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BMC Medical Genetics

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Genome wide association for substance dependence: convergent results from epidemiologic and research volunteer samples

  • Catherine Johnson1,
  • Tomas Drgon1,
  • Qing-Rong Liu1,
  • Ping-Wu Zhang1,
  • Donna Walther1,
  • Chuan-Yun Li1, 2,
  • James C Anthony3,
  • Yulan Ding4,
  • William W Eaton4 and
  • George R Uhl1Email author
Contributed equally
BMC Medical Genetics20089:113

https://doi.org/10.1186/1471-2350-9-113

Received: 21 July 2008

Accepted: 18 December 2008

Published: 18 December 2008

Abstract

Background

Dependences on addictive substances are substantially-heritable complex disorders whose molecular genetic bases have been partially elucidated by studies that have largely focused on research volunteers, including those recruited in Baltimore. Maryland. Subjects recruited from the Baltimore site of the Epidemiological Catchment Area (ECA) study provide a potentially-useful comparison group for possible confounding features that might arise from selecting research volunteer samples of substance dependent and control individuals. We now report novel SNP (single nucleotide polymorphism) genome wide association (GWA) results for vulnerability to substance dependence in ECA participants, who were initially ascertained as members of a probability sample from Baltimore, and compare the results to those from ethnically-matched Baltimore research volunteers.

Results

We identify substantial overlap between the home address zip codes reported by members of these two samples. We find overlapping clusters of SNPs whose allele frequencies differ with nominal significance between substance dependent vs control individuals in both samples. These overlapping clusters of nominally-positive SNPs identify 172 genes in ways that are never found by chance in Monte Carlo simulation studies. Comparison with data from human expressed sequence tags suggests that these genes are expressed in brain, especially in hippocampus and amygdala, to extents that are greater than chance.

Conclusion

The convergent results from these probability sample and research volunteer sample datasets support prior genome wide association results. They fail to support the idea that large portions of the molecular genetic results for vulnerability to substance dependence derive from factors that are limited to research volunteers.

Background

Vulnerability to substance dependence is a complex trait with strong genetic influences that are well documented by data from family, adoption and twin studies [14]. Twin studies support the view that much of the heritable influence on vulnerability to dependence on addictive substances from different pharmacological classes (eg nicotine and stimulants) is shared [2, 3, 5]. Combined data from linkage and genome wide association (GWA) datasets [611] suggest that most of the genetics of vulnerability to dependence on addictive substances is likely to be polygenic, arising from variants in genes whose influences on vulnerability, taken one at a time, are relatively modest. Substance-dependent individuals also differ from control individuals in personality, cognitive domains and co-occurrence of psychiatric diagnoses [1, 12] (reviewed in [13]).

GWA approaches of increasing sophistication have been developed and used to identify the specific genes and genomic variants that predispose to vulnerability to substance dependence. For example, we have assembled a group of research volunteers from the Molecular Neurobiology Branch of the NIH (NIDA) intramural research program in Baltimore between 1990 and 2008 ("MNB"). We have compared allele frequencies at ca 1500, 10,000, 100,000, 500,000 and then 1,000,000 SNP markers in increasing numbers of substance dependent vs control individuals from this growing sample, including 680 substance dependent or control individuals with self reported European ancestries [69, 11], (Drgon et al, submitted).

There is the theoretical concern that this MNB sample, and many of the other samples collected for studies of genetics of dependence on addictive substances, might be biased based on the requirement that subjects were ascertained when they volunteered for research. It is conceivable that "volunteering" might interact with heritable features of personality, cognitive, psychiatric and/or other features by which substance dependent individuals might differ from controls [1, 1225]. GWA findings in such research volunteer samples would then conceivably provide a distorted representation of findings that would otherwise be made in members of the community.

The Baltimore site of the Epidemiological Catchment Area (ECA) Study provides a good comparison group to probe such potential confounding features [24, 26]. This study initially assembled a probability sample of individuals who represented the East Baltimore population, including many of the census tracts in which MNB research volunteers reported their home residences. ECA investigators followed substantial portions of these individuals, interviewing them four times and sampling DNA from most of the 1071 individuals from the initial sample who were interviewed in 2004–05 (see below). The repeated assessment of these individuals provides confident assessment of dependence-related phenotypes that include DSM diagnoses of substance abuse and dependence and Fagerstrom Test for Nicotine Dependence (FTND) diagnoses of nicotine dependence.

We thus now report data that confirms the overlapping areas of Baltimore from which ECA and MNB subjects were sampled. We report genome wide association studies for substance dependence phenotypes for Baltimore ECA subjects. We compare these genome wide association results with those from ethnically-matched MNB research volunteers who were recruited from many of the same areas. We discuss the significance of the substantially-overlapping data that we report, as well as the limitations of the samples and datasets. These data document large molecular genetic overlaps between probability-sample and research volunteer samples for substance dependence.

Methods

ECA Sample

Subjects from the Baltimore (Eastern Baltimore Mental Health Survey) site for the Epidemiological Catchment Area Program (ECA) were ascertained as a probability sample of individuals in dwelling units within census tracts near the Johns Hopkins Medical Institutions and initially interviewed in 1981 [24, 26]. Subsets of these individuals were interviewed in 1982, 1993–96 and 2004–5. Diagnoses came from the Diagnostic Interview Schedule (DIS), a self-report instrument [27] whose validity and reliability has been documented in this sample [27a]. Smoking was also described using the Fagerstrom Test for Nicotine Dependence (FTND) [2830]. Although many of the individuals who were > 65 in 1981 had died by the 2004–05 follow-up, self-report survey data was collected from 662 European-American respondents (63% female) during this follow-up. This subset provided good representation of the composition of the portion of the original Baltimore ECA cohort that was of this race/ethnicity. Blood for DNA extraction and for lymphocyte immortalization was obtained from 74% of these individuals, who did not differ from subjects from whom DNA was not obtained in any obvious feature that related to substance dependence [30a].

Individuals who were dependent on an abused substance were identified by DSM criteria, except for nicotine dependence which was based on FTND criteria. Eighty substance dependent individuals were identified. These individuals were matched for gender and age to eighty control individuals. These control individuals were East Baltimore ECA participants who never used any illegal drugs more then 5 times in their lives, were not dependent on any drug or alcohol, drank less than one drink per day (waves 3 and 4) drank less than 5 drinks/week (waves 1 and 2) and had FTND scores < 7. Generation and analyses of these data were approved by the Johns Hopkins Bloomberg School of Public Health IRB and exempt protocols for pooled genotyping approved by the NIH Office of Human Research Subject Protection.

Comparison research volunteer sample

Data from these European-American ECA subjects was compared to data from European-American MNB research volunteers who provided informed consents, ethnicity data, drug use histories and DSMIII-R or IV diagnoses as previously described [6, 31, 32]. DNA in 34 pools sampled 400 "abusers" with heavy lifetime use of illegal substances and, for virtually all, DSMIII-R/IV dependence on at least one illegal abused substance and 280 "controls" who reported no significant lifetime use of any addictive substance. Generation and analyses of these data were approved by the NIH IRP (NIDA) IRB (protocol #148) and exempt protocols for pooled genotyping approved by the NIH Office of Human Research Subject Protection.

DNA preparation and assessment of allelic frequencies

DNA was prepared from blood (MNB and some ECA subjects) or cell lines (most ECA subjects) [6, 31, 32] and carefully quantitated. DNAs from groups of 20 individuals of the same phenotype were combined. Hybridization probes were prepared with precautions to avoid contamination, as described (Affymetrix assays 500 k [9, 11, 33, 34]). 150 ng of pooled DNA was digested using StyI or NspI, ligated to appropriate adaptors and amplified using a GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA) with 3 min 94°C, 30 cycles of 30 sec 94°C, 45 sec 60°C, 15 sec at 68°C and a final 7 min 68°C extension. PCR products were purified (MinEluteTM 96 UF kits, Qiagen, Valencia, CA) and quantitated. Forty μg of PCR product was digested for 35 min at 37°C with 0.04 unit/μl DNase I to produce 30–100 bp fragments which were end-labeled using terminal deoxynucleotidyl transferase and biotinylated dideoxynucleotides and hybridized to the appropriate 500 k array (Sty I or Nsp I arrays) (Mendel array sets, Affymetrix). Arrays were stained and washed as described (Affymetrix Genechip Mapping Assay Manual) using immunopure strepavidin (Pierce, Milwaukee, WI), biotinylated antistreptavidin antibody (Vector Labs, Burlingame, CA) and R-phycoerythrin strepavidin (Molecular Probes, Eugene, OR). Arrays were scanned and fluorescence intensities quantitated using an Affymetrix array scanner as described [9, 11, 33, 34].

Chromosomal positions for each SNP were sought using NCBI (Build 36.1) and NETAFFYX (Affymetrix) data. Allele frequencies for each SNP in each DNA pool were assessed based on hybridization intensity signals from four arrays, allowing assessment of hybridization to the 12 "perfect match" cells on each array that are complementary to the PCR products from alleles "A" and "B" for each diallelic SNP on sense and antisense strands. We eliminated: i) SNPs on sex chromosomes and iii) SNPs whose chromosomal positions could not be adequately determined.

Each array was analyzed as described [9, 11, 33, 34], with background values subtracted, normalization to the highest values noted on the array, averaging of the hybridization intensities from the array cells that corresponded to the perfect match "A" and "B" cells, calculation of "A/B ratios" by dividing average normalized A values by average normalized B values, arctangent transformations to aid combination of data from arrays hybridized and scanned on different days, and determination of the average arctangent value for each SNP from the 4 replicate arrays. A "t" statistic for the differences between abusers and controls was generated as described [9, 11, 33, 34] for each SNP. We focused on SNPs that displayed t statistics with p < 0.05 for abuser/control differences. We sought evidence for clustering of these SNPs by focusing on chromosomal regions in which at least three of these outlier SNPs, assessed by at least two array types, lay within 25 Kb of each other. We term these clustered, nominally-positive SNPs "clustered positive SNPs", and focus our analyses on regions in which they lie.

To confirm the SNPs within the positive clusters from the current dataset, we sought convergence between data from these clustered nominally-positive SNPs and clustered nominally-positive SNPs, determined in the same way, from 1 M SNP genome wide association studies of the MNB samples (Drgon et al, submitted). To provide insights into some of the genes likely to harbor variants that contribute to individual differences in vulnerability to substance dependence, we sought candidate genes that were identified by overlapping clusters of positive SNPs from each of these samples.

We compare observed results to those expected by chance using Monte Carlo simulation trials, as described [9, 11, 33, 34]. For each trial, a randomly-selected set of SNPs from the current dataset was assessed to see if it provided results equal to or greater than the results that we actually observed. The number of trials for which the randomly-selected SNPs displayed (at least) the same features displayed by the observed results was then tallied to generate an empirical p value. These simulations thus correct for the number of repeated comparisons made in these analyses, an important consideration in evaluating these GWA datasets. We thus focus on genes which display convergence between nominally-significant results obtained from the two dependence vs control samples. We report Monte Carlo probabilities for the observed convergence of clustered nominally positive SNPs within each gene, using simulations that correct for the number of repeated comparisons.

To assess the power of our current approach, we use current sample sizes and standard deviations, the program PS v2.1.31 [35, 36] and α = 0.05. To provide controls for the possibilities that abuser-control differences observed herein were due to a) occult ethnic/racial allele frequency differences or b) noisy assays, we assessed the overlap between the results obtained here and the SNPs that displayed the largest a) allele frequency differences between African-American vs European-American control individuals and b) the largest assay "noise".

We have compared the patterns of human brain expression for the genes identified herein to those identified using a novel tool based on the distribution of expressed sequence tags (ESTs) contained in an annotated set of brain cDNA libraries (CYL, GRU et al, in preparation). Briefly, we identified 846 human cDNA libraries with cDNAs represented in dbEST. These libraries were constructed from regions of brains that appeared to display modest or no pathology. We based the analyses on two sets of criteria: 1) all entries in these libraries and 2) "more reliable" entries that display i) correct genomic orientation and either ii a) evidence for polyA tail or ii b) spliced structure (CYL, GRU et al, in preparation). For each brain region, we assessed the p-value for over-representation of expression of the dependence-associated genes using hypergeometric distribution tests with false discovery rate (FDR) corrections. Q-values < 0.05 were considered statistically significant.

Results

We assessed the extent to which the ECA and NIDA research volunteers came from similar Baltimore neighborhoods by comparing the distributions of available zip codes from these samples. As noted in Figure 1, there was substantial overlap between these zip code distributions, providing impetus for the comparative molecular genetic studies described below.
Figure 1

Substantial overlaps of the zip codes in which subjects reported their residences. Fractional distributions (X axis) of zip codes (Y axis) in which ECA (solid blue bars) or MNB (dotted purple bars) subjects reported their residence. Areas from which NIDA and ECA recruitment efforts were dissimilar include 21214; Lauraville and surrounding areas and 21222, Dundalk and surrounding areas. Zip codes from which fewer than 3 individuals were recruited are not indicated.

We then assessed allele frequencies in multiple pools of DNA from substance dependent and control ECA individuals. There was modest variability among replicate arrays that assessed the same pool (standard error of the mean (SEM) 0.032) and among the different pools that assessed the same phenotype (SEM 0.033). These samples and these estimates of variance thus provided 0.9, 0.74, 0.46 and 0.21 power to detect allele frequency differences of 12.5, 10, 7.5 and 5%, respectively.

28,137 SNPs displayed "nominally positive" t values with p < 0.05 in these ECA samples. 7,620 of these nominally positive SNPs fell into 1660 clusters of at least 3 SNPs that came from both array types were separated from adjacent nominally-positive SNPs by less than 25,000 basepairs. Monte Carlo simulations reveal p < 0.00001 for this degree of clustering.

One hundred seventy two genes are identified by both 1) clusters of nominally-positive SNPs from the ECA samples and 2) overlapping clusters of nominally positive SNPs from MNB samples. We list the 126 of these genes whose nominal Monte Carlo p values are < 0.05 in Table 1. This number of genes is never identified by chance in both samples by any of 10,000 Monte Carlo simulation trials (thus, p < 0.0001). There is also overlap, to extents greater than expected by chance, with the clusters of SNPs whose allele frequencies distinguish MNB African-American polysubstance abusers from controls (p < 0.0001) [9], Japanese methamphetamine abusers from controls (p < 0.0001) [11], Taiwanese methamphetamine abusers from controls (p < 0.0007) [11] and more-frequently nicotine dependent smokers of European ancestry from less-frequently nicotine dependent smokers (p < 0.0001) [10].
Table 1

Genes that contain overlapping clusters of nominally positive SNPs in both ECA and European-American MNB research volunteer samples that display nominal p < 0.05.

    

Clust SNPs

 

gene

ch

bp

description

ECA

MNB

p

A2BP1

16

6,009,133

ataxin 2-binding protein 1

16

42

0.0038

ACCN1

17

28,364,218

neuronal amiloride-sens cation chan 1

5

5

0.0240

ADARB2

10

1,218,073

RNA spec A deaminase B2

4

8

0.0420

ADCY2

5

7,449,345

adenylate cyclase 2

7

6

0.0210

AGBL4

1

48,822,129

ATP/GTP binding protein-like 4

3

8

0.0400

AK5

1

77,520,330

adenylate kinase 5

10

4

0.0090

AKAP6

14

31,868,274

A kinase anchoring protein 6

3

15

0.0210

ALK

2

29,269,144

anaplastic lymphoma kinase (Ki-1)

6

10

0.0470

ANKFN1

17

51,585,835

ankyrin-rep Fn III dom cont 1

3

14

0.0130

ATXN1

6

16,407,322

ataxin 1

3

17

0.0120

C18orf1

18

13,208,795

chromosome 18 open reading frame 1

4

6

0.0440

C3orf21

3

196,270,302

chromosome 3 open reading frame 21

4

4

0.0380

C8A

1

57,093,065

complement component 8 α polypep

3

7

0.0180

C9orf88

9

129,307,439

Chr 9 open reading frame 88

4

4

0.0220

CABIN1

22

22,737,765

calcineurin binding protein 1

4

4

0.0220

CACNA2D3

3

54,131,733

voltage det Ca chan α2/δ3 subunit

12

18

0.0091

CCBE1

18

55,252,124

collagen and calcium binding EGF domains 1

7

10

0.0090

CCDC63

12

109,769,194

coiled-coil domain containing 63

3

4

0.0290

CD180

5

66,513,872

CD180 molecule

5

5

0.0051

CDH13

16

81,218,079

cadherin 13

18

65

0.0019

CDH23

10

72,826,697

cadherin-like 23

7

4

0.0380

CGNL1

15

55,455,997

cingulin-like 1

3

11

0.0040

CHL1

3

213,650

close homolog of L1

3

12

0.0103

CHST11

12

103,370,614

chondroitin 4 sulfotransferase 11

4

4

0.0470

CIT

12

118,607,981

citron rho-interacting, serine/threonine kinase 21

5

6

0.0160

CNTN5

11

98,397,081

contactin 5

11

10

0.0390

CNTNAP2

7

145,444,386

contactin associated protein-like 2

22

9

0.0380

CPVL

7

29,001,772

carboxypeptidase vitellogenic-like

4

4

0.0290

CRYL1

13

19,875,810

crystalline λ1

4

5

0.0170

CSMD1

8

2,782,789

CUB and Sushi multiple domains 1

29

137

0.0014

CUGBP2

10

11,087,290

CUG triplet repeat RNA bind prot 2

16

4

0.0031

DAB1

1

57,236,167

disabled homolog 1

4

24

0.0140

DLC1

8

12,985,243

deleted in liver cancer 1

13

9

0.0059

DNAPTP6

2

200,879,041

DNA polymerase-transactivated protein 6

8

4

0.0140

DOCK2

5

168,996,871

dedicator of cytokinesis 2

3

7

0.0490

DPP6

7

154,060,464

dipeptidyl-peptidase 6

6

4

0.0250

EDNRA

4

148,621,575

endothelin receptor type A

3

4

0.0280

EFCAB4B

12

3,627,370

EF-hand calcium binding domain 4B

3

6

0.0165

EPHB1

3

135,996,950

EPH receptor B1

13

5

0.0090

ESRRG

1

214,743,211

estrogen-related receptor γ

3

14

0.0210

EVI1

3

170,285,244

ecotropic viral integration site 1

3

12

0.0047

F5

1

167,750,033

coagulation factor V

4

11

0.0049

FAM13A1

4

89,866,129

family with seq sim 13 A1

6

4

0.0360

FAM3C

7

120,776,141

family with sequence similarity 3 C

6

4

0.0109

FAM3D

3

58,594,710

family with sequence similarity 3 D

7

4

0.0063

FBXL17

5

107,223,348

F-box and leucine-rich repeat protein 17

6

6

0.0430

FGD2

6

37,081,401

FYVE, RhoGEF PH dom cont 2

3

4

0.0320

FHIT

3

59,710,076

fragile histidine triad gene

24

62

0.0030

FLJ11151

16

12,664,438

hypothetical protein FLJ11151

4

4

0.0380

FLJ32682

13

45,013,433

hypothetical protein FLJ32682

4

5

0.0180

FN1

2

215,933,422

fibronectin 1

3

5

0.0260

FOXP1

3

71,087,426

forkhead box P1

4

17

0.0180

FREM3

4

144,717,905

FRAS1 related extracellular matrix 3

4

6

0.0130

FRMD4A

10

13,725,718

FERM domain containing 4A

3

23

0.0090

GABBR2

9

100,090,187

GABA B receptor 2

11

6

0.0073

GLIS3

9

3,817,676

GLIS family zinc finger 3

13

18

0.0016

GRB10

7

50,625,259

growth factor receptor-bound protein 10

3

13

0.0083

GRID1

10

87,349,292

delta 1 inotropic glutamate rec

7

18

0.0130

GRIK1

21

29,831,125

kainate 1 inotropic glutamate rec

4

12

0.0150

GTF2F2L

4

148,646,691

general transcription fact IIFpolypep 2-L

3

3

0.0170

HPSE2

10

100,208,867

heparanase 2

6

18

0.0160

HS3ST4

16

25,611,240

heparan sulfate 3-O-sulfotransferase 4

4

11

0.0250

IMPA2

18

11,971,455

inositol(myo)-1(or 4)-monophosphatase 2

6

6

0.0064

IQGAP2

5

75,734,905

IQ motif cont GTPase activ prot 2

7

5

0.0170

JAKMIP1

4

6,106,385

janus kinase microtubule interacting protein 1

3

8

0.0138

KCNB1

20

47,421,912

Shab-rel volt-gated K chan 1

5

3

0.0190

KCNIP4

4

20,339,337

Kv channel interacting protein 4

13

9

0.0107

KCNJ6

21

37,918,655

inwardly-rect K chan J 6

3

8

0.0330

KCNMA1

10

78,299,366

large conduct Ca-act K chan M α1

8

8

0.0390

KIAA1576

16

76,379,984

KIAA1576 protein

3

6

0.0260

KREMEN1

22

27,799,106

kringle cont TM prot 1

5

4

0.0190

KSR2

12

116,389,387

kinase suppressor of ras 2

5

4

0.0430

LDLRAD3

11

35,922,188

low density lipoprotein recep cl A dom cont 3

3

6

0.0330

LTF

3

46,452,500

lactotransferrin

3

4

0.0290

MAGI1

3

65,314,946

membr-assoc G kinase WW PDZ dom cont 1

6

9

0.0380

MAGI2

7

77,484,310

membrane assoc G kinase WW PDZ dom 2

9

16

0.0430

MGC23985

5

147,252,464

similar to AVLV472

3

7

0.0085

MICAL2

11

12,088,714

calponin LIM cont microtub monoxygenase 2

8

8

0.0120

MTSS1

8

125,632,212

metastasis suppressor 1

9

9

0.0050

MTUS1

8

17,545,583

mitochondrial tumor suppressor 1

3

4

0.0090

MYO18B

22

24,468,120

myosin XVIIIB

15

8

0.0460

MYO3A

10

26,263,202

myosin IIIA

3

5

0.0018

NAALADL2

3

176,059,805

N-Ac α-linked acidic dipeptidase-L 2

4

5

0.0340

NFIA

1

61,320,881

nuclear factor I/A

4

4

0.0080

NLGN1

3

174,598,938

neuroligin 1

4

4

0.0260

OPCML

11

131,790,085

opioid binding protein/cell adhesion molecule-L

10

13

0.0076

PALM2

9

111,442,893

paralemmin 2

10

16

0.0220

PALM2-AKAP2

9

111,582,410

PALM2-AKAP2 protein

5

10

0.0021

PARD3B

2

205,118,761

par-3 partitioning defective 3 homolog B

3

5

0.0160

PDE4D

5

58,302,468

cAMP spec phosphodiesterase 4D

4

4

0.0140

PKD1L2

16

79,691,991

polycystic kidney disease 1-like 2

4

4

0.0060

PLD5

1

240,318,895

phospholipase D family 5

3

16

0.0300

PRKCA

17

61,729,388

protein kinase C α

6

4

0.0113

PRKCH

14

60,858,268

protein kinase C η

7

22

0.0300

PRKG1

10

52,504,299

cGMP dep protein kinase I

12

10

0.0014

PRPF4

9

115,077,795

PRP4 pre-mRNA process fact 4 homol

6

4

0.0360

PSD3

8

18,432,343

pleckstrin and Sec7 domain containing 3

8

16

0.0240

PTPN14

1

212,597,634

no rec prot Y phosphatase 14

10

4

0.0150

PTPRK

6

128,331,625

recept protein tyrosine phosphatase K

3

8

0.0062

PTPRT

20

40,134,806

recept prot Y phosphatase T

3

15

0.0190

RBMS3

3

29,297,947

sing strand RNA binding motif interact prot

5

13

0.0230

ROR2

9

93,524,705

receptor tyrosine kinase-L orphan recept 2

5

4

0.0290

RORA

15

58,576,755

RAR-related orphan receptor A

4

9

0.0220

SLC2A13

12

38,435,090

solute carrier family 2 13

4

4

0.0086

SLIT3

5

168,025,857

slit homolog 3

3

4

0.0370

SRGAP3

3

8,997,278

SLIT-ROBO Rho GTPase activating protein 3

4

23

0.0087

STK32B

4

5,104,428

serine threonine kinase 32B

3

25

0.0011

STK39

2

168,518,777

serine threonine kinase 39

4

4

0.0036

SYNE1

6

152,484,516

spectrin rep cont nuclear envelope 1

7

5

0.0034

TACC2

10

123,738,679

transforming acidic coil-coil cont prot 2

3

9

0.0430

TBC1D22A

22

45,537,213

TBC1 domain family 22A

3

8

0.0270

TEK

9

27,099,286

TEK tyrosine kinase, endothelial

3

9

0.0420

TG

8

133,948,387

thyroglobulin

4

4

0.0180

THSD4

15

69,220,842

thrombospondin I dom cont 4

3

11

0.0063

TMEM132C

12

127,318,855

transmembrane protein 132C

5

13

0.0084

TMEM132D

12

128,122,224

transmembrane protein 132D

8

11

0.0090

TMEM16D

12

99,712,716

transmembrane protein 16D

11

11

0.0320

TMTC1

12

29,545,024

transmemb tetratricopep rep cont 1

3

3

0.0035

TRPC4

13

37,108,795

transient receptor potential cation channel C 4

8

4

0.0140

TULP4

6

158,653,680

tubby like protein 4

6

9

0.0076

UNC5C

4

96,308,712

unc-5 homolog C

9

4

0.0162

VAMP4

1

169,938,783

vesicle-associated membrane protein 4

3

5

0.0170

VAPB

20

56,397,651

vesicle-assoc memb protein-assoc prot B C

4

11

0.0034

VIT

2

36,777,418

Vitrin

3

9

0.0110

ZNF365

10

63,803,957

zinc finger protein 365

6

4

0.0340

ZNF406

8

135,559,213

zinc finger protein 406

16

4

0.0014

The numbers of nominally-positive SNPs that lay in clusters within the gene's exons and in 10 kb genomic flanking regions are noted for each sample. Chromosome number and initial chromosomal position for the cluster (bp, NCBI Mapviewer Build 36.1) are listed. Nominal p values for each gene are based on 10,000 Monte Carlo simulation trials. For each trial, the number of times randomly-selected segments of the genome that lie within genes are assessed for the same features displayed by the actual gene identified. Note that the very highly significant p values for the overall convergence noted between these two datasets (text) does account for multiple comparisons, while the much more modest p values for many of the individual genes (displayed here) do not. Genes that are identified by clustered nominally positive SNPs in both samples but whose gene-wise p values lie > 0.05 (perhaps, in part, due to the large size of the genes) include: C4orf13, PRKCE, UNC13C, KIAA1303, MYR8, DNAH11, ONECUT2, TGFBR3, TPD52L1, C9orf28, TMEM108, GALNT14, HECW2, NFIB, STS-1, KIAA1217, RAB3C, SNTG1, CPNE4, PTPRM, SLC39A11, MDS1, GPC5, ZNF533, NR5A2, RYR3, C8orf68, CTNNA2, GRM5, ATRNL1, ARL15, BTBD9, CNTNAP5, GALNTL4, PELI2, SNRPN, GPR98, ERC2, NFATC2, FLJ16124, GRM7, SORCS2, NPAS3, PARVB, IGL@ and LRP1B.

We would anticipate the observed, highly-significant clustering of SNPs that display nominally-positive results if many of these reproducibly-positive SNPs lay near and were in linkage disequilibrium with functional allelic variants that distinguished substance-dependent subjects from control subjects. We would not anticipate this degree of clustering if the results were solely due to chance. The Monte Carlo p values noted here are thus likely to receive contributions from both the extent of linkage disequilibrium among the clustered, nominally-positive SNPs and the extent of linkage disequilibrium between these SNPs and the functional haplotype(s) that lead to the association with substance dependence.

Neither controls for occult stratification nor for assay variability appear to provide convincing alternative explanations for most of the data obtained here. When we examined the overlap between the 7620 clustered positive SNPs from the ECA samples and: 1) the 2.5% of the SNPs for which the noise in validating studies was highest and 2) the 2.5% of SNPs that displayed the largest differences between Baltimore African-American vs European American control individuals, we found 15 and 245, respectively, vs 185 expected by chance in each case.

We evaluated evidence for preferential brain and brain regional expression of the 172 genes identified in Table 1 (body and legend). Brain libraries represented in dbEST contained ESTs that corresponded to 91% (157/172) of these genes. Expression for this set of genes (compared to all genes) displayed nominal significance in thalamus (p < 10-8), amygdale (p = 5.6 × 10-9), hippocampus (p = 2.6 × 10-5), frontal cortex (p = 0.015) and medulla (p = 0.039) using hypergeometric tests (p values were Bonferroni corrected for repeated comparisons). Assessments of the "more reliable" subset of ESTs revealed significant overexpression in whole brain (p = 2.1 × 10-7), amygdala (p = 4.4 × 10-6), hippocampus (p = 0.0011), cerebellum (p = 0.0052), thalamus (p = 0.037) and cortex (p = 0.049) (p-values were Bonferroni corrected).

Discussion

The current results 1) provide independent support for GWA results from larger samples of research volunteers studied for substance dependence phenotypes and 2) provide a control for one of the potential confounding features of this previously-studied sample. The possibility that genetic results from members of any sample of research volunteers might not represent the genetics of members of the general population is ever present. In molecular genetic studies of substance dependence, however, features that are both 1) heritable and 2) differentially present in substance dependent individuals might, a priori, be considered to be especially likely to provide confounding influences. Cognitive abilities are highly heritable. Cognitive tests in a number of samples of substance dependent individuals have indicated differences in performance (reviewed in [13]). A study in twin pairs whose members were discordant for substance use concluded that most of these cognitive differences were likely to be heritable antecedents to, not just consequences of, the use of addictive substances [37]. Cognitive abilities have been shown to interact with willingness to volunteer for and/or participate in research protocols in a number of settings [1, 12, 1425]. A number of personality features are also highly heritable [38]. Neuroticism is both one of the more heritable personality features and also the personality feature that has been demonstrated to be elevated in several samples of substance dependent individuals [38]. Personality features have also been linked to willingness to volunteer for participation in research protocols [1, 12, 1425]. A number of psychiatric disorders that might also be linked to differential willingness (or ability) to participate as a research volunteer are also heritable and co-occur with substance dependences at rates much greater than chance [13].

It is also important to keep a number of limitations in mind in considering the present results. 1) The preplanned approach used here demands that multiple nominally-positive SNPs from each sample tag the same genomic region that lies within a gene. Requirements that nominally positive SNPs from the current dataset come from each of the two 500 k array types add a technical control. Monte Carlo approaches that do not require specification of underlying distributions can readily judge the degree to which all of the observations made here could be due to chance. Nevertheless, there have been no unanimous criteria for declaring "replication" or "convergence" for GWA studies, a consideration worth considering in evaluating the current results. 2) The ECA samples are of modest size, limited by the numbers of substance abusing or dependent individuals in the aging Baltimore ECA cohort follow-up samples. Power calculations that document the modest power in European-Americans samples revealed even more modest power for the smaller number of African-American substance dependent individuals in this sample; we have thus not analyzed these samples. Modest power limits interpretation of negative data, substantially restricting inferences about genes identified in the more robust dataset from MNB research volunteers but not in these ECA samples. 3) There is very highly significant confidence in the overall set of convergent positive results reported here. However, the values for each gene, tested individually, provide much more modest levels of statistical assurance. 4) Focus on data from autosomes here allows us to combine data from male and female subjects, but misses potentially important contributions from sex chromosomes. 5) The individuals in the Baltimore ECA cohort were not initially sampled based on their willingness to be volunteers. However, participants needed to consent in order to be able to be followed and studied genetically. Although the overwhelming majority of the European-American participants who were followed did consent to participation in genetic studies, potential contributions that the non-consenting individuals might have made to the present results remain unknown. 6) The pooling approach that we use here provides excellent correlations between individually-genotyped and pooled allele frequency assessments in validation experiments. This approach has allowed us to use these samples without adding additional confidentiality burdens to these intensively-studied individuals. Nevertheless, estimates of allele frequencies based on pooled data represent approximations of "true" allele frequency differences that might be determined by error free individual genotyping of each participant. 7) There is no indication that the overall positive results reported here are based on the SNPs whose assays provide more noise, and no indication that occult stratification on racial/ethnic lines contributed overall to the results that we obtain here. However, we cannot totally exclude contributions of occult stratification that cannot be detected by these overall screens to findings in specific genes. 8) The convergent data derived from studies of individuals with dependence on substances in several different pharmacological classes supports the idea that many allelic variants enhance vulnerability to dependence on a number of substances. These results do not exclude additional contributions from genomic variants that influence vulnerability to specific substances. 9) We focus on identification of genes. Although associations away from annotated genes can also provide interesting results, the genes that we identify in the present work provide a number of interesting views of substance dependence. These data reinforce our observations that many of these genes are likely to contribute to brain differences that are reflected in the mnemonic aspects of addiction, and that some of them also provide tempting targets for antiaddiction therapeutics. We discuss these ideas in more detail elsewhere [12, 13].

More of the genes identified here are represented among cDNAs cloned from brain libraries than is the case for all human genes. The results focus attention on expression in hippocampus, which manifests interesting roles in mnemonic processes and cerebral cortical connections that may provide additional clues to the pathophysiology of human substance dependence. Although detailed discussion of each of these groups of genes is beyond the scope of this report, it is interesting to note that about 15% of the genes enumerated in Table 1 can be related to cell adhesion mechanisms. This is a much larger fraction that the fraction of all genes, about 2%, that are identified as cell adhesion molecules in a recent bioinformatic approach to comprehensively identifying cell adhesion molecules [39], supporting overrepresentation of these genes among addiction-associated genes.

A number of the genes identified in this work are also identified in genome wide association and/or candidate gene datasets for heritable disorders or phenotypes that co-occur with addictions. As we discuss elsewhere, dependence-vulnerability GWA results overlap at levels greater than expected by chance with GWA studies of cognitive abilities, personality features, frontal lobe brain volumes and bipolar disorder [13].

Conclusion

The observations in the present dataset that the findings from a population-based sample converge strongly with those made in larger research volunteer samples are reassuring. They support the idea that many of the molecular genetic findings that we and others have previously reported are not due simply to the methods used for ascertainment of "cases" and "controls" for our studies in research volunteers. It is important to note that this overall conclusion does not exclude contributions for some of these sampling issues to findings in particular genes. Nevertheless, the findings presented here promise to add to ongoing processes for comparing GWA datasets from research volunteers to those from population based samples. For dependence on alcohol, tobacco and other drugs, as for many complex disorders, such data provides an increasingly rich basis for improved understanding and for personalization of prevention and treatment strategies.

Notes

Abbreviations

DSM: 

diagnostic and statistical manual

ECA: 

Epidemiological catchment area

MNB: 

Molecular Neurobiology Research Branch.

Declarations

Acknowledgements

We are grateful for thoughtful advice and discussion from Drs N Ialongo, C Storer and P Zandi. This research was supported financially by the NIH Intramural Research Program, NIDA, DHSS and National Institute of Mental Health grants R01-47447 and T32-14592, and Johns Hopkins Bloomberg School of Public Health IRB H.33.01.03.26.A2 (WE). We are also grateful to the Epidemiologic Catchment Area Program's principal collaborators (D Regier, B Locke, WE and J Burke) and to Drs M Kramer, E Gruenberg, and S Shapiro from the Johns Hopkins site, supported by UO1 MH 33870.

Authors’ Affiliations

(1)
Molecular Neurobiology Branch, NIH-IRP (NIDA)
(2)
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University
(3)
Dept of Epidemiology, Michigan State University
(4)
Department of Mental Health and Hygiene, Johns Hopkins Bloomberg School of Public Health

References

  1. Uhl GR, Elmer GI, Labuda MC, Pickens RW: Genetic influences in drug abuse. Psychopharmacology: The Fourth Generation of Progress. Edited by: Gloom FE, Kupfer DJ. 1995, New York: Raven Press, 1793-2783.Google Scholar
  2. Tsuang MT, Lyons MJ, Meyer JM, Doyle T, Eisen SA, Goldberg J, True W, Lin N, Toomey R, Eaves L: Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch Gen Psychiatry. 1998, 55 (11): 967-972. 10.1001/archpsyc.55.11.967.View ArticlePubMedGoogle Scholar
  3. Karkowski LM, Prescott CA, Kendler KS: Multivariate assessment of factors influencing illicit substance use in twins from female-female pairs. Am J Med Genet. 2000, 96 (5): 665-670. 10.1002/1096-8628(20001009)96:5<665::AID-AJMG13>3.0.CO;2-O.View ArticlePubMedGoogle Scholar
  4. True WR, Heath AC, Scherrer JF, Xian H, Lin N, Eisen SA, Lyons MJ, Goldberg J, Tsuang MT: Interrelationship of genetic and environmental influences on conduct disorder and alcohol and marijuana dependence symptoms. Am J Med Genet. 1999, 88 (4): 391-397. 10.1002/(SICI)1096-8628(19990820)88:4<391::AID-AJMG17>3.0.CO;2-L.View ArticlePubMedGoogle Scholar
  5. Kendler KS, Karkowski LM, Neale MC, Prescott CA: Illicit psychoactive substance use, heavy use, abuse, and dependence in a US population-based sample of male twins. Arch Gen Psychiatry. 2000, 57 (3): 261-269. 10.1001/archpsyc.57.3.261.View ArticlePubMedGoogle Scholar
  6. Uhl GR, Liu QR, Walther D, Hess J, Naiman D: Polysubstance abuse-vulnerability genes: genome scans for association, using 1,004 subjects and 1,494 single-nucleotide polymorphisms. Am J Hum Genet. 2001, 69 (6): 1290-1300. 10.1086/324467.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Liu QR, Drgon T, Walther D, Johnson C, Poleskaya O, Hess J, Uhl GR: Pooled association genome scanning: validation and use to identify addiction vulnerability loci in two samples. Proc Natl Acad Sci USA. 2005, 102 (33): 11864-11869. 10.1073/pnas.0500329102.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Johnson C, Drgon T, Liu QR, Walther D, Edenberg H, Rice J, Foroud T, Uhl GR: Pooled association genome scanning for alcohol dependence using 104,268 SNPs: validation and use to identify alcoholism vulnerability loci in unrelated individuals from the collaborative study on the genetics of alcoholism. Am J Med Genet B Neuropsychiatr Genet. 2006, 141B (8): 844-853. 10.1002/ajmg.b.30346.View ArticlePubMedGoogle Scholar
  9. Liu QR, Drgon T, Johnson C, Walther D, Hess J, Uhl GR: Addiction molecular genetics: 639,401 SNP whole genome association identifies many "cell adhesion" genes. Am J Med Genet B Neuropsychiatr Genet. 2006, 141B (8): 918-925. 10.1002/ajmg.b.30436.View ArticlePubMedGoogle Scholar
  10. Bierut LJ, Madden PA, Breslau N, Johnson EO, Hatsukami D, Pomerleau OF, Swan GE, Rutter J, Bertelsen S, Fox L, et al: Novel genes identified in a high-density genome wide association study for nicotine dependence. Hum Mol Genet. 2007, 16 (1): 24-35. 10.1093/hmg/ddl441.View ArticlePubMedGoogle Scholar
  11. Uhl GR, Drgon T, Liu QR, Johnson C, Walther D, Komiyama T, Harano M, Sekine Y, Inada T, Ozaki N, et al: Genome-wide association for methamphetamine dependence: convergent results from 2 samples. Arch Gen Psychiatry. 2008, 65 (3): 345-355. 10.1001/archpsyc.65.3.345.View ArticlePubMedGoogle Scholar
  12. Uhl GR, Drgon T, Johnson C, Fatusin OO, Liu QR, Contoreggi C, Li CY, Buck K, Crabbe J: "Higher order" addiction molecular genetics: convergent data from genome-wide association in humans and mice. Biochem Pharmacol. 2008, 75 (1): 98-111. 10.1016/j.bcp.2007.06.042.View ArticlePubMedGoogle Scholar
  13. Uhl GR, Drgon T, Johnson C, Li CY, Contoreggi C, Hess J, Naiman D, Liu QR: Molecular genetics of addiction and related heritable phenotypes: genome wide association approaches identify "connectivity constellation" and drug target genes with pleiotropic effects. Ann N Y Acad Sci. 2008, 1141: 318-381.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Annas GJ: Reforming informed consent to genetic research. Jama. 2001, 286 (18): 2326-2328. 10.1001/jama.286.18.2326.View ArticlePubMedGoogle Scholar
  15. Bauer JE, Rezaishiraz H, Head K, Cowell J, Bepler G, Aiken M, Cummings KM, Hyland A: Obtaining DNA from a geographically dispersed cohort of current and former smokers: use of mail-based mouthwash collection and monetary incentives. Nicotine Tob Res. 2004, 6 (3): 439-446. 10.1080/14622200410001696583.View ArticlePubMedGoogle Scholar
  16. Benfante R, Reed D, MacLean C, Kagan A: Response bias in the Honolulu Heart Program. Am J Epidemiol. 1989, 130 (6): 1088-1100.PubMedGoogle Scholar
  17. Bergstrand R, Vedin A, Wilhelmsson C, Wilhelmsen L: Bias due to non-participation and heterogenous sub-groups in population surveys. J Chronic Dis. 1983, 36 (10): 725-728. 10.1016/0021-9681(83)90166-2.View ArticlePubMedGoogle Scholar
  18. Beskow LM, Burke W, Merz JF, Barr PA, Terry S, Penchaszadeh VB, Gostin LO, Gwinn M, Khoury MJ: Informed consent for population-based research involving genetics. Jama. 2001, 286 (18): 2315-2321. 10.1001/jama.286.18.2315.View ArticlePubMedGoogle Scholar
  19. Clayton EW, Steinberg KK, Khoury MJ, Thomson E, Andrews L, Kahn MJ, Kopelman LM, Weiss JO: Informed consent for genetic research on stored tissue samples. Jama. 1995, 274 (22): 1786-1792. 10.1001/jama.274.22.1786.View ArticlePubMedGoogle Scholar
  20. Corbie-Smith G, Thomas SB, Williams MV, Moody-Ayers S: Attitudes and beliefs of African Americans toward participation in medical research. J Gen Intern Med. 1999, 14 (9): 537-546. 10.1046/j.1525-1497.1999.07048.x.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Criqui MH: Response bias and risk ratios in epidemiologic studies. Am J Epidemiol. 1979, 109 (4): 394-399.PubMedGoogle Scholar
  22. Criqui MH, Austin M, Barrett-Connor E: The effect of non-response on risk ratios in a cardiovascular disease study. J Chronic Dis. 1979, 32 (9–10): 633-638. 10.1016/0021-9681(79)90093-6.View ArticlePubMedGoogle Scholar
  23. Durfy SJ, Bowen DJ, McTiernan A, Sporleder J, Burke W: Attitudes and interest in genetic testing for breast and ovarian cancer susceptibility in diverse groups of women in western Washington. Cancer Epidemiol Biomarkers Prev. 1999, 8 (4 Pt 2): 369-375.PubMedGoogle Scholar
  24. Eaton WW, Neufeld K, Chen LS, Cai G: A comparison of self-report and clinical diagnostic interviews for depression: diagnostic interview schedule and schedules for clinical assessment in neuropsychiatry in the Baltimore epidemiologic catchment area follow-up. Arch Gen Psychiatry. 2000, 57 (3): 217-222. 10.1001/archpsyc.57.3.217.View ArticlePubMedGoogle Scholar
  25. McQuillan GM, Porter KS, Agelli M, Kington R: Consent for genetic research in a general population: the NHANES experience. Genet Med. 2003, 5 (1): 35-42.View ArticlePubMedGoogle Scholar
  26. Regier DA, Myers JK, Kramer M, Robins LN, Blazer DG, Hough RL, Eaton WW, Locke BZ: The NIMH Epidemiologic Catchment Area program. Historical context, major objectives, and study population characteristics. Arch Gen Psychiatry. 1984, 41 (10): 934-941.View ArticlePubMedGoogle Scholar
  27. Robins LN, Helzer JE, Croughan J, Ratcliff KS: National Institute of Mental Health Diagnostic Interview Schedule. Its history, characteristics, and validity. Arch Gen Psychiatry. 1981, 38 (4): 381-389.View ArticlePubMedGoogle Scholar
  28. Fagerstrom KO: Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addict Behav. 1978, 3 (3–4): 235-241. 10.1016/0306-4603(78)90024-2.View ArticlePubMedGoogle Scholar
  29. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO: The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict. 1991, 86 (9): 1119-1127. 10.1111/j.1360-0443.1991.tb01879.x.View ArticlePubMedGoogle Scholar
  30. Fagerstrom KO, Schneider NG: Measuring nicotine dependence: a review of the Fagerstrom Tolerance Questionnaire. J Behav Med. 1989, 12 (2): 159-182. 10.1007/BF00846549.View ArticlePubMedGoogle Scholar
  31. Smith SS, O'Hara BF, Persico AM, Gorelick DA, Newlin DB, Vlahov D, Solomon L, Pickens R, Uhl GR: Genetic vulnerability to drug abuse. The D2 dopamine receptor Taq I B1 restriction fragment length polymorphism appears more frequently in polysubstance abusers. Arch Gen Psychiatry. 1992, 49 (9): 723-727.View ArticlePubMedGoogle Scholar
  32. Persico AM, Bird G, Gabbay FH, Uhl GR: D2 dopamine receptor gene TaqI A1 and B1 restriction fragment length polymorphisms: enhanced frequencies in psychostimulant-preferring polysubstance abusers. Biol Psychiatry. 1996, 40 (8): 776-784. 10.1016/0006-3223(95)00483-1.View ArticlePubMedGoogle Scholar
  33. Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE: Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs. BMC Genet. 2007, 8: 10-10.1186/1471-2156-8-10.View ArticlePubMedPubMed CentralGoogle Scholar
  34. Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE, David SP, Niaura R, Lerman C: Molecular genetics of successful smoking cessation: convergent genome-wide association study results. Arch Gen Psychiatry. 2008, 65 (6): 683-693. 10.1001/archpsyc.65.6.683.View ArticlePubMedPubMed CentralGoogle Scholar
  35. Dupont WD, Plummer WD: Power and sample size calculations. A review and computer program. Control Clin Trials. 1990, 11 (2): 116-128. 10.1016/0197-2456(90)90005-M.View ArticlePubMedGoogle Scholar
  36. Dupont WD, Plummer WD: Power and sample size calculations for studies involving linear regression. Control Clin Trials. 1998, 19 (6): 589-601. 10.1016/S0197-2456(98)00037-3.View ArticlePubMedGoogle Scholar
  37. Lyons MJ, Bar JL, Panizzon MS, Toomey R, Eisen S, Xian H, Tsuang MT: Neuropsychological consequences of regular marijuana use: a twin study. Psychol Med. 2004, 34 (7): 1239-1250. 10.1017/S0033291704002260.View ArticlePubMedGoogle Scholar
  38. Costa PT, Widiger TA: Personality Disorders and The Five Factor Model of Personality. 1993, Washington DC: American Psychological AssociationGoogle Scholar
  39. Li CY, Liu QR, Zhang PW, Li XM, Wei L, Uhl GR: OKCAM: an ontology-based, human-centered knowledgebase for cell adhesion molecules. Nucleic Acids Res. 2009, 37 (Database issue): D251-D260. 10.1093/nar/gkn568.View ArticlePubMedGoogle Scholar
  40. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2350/9/113/prepub

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© Johnson et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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