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. Author manuscript; available in PMC: 2014 Sep 1.
Published in final edited form as: Int J Eat Disord. 2013 Apr 9;46(6):594–608. doi: 10.1002/eat.22133

Genetic variants associated with disordered eating

Tracey D Wade 1, Scott Gordon 2, Sarah Medland 2, Cynthia M Bulik 3,4, Andrew C Heath 5, Grant W Montgomery 2, Nicholas G Martin 2
PMCID: PMC3775874  NIHMSID: NIHMS470177  PMID: 23568457

Abstract

Objective

While the genetic contribution to the development of anorexia nervosa (AN) has long been recognized, there has been little progress relative to other psychiatric disorders in identifying specific susceptibility genes. Here we have carried out a GWAS on an unselected community sample of female twins surveyed for eating disorders.

Method

We conducted genome wide association analyses in 2564 female twins for four different phenotypes derived from self-report data relating to lifetime presence of 15 types of disordered eating: anorexia nervosa spectrum, bulimia nervosa spectrum, purging via substances, and a binary measure of no disordered eating behaviors versus 3 or more. To complement the variant level results we also conducted gene-based association tests using VEGAS.

Results

While no variants reached genome-wide significance at the level of p<10−8, six regions were suggestive (p<5×10−7). The current results implicate the following genes: CLEC5A; LOC136242, TSHZ1 and SYTL5 for the anorexia nervosa spectrum phenotype, NT5C1B for the bulimia nervosa spectrum phenotype, and ATP8A2 for the disordered eating behaviors phenotype.

Discussion

As with other medical and psychiatric phenotypes, much larger samples and meta-analyses will ultimately be needed to identify genes and pathways contributing to predisposition to eating disorders.

Twin studies suggest that around 60% of the variance in risk for developing anorexia nervosa (AN) and disordered eating is due to genetic factors,1–3 with more variable estimates attributed to bulimia nervosa (BN, ranging from 28%4 to 83%5). Linkage studies identified regions on chromosomes 1, 2, 4, and 13 as suggestive of linkage for AN6,7 with follow-up significant association of the delta opioid receptor (OPRD1) and serotonin (5-HT) receptor 1D (HTR1D) genes, both on Chromosome 1.8 For BN, significant linkage was observed on chromosome 10 and another region on chromosome 14 was suggestive for genome-wide linkage.9 Well over 200 candidate gene association studies of eating disorders have been conducted, focusing primarily, but not exclusively on serotonergic, dopaminergic, and appetite regulatory genes; however, due largely to an overreliance on small samples, replication has not been universal and clear conclusions remain elusive.10

The current preferred approach to rectifying the nebulous results emerging from a litany of underpowered studies is to boost power throughmeta-analyses of multiple Genome Wide Association Studies (GWAS). In contrast to candidate gene association studies that focus on pre-specified genes of interest, GWAS represent an unbiased scan of the entire genome for common genetic variation in cases versus healthy controls. To date threeGWAS investigations11–13 have been published for eating disorders, none of which have yielded genome-wide significant single-nucleotide polymorphisms (SNPs), where adequate significance is set at P<10−8, as suggested by Li et al14. The first, from the Japanese Genetic Research Group for Eating Disorders,11 showed the strongest associations for AN in 320 cases and 341 controls at 1q41 (with the most significant association observed at SNP rs2048332) and 11q22 (associated with 4 SNP markers, rs6590474, D11S0268i, rs737582, rs7947224). The second study of 1033 AN cases and 3733 pediatric controls12 hadtop association signals detected near ZNF804B, CSRP2BP, NTNG1, AKAP6 and CDH9. This latter gene codes for a neuronal cell-adhesion proteins that influences how neurons communicate with each other in the brain and has been associated with autism spectrum disorders. The third study13 which examined six eating disorder-related symptoms, behaviours and personality traits in 2,698 individuals detected association of eight genetic variants with P<10−5, and an associated meta-analysis showing five SNP markers (and associated genes) met genome-wide significance level:rs6894268 (RUFY1), rs7624327 (CCNL1), rs10519201 (SHC4), rs4853643 (SDPR), rs218361 (TRPS1). A further GWAS of AN, conducted by the International Wellcome Trust Case Control Consortium (WTCCC3) on 2,907 patients with AN and 14,860 geographically matched controls, is in progress.15

Eating disorders are associated with the highest mortality of any psychiatric disorder.16–19 Best evidence treatment approaches have been identified for bulimic disorders20 butthe evidence base for how best to treat AN is weak.21 There are no medications that are currently considered to be effective in the treatment of AN and progress in this area has been hampered by a lack of knowledge about the underlying neurobiology of the condition. The clear-cut identification of genomic variation that predisposes to eating disorders can provide the basis for the next generation of research into etiology, treatment, and prevention.

In line with evidence that shows that large-scale collaborative GWAS studies and larger sample sizes can achieve the necessary power to identify specific loci in psychiatric disorders,22,23 the aim of this study is to contribute to the accumulation of a larger sample size related to disordered eating. The current study conducted a GWAS of four different phenotypes of disordered eating in an unselected sample of 2564 female twins in order to further our knowledge of the genomic variation that predisposes to core features of eating disorders. This represents only the fourth published GWAS in eating disorders, and so a secondary aim was to see whether we could achieve any replication with the previouspublished studies11–13.

Materials and methods

Participants

Participants were from the volunteer adult Australian Twin Registry (ATR) maintained by the National Health and Medical Research Council. These data are from two cohorts of women who completed a mailed questionnaire survey 1988–92, as shown in Figure 1. The first cohort, born before 1964, hasbeen previously described,3,24,25 and an examination of their socio-demographic features, including age, marital status, educational background, workforce participation, major lifetime occupation, and religious denomination, suggests that the sample is not notably different from the Australian female population (using data obtained from the Australian Bureau of Statistics between 1986 and 1992). The second cohort included women born between 1964 and 1971 and has also been previously described.26,27 Most of these twins had been recruited when at school some ten years earlier. All applicable institutional regulations concerning the ethical use of human volunteers were followed during this research. The final combined sample where there were both phenotypic data for disordered eating and genotypes comprised 2564 women.

Figure 1.

Figure 1

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Flow diagram depicting sample and data used in the GWAS

Phenotypes

The 1988–92 surveys mailed to female twins contained five questions assessing disordered eating and these are shown in Table 1. These questions produced a total of 15 variables relating to disordered eating. A previous examination of these items along with two subsequent measures of eating disordered behavior indicated that 60% (95% CI: 50–68) of the variance could be attributed to additive genetic influences.3 In the younger cohort, a follow-up telephone interview was conducted in 2001–2003 when they were aged 28 to 40 years of age (about 10 years after the self-report questionnaire) using the Eating Disorder Examination (EDE28) with 1,083 women, indicating a moderate association (r=0.31 and 0.38 for Twin 1 and 2 respectively) between the mean number of 16 possible problems endorsed in the self-report questionnaire and total number of 6 possible eating disorder behaviors endorsed at interview.27 Moderate agreement is also obtained between two different interview schedules (including the EDE) assessing eating disorders18–24 months apart, achieving a kappa less than 0.60.29

Table 1.

Endorsement of 15 self-report questionnaire items relating to eating and exploratory factor analysis in the total sample (N=6002) using varimax rotation of the 15 eating items from Table 1: items loading ≥ 0.2 are in bold

Item >1 item answered (%, N=6104) Genotyped females (%, N=2564) Factor 1
Anorexia nervosa spectrum		 Factor 2
Bulimia nervosa spectrum		 Factor 3
Purging via substances		 Factor 4
Disordered eating behaviors
Do you feel that you have difficulty controlling weight? 46.0 47.5 −0.084 −0.08 −0.132 0.438
Do you feel you have had problems with disordered eating? 23.9 23.8 0.003 −0.015 −0.138 0.375
Do you feel you have been preoccupied with thoughts of food or body weight? 36.9 37.1 −0.04 −0.051 −0.111 0.402
Have you ever used any of the following methods to control your body weight?
Starvation 12.4 11.9 0.055 0.027 0.172 0.076
Excessive exercise 13.6 12.6 0.015 0 0.068 0.164
Laxatives 7.7 7.8 0.013 −0.022 0.461 −0.125
Fluid tablets 7.4 7.6 −0.016 −0.08 0.506 −0.163
Slimming tablets 16.3 17.4 −0.067 −0.064 0.324 0.059
Self-induced vomiting 4.5 3.8 −0.041 0.28 0.207 −0.087
Have you ever suffered from or been treated for:
Binge eating 2.6 2.9 −0.084 0.455 −0.139 0.027
Bulimia 1.0 0.9 −0.105 0.525 −0.032 −0.102
Eating disorder 3.5 3.3 0.208 0.156 −0.09 0.014
Anorexia nervosa 1.8 1.7 0.301 0.023 0.014 −0.067
Low body weight 5.0 5.1 0.426 −0.148 −0.017 −0.052
Weight loss 5.9 5.8 0.394 −0.158 0.003 −0.011
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As shown in Figure 1, four different phenotypes relating to disordered eating were examined. The first three phenotypes were derived from an exploratory factor analysis of the 15 variables for all available data, whether women had been genotyped or not. The resultant factors are shown in Table 1, where items with factor loadings ≥ 0.2 are highlighted. Of interest to the current investigation were those factors that related to disordered eating, namely Factor 1 (anorexia nervosa spectrum), Factor 2 (bulimia nervosa spectrum) and Factor 3 (purging via substances).

For the fourth phenotype (disordered eating behaviors), the item relating to “difficulty controlling weight” was excluded as it was endorsed so widely that it was considered not to be indicative of disordered eating but rather of the normative struggle many women feel that they have with their weight. The remaining 14 items were reduced to a binary variable, where women who endorsed “no” for all items were grouped as “controls”, and women who endorsed 3 or more problems were grouped as “cases”.

Genotyping

Genotypes were drawn from an existing QIMR Genetic EpidemiologyLaboratory GWAS data for >19,000 individuals (comprised of twin pairs, nuclear families, or singletons), which integrates data from eight batches of genotyping obtained using standard Illumina chips. The subset used here includes individuals typed withthe 610K-quad chip(1138 individuals); 370K or 370K-duo chips (738 individuals); or the Illumina 317K chip (644 individuals); 316 individuals were genotyped on more than one chip either for deliberate QC reasons or to obtain highercoverage than an early generation chip used previously. Individual genotypes were eliminated where they conflict between monozygotic twins or repeat genotypings, as well as (within each family) all genotypes for markers with Mendelian errors. All twin-family members were used in the genetic analysis, taking account of their relatedness (see below).

Within each batch, genotypes were called using the Genotyping Module in Beadstudio and then exported. Cleaning was later performed (a) per-SNP to remove SNPs with (1) MAF <1%; (2) call rate <95%; (3) mean GenCall score <0.7; or (4) Hardy-Weinberg p-value <10−6; and (b) per-individual to remove individuals with (in their batch) a call rate <95% or other obvious quality issues; or (c) in the integrated dataset, having (1) an unresolvable sample mix-up, zygosity or pedigree issue after archival investigation of outlier families from IBS and IBD-based relatedness checks; or (2) being an ancestry outlier based on lying >6sd from the PC1 or PC2 mean for Europeans in a Principal Components Analysis run in SMARTPCA v3, with all HapMap Phase II/III and non-QIMR EUTWIN populations used as a training set. The dataset contains verified pedigree data for all individuals barring a small number of distant relationships (typical π-hat<0.1).

Measured genotypes for the ~281,000 SNPs passing QC in all genotyping batcheswere used to impute to 1000 Genomes SNPs (Release 20100804) via the recommended pre-phasing method in MACH and Minimac30, using the publicly available EUR phased haplotypes as reference panel (from the formatted 1000 Genomes haplotype files supplied by the software authors’ web site, for this purpose). In all, 7262007 SNPs were initially analysed (this is after the R2 quality control test but not the MAF test), and 6150213 SNPs remained after filtering out those with MAF (Minor Allele Frequency) < 2%. Since people genotyped already had their zygosity assessed previously in various ways, no twin pairs needed to be discarded due to discordance revealed by genotyping. The number of twins passing quality control varied by phenotype: 2524 for the anorexia nervosa spectrum, 2442 for the bulimia nervosa spectrum, 2521 for purging via substances, 1659 for the 14-item disordered eating score.

Statistical analysis

Four case/control phenotypes were analyzed. To allow for both developmental and secular cohort effects on these phenotypes we included age, age2, cohort, age*cohort, age2*cohort as covariates. Analyses were conducted using MERLIN-OFFLINE, which implements a total test of association using allele dosage scores while explicitly modeling the relationship structure within our MZ and DZ twin families.31 Variants with poor imputation accuracy (R2<0.3) and rare variants (MAF<0.02) were excluded from analyses.

Gene-based association tests were run on the association results for common variantsusing VEGAS32(v0.8.27). Note that VEGAS as currently configured identifies SNPs within genes based on the geneboundaries as defined by Build 36 (hg18) coordinates, and returns results in these coordinates. VEGAS results reported here have been converted to Build 37 (hg19) for consistency with other quoted positions. Due to software limitations, only SNPs found in HapMap II genotypes were analyzed, and results for the X chromosome are not available from VEGAS.

Results

Genome-wide association of SNP data

The results of the GWAS analyses for each of our four binary eating disorder variables are summarized in the Manhattan plots presented in Figure 2. LD pruned results for variants p<10−5are provided in Table 2. The top 100 gene-based results from VEGAS are listed in Table 3.

Figure 2.

Figure 2

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Manhattan plots: 1000 Genomes-based dosage scores (SNPs with R2>0.3& MAF>0.02) for the four disordered eating phenotypes analysed. Vertical scale is −log10(p); p<10−8 is considered significant. Horizontal scale is hg19/Build 37 position. Green for SNPs with p<10−5, otherwise alternate colours for alternate chromosomes.

Table 2.

Single-SNP Association Peaks for individual 1000 Genomes SNPs - peaks highlighted in bold are plotted in Figure 3

Chr Start (bp, Build 37) End (bp, Build 37) # SNPs (p<10−5) SNP with lowest p lowest p- value Effect allele Other allele Effect =Beta SE Imputation R2 Imputed Allele freq (%) Genes at these SNPs Genes within (approx) +/− 50kb
Anorexia nervosa syndrome factor case/control
7 141450588 1416658110 65 rs145241704 1.51E-07 T G −0.143 0.027 0.542 95.2 CLEC5A; LOC136242 KIAA1147; MGAM; OR9A4; SSBP1; TAS2R3; TAS2R4; TAS2R5; TAS2R38; WEE2
18 72986495 73072779 26 rs62090893 2.84E-07 G A −0.085 0.017 0.876 92.1 TSHZ1 C18orf62
X 37905642 38009352 55 rs56156506 9.51E-07 A T −0.053 0.011 0.994 81.3 SYTL5
10 87692965 87694292 2 rs76765968 2.21E-06 T C −0.064 0.014 0.716 85.6 GRID1
10 77298609 1 rs2043090 3.26E-06 A G −0.119 0.026 0.727 95.9
5 94148538 1 rs469339 3.45E-06 A G −0.144 0.031 0.875 97.7 MCTP1
7 12193432 1 rs114945094 3.60E-06 G A −0.135 0.029 0.464 95.9
8 87874292 96504472 3 rs77742018 3.83E-06 A G −0.117 0.025 0.609 94.6 CNBD1
1 79218940 79227956 7 rs1937020 4.45E-06 T C −0.041 0.009 1.000 68.1
10 12702569 1 rs75263140 6.44E-06 A G −0.172 0.038 0.435 97.4 CAMK1D
16 79184753 79186886 2 rs8050187 6.57E-06 T C −0.044 0.010 0.939 73.6 WWOX
2 223353446 1 rs17496827 7.29E-06 C A −0.042 0.009 0.767 55.0 SGPP2
1 180128044 180130723 2 rs55946907 8.54E-06 C T −0.066 0.015 0.888 90.1 QSOX1 CEP350
13 85548207 85549736 2 rs9531686 8.90E-06 T G −0.038 0.008 0.995 57.1
1 19206334 1 rs28441017 8.93E-06 G A −0.086 0.019 0.335 82.7 ALDH4A1 TAS1R2
1 32668428 1 rs6425793 9.63E-06 A G −0.066 0.015 0.357 69.7 CCDC28B C1orf91; DCDC2B; EIF3I; FAM167B; IQCC; KPNA6; LCK; TXLNA
Bulimia nervosa syndrome factor case/control
2 18794610 18867580 43 rs1445130 1.08E-07 A G −0.056 0.01 0.974 86.4 NT5C1B
8 63258917 1 rs142014203 8.83E-07 T G −0.126 0.026 0.765 97.4 NKAIN3
21 19531442 1 rs77600076 1.17E-06 A C −0.124 0.025 0.588 97.1 CHODL; TMPRSS15
16 11386960 1 rs117096873 1.95E-06 C T −0.129 0.027 0.654 97.4 PRM1; PRM2; PRM3; SOCS1; TNP2
1 58288972 58319828 2 rs985795 2.22E-06 T G −0.094 0.020 0.652 94.6 DAB1
22 31438361 1 rs111383589 2.25E-06 C T −0.087 0.018 0.383 89.2 SMTN
6 138426032 1 rs1556640 2.33E-06 T C −0.075 0.016 0.437 88.0 PERP
5 134321546 1 rs299362 2.52E-06 A G −0.062 0.013 0.766 88.6 CATSPER3 PITX1; PCBD2
4 63845629 63893278 27 rs145379083 3.26E-06 G A −0.037 0.008 0.813 51.0
15 85699207 85719207 9 rs8040855 3.32E-06 C G 0.035 0.007 0.972 63.4 PDE8A
6 67645244 67653279 6 rs28631020 3.45E-06 G A −0.080 0.017 0.718 92.5
19 29897537 29918577 4 rs12986207 3.90E-06 G A −0.044 0.01 0.963 81.7 VSTM2B
4 88053335 88126797 4 rs115694618 3.91E-06 A G −0.123 0.027 0.760 97.9 AFF1; KLHL8 C4orf36; HSD17B13; HSD17B11
2 53727034 53756542 4 rs56148675 4.50E-06 T C −0.076 0.017 0.905 94.2
5 177808675 1 rs2910124 5.80E-06 C T −0.059 0.013 0.610 85.8 COL23A1
1 114226143 1 rs61742849 5.82E-06 G A −0.179 0.039 0.326 97.5 MAGI3 PHTF1
4 31152756 31156178 3 rs74879986 5.86E-06 G A −0.140 0.031 0.619 97.5
3 133260874 1 rs11708304 6.09E-06 C T −0.059 0.013 0.598 85.3 CDV3
15 87710066 87710066 10 rs8024343 6.14E-06 A T −0.045 0.010 0.901 83.1
5 150585867 150596254 12 rs7724774 6.93E-06 G A −0.054 0.012 0.899 88.4 CCDC69 GM2A
3 163855069 1 rs78661745 7.15E-06 C T −0.068 0.015 0.645 90.8
8 10086411 1 rs6999631(a) 8.01E-06 C G −0.090 0.020 0.854 96.5 MSRA
21 34369761 1 rs117124364 8.93E-06 C T −0.160 0.036 0.374 97.7 OLIG2
Purging via substances factor case/control
5 80406566 1 rs138206701 9.65E-08 A G −0.327 0.061 0.535 98.0 RASGRF2
8 134771894 134781276 3 rs74566133 6.65E-07 C T −0.249 0.050 0.465 96.9
2 232298076 1 rs12475512 6.82E-07 G A 0.108 0.022 0.349 54.3 NCL; PTMA; PDE6D
3 58101471 58138528 10 rs13077017 1.00E-06 C T −0.073 0.015 0.933 71.0 FLNB DNASE1L3
2 228667258 228672579 6 rs10175070 1.94E-06 A G 0.124 0.026 0.341 75.0 SPHKAP; CCL20
3 76261724 76261820 2 rs1516459 3.37E-06 C T −0.270 0.058 0.383 96.8
10 70014230 1 rs10998035 3.61E-06 C T −0.151 0.033 0.775 94.5 ATOH7
9 130503612 130517973 5 rs514024 4.51E-06 A G 0.061 0.013 0.999 57.2 PKN3 SET; WDR34; ZDHHC12; ZER1
8 3156220 3156271 3 rs142816172 5.60E-06 C T −0.273 0.060 0.524 97.6 CSMD1
2 60126311 1 rs145433814 6.25E-06 G A −0.239 0.053 0.559 97.6
3 31036738 31042738 8 rs1506203 7.71E-06 G T −0.083 0.018 0.952 84.9 GADL1
5 140668925 1 rs113951537 9.77E-06 G T −0.163 0.037 0.875 96.2 PCDHGA*; PCDHGB*; SCL25A2; TAF7
14-item case/control disordered eating behaviours
13 25994044 26022597 43 rs7322916 7.68E-07 G A 0.089 0.018 0.899 50.1 ATP8A2
1 152295942 152407207 82 rs3120667 1.66E-06 A G −0.118 0.025 0.956 84.5 FLG; FLG2; CRNN
6 39117698 1 rs2115200 2.25E-06 T G 0.098 0.021 0.980 76.8 C6orf64; KCNK5
10 12875208 1 rs10906233 3.65E-06 C T −0.288 0.062 0.953 97.9 CAMK1D
20 15120744 15121081 2 rs11087123(b) 3.83E-06 A G −0.12 0.026 0.536 73.8 MACROD2
5 80406566 1 rs138206701 4.25E-06 A G −0.425 0.092 0.535 98.0 RASGRF2
16 10663627 10673844 7 rs2221433 4.99E-06 G T −0.087 0.019 0.926 68.2 EMP2 TEKT5
4 100395414 100418353 10 rs148915469 7.90E-06 C T −0.279 0.062 0.953 97.9 ADH7; C4orf17
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Notes:

*

many genes/isoforms in that family

(a)

rs6999631 (Bulimia case/control) is 1235 bp from SNP rs141680122 (p~8.0×10−10, MAF~1.1%) which fails our 2% MAF filter. However there is no apparent association signal apart from these two SNPs even without that filter.

(b)

rs11087123 (14-item case/control) is in a wide block of associated SNPs down to p~1.3×10−5 [40 with p ≤ 10−4] which fail the p-value filter used here.

Table 3.

Gene-based associations at p<10−3 [plus other top 100 genes in same block] for each phenotype. Obtained using VEGAS software based on 1000 Genomes per-SNP p-values. Due to software limitations this only considers SNPs found in HapMap Phase II, and was not run for the X chromosome. Genes have been merged into one entry and shown for the lowest p-value where multiple genes in the same LD block are associated. The number of underlying SNPs (or range of numbers, if multiple genes) is shown. In most cases there are many other genes within ~200 kbp. Figure 3 includes plots of per-SNP association for entries highlighted in bold [reference SNP for the plot may differ from the one quoted here].

Chr Start (bp; hg19/Build 37) End (bp) Most associated gene in block Most associated HapMap (II) SNP within most associated gene Other gene(s) associated, top 100 for phenotype
Gene name Gene p- value # SNPs SNP name p-value Effect allele Other allele Effect =Beta SE Imputation R2 Effect allele freq (%)
Anorexia spectrum factor case/control
7 141536085 141646783 OR9A4 4.30E-05 72 rs1285957 1.00E-06 C T −0.056 0.012 0.968 82.6 LOC136242; CLEC5A
3 130613433 131069303 ASTE1 8.50E-05 84 rs13076493 3.34E-05 C T −0.043 0.010 0.982 78.8 ATP2C1; NEK11
16 29674299 29709314 SPN 1.28E-04 28 rs9933310 3.30E-05 A G 0.043 0.010 0.638 58.9 QPRT
10 124320180 124459338 C10orf120 1.89E-04 54 rs2421031 4.62E-04 T C 0.048 0.014 0.478 74.0 DMBT1
10 87359311 88495824 LDB3 2.78E-04 154 rs2803546 2.79E-04 G A 0.034 0.009 0.843 54.6 OPN4; GRID1
2 74682198 74875164 LOXL3 2.87E-04 36 rs17010021 1.00E-05 T A −0.105 0.024 0.696 95.8 ZNHIT4; WBP1; GCS1; MRPL53; CCDC142; TTC31; LBX2; PCGF1; TLX2; DQX1; AUP1; HTRA2; DOK1; C2orf65
15 80137317 80263643 MTHFS 3.48E-04 164 rs1113983 1.30E-04 C A −0.033 0.009 0.988 63.1 ST20; C15orf37; BL2A1
1 68511644 68516460 DIRAS3 3.88E-04 64 rs12069862 5.42E-04 G A −0.110 0.032 0.406 95.9
10 102672325 102747272 FAM178A 4.49E-04 118 rs11190790 2.02E-04 C A 0.032 0.009 0.999 64.1 SEMA4G; MRPL43
5 118407083 118584822 DMXL1 6.14E-04 129 rs4895185 1.69E-04 A G −0.033 0.009 0.999 66.8
7 138818523 138874546 TTC26 7.70E-04 82 rs7798474 6.90E-05 T G −0.039 0.010 0.992 75.4
8 86019376 86132643 LRRCC1 9.53E-04 34 rs4150880 1.70E-05 A T −0.045 0.010 0.912 76.2 LRRCC1; E2F5; C8orf59
4 5822490 5894785 CRMP1 9.67E-04 205 rs3774895 2.00E-05 T A −0.036 0.008 0.981 50.4
Bulimia nervosa spectrum factor case/control
5 140682195 140892546 SLC25A2 1.18E-04 82 rs10491309 1.67E-04 A G −0.095 0.025 0.547 96.1 TAF7; PCDHGA1; PCDHGA3
2 42396515 42721237 KCNG3 1.58E-04 133 rs1874449 6.30E-05 T G 0.030 0.007 0.926 57.0 EML4; COX7A2L
16 69796273 69997889 LOC348174-1 2.10E−04 30 rs904809 4.30E-05 G A −0.033 0.008 0.878 67.6 WWP2
3 38035077 38071133 PCLD1 2.48E-04 85 rs6809649 2.44E-04 T C 0.036 0.010 0.957 82.2 VILL
1 10093015 10480201 KIF1B 2.54E-04 173 rs12131785 1.50E-05 C T −0.042 0.010 0.752 75.4 PGD; UBE4B
7 100218038 100395419 POP7 3.02E-04 42 rs221795 5.50E-05 T C −0.029 0.007 1.000 65.0 GNB2; GIGYF1; EPO; TFR2; ACTL6B; ZAN
14 69517641 69709072 EXDL2 3.56E-04 87 rs4902704 1.63E-04 C G −0.028 0.007 0.969 61.1 WDR22
5 169064292 169510381 LOC100131897 4.71E-04 300 rs30080 4.70E-05 C G −0.030 0.007 0.997 60.7 DOCK2
5 175511908 175543457 FAM153B 5.58E-04 30 rs7443800 3.22E-04 G A −0.027 0.007 0.943 57.5
22 40742503 40806293 ADSL 5.66E-04 52 rs2235318 2.68E-04 C T −0.037 0.010 0.866 81.4 SGSM3
21 27096790 27144771 GABPA 5.66E-04 81 rs10482968 2.41E-04 C A −0.043 0.012 0.959 89.3 ATP5J
14 99947738 99977852 CCNK 7.06E-04 87 rs4905848 9.78E-04 G A −0.026 0.008 0.796 48.4 CCNK
1 225965530 225978164 SRP9 7.29E-04 101 rs12118223 6.34E-04 A T −0.061 0.018 0.412 90.4 SRP9
7 138728265 138874546 ZC3HAV1 7.56E-04 123 rs1814170 3.40E-05 A T −0.056 0.014 0.797 90.2 TTC26
1 23755055 23886322 E2F2 8.03E-04 64 rs3218148 1.97E-04 A G −0.028 0.008 0.905 54.7 DDEFL1; ID3
2 228474805 228497888 DKFZp547H025 8.18E-04 158 rs2396468 1.47E-04 A C −0.046 0.012 0.786 87.1 C2orf83
19 49588464 49715093 LIN7B 8.35E-04 71 rs8044 1.02E-03 G T −0.024 0.007 0.979 60.6 SNRP70; FLJ10490; PPFIA3; HRC; TRPM4
16 31470316 31540124 TGFB1I1 8.98E-04 44 rs7187900 7.53E-04 A G −0.025 0.007 0.956 48.5 ARMC5; SLC5A2; C16orf58; ERAF
15 74528666 74660081 CCDC33 9.55E-04 184 rs2930313 1.23E-04 A G −0.059 0.015 0.690 91.1 CYP11A1
15 43568478 43941039 LCMT2 9.58E-04 62 rs2412779 3.33E-04 A G −0.043 0.012 0.917 89.8 ADAL; ZSCAN29; TUBGCP4; TP53BP1; HISPPD2A; CKMT1B; STRC; CATSPER2; MAP1A; TGM7
Purging via substances factor case/control
9 130374567 130617047 SH2D3C 3.00E-06 78 rs514024 5.00E-06 A G 0.061 0.013 0.999 57.2 STXBP1; C9orf117; PTRH1; TTC16; TOR2A; CDK9; FPGS; ENG
1 229406878 229478688 C1orf96 9.90E-05 84 rs163771 6.80E-05 G A −0.088 0.022 0.369 62.2 RAB4A; SPHAR
3 170075515 170151885 SKIL 1.12E-04 67 rs13101192 3.80E-05 G C 0.074 0.018 0.934 83.4 CLDN11
6 35911292 36200567 MAPK13 1.22E-04 72 rs7752459 8.10E-05 C T −0.093 0.024 0.949 89.8 MAPK14; SLC26A8; BRPF3
12 38710556 39299420 CPNE8 1.44E-04 269 rs864324 6.20E-05 A G −0.053 0.013 0.977 53.6 ALG10B
1 955502 1051736 AGRN 1.71E-04 19 rs7545952 1.68E-04 A G −0.177 0.047 0.303 94.3 C1orf159
8 124084919 124222318 WDR67 2.08E-04 200 rs2385165 3.80E-05 A C 0.061 0.015 1.000 75.2 FAM93A
6 131466460 131604673 AKAP7 3.22E-04 181 rs3777474 8.10E-05 A G 0.054 0.014 0.975 63.7 AKAP7
2 228549925 228682280 CCL20 3.71E-04 81 rs13385901 4.00E-06 C A 0.096 0.021 0.811 84.0 SLC19A3
3 119885878 119962945 GPR156 4.16E-04 169 rs4676822 1.07E-04 T G −0.101 0.026 0.963 92.9
5 140603077 140892546 PCDHB15 4.61E-04 89 rs10044936 1.20E-05 C T −0.151 0.035 0.860 95.6 PCDHB14; SLC25A2; TAF7; PCDHGA*; PCDHGB*
2 216807313 216967494 PECR 5.90E-04 113 rs934154 4.20E-05 T C 0.058 0.014 0.978 69.0 MREG; TMEM169
3 57994126 58157977 FLNB 7.96E-04 287 rs13077017 1.00E-06 C T −0.073 0.015 0.933 71.0
7 82993221 83278324 SEMA3E 9.90E-04 425 rs2713189 1.39E-04 C T −0.050 0.013 0.996 53.9
14-item case/control for disordered eating behaviours
1 152184557 152386728 FLG2 "0" (next lowest is 3E-6) 74 rs3120667 1.66E-06 A G −0.118 0.025 0.956 84.5 FLG; CRNN; HRNR
10 91061705 91180753 IFIT3 1.31E-04 74 rs627524 1.83E-05 C A −0.076 0.018 0.998 47.8 IFIT1L; IFIT1; IFIT5; IFIT2
5 65222383 65376850 ERBB2IP 1.42E-04 134 rs251614 5.70E-05 C G −0.104 0.026 0.852 85.2 ERBB2IP
5 140588290 140683612 PCDHB15 2.15E-04 89 rs2910330 5.07E-04 G T −0.081 0.023 0.990 83.6 PCDHB12; PCDHB13; PCDHB14; SCL25A2
2 234160216 234255701 ATG16L1 2.65E-04 128 rs6759896 1.70E-04 A G 0.070 0.019 0.863 58.4 SAG
3 170075515 170151885 CLDN11 2.81E-04 81 rs4292231 2.45E-04 G C 0.092 0.025 0.791 80.4 SKIL
4 699572 1381837 PCGF3 3.73E-04 93 rs6816483 7.00E-04 C T −0.064 0.019 0.965 68.5 CPLX1; SPON2; KIAA1530
10 102672325 102800998 LZTS2 3.81E-04 63 rs807029 1.86E-04 C T 0.077 0.021 0.869 72.5 FAM178A; SEMA4G; MRPL43; C10orf2; PDZD7; SFXN3
11 69924407 70053486 TMEM16A 4.08E-04 210 rs2509175 9.80E-05 T A 0.106 0.027 0.586 77.8 FADD
19 18045904 18124911 KCNN1 4.53E-04 76 rs4808105 3.67E-04 C T −0.065 0.018 0.980 67.4 CCDC124; ARRDC2
4 156587877 156728056 GUCY1B3 5.09E-04 139 rs17033585 2.52E-04 G A 0.128 0.035 0.366 78.4 GUCY1A3
16 27471933 28074830 GSG1L 5.18E-04 312 rs1645336 1.24E-03 T C −0.068 0.021 0.998 75.7 GTF3C1; KIAA0556
1 955502 1051736 C1orf159 5.70E-04 31 rs6689308 5.62E-04 A G −0.087 0.025 0.885 83.9 AGRN
17 3827168 4046253 ATP2A3 5.97E-04 85 rs9914203 2.96E-04 G A 0.219 0.060 0.458 95.2 ZZEF1
19 5455425 5456867 ZNRF4 7.27E-04 69 rs529515 3.76E-03 A G 0.074 0.025 0.469 52.3 ZNRF4
4 69681728 69696620 UGT2B10 9.71E−04 62 rs9329034 1.29E-03 T C 0.096 0.030 0.827 89.6 UGT2B10
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Note:

*

= many genes in that family

Many of those with one (or few) associated SNPs per peak appear to represent false positive signals, as either they are not in LD with adjoining SNPs, or are in LD but adjoining SNPs are not also associated. Peaks shown with ≤2 SNPs in Table 2 were all manually inspected to ascertain if they contained a signal off the listed SNP(s). In the majority of instances there is no association signal off the listed SNP(s) even without applying the ‘MAF≥2%’ filter to association results. In others there are other mildly associated SNPs with no signal in between. The most notable such exceptions have been footnoted in Table 2.

The initial GWAS analyses yielded a number of suggestive association signals, althoughnone reached genome-wide significance for common variants within1KGP imputed data of p<10−8. Regional association plots for the sesuggestive signals are shown in Figure 3. The power associated with our strongest SNPs (at p<10−5) was R2<0.5 for 9, R2<0.6 for 15, and R2<0.7 for 21, indicating that they were well imputed.

Figure 3.

Figure 3

Figure 3

Figure 3

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Association peak regional plots of per-SNP association p-values for (1) the most highly associated but plausible association peaks for each phenotype (i.e. containing a group of adjoining associated SNPs in high LD);(2) additional associated genes (highlighted in bold in Tables 3 and 4). Obtained for Build 37/hg19 coordinates using v1.1 of LocusZoom, with LD data for 1000 Genomes release 20101123 (http://genome.sph.umich.edu/wiki/LocusZoom_Standalone). Shown with recombination rate (underlying blue graph) and annotated with names and positions of known genes if any (box below each plot). Symbols for SNPs are: filled diamond for most associated SNP (as named); filled triangle if genotyped or open triangle if purely imputed. Colouring indicates LD with the named SNP (grey = LD unknown) based on genotypes from 1000 Genomes release ‘20101123’. The phenotype name is labeled below each panel.

Attempted replication of results from the previous GWAS studies

We examined our results for the regions containing SNPs and CNV regions reported as associated with AN by Wang et al,12 and the other previously-reported associated SNPs reported earlier13,33 and in a Japanese population,11 replication of which was tested in Wang et al. The p-values for the relevant SNPs in our data are reported in Table 4, along with MAF from our imputed data and the referenced papers (all for Europeans for Wang et al12; for Japanese by Nakabayashi et al11) for rs2048332. Our frequencies are consistent with the range between case and control frequencies for Wang et al12 (suggesting good imputation) but we fail to replicate (in any of our phenotypes)their associated SNPs for AN, or those reported earlier.11,13,33 We do find a nominally significant association (p~0.01) in both the BN spectrum and 14-item disordered eating behavior variable for rs906281, which Wang et al12 investigated as a proxy for rs2048332 which was itself reported by Nakabayashi et al.11 However this is significant only in terms of the limited number of tests shown in Table 5, and is for a different population.

Table 4.

Replication of previous studies: Per-SNP association p-values for SNPs reported associated with AN in previous literature (as labeled) where available in our analysis. rs674386 (from Brown et al) was not available, observed or imputed. Imputed dosages cover all ~2557 phenotyped individuals. Observed genotypes cover ~1217 phenotyped individuals (rs17725255, 2383378, rs830998); ~1497 (rs533123); otherwise ~2550 (less minor dropout for each phenotype).

Reported SNP p-values for Anorexia Nervosa spectrum Factor case/Control p-values for Bulimia Nervosa spectrum case/Control p-values for Tablet Purging factor case/Control p-values for 14-item case/Control disordered eating behaviour Imputed MAF (%) - here MAF (%)in referenced paper [AN case; control]
observed genotypes 1000G dosage observed genotypes 1000G dosage observed genotypes 1000G dosage observed genotypes 1000G dosage
SNPs associated in Table 1 of Wang et al6
rs6959888 0.038 0.035 0.950 0.846 0.300 0.207 0.330 0.243 11.8 15; 11
rs17725255 0.074 0.051 0.104 0.100 0.440 0.669 0.990 0.586 12.5 14; 11
rs10494067 0.870 0.852 0.650 0.658 0.062 0.061 0.260 0.265 6.1 3; 6
rs2383378 0.460 0.809 0.660 0.621 0.810 0.100 0.780 0.144 37.2 35; 41
rs410644 0.730 0.708 0.200 0.180 0.460 0.408 0.640 0.562 45.7 41; 47
rs4479806 0.320 0.346 0.670 0.687 0.250 0.305 0.450 0.538 8.7 6; 10
rs957788 0.800 0.805 0.250 0.250 0.240 0.334 0.580 0.643 33.2 37; 31
rs830998 0.170 0.147 0.137 0.975 0.420 0.348 0.810 0.372 20.7 23; 19
rs6782029 0.810 0.887 0.570 0.530 0.570 0.595 0.470 0.518 23.2 19; 24
rs512089 0.870 0.844 0.190 0.234 1.000 0.897 0.490 0.610 25.6 28; 24
rs3808986 0.400 0.386 0.860 0.844 0.510 0.503 0.980 0.994 6.9 5; 8
SNPs associated in Brown et al24
rs569356 0.841 0.511 0.999 0.683 13.3 ?
rs856510 0.551 0.785 0.564 0.591 31.9 ?
SNPs associated (in Japanese) in Nakabayashi et al25
rs2048332 0.696 0.262 0.711 0.824 31.1 ?
SNPs which Wang et al6 investigated (as proxies for SNPs associated by Brown et al)
rs533123 0.160 0.993 0.270 0.903 0.380 0.905 0.090 0.857 18.9 21.7; 18.6
rs7532266 0.640 0.667 0.660 0.670 0.830 0.799 0.880 0.843 31.2 31.1; 32.0
SNPs which Wang et al6 investigated (as proxies for markers associated in Nakabayashi et al)
rs6604568 0.490 0.517 0.260 0.275 0.750 0.760 0.790 0.783 27.9 28.0; 29.7
rs906281 0.099 0.111 0.011 0.010 0.035 0.036 0.010 0.010 22.1 ?
Body Dissatisfaction (BD) phenotype SNPs (with p<10−5) from Table III in Boraska et al.13 EAF from paper (%)
rs6894268 0.74 0.601 0.41 0.599 0.27 0.994 0.74 0.316 31.9 35.4
Bulimia phenotype SNPs (with p<10−5) from Table III in Boraska et al.13
rs7624327 0.21 0.205 0.65 0.635 0.54 0.567 0.71 0.760 10.9 9.8
“OCPD” phenotype SNPs (with p<10−5) from Table III in Boraska et al.13
rs7690467 0.91 0.931 0.016 0.017 0.093 0.094 0.54 0.532 29.2 28.5
rs1898111 0.87 0.850 0.0046 0.0043 0.016 0.016 0.0076 0.008 17.0 16.3
rs10519201 0.91 0.927 0.38 0.380 0.13 0.125 0.91 0.921 13.7 13.2
rs1557305 0.56 0.563 0.34 0.351 0.78 0.835 0.94 0.824 36.9 37.2
Weight Fluctuation (WF) phenotype SNPs (with p<10−5) from Table III in Boraska et al.13
rs4853643 0.19 0.198 0.42 0.421 0.59 0.577 0.43 0.457 18.4 17.8
rs218361 0.19 0.207 0.56 0.584 0.68 0.797 0.67 0.633 41.2 42.9
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Discussion

The current study represents only the fourth published GWAS for eating disorders-related phenotypes and extends the literature by examining four broad eating disorder phenotypes assessed by self-report - anorexia nervosa spectrum, bulimia nervosa spectrum, purging via substances, and disordered eating behaviors. A number of suggestive signals were identified, although none reached genome-wide significance at the level of p<10−8. The strongest evidence of association was observed at rs145241704, rs62090893 and rs56156506 for the anorexia nervosa spectrum phenotype, rs1445130 for the bulimia nervosa spectrum phenotype, rs138206701 for the purging phenotype, and rs7322916 for the disordered eating behaviors phenotype.

The strongest signal for our anorexia nervosa spectrum variable is located in a gene rich region on chromosome 7 (141.5Mb). Within this region are a number of promising positional candidates. The peak variant in this region, rs145241704, is located within the mRNA DQ571874 which has previously been identified as a Piwi-interacting RNA playing a role in gamete development. However, the LD block within this region includes a number of taste receptor genes including TAS2R3, TAS2R4 and TAS2R5, which encode bitter taste receptors. Such receptors have previously been shown to influence perception and eating behaviors with respect to certain foods. Also within this region is CLEC5A, which is a carbohydrate-binding protein domain which has a diverse range of functions including cell-cell adhesion, immune response to pathogens and apoptosis. The next strongest signal, which peaked at rs62090893encompasses theTSHZ1gene. Notably, in a recent study examine changes in gene expression in response to bariatric surgery in a sample of patients with Type 2 diabetes34, changes in expression of TSHZ1 were correlated with changes in weight, fasting plasma glucose and glycosylated hemoglobin.

The strongest result for the for the BN spectrum phenotype, was located in an intergenic region centered around rs1445130 on chromosome 2. Recent results from the ENCODE consortium have shown enrichment of the H3K27Ac histone marks within this region suggesting that there may be an active regulatory region nearby. The closest gene, NT5C1B, plays a role in the production of adenosine, which plays an important role in biochemical processes, such as energy transfer.

Consistent with research in other areas of psychiatric genetics prior to accumulation of large sample sizes, there was no meaningful replication between previous genome-wide studies of AN and our current results. If eating disorders follows the same scientific trajectory of other medical and psychiatric disorders, which is increased replication and clarity with increasingly large sample sizes35 - and there are not theoretical reasons why they should not - then we would expect more concrete results as we combine samples into meta-analyses.

The current study has a number of limitations; first, we used self-report data that are not directly reflective of the diagnostic criteria for eating disorders. While our data cluster in recognizable eating disorder syndromes,25 the phenotypes represent rather a blunt instrument for identifying specific eating disorders. Second, as with other studies of psychiatric illness that have used population based samples, the analyses are underpowered. Third, there are there are only 45persons who would qualify for a diagnosis of BN or AN in our genotypedsample,36 so our ability to contribute cases to larger case-control samples is limited. However, GWAS now exist that are not focused on diagnosis but on eating disorder-related symptoms and behaviors.13 As GWAS meta-analysis by definition requires the availability of a number of samples, and a review of the genetic architecture of psychiatric disorders shows that sample size is of greater importance than heritability with respect to the identification of specific loci,22 our analyses should make a useful contribution towards improving the power to identify genetic variants influencing symptoms and behaviours related to eating disorders through the conduct of meta- and mega-analyses with other such GWAS.

Acknowledgments

Supported by National Institutes of Health Grants AA07535, AA07728, AA13320, AA13321, AA14041, AA11998, AA17688, DA012854, and DA019951; by Grants from the Australian National Health and Medical Research Council (241944, 339462, 389927,389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, and 552498); and by the 5th Framework Programme (FP-5) GenomEUtwin Project (QLG2-CT-2002-01254). Genome-wide association study genotyping at Center for Inherited Disease Research was supported by a Grant to the late Richard Todd, M.D., Ph.D., former Principal Investigator of Grant AA13320. SEM and GWM are supported by the National Health and Medical Research Council Fellowship Scheme. We also thank Dixie Statham and Anjali Henders (phenotype collection); Lisa Bowdler and Steven Crooks (DNA processing); and David Smyth (Information Technology support) at Queensland Institute of Medical Research, Brisbane Australia. Last, but not least, we thank the twins and their families for their participation.

Footnotes

Disclosure of conflicts

All authors report no biomedical financial interest or potential conflicts of interest.

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