Skip Navigation


Human Molecular Genetics Advance Access originally published online on April 4, 2007
Human Molecular Genetics 2007 16(11):1327-1334; doi:10.1093/hmg/ddm081
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
16/11/1327    most recent
ddm081v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Lachman, H. M.
Right arrow Articles by Knowles, J. A.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Lachman, H. M.
Right arrow Articles by Knowles, J. A.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Genomewide suggestive linkage of opioid dependence to chromosome 14q

Herbert M. Lachman1,*, Cathy S.J. Fann2,{dagger}, Michael Bartzis3,{dagger}, Oleg V. Evgrafov3,{dagger},{ddagger}, Richard N. Rosenthal5, Edward V. Nunes4, Christian Miner6, Maria Santana1, Jebediah Gaffney1, Amy Riddick1, Chia-Lin Hsu2,{dagger} and James A. Knowles3,{ddagger}

1 Division of Basic Research, Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine Bronx, NY 10461, USA, 2 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan, Republic of China, 3 Department of Psychiatry, New York State Psychiatric Institute, 4 Division on Substance Abuse, Department of Psychiatry, New York State Psychiatric Institute, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA, 5 Department of Psychiatry, St Luke's Roosevelt Hospital Center, Columbia University College of Physicians and Surgeons, New York, NY 10019, USA and 6 Department of Psychiatry, Beth Israel Medical Center, New York, NY 10003-3896, USA

* To whom correspondence should be addressed at: Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA. Tel: +1 7184302428; Fax: +1 7184308772; Email: lachman{at}aecom.yu.edu

Received November 1, 2006; Accepted March 26, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
The genetic predisposition to addiction to opioids and other substances is transmitted as a complex genetic trait, which investigators are attempting to characterize using genetic linkage and association. We now report a high-density genome-wide linkage study of opioid dependence. We ascertained 305 DSM-IV opioid dependent affected sibling pairs from an ethnically mixed population of methadone maintained subjects and genotyped their DNA using Affymetrix 10K v2 arrays. Analysis with MERLIN identified a region on chromosome 14q with a non-parametric lod (NPL) of 3.30. Secondary analyses indicated that this locus was relatively specific to the self-identified Puerto Rican subset, as the NPL increased from 3.30 to 5.00 (NPLCaucasian = 0.05 and NPLAfrican Amer. = 0.15). The 14q peak encompasses the NRXN3 gene (neurexin 3), which was previously identified as a potential candidate gene for addiction. Secondary analyses also identified several regions with gender-specific NPL scores greater than 2.00. The most significant was a peak on (10q) that increased from 0.90 to 3.22 when only males were considered (NPLfemale = 0.05). Our linkage data suggest specific chromosomal loci for future fine-mapping genetic analysis and support the hypothesis that ethnic and gender specific genes underlie addiction susceptibility.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
Addiction to heroin is a major public health problem both in the USA and throughout the world. Nearly four million people in the USA have tried heroin, and approximately one million are currently addicted (1). Tens of billions of dollars are spent every year on the adverse health effects and criminal consequences of heroin use. Although statistics vary in different parts of the USA, approximately one-fifth of intravenous users of heroin are infected with human immunodeficiency virus and three-quarters with hepatitis C virus (2). A substantial body of evidence from family, twin and adoption studies indicates that a genetic component underlies addiction disorders (3–11). Twin studies show a 2–4-fold difference in concordance for substance abuse disorder between monozygotic and dizygotic twins (12,13). Calculations by Pickens et al. (12) suggest that genetic factors contribute 31% of the vulnerability in males for drug abuse/dependence disorders. Higher values (45% and 79%) were reported by Grove et al. and Van den Bree et al., respectively (5,13). Both drug-specific and common genetic factors contribute to addiction (14–16). In a study by Tsuang et al. (15), the 54% overall heritability for opiate dependence could be apportioned into a component accounting for unique genetic variance (38%) and another for shared liability (16%) with other substances. Kendler et al. (16), on the other hand, in their study on male twins, attributed all of the genetic liability for six substances, including opiates, to a common genetic factor. There is also some evidence that genetic factors may play less of a role in opiate use liability in females (12,13,17).

An underlying genetic vulnerability is consistent with current models of addiction as a brain disease, which follows three decades of neurobiological research of the dopaminergic reward circuitry (18–21). It is hypothesized that polymorphic variation in the genes involved in regulating the reward circuitry may cause interindividual differences in drug use initiation, the rewarding effects of addicting substances and the brain's adaptation to them (4,17,22,23).

An underlying genetic component has also been observed in animal models of addiction. For example, the Lewis rat strain is more vulnerable to self-administration of addicting substances, compared to the Fisher 344 and Holtzman strains (24,25). This suggests that a polymorphic behavioral trait or physiological process has emerged in the evolution of higher animals that manifests in humans (and artificially in laboratory animals) as an increased vulnerability to compulsively seek addicting substances without regard to adverse consequences, following an initial exposure to those substances.

Mendelian inheritance does not explain the pattern of drug addiction susceptibility seen in families. Rather, the inheritance is more consistent with a complex inherited trait influenced by environmental factors. Most previous studies carried out to identify genetic factors in addiction have focused on analyzing candidate genes based on knowledge of the brain dopaminergic reward pathways, and the receptor and transporter proteins targeted by addicting substances, such as DRD2 and OPRM1. However, since neurotransmitter and signal transduction pathways in the brain interact with each other on many levels, and trigger complex intracellular events affecting hundreds of different proteins and genes, identifying the specific receptors and transporters targeted by addicting substances does not necessarily translate into choosing the correct candidate genes for analysis. A hypothesis-free, whole genome approach offers the potential to capture the full range of genetic possibilities in an unbiased manner.

An early genome-wide study relevant to addiction was carried out by Berrettini et al. (26), who used quantitative trait locus (QTL) mapping to identify several loci for self-administration in mice, one of which turned out to be the Oprm1 gene. Researchers in human addiction subsequently carried out genome-wide linkage studies in alcoholism, nicotine, cocaine and opiate dependence. Positive loci on chromosomes 1, 4 and 7 were reported in the initial linkage reports in alcoholism (27–30). The chromosome 4 linkage peak at 4p13–p12 encompasses the GABAA gene cluster. Eventually, Edenberg et al. narrowed the search using linkage disequilibrium (LD) mapping and identified SNPs and SNP haplotypes in the GABRA2 gene that were significantly association with alcoholism, a finding that was replicated by Covault et al. (31,32). Several loci for nicotine addiction have also been mapped, and eventually, CHRN4, which codes for the nicotinic acetylcholine receptor {alpha}4 subunit, and GABAB2, which codes for the type ß receptor subunit 2 of the GABA receptor, were targeted as likely susceptibility genes (33,34). Recently, Gelernter et al. (35) published the results of a genome-wide linkage scan using a 400 marker set in 528 sibling pairs concordant for cocaine dependence. Two significant linkage peaks were identified on chromosomes 9 and 12. Glatt et al. (36) recently reported the first genome-wide linkage study in heroin addicts in Han Chinese families; no markers achieved genome-wide significance, although two non-parametric lod (NPL) Z-scores >2.0 were found (2.19 at marker D4S1644 and 2.36 at marker D17S1880).

An alternative to linkage mapping—whole genome association using LD—has also been used to map susceptibility genes for drug addiction. Using this approach, dozens of novel candidate genes have been targeted including NRXN3 (neurexin 3) and other cell adhesion molecules involved in synaptogenesis and neuronal development (37,38).

We now report a genome-wide linkage study to map susceptibility genes in opiate dependence using concordant sibling pairs from the New York City metropolitan area. Subjects were recruited from methadone maintenance treatment programs (MMTPs), a clinical population suited for genetic studies as they are some of the most severely addicted patients in the world, exhibiting a high degree of polysubstance abuse. Cocaine use is found in 40–75% of patients, alcohol abuse in an estimated 15–30% and more than 90% smoke cigarettes (39–44). Since heritability increases sharply with a diagnosis of drug dependence, the unambiguous nature of opiate dependence in this population, combined with the high prevalence of polysubstance abuse, is likely to increase the probability of genetic loading (13).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
A total of ~25 000 patients from ~60 participating MMTPs were screened. Clinical data were collected and DNA obtained from a total of 667 subjects from 296 subject-described families. Every subject met DSM-IV criteria for lifetime opioid dependence; 73% had a lifetime diagnosis of cocaine abuse/dependence. Two-thirds of the subjects in our study were of Hispanic origin (Table 1). Although Hispanics constitute a heterogeneous mix of cultures and ethnicities, our population was fairly homogenous in that the overwhelming majority were of Puerto Rican heritage, reflecting the demographics of the neighborhoods serving the clinics we used for recruitment, which were primarily in Bronx County and the lower East Side of Manhattan. Ninety-four percent of the Hispanic subjects in our study reported that all four grandparents were born in Puerto Rico; 2% had four grandparents born in Dominican Republic; 1% had four Cuban-born grandparents; and the remainder reported mixed heritage that included grandparents born in St Thomas, Venezuela, Spain, Mexico, along with Puerto Rico and Dominican Republic. The ‘other’ category (Table 1) consisted primarily of self-identified African Americans who reported having a White or Native American parent or grandparent.


View this table:
[in this window]
[in a new window]

  		Table 1. Basic demographics

 
There was a significant correlation between self-reported ethnicity and cluster analysis using STRUCTURE (Pearson Correlation Coefficient = 0.59613; P < 0.0001; Supplementary Material, Figure S1).

After eliminating singletons whose siblings did not return for ascertainment, we genotyped 591 DNA samples for 10,204 SNPs distributed genome-wide. Nineteen samples had call rates below 94% and the assays were repeated. The overall genotype call rate after these samples had been repeated was 97.33% (~9932 genotypes/person) and data from 9769 autosomal SNPs that were both polymorphic and had call rates in the total sample of >90% were used for subsequent analysis. One hundred and sixty-nine markers were not in Hardy Weinberg equilibrium (HWE) (empirical P-value <0.001), leaving 9600 for further analysis. Of those, 1188 were found to be in LD with another marker (r2 > 0.4) and were eliminated. Consequently the final set of markers for the linkage analysis contained 8412 SNPs.

After estimation of allele frequencies, the true genetic relationships between self-reported sibling pairs were examined by using RELCHECK and PREST (45,46). These analyses resulted in the deletion of 51 families in which self-reported siblings were found to be either unrelated, or parent/offspring pairs. Based on the results using RELCHECK, PREST and ALTERTEST, we also found 15 pairs of self-reported full siblings to be half-siblings, five pairs of ‘siblings’ to be cousins and two pairs of ‘siblings’ to be half sibs + cousins. The relationship in several other pairs could not be determined with certainty (more related than half-sibs, less related than full sibs) and were not included in the linkage analysis. The final sample for linkage analysis contained 490 subjects. These included 201 sibpairs, 22 sibtrios, three sibquads and two sibquints for the total of 305 affected sibling pairs. Of these, 237 were full sibs and 68 were half sibs. There were 140 male–male pairs, 88 female–female pairs and 77 male–female pairs (Table 1).

Nonparametric linkage analyses, both two-point and multipoint, were carried out on the entire data set using MERLIN (47). To estimate empirical significance levels of our data set, we used MERLIN to generate 500 replicates of the entire genome, retaining the family structure. The threshold value for observing a genome-wide type I error once per scan was a –log(P) NPL score of 3, under the hypothesis of no linkage. In our primary analysis, using all individuals with DSM-IV opiate dependence as affected, an NPL score greater than 3.00 was achieved for a wide region of markers located on chromosome 14q, suggestive of genome-wide significance (48). Two peaks ~12 cM apart were seen: 3.30 at 87.21–90.40 cM and 3.22 at 75.55–73.63 cM (Fig. 1; Supplementary Material, Figure S2 for more detailed individual chromosome data).


Figure 1
View larger version (39K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 1. Linkage analysis-entire data set.

 
We also carried out secondary analyses stratified by gender and ethnicity (Figs 2 and 3 respectively; Supplementary Material, Figures S3 and S4). Even though the number of female–female pairs (n = 88) is less than the number of male–male pairs (n = 140), the majority of the contribution to the 14q peak observed in using all pairs appeared to be coming primarily from the female siblings. NPL scores of 3.22 (73.52–73.73 cM) and 3.16 (78.15–78.41 cM) were found in females, whereas the corresponding NPL scores in this region for males were 1.15 and 1.40 (Fig. 2). Likewise, a putative male-specific locus on chromosome 10q was observed with an NPL score of 3.22 (103.94 cM). A region ~18 cM away was also positive in males (2.89 at 86.02–86.93 cM). The female-specific scores in this region of chromosome 10q were essentially zero (Fig. 2).


Figure 2
View larger version (46K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 2. Linkage analysis-gender.

 


Figure 3
View larger version (41K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Figure 3. Linkage analysis-ethnicity.

 
The ethnic-stratified results also showed differential contributions to the 14q linkage peak (Fig. 3). An NPL score of 5.00 was observed for the Hispanic group at 87.22–87.46 cM on chromosome 14q, while the scores in the region were well below 1.0 for the Caucasian and African American groups. After applying a Bonferroni correction for three genome scans, the NPL score of 5.00 is ‘suggestive’ of genome-wide evidence for linkage using the Lander and Kruglyak significance criteria (48).

Other NPL scores greater than ~2.0 are shown in Table 2. The most significant were found in the Caucasian subgroup. However, considering that less than 11% of the subjects in this study were Caucasian, these findings could easily be due to type I error. Indeed, the reduction in sample size necessitated by the secondary analysis of subgroups suggests that even the more robust gender and ethnic-specific findings shown in Table 2 should be viewed with caution.


View this table:
[in this window]
[in a new window]

  		Table 2. Summary of linkage findings

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 
In the past few years, several groups have used non-parametric linkage analysis and whole genome association to map loci in various addiction disorders and identify positional candidate genes. One of the earliest was the Collaborative Study of the Genetics of Alcoholism (COGA), which culminated in the identification of GABRA2, and more recently, GABRA1, as positional candidate genes for alcoholism, alcoholism-related behaviors, co-morbid drug use and antisocial personality (27,31,49–52). Later, other groups identified potential nicotine, cocaine and heroin addiction loci (33–36,53–55). Two positional candidate genes for nicotine addiction, which are also feasible physiological and pharmacological candidates—GABBR2 and CHRNA4—were subsequently identified.

Adding to this growing list of addiction susceptibility genes and loci, we now report the results of a genome-wide linkage study in heroin addiction. In our primary analysis, two peaks on 14q with NPL scores greater than 3.0 were identified, although it is not clear at this time whether these represent two distinct regions of interest on the chromosome. In addition, our gender-specific analysis identified several other regions of potential interest. None of these loci map in the vicinity of genes previously considered as potential candidates for opioid dependence susceptibility, including OPRM1, DRD2, OPRD1, OPRK1, PENK and PDYN (56). In addition, with one possible exception, there was no significant overlap in our findings with regions mapped in the two other genome-wide linkage studies carried out in drug-dependent populations that most closely resemble ours (35,36). This could be due to the presence of drug, ethnic or gender-specific genetic factors. For example, these other linkage studies were carried out in European and African Americans (35) and in a Han Chinese cohort (36), whereas our study population was largely of Puerto Rican ancestry. In addition, the linkage study reported by Gelernter et al. (35) was primarily carried out in cocaine-dependent sibling pairs. Although some subjects were also opiate abusers, the number with this diagnosis, their cluster 4, was relatively small (n = 151) and, therefore, may have been underpowered to find significant linkage signals at the loci we identified (35). Alternatively, unique behavioral features characteristic of the patient populations used in each study could account for the heterogeneity in the linkage findings. Patients enrolled in MMTPs, the primary recruitment source for opioid-dependent subjects in this study, are characterized by polysubstance abuse and a high prevalence of personality disorders, notably antisocial personality, social dysfunction and axis 1 co-morbidity, factors that all influence drug use initiation (57). Thus, the apparent genetic heterogeneity across studies could be due to the contribution of the different genetic factors influencing underlying co-morbid behavioral traits in the various cohorts, as well as ethnicity. Replication in sufficiently powerful data sets and a closer examination of co-morbid traits using a case control approach will be needed to resolve these alternative hypotheses.

The exception to the heterogeneity in the linkage findings may occur on chromosome 17. An NPL score of 2.40 was found in our male cohort at 51.41–56.78 cM, which, as seen in Table 3, overlaps with a region mapped by Glatt et al. their Han Chinese opioid-dependent cohort (36). It is interesting to note that 73% of the subjects in the Glatt et al. study were male.


View this table:
[in this window]
[in a new window]

  		Table 3. Summary of other addiction linkage studies

 
While there are more differences than similarities when the different linkage studies are compared, potential regions of overlap were found between our study and the whole genome association studies reported by Liu et al. (37,38). In their initial report involving an analysis of 11 482 SNPs, associations were found between 38 markers and heavy drug use in European American and African American subjects. Two of these, in the NRXN3 and SYT16 genes, fall within the chromosome 14 linkage signals reported here (Table 4). In fact, the highest NPL score in our study was found with markers that map only 50 kb upstream of the NRNX3 gene. Neurexins are presynaptic transmembrane proteins that bind to postsynaptic neuroligans, an interaction that aligns synaptic termini and dendrites, bringing them into close juxtaposition (58). SYT16 is a member of the synaptotagmin family, which are transmembrane proteins intimately associated with the vesicle fusion machinery, an interaction that culminates in exocytosis.


View this table:
[in this window]
[in a new window]

  		Table 4. Regions of overlap with whole genome association studies

 
Liu et al. (38) expanded their whole genome association study by using 639 401 SNPs and identified an additional two-dozen genes encoding cell adhesion proteins associated with high drug use. Adhesion proteins involved in regulating synapse formation and memory—cadherins and NrCAM—have previously been implicated in the pathogenesis of drug addiction and the effects of prenatal cocaine (59,60). Interestingly, in our secondary analysis to identify gender-specific loci, we detected several putative linkage peaks that map closely to three adhesion genes associated with high drug use by Lui et al. (38) in their expanded association study; CTNNA3, CTNND2 and CSMD2 (Table 4). These observations are consistent with novel models of addiction focusing on alterations in neuronal plasticity and synaptic architecture, and in the process, new candidate genes in addiction vulnerability have been targeted, beyond the level of dopamine and opioid receptor/transporter pathways that have received most of the attention over the past two decades (37,38,61–65).

Overall, the findings reported here are consistent with a heterogeneous genetic model of addiction generated by ethnic, gender and phenotypic differences, similar to the genetic heterogeneity found in the numerous linkage studies carried out in schizophrenia, major depression and bipolar disorder.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
SUPPLEMENTARY MATERIAL
 REFERENCES
 
Subjects
The primary recruitment was carried out in MMTPs located in the New York City Metropolitan area, primarily in Bronx County, Kings County and lower Manhattan. Potential probands were approached in their home clinics by Masters level research assistants and answered a simple screening questionnaire to determine eligibility. The primary criterion was having one or more siblings (or half-siblings) currently enrolled in a MMTP in the New York City metropolitan region. We also included siblings who were previously enrolled in a MMTP, if they were able to provide proof of prior enrollment, such as an old picture ID. In order to maintain patient confidentiality, probands were asked to have their siblings contact either a study coordinator or their clinic counselor to assess eligibility. Siblings were ascertained either in their home clinic or in the proband's home clinic. All subjects who were not interviewed in their home clinic were told to bring their MMTP identification cards. Many were also asked to bring a birth certificate to reduce the risk of deception for the financial incentive being offered (especially half siblings with different surnames, and opposite sex sibling pairs). If eligible for ascertainment, subjects signed informed consents that had been approved by the parent academic institution or local institutional review boards.

Subjects were then interviewed by the research assistants with an instrument to collect basic demographic information and were administered the Structured Clinical Interview for DSM-IV (SCID) to confirm the diagnosis of opioid dependence, and establish comorbid drug or alcohol dependencies (66). The SCID is a widely used structured diagnostic interview designed to assess the presence of current and lifetime DSM-IV diagnosis, and has been demonstrated to have acceptable levels of internal consistency and interater reliability (67). Masters level research assistants were trained in the administration of the SCID during a 3-month training period using practice subjects recruited from MMTPs in the Beth Israel Medical Center system. Phlebotomy was carried out and blood samples were sent to the Rutgers University Cell and DNA Repository for processing (DNA extraction and EB-virus transformation).

Genotyping
DNA samples were genotyped for 10 204 SNPs distributed genome-wide using GeneChip®; mapping 10K 2.0 arrays (Affymetrix, Inc.). DNA samples were processed for hybridization to the arrays in 96-well plates, according to the manufacturer's protocol (https://www.affymetrix.com/support/downloads/manuals/10k2_manual.pdf, registration required), with one exception. We found that increasing the amount of purified PCR product from 20 to 23 µg into the fragmentation step of the protocol resulted in a more robust reaction, thereby increasing call rate (>98%). Arrays were hybridized in a GeneChip®; Hybridization Oven, washed on a GeneChip®; Fluidics Station 400 and scanned with a GeneChip®; Scanner 3000, according to the manufacturer's protocol (Affymetrix, Inc.). SNP genotype calls were made using GeneChip®; DNA Analysis Software (GDAS) (Affymetrix, Inc.), then stored and output in file formats suitable for analysis using Progeny Lab 6 (Progeny Software, LLC).

Genotyping data can be accessed at the NIDA web site through the following link: http://www.Nidagenetics.org/Results/data2.html.

Statistical analysis
Due to the lack of parental genotypic information, allele frequencies were estimated by randomly choosing one affected member from each family. The allele frequencies used in the linkage analysis were the average from 20 such repeated estimations. A goodness of fit test to determine Hardy–Weinberg equilibrium (HWE) was carried out by using the SAS/Genetics 9.1.3 ‘PROC ALLELE’ module. The critical P-value used to determine departure from HWE was set at 0.001, the default setting on Haploview, which takes into consideration an adjustment for multiple testing. Relationships between self-reported sibpairs were examined by using RELCHECK and PREST and the results were very highly correlated (45,46). ALTERTEST was used to assign true familial relationships when self-reported siblings were neither full nor half-sibs (46). LD between markers was determined using SNPLINK (68), as LD between markers can produce type I error in multipoint linkage studies when dense sets of markers are used and no parental information is available (69). The between marker LD cutoff was r2 > 0.4, which is the default value of SNPLINK (68). Non-parametric linkage analyses were performed using MERLIN (47). Both two-point and multipoint analyses were carried out. The –log(P) from the NPL score that Merlin calculates was extracted to generate genome-wide scan graphical plots where the x-axis corresponds to the Marshfield genetic map. The primary analysis considered all individuals with DSM-IV opiate dependence as affected. We also performed secondary analyses stratified by sex, and then by self-reported ethnicity. Self-reported ethnicity was highly correlated with cluster, which was determined by structure analysis computed using an admixture model with K = 3, a Markov Chain Monte Carlo burning period of 5000, and 5000 iterations (STRUCTURE; Pearson Correlation Coefficient = 0.59613; P < 0.0001) (70,71) (see Supplementary Material, Figure S1).

To estimate empirical significance levels of our data set, we used MERLIN to generate 500 replicates of the entire genome, retaining the family structures. The threshold value for observing a genome-wide type I error once per scan corresponds to an NPL score of 3 under the hypothesis of no linkage.


   SUPPLEMENTARY MATERIAL
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
REFERENCES
 
Supplementary Material is available at HMG Online.


   ACKNOWLEDGEMENTS
 
The authors wish to thank the patients who participated in the study and the MMTPs that provided access to their clients. These include the programs affiliated with the Division of Substance Abuse (DOSA) in the Department of Psychiatry and Behavioral Sciences at the Albert Einstein College of Medicine (Ira Marion), Montefiore Medical Center (Dr Merrill Herman), the Beth Israel Medical Center (Dr Randy Seewald and Dr Edward Salsitz), St Luke's Roosevelt Hospital Center of New York, Long Island Jewish Hospital (LIJ), Nassau Country Medical Center, The Addiction Research and Treatment Center (Dr Lawrence S. Brown, Jr; Melissa Chu, and Dr Steven Kritz, MD), Greenwich House, West Midtown Medical Center, Harlem East Life Plan, Narco Freedom and the Bridge Plaza Treatment and Rehabilitation Clinic (Dr Ronald Brady). We would also like to thank Victor Kamensky, Leonid Strizhevskiy and Mindy Ginsberg for data entry, Eva Kourniotis for her help in setting up our ascertainment at LIJ, and Kristin Pound.

Conflict of Interest statement. No conflicts of interest were reported by the investigators.


   FOOTNOTES
 
{dagger} These authors contributed equally to this study. Back

{ddagger} Present address: Zilkha Neurogenetic Institute, Department of Psychiatry, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Back


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 SUPPLEMENTARY MATERIAL
 REFERENCES
 

  1. Kreek M.J., Bart G., Lilly C., LaForge K.S., Nielsen D.A. Pharmacogenetics and human molecular genetics of opiate and cocaine addictions and their treatments. Pharmacol. Rev. (2005) 57:1–26.[Abstract/Free Full Text]

  2. Piccolo P., Borg L., Lin A., Melia D., Ho A., Kreek M.J. Hepatitis C virus and human immunodeficiency virus-1 co-infection in former heroin addicts in methadone maintenance treatment. J. Addict. Dis. (2002) 21:55–66.[ISI][Medline]

  3. Caderet R.J., Trotighton E., O'Gorman T.W., He E. An adoption study of genetic and environmental factors in drug abuse. Arch. Gen. Psych. (1986) 52:42–52.

  4. Cloninger C.R., Sigvardsson S., Gilligan S.B., von Knorring A.L., Reich T., Bohnian M. Genetic heterogeneity and the classification of alcoholism. Adv. Alcohol Subst. Abuse (1988) 7:3–16.[Medline]

  5. Grove W., Eckert E., Heston L., Bouchard T., Segal N., Lykken D. Heritability of substance abuse and antisocial behavior: a study of monozygotic twins reared apart. Biol. Psychiatry (1990) 27:1293–1304.[CrossRef][ISI][Medline]

  6. Pickens S.W., Svikis D.S., McGue M., LaBuda M. Common genetic mechanisms in alcohol, drug and mental disorder comorbidity. Drug Alcohol Dep. (1995) 39:129–138.[CrossRef][ISI][Medline]

  7. Uhl O.R., Elmer G., Labuda M.C., Pickens R.W. Genetic influences in drug abuse. In: Psychopharmacology: The Fourth Generation of Progress—Bloom F.E., Kupfer D.J., eds. (1995) New York: Raven Press.

  8. Johnson E.O., van den Bree M.B.M., Uhl G.R., Pickeris R.W. Indicators of genetic and environmental influences in drug abusing individuals. Drug Alcohol Depend. (1996) 41:17–23.[CrossRef][ISI][Medline]

  9. Tsuang M.T., Lyons M.J., Eisen S.A., Goldberg J., True W., Nong L., Meyer J.M., Toomey R., Faraone S.V., Eaves L. Genetic influences on DSM-III-R drug abuse and dependence: a study of 3 372 twin pairs. Am. J. Med. Genet. (Neuropsychiatric Genet.) (1996) 67:473–477.[CrossRef]

  10. Bierut L.J., Dinwiddie S.H., Begleiter H., Crowe R.R., Hesselbrock V., Nurnberger J.I., Jr, Porjesz B., Schuckit M.A., Reich T. Familial transmission of substance dependence: alcohol, marijuana, cocaine, and habitual smoking: a report from the Collaborative Study on the Genetics of Alcoholism. Arch. Gen. Psych. (1998) 55:982–988.[Abstract/Free Full Text]

  11. Merikangas K.R., Stolar M., Stevens D.E., Goulet J., Preisig M.A., Fenton B., Zhang H., O'Malley S.S., Rounsaville B.J. Familial transmission of substance use disorders. Arch. Gen. Psych. (1998) 55:973–979.[Abstract/Free Full Text]

  12. Pickens R.W., Svikis D.S., McGue M., Lykken D.T., Heston L.L., Clayton P.J. Heterogeneity in the inheritance of alcoholism. A study of male and female twins. Arch. Gen. Psych. (1991) 48:19–26.[Abstract]

  13. Van den Bree M.B.M., Johnson E.Q., Neale M.C., Pickens R.W. Genetic and environment influences on drug use and abuse/dependence in male and female twins. Drug Alcohol Depend. (1998) 52:231–241.[CrossRef][ISI][Medline]

  14. Swan G.E., Carmelli D., Cardon L.R. Heavy consumption of cigarettes, alcohol and coffee in male twins. J. Stud. Alcohol (1997) 58:182–190.[ISI][Medline]

  15. Tsuang M.T., Lyons M.J., Meyer J.M., Doyle T., Eisen S.A., Goldberg J., True W., Nong L., Toomey R., Eaves L. Co-occurrence of abuse of different drugs in men: the role of drug-specific and shared vulnerabilities. Arch. Gen. Psych. (1998) 55:967–972.[Abstract/Free Full Text]

  16. Kendler K.S., Jacobson K.C., Prescott C.A., Neale M.C. Specificity of genetic and environmental risk factors for use and abuse/dependence of cannabis, cocaine, haucinogens, sedatives, stimulants, and opiates in male twins. Am. J. Psychiatry (2003) 160:687–695.[Abstract/Free Full Text]

  17. Karkowski L.M., Prescott C.A., Kendler K.S. Multivariate assessment of factors influencing illicit substance use in twins from female–female pairs. Am. J. Med. Genet (2000) 96:665–670.[CrossRef][ISI][Medline]

  18. Nestler E.J. Genes and addiction. Nat. Genet. (2000) 26:277–281.[CrossRef][ISI][Medline]

  19. Nesler E.J. Is there a common molecular pathway for addiction. Nat. Neurosci. (2005) 8:1445–1449.[CrossRef][ISI][Medline]

  20. Kalivas P.W., Volkow N.D. The neural basis of addiction: a pathology of motivation and choice. Am. J. Psych. (2005) 162:1403–1413.[Abstract/Free Full Text]

  21. Hyman S.E. Addiction: a disease of learning and memory. Am. J. Psych. (2005) 162:1414–1422.[Abstract/Free Full Text]

  22. Caderet R.J., Yates W.R., Troughton E., Woodworth O., Stewart M.A. Adoption study demonstrating two genetic pathways to drug abuse. Arch. Gen. Psych. (1995) 52:42–52.[Abstract]

  23. Kreek M.J., Nielsen D.A., Butelman E.R., Laforge K.S. Genetic influences on impulsivity, risk taking, stress responsivity and vulnerability to drug abuse and addiction. Nat. Neurosci. (2005b) 8:1450–1457.[CrossRef][ISI][Medline]

  24. Brower V.G., Fu Y., Matta S.G., Sharp B.M. Rat strain differences in nicotine self-administration using an unlimited access paradigm. Brain. Res. (2002) 930:12–20.[CrossRef][ISI][Medline]

  25. Kosten T.A., Ambrosio E. HPA axis function and drug addictive behaviors: insights from studies with Lewis and Fischer 344 inbred rats. Psychoneuroendo. (2002) 27:35–69.[CrossRef]

  26. Berrettini W.H., Ferraro T.N., Alexander R.C., Buchberg A.M., Vogel W.H. Quantitative trait loci mapping of three loci controlling morphine preference using inbred mouse strains. Nat. Genet. (1994) 7:54–58.[CrossRef][ISI][Medline]

  27. Reich T., Edenberg H.J., Goate A., Williams J.T., Rice J.P., Van Eerdewegh P., Foroud T., Hesselbrock V., Schuckit M.A., Bucholz K., et al. Genome-wide search for genes affecting the risk for alcohol dependence. Am. J. Med. Genet. (1998) 81:207–215.[CrossRef][ISI][Medline]

  28. Long J.C., Knowler W.C., Hanson R.L., Robin R.W., Urbanek M., Moore E., Bennett P.H., Goldman D. Evidence for genetic linkage to alcohol dependence on chromosomes 4 and 11 from an autosome-wide scan in an American Indian population. Am. J. Med. Genet. (1998) 81:216–221.[CrossRef][ISI][Medline]

  29. Foroud T., Edenberg H.J., Goate A., Rice J., Flury L., Koller D.L., Bierut L.J., Conneally P.M., Nurnberger J.I., Bucholz K.K., et al. Alcoholism susceptibility loci: confirmation studies in a replicate sample and further mapping. Alcohol Clin. Exp. Res. (2000) 24:933–945.[CrossRef][ISI][Medline]

  30. Edenberg H.J. The collaborative study on the genetics of alcoholism: an update. Alcohol Res. Health (2002) 26:214–218.[Medline]

  31. Edenberg H.J., Dick D.M., Xuei X., Tian H., Almasy L., Bauer L.O., Crowe R.R., Goate A., Hesselbrock V., Jones K., et al. Variations in GABRA2, encoding the alpha 2 subunit of the GABA(A) receptor, are associated with alcohol dependence and with brain oscillations. Am J. Hum. Genet. (2004) 74:705–714.[CrossRef][ISI][Medline]

  32. Covault J., Gelernter J., Hesselbrock V., Nellissery M., Kranzler H.R. Allelic and haplotypic association of GABRA2 with alcohol dependence. Am. J. Med. Genet. (Neuropsychiatric Genet.) (2004) 129:104–109.

  33. Beuten J., Ma J.Z., Payne T.J., Dupont R.T., Crews K.M., Somes G., Williams N.J., Elston R.C., Li M.D. Single- and multilocus allelic variants within the GABA(B) receptor subunit 2 (GABAB2) gene are significantly associated with nicotine dependence. Am. J. Hum. Genet. (2005) 76:859–864.[CrossRef][ISI][Medline]

  34. Li M.D., Beuten J., Ma J.Z., Payne T.J., Lou X.Y., Garcia V., Duenes A.S., Crews K.M., Elston R.C. Ethnic- and gender-specific association of the nicotinic acetylcholine receptor alpha4 subunit gene (CHRNA4) with nicotine dependence. Hum. Mol. Genet. (2005) 14:211–219.

  35. Gelernter J., Panhuysen C., Weiss R., Brady K., Hesselbrock V., Rounsaville B., Poling J., Wilcox M., Farrer L., Kranzler H.R. Genomewide linkage scan for cocaine dependence and related traits: significant linkages for a cocaine-related trait and cocaine-induced paranoia. Am. J. Med. Genet. (Neuropsychiatric Genet.) (2005) 136:45–52.

  36. Glatt S.J., Su J.A., Zhu S.C., Zhang R., Zhang B., Li J., Yuan X., Li J., Lyons M.J., Faraone S.V., Tsuang M.T. Genome-wide linkage analysis of heroin dependence in Han Chinese: results from wave one of a multi-stage study. Am. J. Med. Genet. (Neuropsychiatric Genet.) (2006) 141:648–652.

  37. Liu Q.R., Drgon T., Walther D., Johnson C., Poleskaya O., Hess J., Uhl G.R. Pooled association genome scanning: validation and use to identify addiction vulnerability loci in two samples. Proc. Natl Acad. Sci. USA (2005) 102:11864–11869.[Abstract/Free Full Text]

  38. Liu Q.R., Drgon T., Johnson C., Walther D., Hess J., Uhl G.R. Addiction molecular genetics: 639 401 SNP whole genome association identifies many ‘cell adhesion’ genes. Am. J. Med. Genet. (Neuropsychiatr Genet.) (2006) 141:918–925.

  39. Kosten T.R., Schumann B., Wright D., Carney M.K., Gawin F.H. A preliminary study of desipramine in the treatment of cocaine abuse in methadone maintenance patients. J. Clin. Psych. (1987) 48:442–444.[ISI][Medline]

  40. Condelli W.S., Fairbank J.A., Dennis M.L., Rachal J.V. Cocaine use by clients in methadone programs: significance, scope and behavioral interventions. J. Subst. Abuse Treat. (1991) 8:203–212.[CrossRef][ISI][Medline]

  41. Schottenfeld R.S., Pakes J.R., Oliveto A., Ziedonis D., Kosten T.R. Buprenorphine versus methadone maintenance treatment for concurrent opioid dependence and cocaine abuse. Arch. Gen. Psych. (1997) 54:713–720.[Abstract]

  42. Stimmel B., Cohen M., Sturiano V., Hanbury R., Korts D., Jackson G. Is treatment for alcoholism effective in persons on methadone maintenance? Am. J. Psych. (1983) 140:862–866.[Abstract/Free Full Text]

  43. Anglin M.D., Almog I.J., Fisher D.G., Peters K.R. Alcohol use by heroin addicts: evidence for an inverse relationship: a study of methadone maintenance and drug-free treatment samples. Am. J. Drug Alcohol Abuse (1989) 15:191–207.[ISI][Medline]

  44. el-Bassel N., Schilling R.F., Turnbull J.E., Su K.H. Correlates of alcohol use among methadone patients. Alcohol Clin. Exp. Res. (1993) 17:681–686.[CrossRef][ISI][Medline]

  45. Boehnke M., Cox N.J. Accurate inference of relationships in sib-pair linkage studies. Am. J. Hum. Genet. (1997) 61:423–429.[ISI][Medline]

  46. McPeek M.S., Sun L. Statistical tests for detection of misspecified relationships by use of genome-screen data. Am. J. Hum. Genet. (2000) 66:1076–1094.[CrossRef][ISI][Medline]

  47. Abecasis G.R., Cherny S.S., Cookson W.O., Cardon L.R. Merlin-rapid analysis of dense genetic maps using sparse gene flow trees. Nat. Genet. (2002) 30:97–101.[CrossRef][ISI][Medline]

  48. Lander E., Kruglyak L. Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results. Nat. Genet. (1995) 11:241–247.[CrossRef][ISI][Medline]

  49. Agrawal A., Edenberg H.J., Foroud T., Bierut L.J., Dunne G., Hinrichs A.L., Nurnberger J.I., Crowe R., Kuperman S., Schuckit M.A., et al. Association of GABRA2 with Drug Dependence in the Collaborative Study of the Genetics of Alcoholism Sample. Behav. Genet. (2006) Epub ahead of print.

  50. Dick D.M., Agrawal A., Schuckit M.A., Bierut L., Hinrichs A., Fox L., Mullaney J., Cloninger C.R., Hesselbrock V., Nurnberger J.I. Jr, et al. Marital status, alcohol dependence, and GABRA2: evidence for gene-environment correlation and interaction. J. Stud. Alcohol (2006A) 67:185–194.[ISI][Medline]

  51. Dick D.M., Bierut L., Hinrichs A., Fox L., Bucholz K.K., Kramer J., Kuperman S., Hesselbrock V., Schuckit M., Almasy L., et al. The Role of GABRA2 in risk for conduct disorder and alcohol and drug dependence across developmental stages. Behav. Genet. (2006B) 36:577–590.[CrossRef][ISI][Medline]

  52. Dick D.M., Plunkett J., Wetherill L.F., Xuei X., Goate A., Hesselbrock V., Schuckit M., Crowe R., Edenberg H.J., Foroud T. Association between GABRA1 and drinking behaviors in the collaborative study on the genetics of alcoholism sample. Alcohol Clin. Exp. Res. (2006C) 30:1101–1110.[CrossRef][ISI][Medline]

  53. Feng Y., Niu T., Xing H., Xu X., Chen C., Peng S., Wang L., Laird N., Xu X. A common haplotype of the nicotine acetylcholine receptor alpha 4 subunit gene is associated with vulnerability to nicotine addiction in men. Am. J. Hum. Genet. (2004) 75:112–121.[CrossRef][ISI][Medline]

  54. Straub R.E., Sullivan P.F., Ma Y., Myakishev M.V., Harris-Kerr C., Wormley B., Kadambi B., Sadek H., Silverman M.A., Webb B.T., et al. Susceptibility genes for nicotine dependence: a genome scan and followup in an independent sample suggest that regions on chromosomes 2, 4, 10, 16, 17 and 18 merit further study. Mol. Psychiatry (1999) 4:129–144.[CrossRef][ISI][Medline]

  55. Swan G.E., Hops H., Wilhelmsen K.C., Lessov-Schlaggar C.N., Cheng L.S., Hudmon K.S., Amos C.I., Feiler H.S., Ring H.Z., Andrews J.A., et al. A genome-wide screen for nicotine dependence susceptibility loci. Am. J. Med. Genet. B (Neuropsychiatr Genet.) (2006) 141:354–360.[Medline]

  56. Lachman H.M. An overview of the genetics of substance use disorders. Current Psychiatric Rev. (2006) 8:133–143.[CrossRef]

  57. Ross J., Teesson M., Darke S., Lynskey M., Ali R., Ritter A., Cooke R. The characteristics of heroin users entering treatment: findings from the Australian treatment outcome study (ATOS). Drug Alcohol Rev. (2005) 24:411–418.[CrossRef][ISI][Medline]

  58. Dean C., Dresbach T. Neuroligins and neurexins: linking cell adhesion, synapse formation and cognitive function. Trends Neurosci. (2006) 29:21–29.[CrossRef][ISI][Medline]

  59. Ishiguro H., Liu Q.R., Gong J.P., Hall F.S., Ujike H., Morales M., Sakurai T., Grumet M., Uhl G.R. NrCAM in addiction vulnerability: positional cloning, drug-regulation, haplotype-specific expression, and altered drug reward in knockout mice. Neuropsychopharmacology (2006) 31:572–584.[CrossRef][ISI][Medline]

  60. Novikova S.I., He F., Bai J., Lidow M.S. Neuropathology of the cerebral cortex observed in a range of animal models of prenatal cocaine exposure may reflect alterations in genes involved in the Wnt and cadherin systems. Synapse (2005) 56:105–116.[CrossRef][ISI][Medline]

  61. Del Olmo N., Miguens M., Higuera-Matas A., Torres I., Garcia-Lecumberri C., Solis J.M., Ambrosio E. Enhancement of hippocampal long-term potentiation induced by cocaine self-administration is maintained during the extinction of this behavior. Brain. Res. (2006) 1116:120–126.[CrossRef][ISI][Medline]

  62. Bolanos C.A., Nestler E.J. Neurotrophic mechanisms in drug addiction. Neuromol. Med (2004) 5:69–83.[CrossRef]

  63. Allen P.B., Zachariou V., Svenningsson P., Lepore A.C., Centonze D., Costa C., Rossi S., Bender G., Chen G., Feng J., et al. Distinct roles for spinophilin and neurabin in dopamine-mediated plasticity. Neuroscience (2006) 140:897–911.[CrossRef][ISI][Medline]

  64. Abul-Husn N.S., Devi L.A. Neuroproteomics of the synapse and drug addiction. J. Pharmacol. Exp. Ther. (2006) 318:461–468.[Abstract/Free Full Text]

  65. Yoshihara M., Montana E.S. The synaptotagmins: calcium sensors for vesicular trafficking. Neuroscientist (2004) 10:566–574.[Abstract/Free Full Text]

  66. Spitzer R.L., Williams J.B., Gibbon M., First M.B. The Structured Clinical Interview for DSM-III-R (SCID). I: History, rationale, and description. Arch. Gen. Psych. (1992) 49:624–629.[Abstract]

  67. Kiehl K.A., Smith A.M., Hare R.D., Liddle P.F. An event-related potential investigation of response inhibition in schizophrenia and psychopathy. Biol. Psychiatry (2000) 48:210–221.[CrossRef][ISI][Medline]

  68. Webb E.L., Sellick G.S., Houlston R.S. SNPLINK: multipoint linkage analysis of densely distributed SNP data incorporating automated linkage disequilibrium removal. Bioinformatics (2005) 21:3060–3061.[Abstract/Free Full Text]

  69. Boyles A.L., Scott W.K., Martin E.R., Schmidt S., Li Y.J., Ashley-Koch A., Bass M.P., Schmidt M., Pericak-Vance M.A., Speer M.C., Hauser E.R. Linkage disequilibrium inflates type I error rates in multipoint linkage analysis when parental genotypes are missing. Hum. Hered. (2005) 59:220–227.[ISI][Medline]

  70. Pritchard J.K., Stephens M., Donnelly P. Inference of population structure using multilocus genotype data. Genetics (2000) 155:945–959.[Abstract/Free Full Text]

  71. Pritchard J.K., Stephens M., Rosenberg N.A., Donnelly P. Association mapping in structured populations. Am. J. Hum. Genet. (2000) 67:170–181.[CrossRef][ISI][Medline]


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Arch Gen PsychiatryHome page
G. R. Uhl, Q.-R. Liu, T. Drgon, C. Johnson, D. Walther, J. E. Rose, S. P. David, R. Niaura, and C. Lerman
Molecular Genetics of Successful Smoking Cessation: Convergent Genome-Wide Association Study Results
Arch Gen Psychiatry, June 1, 2008; 65(6): 683 - 693.
[Abstract] [Full Text] [PDF]

Home page
 Hum Mol GenetHome page
A. Hishimoto, Q.-R. Liu, T. Drgon, O. Pletnikova, D. Walther, X.-G. Zhu, J. C. Troncoso, and G. R. Uhl
Neurexin 3 polymorphisms are associated with alcohol dependence and altered expression of specific isoforms
Hum. Mol. Genet., December 1, 2007; 16(23): 2880 - 2891.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
16/11/1327    most recent
ddm081v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Lachman, H. M.