer et al., 2012). Although candidate gene studies have supported the association between ALDH1A1 variants and alcohol use disorders (AUD) in different ancestry groups (Crawford et al., 2014; Lind et al., 2008; Liu et al., 2011), no GWAS of AUD showed this gene to be relevant; indeed our previous AD GWAS supported association in several alcohol dehydrogenase genes in both AAs and EAs, but not this particular locus. However, ALDH1A1 variants were associated with blood alcohol concentration (Lind et al., 2012), confirming the role of this gene in alcohol metabolism. ALDH1A1, the protein product of which also catalyzes conversion of retinaldehyde to retinoic acid, is involved in different molecular processes, such as regulation of marrow adiposity, antioxidant defense, carcinogenesis, and neurodegeneration (Grunblatt and Riederer, 2014; Li et al., 2014; Nallamshetty et al., 2014). Furthermore, recent animal experiments on Aldh1a1−/− mice demonstrated that the enzyme and its substrate retinaldehyde were involved in adipocyte plasticity and adaptive thermogenesis (Kiefer et al., 2012). These data are all consistent with the association of rs1630623 with BMI in AD, suggesting that ALDH1A1 can play a relevant role in determining BMI in subjects with AD.

Our gene-based analysis in stage-1 EAs with AD identified six significant genes (q < 0.05). All these genes – KRTAP4-1, KRTAP4-3, KRTAP4-4, KRTAP4-5, KRTAP4-2, and KRTAP9-2 - encode keratin-associated proteins, but because they are located in a tight gene cluster, the significant signals are not independent. However, although we cannot identify a specific source of the signal, the significant signal in the keratin-associated gene cluster appears to be reliable. Keratin and keratin-associated genes encode intermediate filament proteins, are expressed specifically in epithelial cells and their appendages, and are currently used as markers for various malignancies and other diseases (Upasani et al., 2004). One study highlighted a synergistic effect of alcohol consumption and BMI on serum concentrations of keratin-18 (Gonzalez-Quintela et al., 2011), a keratin marker of epithelial neoplasms. The authors suggested that this result probably reflects liver disease in obese subjects with risky alcohol drinking. The results of our gene-based analysis raise the possibility of a new scenario, in which keratin-related functions interact with alcohol drinking to influence BMI. However, further investigations are needed to elucidate the biological meaning of the association.

Functional annotation clustering analysis based on gene-based association identified three significant clusters. Two of these clusters are related to keratin and keratin-associated genes, and, for the reason discussed above, are due to non-independent results. In contrast, the top cluster is related to 36 KRAB genes, many of which are located on different chromosomes. The KRAB protein family includes 400 human zinc finger protein-based transcription factors (Margolin et al., 1994). Although KRAB proteins operate a well-defined transcriptional repression mechanism, there are few known biological roles or target genes of these proteins (Lupo et al., 2013). However, in vivo studies indicated that KRAB genes may be involved in obesity-related traits and metabolic homeostasis (Krebs et al., 2014; Scherneck et al., 2009). Furthermore, animal models indicated that alcohol consumption affected the gene expression regulation of zinc finger proteins (Curry-McCoy et al., 2013; Sun et al., 2014). On the basis of these findings, the significant functional annotation cluster related to KRAB genes may reflect underlying biology in which KRAB gene expression deregulation due to alcohol consumption in AD subjects is associated with metabolic changes that affect body mass.

Our PPI-based association analysis identified one significant module via the dual evaluation of stage-1 AA and EA samples. This module included 11 genes, eight of which were loci associated with BMI in AD (p < 10−4). Enrichment analysis identified several significant terms related to genes involved in the PPI module significantly associated with BMI in AD subjects. The two most highly significant enriched terms were related to BMI-associated loci (i.e., FTO and TMEM18). The subsequent two significant terms were related to negative regulation in molecular processes, involving the genes GPS1, UBC, and CDC20. However, GPS1 is the only gene associated with BMI in AD (p = 3.14*10−4), whereas no significant associations were present for UBC and CDC20. Two other significant terms are linked to keratin-related genes, which were non-independent in the gene-based analysis. The remaining significant terms were related to UBC and CDC20, which are linked to different ubiquitin-dependent processes. A consistent literature describes ubiquitin-dependent processes, and some evidence is also available about the role of these processes in adipocyte-related mechanisms (Dai et al., 2013; Kim et al., 2014; Nian et al., 2010). However, most of BMI-associated genes in the significant PPI module are not involved in the significant enriched terms, indicating no known pathways or mechanisms in this PPI module.

In conclusion, our GWAS of BMI in subjects with DSM-IV diagnosis of lifetime AD identified novel pathogenic mechanisms, indicating AD-specific genetic components of BMI. We believe that these help to elucidate a specific relationship between AD and BMI. The most intriguing findings suggested that i) AD could affect the genetic architecture of BMI via links between AD and intra-uterine growth and social stress during pregnancy; ii) there are interactions between alcohol metabolism and adipocyte plasticity in AD subjects; and iii) there is the potential involvement of keratin-associated and KRAB genes in the genetics of BMI in AD subjects. Although we obtained some provocative insights about genetic predisposition to BMI in AD subjects, larger study populations are needed to investigate this topic further. Taking all of the association evidence together, the present study demonstrates that GWAS can be useful in investigating the biological mechanisms related to the effects of AD on molecular processes. Because morbidity and mortality consequent to AD are also related to the adverse effects of alcohol use on a range of metabolic processes (including those that affect weight regulation), our results provide insights that may be useful in developing novel preventive and therapeutic interventions.

Supplementary Material

Supp FigureS1-S3

Supplemental Figure 1: Q-Q plot of GWAS meta-analysis of stage-1 EAs with AD.

Supplemental Figure 2: Q-Q plot of GWAS meta-analysis of stage-2 AAs with AD.

Supplemental Figure 3: Regional Manhattan Plots of the three GWAS hits in the stage-1 cohort.

Supp TableS1-S8

Acknowledgments

This study was supported by National Institutes of Health grants RC2 DA028909, R01 DA12690, R01 DA12849, R01 DA18432, R01 AA11330, R01 AA017535 and the VA Connecticut and Philadelphia VA MIRECCs. Henry Kranzler has been a consultant or advisory board member for the following pharmaceutical companies: Alkermes, Lundbeck, Otsuka, and Pfizer. He is also a member of the American Society of Clinical Psychopharmacology’s Alcohol Clinical Trials Initiative that is supported by Lilly, Lundbeck, AbbVie, Ethypharm, and Pfizer. The other authors reported no biomedical financial interests or potential conflicts of interest.

We appreciate the work in recruitment and assessment provided at Yale University School of Medicine and the APT Foundation by James Poling, PhD; at McLean Hospital by Roger Weiss, M.D., at the Medical University of South Carolina by Kathleen Brady, MD, Ph.D. and at the University of Pennsylvania by David Oslin, MD. Genotyping services for a part of our GWAS study were provided by the Center for Inherited Disease Research (CIDR) and Yale University (Center for Genome Analysis). CIDR is fully funded through a federal contract from the National Institutes of Health to The Johns Hopkins University (contract number N01-HG-65403). We are grateful to Ann Marie Lacobelle, Catherine Aldi and Christa Robinson for their excellent technical assistance, to the SSADDA interviewers, led by Yari Nunez and Michelle Slivinsky, who devoted substantial time and effort to phenotype the study sample and to John Farrell and Alexan Mardigan for database management assistance. The publicly available data sets used for the analyses described in this manuscript were obtained from dbGaP through dbGaP accession number phs000092.v1.p. Funding support for the Study of Addiction: Genetics and Environment (SAGE) was provided through the NIH Genes, Environment and Health Initiative [GEI] (U01 HG004422). SAGE is one of the genome-wide association studies funded as part of the Gene Environment Association Studies (GENEVA) under GEI. Assistance with phenotype harmonization and genotype cleaning, as well as with general study coordination, was provided by the GENEVA Coordinating Center (U01 HG004446). Assistance with data cleaning was provided by the National Center for Biotechnology Information. Support for collection of data sets and samples was provided by the Collaborative Study on the Genetics of Alcoholism (COGA; U10 AA008401), the Collaborative Genetic Study of Nicotine Dependence (COGEND; P01 CA089392) and the Family Study of Cocaine Dependence (FSCD; R01 DA013423). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the NIH GEI (U01HG004438), the National Institute on Alcohol Abuse and Alcoholism, the National Institute on Drug Abuse and the NIH contract‘ High throughput genotyping for studying the genetic contributions to human disease’ (HHSN268200782096C).

Footnotes

Authors contributions

RP, HuZ, and JG were responsible for the study concept and design. HRK and JG were responsible for the recruitment of the samples. LAF was responsible for the genotyping and imputation. RP, AHS, HoZ and JG assisted with data analysis and interpretation of findings. RP drafted the manuscript. All authors provided critical revision of the manuscript for important intellectual content and approved final version for publication.

References

  1. Ali Khan A, Rodriguez A, Sebert S, Kaakinen M, Cauchi S, Froguel P, Hartikainen AL, Pouta A, Jarvelin MR. The interplay of variants near LEKR and CCNL1 and social stress in relation to birth size. PLoS One. 2012;7:e38216. doi: 10.1371/journal.pone.0038216. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Berthoud HR, Lenard NR, Shin AC. Food reward, hyperphagia, and obesity. Am J Physiol Regul Integr Comp Physiol. 2011;300:R1266–1277. doi: 10.1152/ajpregu.00028.2011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bierut LJ, Agrawal A, Bucholz KK, Doheny KF, Laurie C, Pugh E, Fisher S, Fox L, Howells W, Bertelsen S, Hinrichs AL, Almasy L, Breslau N, Culverhouse RC, Dick DM, Edenberg HJ, Foroud T, Grucza RA, Hatsukami D, Hesselbrock V, Johnson EO, Kramer J, Krueger RF, Kuperman S, Lynskey M, Mann K, Neuman RJ, Nothen MM, Nurnberger JI, Jr, Porjesz B, Ridinger M, Saccone NL, Saccone SF, Schuckit MA, Tischfield JA, Wang JC, Rietschel M, Goate AM, Rice JP, Gene EASC. A genome-wide association study of alcohol dependence. Proc Natl Acad Sci U S A. 2010;107:5082–5087. doi: 10.1073/pnas.0911109107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Bjerregaard LG, Rasmussen KM, Michaelsen KF, Skytthe A, Mortensen EL, Baker JL, Sorensen TI. Effects of body size and change in body size from infancy through childhood on body mass index in adulthood. Int J Obes (Lond) 2014;38:1305–1311. doi: 10.1038/ijo.2014.108. [DOI] [PubMed] [Google Scholar]
  5. Boraska V, Davis OS, Cherkas LF, Helder SG, Harris J, Krug I, Liao TP, Treasure J, Ntalla I, Karhunen L, Keski-Rahkonen A, Christakopoulou D, Raevuori A, Shin SY, Dedoussis GV, Kaprio J, Soranzo N, Spector TD, Collier DA, Zeggini E. Genome-wide association analysis of eating disorder-related symptoms, behaviors, and personality traits. Am J Med Genet B Neuropsychiatr Genet. 2012;159B:803–811. doi: 10.1002/ajmg.b.32087. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bucholz KK, Cadoret R, Cloninger CR, Dinwiddie SH, Hesselbrock VM, Nurnberger JI, Jr, Reich T, Schmidt I, Schuckit MA. A new, semi-structured psychiatric interview for use in genetic linkage studies: a report on the reliability of the SSAGA. J Stud Alcohol. 1994;55:149–158. doi: 10.15288/jsa.1994.55.149. [DOI] [PubMed] [Google Scholar]
  7. Chawla R, Badon S, Rangarajan J, Reisetter A, Armstrong LL, Lowe LP, Urbanek M, Metzger BE, Hayes MG, Scholtens DM, Lowe WL., Jr A Genetic Risk Score For Prediction of Newborn Adiposity and Large for Gestational Age Birth. J Clin Endocrinol Metab. 2014 doi: 10.1210/jc.2013-4221. jc20134221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Chen MH, Yang Q. GWAF: an R package for genome-wide association analyses with family data. Bioinformatics. 2010;26:580–581. doi: 10.1093/bioinformatics/btp710. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Cowley MJ, Pinese M, Kassahn KS, Waddell N, Pearson JV, Grimmond SM, Biankin AV, Hautaniemi S, Wu J. PINA v2.0: mining interactome modules. Nucleic Acids Res. 2012;40:D862–865. doi: 10.1093/nar/gkr967. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Crawford A, Dalvie S, Lewis S, King A, Liberzon I, Fein G, Koenen K, Ramesar R, Stein DJ. Haplotype-based study of the association of alcohol and acetaldehyde-metabolising genes with alcohol dependence (with or without comorbid anxiety symptoms) in a Cape Mixed Ancestry population. Metab Brain Dis. 2014;29:333–340. doi: 10.1007/s11011-014-9503-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Curry-McCoy TV, Guidot DM, Joshi PC. Chronic alcohol ingestion in rats decreases Kruppel-like factor 4 expression and intracellular zinc in the lung. Alcohol Clin Exp Res. 2013;37:361–371. doi: 10.1111/j.1530-0277.2012.01946.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Dabney A, Storey JD. R package version 303. 2010. qvalue: Q-value estimation for false discovery rate control. [Google Scholar]
  13. Dai Z, Qi W, Li C, Lu J, Mao Y, Yao Y, Li L, Zhang T, Hong H, Li S, Zhou T, Yang Z, Yang X, Gao G, Cai W. Dual regulation of adipose triglyceride lipase by pigment epithelium-derived factor: a novel mechanistic insight into progressive obesity. Mol Cell Endocrinol. 2013;377:123–134. doi: 10.1016/j.mce.2013.07.001. [DOI] [PubMed] [Google Scholar]
  14. de Timary P, Cani PD, Duchemin J, Neyrinck AM, Gihousse D, Laterre PF, Badaoui A, Leclercq S, Delzenne NM, Starkel P. The loss of metabolic control on alcohol drinking in heavy drinking alcohol-dependent subjects. PLoS One. 2012;7:e38682. doi: 10.1371/journal.pone.0038682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Edenberg HJ, Koller DL, Xuei X, Wetherill L, McClintick JN, Almasy L, Bierut LJ, Bucholz KK, Goate A, Aliev F, Dick D, Hesselbrock V, Hinrichs A, Kramer J, Kuperman S, Nurnberger JI, Jr, Rice JP, Schuckit MA, Taylor R, Todd Webb B, Tischfield JA, Porjesz B, Foroud T. Genome-wide association study of alcohol dependence implicates a region on chromosome 11. Alcohol Clin Exp Res. 2010;34:840–852. doi: 10.1111/j.1530-0277.2010.01156.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Elks CE, Heude B, de Zegher F, Barton SJ, Clement K, Inskip HM, Koudou Y, Cooper C, Dunger DB, Ibanez L, Charles MA, Ong KK. Associations Between Genetic Obesity Susceptibility and Early Postnatal Fat and Lean Mass: An Individual Participant Meta-analysis. JAMA Pediatr. 2014 doi: 10.1001/jamapediatrics.2014.1619. [DOI] [PubMed] [Google Scholar]
  17. Gamero-Villarroel C, Gonzalez LM, Gordillo I, Carrillo JA, Garcia-Herraiz A, Flores I, Rodriguez-Lopez R, Gervasini G. Impact of NEGR1 genetic variability on psychological traits of patients with eating disorders. Pharmacogenomics J. 2014 doi: 10.1038/tpj.2014.53. [DOI] [PubMed] [Google Scholar]
  18. Gazdzinski S, Durazzo TC, Mon A, Meyerhoff DJ. Body mass index is associated with brain metabolite levels in alcohol dependence--a multimodal magnetic resonance study. Alcohol Clin Exp Res. 2010;34:2089–2096. doi: 10.1111/j.1530-0277.2010.01305.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gelernter J, Kranzler HR, Sherva R, Almasy L, Koesterer R, Smith AH, Anton R, Preuss UW, Ridinger M, Rujescu D, Wodarz N, Zill P, Zhao H, Farrer LA. Genome-wide association study of alcohol dependence:significant findings in African- and European-Americans including novel risk loci. Mol Psychiatry. 2014;19:41–49. doi: 10.1038/mp.2013.145. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gonzalez-Quintela A, Tome S, Fernandez-Merino C, Rey J, Meijide L, Gude F. Synergistic effect of alcohol consumption and body mass on serum concentrations of cytokeratin-18. Alcohol Clin Exp Res. 2011;35:2202–2208. doi: 10.1111/j.1530-0277.2011.01570.x. [DOI] [PubMed] [Google Scholar]
  21. Grant BF, Stinson FS, Harford TC. Age at onset of alcohol use and DSM-IV alcohol abuse and dependence: a 12-year follow-up. J Subst Abuse. 2001;13:493–504. doi: 10.1016/s0899-3289(01)00096-7. [DOI] [PubMed] [Google Scholar]
  22. Green MA, Strong M, Razak F, Subramanian SV, Relton C, Bissell P. Who are the obese? A cluster analysis exploring subgroups of the obese. J Public Health (Oxf) 2015 doi: 10.1093/pubmed/fdv040. [DOI] [PubMed] [Google Scholar]
  23. Grunblatt E, Riederer P. Aldehyde dehydrogenase (ALDH) in Alzheimer’s and Parkinson’s disease. J Neural Transm. 2014 doi: 10.1007/s00702-014-1320-1. [DOI] [PubMed] [Google Scholar]
  24. GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45:580–585. doi: 10.1038/ng.2653. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Guo L, Du Y, Chang S, Zhang K, Wang J. rSNPBase: a database for curated regulatory SNPs. Nucleic Acids Res. 2014;42:D1033–1039. doi: 10.1093/nar/gkt1167. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Hebebrand J, Volckmar AL, Knoll N, Hinney A. Chipping away the ‘missing heritability’: GIANT steps forward in the molecular elucidation of obesity - but still lots to go. Obes Facts. 2010;3:294–303. doi: 10.1159/000321537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Horikoshi M, Yaghootkar H, Mook-Kanamori DO, Sovio U, Taal HR, Hennig BJ, Bradfield JP, St Pourcain B, Evans DM, Charoen P, Kaakinen M, Cousminer DL, Lehtimaki T, Kreiner-Moller E, Warrington NM, Bustamante M, Feenstra B, Berry DJ, Thiering E, Pfab T, Barton SJ, Shields BM, Kerkhof M, van Leeuwen EM, Fulford AJ, Kutalik Z, Zhao JH, den Hoed M, Mahajan A, Lindi V, Goh LK, Hottenga JJ, Wu Y, Raitakari OT, Harder MN, Meirhaeghe A, Ntalla I, Salem RM, Jameson KA, Zhou K, Monies DM, Lagou V, Kirin M, Heikkinen J, Adair LS, Alkuraya FS, Al-Odaib A, Amouyel P, Andersson EA, Bennett AJ, Blakemore AI, Buxton JL, Dallongeville J, Das S, de Geus EJ, Estivill X, Flexeder C, Froguel P, Geller F, Godfrey KM, Gottrand F, Groves CJ, Hansen T, Hirschhorn JN, Hofman A, Hollegaard MV, Hougaard DM, Hypponen E, Inskip HM, Isaacs A, Jorgensen T, Kanaka-Gantenbein C, Kemp JP, Kiess W, Kilpelainen TO, Klopp N, Knight BA, Kuzawa CW, McMahon G, Newnham JP, Niinikoski H, Oostra BA, Pedersen L, Postma DS, Ring SM, Rivadeneira F, Robertson NR, Sebert S, Simell O, Slowinski T, Tiesler CM, Tonjes A, Vaag A, Viikari JS, Vink JM, Vissing NH, Wareham NJ, Willemsen G, Witte DR, Zhang H, Zhao J, Wilson JF, Stumvoll M, Prentice AM, Meyer BF, Pearson ER, Boreham CA, Cooper C, Gillman MW, Dedoussis GV, Moreno LA, Pedersen O, Saarinen M, Mohlke KL, Boomsma DI, Saw SM, Lakka TA, Korner A, Loos RJ, Ong KK, Vollenweider P, van Duijn CM, Koppelman GH, Hattersley AT, Holloway JW, Hocher B, Heinrich J, Power C, Melbye M, Guxens M, Pennell CE, Bonnelykke K, Bisgaard H, Eriksson JG, Widen E, Hakonarson H, Uitterlinden AG, Pouta A, Lawlor DA, Smith GD, Frayling TM, McCarthy MI, Grant SF, Jaddoe VW, Jarvelin MR, Timpson NJ, Prokopenko I, Freathy RM Meta-Analyses of G, Insulin-related traits C, Early Growth Genetics C. New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism. Nat Genet. 2013;45:76–82. doi: 10.1038/ng.2477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Huang da W, Sherman BT, Lempicki RA. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 2009;37:1–13. doi: 10.1093/nar/gkn923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Jia P, Zheng S, Long J, Zheng W, Zhao Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2011;27:95–102. doi: 10.1093/bioinformatics/btq615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D. The human genome browser at UCSC. Genome Res. 2002;12:996–1006. doi: 10.1101/gr.229102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Kiefer FW, Vernochet C, O’Brien P, Spoerl S, Brown JD, Nallamshetty S, Zeyda M, Stulnig TM, Cohen DE, Kahn CR, Plutzky J. Retinaldehyde dehydrogenase 1 regulates a thermogenic program in white adipose tissue. Nat Med. 2012;18:918–925. doi: 10.1038/nm.2757. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. 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]
  33. Kim JH, Park KW, Lee EW, Jang WS, Seo J, Shin S, Hwang KA, Song J. Suppression of PPARgamma through MKRN1-mediated ubiquitination and degradation prevents adipocyte differentiation. Cell Death Differ. 2014;21:594–603. doi: 10.1038/cdd.2013.181. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Ko A, Cantor RM, Weissglas-Volkov D, Nikkola E, Reddy PM, Sinsheimer JS, Pasaniuc B, Brown R, Alvarez M, Rodriguez A, Rodriguez-Guillen R, Bautista IC, Arellano-Campos O, Munoz-Hernandez LL, Salomaa V, Kaprio J, Jula A, Jauhiainen M, Heliovaara M, Raitakari O, Lehtimaki T, Eriksson JG, Perola M, Lohmueller KE, Matikainen N, Taskinen MR, Rodriguez-Torres M, Riba L, Tusie-Luna T, Aguilar-Salinas CA, Pajukanta P. Amerindian-specific regions under positive selection harbour new lipid variants in Latinos. Nat Commun. 2014;5:3983. doi: 10.1038/ncomms4983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Krebs CJ, Zhang D, Yin L, Robins DM. The KRAB zinc finger protein RSL1 modulates sex-biased gene expression in liver and adipose tissue to maintain metabolic homeostasis. Mol Cell Biol. 2014;34:221–232. doi: 10.1128/MCB.00875-13. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kurima K, Peters LM, Yang Y, Riazuddin S, Ahmed ZM, Naz S, Arnaud D, Drury S, Mo J, Makishima T, Ghosh M, Menon PS, Deshmukh D, Oddoux C, Ostrer H, Khan S, Riazuddin S, Deininger PL, Hampton LL, Sullivan SL, Battey JF, Jr, Keats BJ, Wilcox ER, Friedman TB, Griffith AJ. Dominant and recessive deafness caused by mutations of a novel gene, TMC1, required for cochlear hair-cell function. Nat Genet. 2002;30:277–284. doi: 10.1038/ng842. [DOI] [PubMed] [Google Scholar]
  37. Lahti J, Lahti M, Pesonen AK, Heinonen K, Kajantie E, Forsen T, Wahlbeck K, Osmond C, Barker DJ, Eriksson JG, Raikkonen K. Prenatal and childhood growth, and hospitalization for alcohol use disorders in adulthood: the Helsinki birth cohort study. PLoS One. 2014;9:e87404. doi: 10.1371/journal.pone.0087404. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Li XS, Xu Q, Fu XY, Luo WS. ALDH1A1 overexpression is associated with the progression and prognosis in gastric cancer. BMC Cancer. 2014;14:705. doi: 10.1186/1471-2407-14-705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Lichenstein SD, Jones BL, O’Brien JW, Zezza N, Stiffler S, Holmes B, Hill SY. Familial risk for alcohol dependence and developmental changes in BMI: the moderating influence of addiction and obesity genes. Pharmacogenomics. 2014;15:1311–1321. doi: 10.2217/pgs.14.86. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lind PA, Eriksson CJ, Wilhelmsen KC. The role of aldehyde dehydrogenase-1 (ALDH1A1) polymorphisms in harmful alcohol consumption in a Finnish population. Hum Genomics. 2008;3:24–35. doi: 10.1186/1479-7364-3-1-24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Lind PA, Macgregor S, Heath AC, Madden PA, Montgomery GW, Martin NG, Whitfield JB. Association between in vivo alcohol metabolism and genetic variation in pathways that metabolize the carbon skeleton of ethanol and NADH reoxidation in the alcohol challenge twin study. Alcohol Clin Exp Res. 2012;36:2074–2085. doi: 10.1111/j.1530-0277.2012.01829.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Liu J, Zhou Z, Hodgkinson CA, Yuan Q, Shen PH, Mulligan CJ, Wang A, Gray RR, Roy A, Virkkunen M, Goldman D, Enoch MA. Haplotype-based study of the association of alcohol-metabolizing genes with alcohol dependence in four independent populations. Alcohol Clin Exp Res. 2011;35:304–316. doi: 10.1111/j.1530-0277.2010.01346.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, Hayward NK, Montgomery GW, Visscher PM, Martin NG, Macgregor S Investigators A. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. 2010;87:139–145. doi: 10.1016/j.ajhg.2010.06.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Lupo A, Cesaro E, Montano G, Zurlo D, Izzo P, Costanzo P. KRAB-Zinc Finger Proteins: A Repressor Family Displaying Multiple Biological Functions. Curr Genomics. 2013;14:268–278. doi: 10.2174/13892029113149990002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Margolin JF, Friedman JR, Meyer WK, Vissing H, Thiesen HJ, Rauscher FJ., 3rd Kruppel-associated boxes are potent transcriptional repression domains. Proc Natl Acad Sci U S A. 1994;91:4509–4513. doi: 10.1073/pnas.91.10.4509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Maunakea AK, Nagarajan RP, Bilenky M, Ballinger TJ, D’Souza C, Fouse SD, Johnson BE, Hong C, Nielsen C, Zhao Y, Turecki G, Delaney A, Varhol R, Thiessen N, Shchors K, Heine VM, Rowitch DH, Xing X, Fiore C, Schillebeeckx M, Jones SJ, Haussler D, Marra MA, Hirst M, Wang T, Costello JF. Conserved role of intragenic DNA methylation in regulating alternative promoters. Nature. 2010;466:253–257. doi: 10.1038/nature09165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069–2070. doi: 10.1093/bioinformatics/btq330. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Actual causes of death in the United States, 2000. JAMA. 2004;291:1238–1245. doi: 10.1001/jama.291.10.1238. [DOI] [PubMed] [Google Scholar]
  49. Monda KL, Chen GK, Taylor KC, Palmer C, Edwards TL, Lange LA, Ng MC, Adeyemo AA, Allison MA, Bielak LF, Chen G, Graff M, Irvin MR, Rhie SK, Li G, Liu Y, Liu Y, Lu Y, Nalls MA, Sun YV, Wojczynski MK, Yanek LR, Aldrich MC, Ademola A, Amos CI, Bandera EV, Bock CH, Britton A, Broeckel U, Cai Q, Caporaso NE, Carlson CS, Carpten J, Casey G, Chen WM, Chen F, Chen YD, Chiang CW, Coetzee GA, Demerath E, Deming-Halverson SL, Driver RW, Dubbert P, Feitosa MF, Feng Y, Freedman BI, Gillanders EM, Gottesman O, Guo X, Haritunians T, Harris T, Harris CC, Hennis AJ, Hernandez DG, McNeill LH, Howard TD, Howard BV, Howard VJ, Johnson KC, Kang SJ, Keating BJ, Kolb S, Kuller LH, Kutlar A, Langefeld CD, Lettre G, Lohman K, Lotay V, Lyon H, Manson JE, Maixner W, Meng YA, Monroe KR, Morhason-Bello I, Murphy AB, Mychaleckyj JC, Nadukuru R, Nathanson KL, Nayak U, N’Diaye A, Nemesure B, Wu SY, Leske MC, Neslund-Dudas C, Neuhouser M, Nyante S, Ochs-Balcom H, Ogunniyi A, Ogundiran TO, Ojengbede O, Olopade OI, Palmer JR, Ruiz-Narvaez EA, Palmer ND, Press MF, Rampersaud E, Rasmussen-Torvik LJ, Rodriguez-Gil JL, Salako B, Schadt EE, Schwartz AG, Shriner DA, Siscovick D, Smith SB, Wassertheil-Smoller S, Speliotes EK, Spitz MR, Sucheston L, Taylor H, Tayo BO, Tucker MA, Van Den Berg DJ, Edwards DR, Wang Z, Wiencke JK, Winkler TW, Witte JS, Wrensch M, Wu X, Yang JJ, Levin AM, Young TR, Zakai NA, Cushman M, Zanetti KA, Zhao JH, Zhao W, Zheng Y, Zhou J, Ziegler RG, Zmuda JM, Fernandes JK, Gilkeson GS, Kamen DL, Hunt KJ, Spruill IJ, Ambrosone CB, Ambs S, Arnett DK, Atwood L, Becker DM, Berndt SI, Bernstein L, Blot WJ, Borecki IB, Bottinger EP, Bowden DW, Burke G, Chanock SJ, Cooper RS, Ding J, Duggan D, Evans MK, Fox C, Garvey WT, Bradfield JP, Hakonarson H, Grant SF, Hsing A, Chu L, Hu JJ, Huo D, Ingles SA, John EM, Jordan JM, Kabagambe EK, Kardia SL, Kittles RA, Goodman PJ, Klein EA, Kolonel LN, Le Marchand L, Liu S, McKnight B, Millikan RC, Mosley TH, Padhukasahasram B, Williams LK, Patel SR, Peters U, Pettaway CA, Peyser PA, Psaty BM, Redline S, Rotimi CN, Rybicki BA, Sale MM, Schreiner PJ, Signorello LB, Singleton AB, Stanford JL, Strom SS, Thun MJ, Vitolins M, Zheng W, Moore JH, Williams SM, Ketkar S, Zhu X, Zonderman AB, BioBank Japan P, Kooperberg C, Papanicolaou GJ, Henderson BE, Reiner AP, Hirschhorn JN, Loos RJ, North KE, Haiman CA Consortium N, Consortium U, Consortium A. A meta-analysis identifies new loci associated with body mass index in individuals of African ancestry. Nat Genet. 2013;45:690–696. doi: 10.1038/ng.2608. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Muller TD, Greene BH, Bellodi L, Cavallini MC, Cellini E, Di Bella D, Ehrlich S, Erzegovesi S, Estivill X, Fernandez-Aranda F, Fichter M, Fleischhaker C, Scherag S, Gratacos M, Grallert H, Herpertz-Dahlmann B, Herzog W, Illig T, Lehmkuhl U, Nacmias B, Ribases M, Ricca V, Schafer H, Scherag A, Sorbi S, Wichmann HE, Hebebrand J, Hinney A. Fat mass and obesity-associated gene (FTO) in eating disorders: evidence for association of the rs9939609 obesity risk allele with bulimia nervosa and anorexia nervosa. Obes Facts. 2012;5:408–419. doi: 10.1159/000340057. [DOI] [PubMed] [Google Scholar]
  51. Nallamshetty S, Le PT, Wang H, Issacsohn MJ, Reeder DJ, Rhee EJ, Kiefer FW, Brown JD, Rosen CJ, Plutzky J. Retinaldehyde dehydrogenase 1 deficiency inhibits PPARgamma-mediated bone loss and marrow adiposity. Bone. 2014;67:281–291. doi: 10.1016/j.bone.2014.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Nian Z, Sun Z, Yu L, Toh SY, Sang J, Li P. Fat-specific protein 27 undergoes ubiquitin-dependent degradation regulated by triacylglycerol synthesis and lipid droplet formation. J Biol Chem. 2010;285:9604–9615. doi: 10.1074/jbc.M109.043786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Pierucci-Lagha A, Gelernter J, Feinn R, Cubells JF, Pearson D, Pollastri A, Farrer L, Kranzler HR. Diagnostic reliability of the Semi-structured Assessment for Drug Dependence and Alcoholism (SSADDA) Drug Alcohol Depend. 2005;80:303–312. doi: 10.1016/j.drugalcdep.2005.04.005. [DOI] [PubMed] [Google Scholar]
  54. Polimanti R, Yang C, Zhao H, Gelernter J. Dissecting ancestry genomic background in substance dependence genome-wide association studies. Pharmacogenomics. 2015:1–12. doi: 10.2217/pgs.15.91. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–909. doi: 10.1038/ng1847. [DOI] [PubMed] [Google Scholar]
  56. Quillen EE, Chen XD, Almasy L, Yang F, He H, Li X, Wang XY, Liu TQ, Hao W, Deng HW, Kranzler HR, Gelernter J. ALDH2 is associated to alcohol dependence and is the major genetic determinant of “daily maximum drinks” in a GWAS study of an isolated rural Chinese sample. Am J Med Genet B Neuropsychiatr Genet. 2014;165B:103–110. doi: 10.1002/ajmg.b.32213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Scherneck S, Nestler M, Vogel H, Bluher M, Block MD, Berriel Diaz M, Herzig S, Schulz N, Teichert M, Tischer S, Al-Hasani H, Kluge R, Schurmann A, Joost HG. Positional cloning of zinc finger domain transcription factor Zfp69, a candidate gene for obesity-associated diabetes contributed by mouse locus Nidd/SJL. PLoS Genet. 2009;5:e1000541. doi: 10.1371/journal.pgen.1000541. [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, Grundberg E, Waldenberger M, Richards JB, Mohney RP, Milburn MV, John SL, Trimmer J, Theis FJ, Overington JP, Suhre K, Brosnan MJ, Gieger C, Kastenmuller G, Spector TD, Soranzo N Multiple Tissue Human Expression Resource C. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46:543–550. doi: 10.1038/ng.2982. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Sobczyk-Kopciol A, Broda G, Wojnar M, Kurjata P, Jakubczyk A, Klimkiewicz A, Ploski R. Inverse association of the obesity predisposing FTO rs9939609 genotype with alcohol consumption and risk for alcohol dependence. Addiction. 2011;106:739–748. doi: 10.1111/j.1360-0443.2010.03248.x. [DOI] [PubMed] [Google Scholar]
  60. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, Lango Allen H, 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 Wingerden S, 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, den 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, Magic, 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, Vatin V, Viikari J, Visvikis-Siest S, Vitart V, Vogel CI, Voight BF, Waite LL, Wallaschofski H, Walters GB, Widen E, Wiegand S, Wild SH, Willemsen G, Witte DR, Witteman JC, Xu J, Zhang Q, Zgaga L, Ziegler A, Zitting P, Beilby JP, Farooqi IS, Hebebrand J, Huikuri HV, James AL, Kahonen M, Levinson DF, Macciardi F, Nieminen MS, Ohlsson C, Palmer LJ, Ridker PM, Stumvoll M, Beckmann JS, Boeing H, Boerwinkle E, Boomsma DI, Caulfield MJ, Chanock SJ, Collins FS, Cupples LA, Smith GD, Erdmann J, Froguel P, Gronberg H, Gyllensten U, Hall P, Hansen T, Harris TB, Hattersley AT, Hayes RB, Heinrich J, Hu FB, Hveem K, Illig T, Jarvelin MR, Kaprio J, Karpe F, Khaw KT, Kiemeney LA, Krude H, Laakso M, Lawlor DA, Metspalu A, Munroe PB, Ouwehand WH, Pedersen O, Penninx BW, Peters A, Pramstaller PP, Quertermous T, Reinehr T, Rissanen A, Rudan I, Samani NJ, Schwarz PE, Shuldiner AR, Spector TD, Tuomilehto J, Uda M, Uitterlinden A, Valle TT, Wabitsch M, Waeber G, Wareham NJ, Watkins H, Procardis C, Wilson JF, Wright AF, Zillikens MC, Chatterjee N, McCarroll SA, Purcell S, Schadt EE, Visscher PM, Assimes TL, Borecki IB, Deloukas P, Fox CS, Groop LC, Haritunians T, Hunter DJ, Kaplan RC, Mohlke KL, O’Connell JR, Peltonen L, Schlessinger D, Strachan DP, van Duijn CM, Wichmann HE, Frayling TM, Thorsteinsdottir U, Abecasis GR, Barroso I, Boehnke M, Stefansson K, North KE, McCarthy MI, Hirschhorn JN, Ingelsson E, Loos RJ. 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]
  61. Sun Q, Li Q, Zhong W, Zhang J, Sun X, Tan X, Yin X, Sun X, Zhang X, Zhou Z. Dysregulation of hepatic zinc transporters in a mouse model of alcoholic liver disease. Am J Physiol Gastrointest Liver Physiol. 2014;307:G313–322. doi: 10.1152/ajpgi.00081.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Trace SE, Thornton LM, Baker JH, Root TL, Janson LE, Lichtenstein P, Pedersen NL, Bulik CM. A behavioral-genetic investigation of bulimia nervosa and its relationship with alcohol use disorder. Psychiatry Res. 2013;208:232–237. doi: 10.1016/j.psychres.2013.04.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Upasani OS, Vaidya MM, Bhisey AN. Database on monoclonal antibodies to cytokeratins. Oral Oncol. 2004;40:236–256. doi: 10.1016/j.oraloncology.2003.08.022. [DOI] [PubMed] [Google Scholar]
  64. Urbanek M, Hayes MG, Armstrong LL, Morrison J, Lowe LP, Badon SE, Scheftner D, Pluzhnikov A, Levine D, Laurie CC, McHugh C, Ackerman CM, Mirel DB, Doheny KF, Guo C, Scholtens DM, Dyer AR, Metzger BE, Reddy TE, Cox NJ, Lowe WL, Jr, Group HSCR. The chromosome 3q25 genomic region is associated with measures of adiposity in newborns in a multi-ethnic genome-wide association study. Hum Mol Genet. 2013;22:3583–3596. doi: 10.1093/hmg/ddt168. [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Velez Edwards DR, Naj AC, Monda K, North KE, Neuhouser M, Magvanjav O, Kusimo I, Vitolins MZ, Manson JE, O’Sullivan MJ, Rampersaud E, Edwards TL. Gene-environment interactions and obesity traits among postmenopausal African-American and Hispanic women in the Women’s Health Initiative SHARe Study. Hum Genet. 2013;132:323–336. doi: 10.1007/s00439-012-1246-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Wang F, Gelernter J, Kranzler HR, Zhang H. Identification of POMC exonic variants associated with substance dependence and body mass index. PLoS One. 2012;7:e45300. doi: 10.1371/journal.pone.0045300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Wang L, Liu X, Luo X, Zeng M, Zuo L, Wang KS. Genetic variants in the fat mass- and obesity-associated (FTO) gene are associated with alcohol dependence. J Mol Neurosci. 2013;51:416–424. doi: 10.1007/s12031-013-0044-2. [DOI] [PubMed] [Google Scholar]
  68. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res. 2012;40:D930–934. doi: 10.1093/nar/gkr917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–2191. doi: 10.1093/bioinformatics/btq340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Zuo L, Gelernter J, Zhang CK, Zhao H, Lu L, Kranzler HR, Malison RT, Li CS, Wang F, Zhang XY, Deng HW, Krystal JH, Zhang F, Luo X. Genome-wide association study of alcohol dependence implicates KIAA0040 on chromosome 1q. Neuropsychopharmacology. 2012;37:557–566. doi: 10.1038/npp.2011.229. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp FigureS1-S3

Supplemental Figure 1: Q-Q plot of GWAS meta-analysis of stage-1 EAs with AD.

Supplemental Figure 2: Q-Q plot of GWAS meta-analysis of stage-2 AAs with AD.

Supplemental Figure 3: Regional Manhattan Plots of the three GWAS hits in the stage-1 cohort.

NIHMS731130-supplement-Supp_FigureS1-S3.pdf (254.5KB, pdf)
Supp TableS1-S8
NIHMS731130-supplement-Supp_TableS1-S8.docx (72.7KB, docx)

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