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Human Molecular Genetics Advance Access originally published online on December 12, 2006
Human Molecular Genetics 2007 16(2):142-153; doi:10.1093/hmg/ddl450
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Fine mapping of a linkage region on chromosome 17p13 reveals that GABARAP and DLG4 are associated with vulnerability to nicotine dependence in European-Americans

Xiang-Yang Lou1, Jennie Z. Ma2, Dongxiao Sun1, Thomas J. Payne3 and Ming D. Li1,*

1 Department of Psychiatry and Neurobehavioral Sciencesm, 2 Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA and 3 ACT Center for Tobacco Treatment, Education and Research, University of Mississippi Medical Center, Jackson, MS, USA

* To whom correspondence should be addressed at: 1670 Discovery Drive, Suite 110, Charlottesville, VA 22911, USA. Tel: +1 4342430566; Fax: +1 4349737031; Email: ml2km{at}virginia.edu

Received October 27, 2006; Accepted November 24, 2006


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
A two-stage association study was conducted targeting a genomic region on chromosome 17p13 that we reported likely to harbor susceptibility gene(s) for nicotine dependence (ND). Participants were 2037 subjects from 602 nuclear families of either African-American (AA) or European-American (EA) origin from our Mid-South Tobacco Family (MSTF) cohort. We first examined 10 single nucleotide polymorphisms (SNPs) in six genes within the targeted region of about 90 kb to determine which SNP/gene was associated with ND, assessed by smoking quantity (SQ), the heaviness of smoking index (HSI) and the Fagerström Test for ND (FTND). Individual SNP analysis revealed that SNPs rs17710 and rs222843 in GABAA receptor-associated protein (GABARAP) exhibited a significant association with at least one age- and gender-adjusted ND measure in the EA sample and rs222843 remained significant with the FTND after correction for multiple testing (P = 0.009). Although no SNP in DLG4 was significantly associated with ND, we found a G-G haplotype with a frequency of 14.2% formed by SNPs rs2242449 and rs507506 within the gene that showed significant inverse associations with all three ND measures [P = 0.003, 0.015 and 0.024, for SQ (defined as the number of cigarettes smoked per day), HSI and FTND, respectively]. We also found an A-A haplotype with a frequency of 8.8% formed by SNPs rs17710 and rs222843 in GABARAP, which revealed significant associations with all three ND measures (P = 0.006, 0.019 and 0.024, for SQ, HSI and FTND, respectively). To confirm these findings with a better coverage of GABARAP and DLG4, we conducted a second-stage association analysis by genotyping four more SNPs for GABARAP and nine more for DLG4 on the same set of samples. Our results from the second stage of individual SNP- and/or haplotype-based association analysis supported our finding of significant association of the DLG4 gene with ND. No significant association of GABARAP or DLG4 with ND was detected in the AA sample. Further, by comparing the linkage signal before and after adjustment for the SNPs of GABARAP and DLG4, we found that inclusion of the SNPs of the two genes as covariates largely reduced the linkage signal in the EA sample, but kept nearly unchanged in the AA sample. Taken together, our two-stage association analysis and linkage analysis results indicate that the GABARAP and DLG4 genes are involved in the etiology of ND in EA smokers. Further investigation of neurobiological mechanisms of the two genes in the etiology of ND is thus warranted.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Nicotine is the primary component of tobacco responsible for addiction, which directly or indirectly causes approximately 435 000 deaths per year and costs approximately $157.7 billion annually to the US economy (1,2). Epidemiological studies have demonstrated that genetic factors contribute greatly to individual differences in liability for nicotine dependence (ND) (reviewed in 3,4). Identification of susceptibility genes for ND will provide greater insight into the etiology of smoking addiction, and ultimately facilitate the development of more effective prevention and treatment approaches, including individualized medicine regimens. Considerable effort is being expended in attempts to identify such genetic determinants (5).

Our previous linkage analysis revealed a ‘suggestive’ linkage within the chromosome 17p13 region for ND in the Framingham Heart Study (FHS) sample (6,7). Additionally, three independent studies reported a linkage for smoking behavior (8), attention-deficit/hyperactivity disorder (9) and conduct disorder (10) on chromosome 17 that overlapped with our reported linkage for ND. Within the linkage peak for ND on 17p13, there is a relatively small region approximately 90 kb in length that appears to be interesting because a few genes located within this region have been implied to play a significant role in drug addiction (Fig. 1). For example, GABAA receptor-associated protein (GABARAP) belongs to a family of microtubule-associated proteins that includes GABARAP, GABAA-receptor-associated protein like 1 (GABARAPL1, also called glandular epithelial cell 1 or GEC1), GABARAPL2, the yeast protein Apg8p/Aut7 and light chain 3 of microtubule-associated protein 1 (MAP1-LC3) (11–15). Of the members of the family, GABARAP has been investigated extensively and found to interact with the {gamma}2 subunit of the GABAA receptor. Such interactions among GABAA receptor, GABARAP, and tubulin promote clustering of the receptor, alter its channel kinetics and enhance its trafficking to the plasma membrane in neurons (12,16,17). Furthermore, our recent microarray study indicated that GABARAPL2 was highly regulated by nicotine in multiple rat brain regions in a time- and region-dependent manner (18).


Figure 4501
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  		Figure 1. An illustration of the structure of genomic region ~17p13 and genes located within the region. The distribution of SNPs genotyped in this study is also provided in the figure. All information included were obtained from NCBI databases.

 
Another gene of potential interest within this region is DLG4 (discs, large homolog 4; also called post-synaptic density-95 protein; PSD95), which has been demonstrated to play an important role in coupling the N-methyl-D-aspartate (NMDA) receptor to pathways that control bidirectional synaptic plasticity and learning (19,20). DULLARD is the third gene of interest in this study, which was recently reported as a regulatory factor of neural tissue in development (21). Additionally, several other genes are located within this region, however, no or limited information is available regarding their function and potential involvement in drug addiction, and thus they will not be discussed further.

To determine whether any particular variant/gene of this region is associated with ND, we adopted a two-stage approach in this study, as routinely employed in genetic association studies for complex traits. The first stage involves a map-based association test or region-wide random single nucleotide polymorphism (SNP) method (22). Following the identification of positive markers at stage one, additional markers are then genotyped to assure a better and more uniform coverage of the gene(s) of interest.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 
Hardy–Weinberg Equilibrium test, heterogeneity test and pair-wise linkage disequilibrium analysis
The tests for Hardy–Weinberg Equilibrium (HWE) suggested that no SNP at the two stages of genotyping was deviated significantly from HWE in either the African-American (AA) or European-American (EA) sample (minimum P = 0.224 and 0.390 for the AA and EA samples, respectively), confirming the high quality of our genotyping data used in the current study. To determine whether population stratification and admixture existed, we performed heterogeneity tests in the combined (AA + EA) sample and found significant differences in the allele frequencies for all informative SNPs (P < 0.05) except for SNP rs222853. This suggests the presence of distinct allele distributions across the two ethnical samples, an indication that it is appropriate to analyse these two ethnical samples separately.

Figure 2 displays the pair-wise normalized linkage disequilibrium (LD) (D') values for the SNPs, calculated using the Haploview program (23). There were high D' values (range 0.93–1.00) for the SNP pairs rs222836–rs222851 and rs414206–rs402514 in both the EA and AA samples, and these pairs were assigned to two haplotype blocks spanning 6 and 3 kb, respectively, according to criteria specified by Gabriel et al. (24). Seven SNPs located between rs13331 and rs390200, and three between rs929229 and rs390200, constituted a block of 16 and 4 kb in the DLG4 gene for the EA and the AA samples, respectively. There were low (<0.80) values for most SNP pairs between rs222853, rs17710, rs7216047, rs222843 and rs5418 regardless of the physical distance, even for SNP pairs rs222843–rs17710 and rs222843–rs7216047 in the GABARAP gene that are spaced <2 kb apart, and rs222837 and rs222836 in the DVL2 gene that are only approximately 0.6 kb away from each other.


Figure 4502
Figure 4502
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  		Figure 2. LD structures of the SNPs in the AA (A) and EA (B) samples. The Haploview (v.3.2) program (23) was used to calculate D' values between SNPs. Haplotype block was defined according to the criteria of Gabriel et al. (24). Only informative SNPs are provided. The number in each box represents the D' value for each SNP pair.

 
Single-locus association analysis at stage one
Of the region of interest, we genotyped 10 SNPs in six genes spanning about 90 kb of sequence that gave an average density of 9 kb per SNP. Individual SNP-based analysis revealed a significant association for two of the 10 SNPs at the first stage, i.e. rs17710 and rs222843 in the GABARAP gene, with at least one age- and gender-adjusted ND measure in the EA sample (Table 1). The association of rs222843 with adjusted FTND remained significant after correction for multiple testing based on the SNP spectral decomposition (SNPSpD) approach (25,26), suggesting that GABARAP is potentially associated with ND in the EA sample. For the AA sample, only SNP rs5418 in the SLC2A4 gene was significantly associated with FTND (P = 0.046), but this was no longer significant after correction for multiple testing.


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  		Table 1. P-values for association of the 10 SNPs at the first stage of association analysis with three ND measures in the AA and EA samples

 
Haplotype-based association analysis at stage one
To capture additional information on the LD structure and provide greater power in detecting association than single SNP-based association analysis, we performed haplotype-based association analysis wherein different combinations of SNPs were considered within each gene, with the exceptions of DULLARD, DERP6 and SLC2A4, which were analysed together because only one SNP was genotyped for each gene. This approach was further supported by haplotype block determination results in which SNPs rs414206 in the DULLARD and rs402514 in the DERP6 were in a haplotype block for both AA and EA samples. Haplotype-based association analysis indicated that the haplotypes A-A (8.8%) formed by SNPs rs17710 and rs222843 in GABARAP, and G-G (14.2%) formed by SNPs rs2242449 and rs507506 in DLG4, showed significant negative associations with the three ND measures in the EA sample, implicating these haplotypes as protective variants. Associations between these haplotypes with smoking quantity (SQ) remained significant after correction for multiple testing under both the additive and dominant models (Table 2). The G-A haplotype, occurring at a frequency of 51.4% in the DLG4, also showed significant negative associations with heaviness of smoking index (HSI) (P = 0.036) and FTND (P = 0.04) in the AA sample prior to correction for multiple testing. The significant haplotypic associations with the DLG4 suggest its involvement in the etiology of ND. On the other hand, no significant association was found for all SNP combinations within DVL2, DULLARD, DERP6 and SLC2A4 genes in either sample.


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  		Table 2. Z- and P- values (in parenthesis) for association of major haplotypes from the first stage of genotyping with three ND measures in the AA and EA samples

 
Association analysis at stage two
Based on the results from the first stage of association analysis, we conclude that GABARAP and DLG4 within the 17p13 region are significantly associated with ND. To confirm these findings, we conducted a second stage of association analysis by genotyping additional SNPs. Unfortunately, we found that second-stage SNPs rs12452520 in the DLG4 gene, and rs3177209, rs10999 and rs2134510 in the GABARAP gene were monomorphic in our AA and EA samples and were thus excluded from further analysis. Individual SNP-based analysis indicated that SNPs rs13331, rs929229 and rs1875673 in DLG4 were significantly associated with at least one age- and gender-adjusted ND measure in the EA sample; the association of rs13331 with FTND remained significant (P = 0.009) after correction for multiple testing (Table 3). Although we assayed four more SNPs in GABARAP at the second stage of genotyping, we found none of them to be useful for association analysis. Of them, three were monomorphisms in both samples, and the fourth SNP (i.e. rs7216047) did not have a sufficient number of informative families in the EA sample and was not significant in the AA sample. Since there were no other polymorphic SNPs available in this small gene of approximately 2.0 kb, no more additional SNPs were genotyped for GABARAP. Given we had genotyped two SNPs at the first stage for the gene, an average intensity of one SNP per 1.0 kb of sequence should be sufficient to cover this entire gene.


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  		Table 3. P-values for association of additional SNPs for DLG4 and GABARAP at the second stage of genotyping with three ND measures in the AA and EA samples

 
For the DLG4 gene, we considered all SNPs together within the gene in a haplotype-based analysis defined by contiguous 2–10 SNP combinations from sliding windows to determine which subsets of SNPs are potentially important for ND. We found that the haplotypes G-G-C-G-G-A-C formed by SNPs rs2242449, rs17203281, rs3826408, rs929229, rs1875673, rs390200 and rs222853 in the EA sample and G-G-G-C-G-G formed by rs929229, rs1875673, rs390200, rs222853, rs507506 and rs421019 in the AA sample, gave the most substantial associations (Table 4). The association of the haplotype G-G-C-G-G-A-C with the ND remained significant after Bonferroni correction in the EA sample (P = 0.003, 0.009 and 0.004 for SQ, HSI and FTND, under both additive and dominant models, respectively).


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  		Table 4. Z- and P- values for association of major haplotypes in the DLG4 gene with three ND measures in the AA and EA samples

 
Contributions of GABARAP and DLG4 to the linkage signal on chromosome 17
The magnitude of the decrease in the previously detected suggestive linkage signal after covariate adjustment can be used to evaluate the contribution of the target gene(s). Since the results for the three ND measures were almost identical to each other, only the results for HSI are shown in Figure 3. By comparing the linkage analysis results before and after adjustment for the SNPs of GABARAP and DLG4, we found that the inclusion of SNPs as covariates largely reduced, although could not completely eliminate, the detected linkage signal in the EA sample, but kept nearly unchanged in the AA sample (Fig. 3). The adjustments for the GABARAP and DLG4 SNPs decreased the linkage signal by 22.7 and 39.6%, respectively, for HSI in the EA sample. These results indicated that GABARAP and DLG4 are indeed plausible genes that contribute to the detected linkage signal on chromosome 17 for ND in the EA sample.


Figure 4503
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  		Figure 3. Determination of contribution of GABARAP and DLG4 variants to the detected linkage signal for HSI in the EA and AA samples. Linkage results on chromosome 17 with adjustments for no SNP (labeled as HSI), the SNPs of GABARAP (labeled as HSI/GABARAP) and the SNPs of DLG4 (labeled as HSI/DLG4) for HSI were shown in the EA (A) and AA (B) samples, respectively.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
MATERIALS AND METHODS
 REFERENCES
 
Within the linkage region identified in our previous studies on the FHS sample (6,7), we conducted a positional candidate gene-based association study in an independent Mid-South Tobacco Family (MSTF) cohort. Considering multiple genes with biological relevance to addiction are located within this region, we attempted to identify likely candidates for further investigation. A two-stage approach was adopted with the objective of the first stage being to determine which gene(s) is most likely to be a candidate for ND, and the second stage to confirm those findings by genotyping additional SNPs on positive genes. Of the candidate polymorphisms investigated, we found the GABARAP and DLG4 genes were significantly associated with ND in the EA sample based on individual SNP- and/or haplotype-based association analysis. The observed associations at the first stage were then validated by our second-stage association analysis. Taken together, our two-stage association analysis results revealed that GABARAP and DLG4 were significantly associated with ND in the EA sample. On the other hand, associations for GABARAP and DLG4 with ND were marginal in our AA sample, and ultimately non-significant after correction for multiple testing. The assessment of contributions to the detected linkage signal suggested that GABARAP and DLG4 largely reduced the linkage signal in the EA sample but kept nearly unchanged in the AA sample, supporting that GABARAP and DLG4 are indeed potential contributors to the linkage signal on chromosome 17 for ND in the EA sample.

Both GABARAP and DLG4 have been implicated in neural signal transduction (27,28). Type-A receptors (GABAA) that regulate {gamma}-aminobutyric acid (GABA), an important inhibitory neurotransmitter, contain ligand-gated chloride channels that mediate fast synaptic inhibition in the central nervous system. Prior evidence suggests variations of the {alpha}2 subunit gene of the GABAA receptor are associated with alcohol dependence (29–31). Further, we previously reported that GABAB receptor subunit 2 gene is associated with ND (32). GABARAP has been identified as a bridge between the GABAA receptors and the underlying cytoskeleton (12). Through interacting with the {gamma}2 subunit of the GABAA receptors, tubulin and microtubules, GABARAP transports GABAA receptors to and from the plasma membrane in neurons, organizes them into post-synaptic clusters and regulates the steady-state receptor density (17,27). Moreover, our recent microarray study of gene expression profiling indicated that the expression of GABARAPL2 was highly regulated by nicotine in rodent brain (18). Finally, we found that all the members of the GABARAP family were modulated by nicotine in SH-SY5Y cells (33). These findings strongly indicate that GABARAP family members are important contributors to drug addiction, although no genetic study to date has directly examined this association.

The predicted 723-amino-acid DLG4 (or PSD95) protein is a member of the membrane-associated guanylate kinase (MAGUK) family (34,35). By forming a heterodimer with DLG2, these two MAGUK proteins may interact at post-synaptic sites to form a multimeric scaffold for the clustering of NMDA receptors, ion channels and associated signaling proteins (36–39). Moreover, the expression of DLG4 has been reported to be modulated in the brain of elderly patients with schizophrenia, autism and bipolar disorder (28,40,41), suggesting this gene may play a role in the etiology of these neurological disorders. Although it remains to be determined how DLG4 is regulated by nicotine, our finding of a significant association between DLG4 and ND indicate its importance in understanding molecular mechanisms underlying ND.

On the other hand, we found no convincing evidence for associations of either GABARAP or DLG4 with ND in the AA sample. Such a finding is consistent with our linkage analysis results for ND on the FHS and MSTF samples, where a significant linkage to ND was only detected in the FHS (the majority of the FHS sample is of EA origin) and in the EA samples of MSTF cohorts but not in the AA sample of MSTF cohort (6,7,42). This reflects potential genetic differences on ND between ethnic groups that possibly arise from differences in background risk factors, and GABARAP and DLG4 themselves. Ethnic differences have been reported on nicotine metabolism, reasons for smoking, exposure to smoking and use of mentholated cigarettes (43–45), and these risk factors may yield different background variations across ethnic populations and/or affect ND in concert with GABARAP and DLG4, leading to the ethnic-specific finding. As shown in Figure 2 and Table 2, significant differences existed in allele frequencies and LD structures between the two samples. For example, the AA sample demonstrated a weaker LD than the EA sample. The MAF of rs17710 is 0.013 in the AA sample and 0.094 in the EA sample, respectively. This implies that frequency of causative variant(s) in these genes and their LDs with the genotyped SNPs in the study are potentially ethnic-dependent.

In summary, our association and linkage results revealed that GABARAP and DLG4 are significantly associated with ND in EA smokers. Although these two genes have been implicated in the regulation of GABAA receptor function and plasticity, no human genetics study has been conducted to investigate their associations with drug addiction or other psychiatric disorders, including ND. Thus, the present study is the first to provide evidence for the association of GABARAP and DLG4 with ND in EAs. For AA smokers, independent studies are needed, as our findings revealed only marginal associations.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
REFERENCES
 
Study participants and ND measurement
The participants involved in this study were from the US MSTF cohort, recruited during 1999–2004. Levels of ND for probands and other smoker participants were assessed using the Fagerström Test for ND (FTND) (46). Once a proband and a full-sib who was also nicotine dependent (for a majority of our families) were recruited, additional siblings and parents were included into the study whenever possible, regardless of smoking status. Participants included 1366 individuals from 402 AA families and 671 individuals from 200 EA families, yielding a total of 2037. The families varied in size from two to nine with an average size of 3.14 (± 0.75; SD) for AAs and 3.17 (± 0.69) for EAs. Average age ± SD was 39.4 ± 14.4 years for the AA and 40.5 ± 15.5 years for the EA participants. Detailed demographic and clinical characteristics of this sample have been reported elsewhere (42,47), and will not be included here. All participants provided informed consent. The study protocol and forms/procedures were approved by all participating Institutional Review Boards.

The level of ND for each smoker was ascertained based on three measures most commonly used in the literature: SQ, the HSI (0–6 scale), which includes SQ and smoking urgency (i.e. how soon after waking up do you smoke your first cigarette?), and the FTND score on a 0–10 scale (46). Given the content overlap of these measures, there exists a robust correlation among them. The estimated pair-wise correlation coefficients of SQ-HSI, SQ-FTND and HSI-FTND are 0.94, 0.89 and 0.97, respectively, for AAs, and 0.94, 0.91 and 0.97, respectively, for EAs (all P-values < 0.0001). The average FTND score ± SD for smokers was 6.26 ± 2.15 for AAs and 6.33 ± 2.22 for EAs. The average number of cigarettes smoked per day ± SD was 19.4 ± 13.3 for AA smokers and 19.5 ± 13.4 for EA smokers. Our primary reasons for examining all three ND measures were: (a) the current lack of consensus regarding the best approach to assess ND as a phenotype and (b) to permit maximum cross-reference with previous studies of ND. The FTND has been accepted as a standard in both clinical and research settings, although recent evidence suggests ND is a broader and more complex construct than it was previously considered to be (48,49). Thus, although it is premature to endorse other measures that have received interesting but limited support, we believe it is prudent to examine our genetic findings with respect to the three aforementioned measures.

SNP genotyping
DNA was extracted from peripheral blood samples of each participant using a kit available from Qiagen Inc. (Valencia, CA, USA). A relatively small region within a suggestive linkage peak identified before (6,7) in the FHS cohort was considered as higher priority to follow up in this study because it contains a couple of genes potentially involved in drug addiction and thus is more likely to harbor susceptibility genes for ND (Fig. 1). To avoid overlooking potentially novel genetic determinants, we selected SNPs that are evenly distributed along this region and have an average density approximate to that suggested in the literature (50–52) at stage one. Selection of the SNPs for association analysis was based on (i) availability (i.e. high heterozygosity with a minor allele frequency [MAF] being at least 0.15 for stage one and 0.05 for stage two, according to the NCBI dbSNP database), and (ii) coverage of the genes that was as uniform as possible. Ten SNPs in six genes were chosen from the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/SNP/) and were used to test for association with ND in the first stage: DLG4, DVL2, GABARAP, DULLARD, DERP6 and SLC2A4 (see Table 5 for information on gene structures). To validate the observed significant associations in the first stage by obtaining better coverage, we genotyped an additional nine and four SNPs for DLG4 and GABARAP, respectively. Detailed information on the SNPs used for both stages is summarized in Table 6, which includes domains within the gene, chromosomal positions, allelic variants, MAF and the primer/probe sequences.


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  		Table 5. Information on the genes used in this study

 


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  		Table 6. Information on the SNPs for both the first and second stages of genotyping

 
The SNPs were genotyped using the TaqMan assay in a 384-well microplate format (Applied Biosystems Inc., Foster City, CA, USA). Briefly, 15 ng of DNA was amplified in a total volume of 7 µl containing an MGB probe and 2.5 µl of TaqMan universal PCR master mix. Amplification conditions were 2 min at 50°C and 10 min at 95°C followed by 40 cycles of 95°C for 25 s and 60°C for 1 min. Allelic discrimination analysis was performed on the ABI Prism 9700HT Sequence Detection System (ABI, Foster City, CA, USA). To ensure the quality of the genotyping for a given SNP, eight SNP-specific positive controls for both alleles were added to every 384-well plate.

Data analysis
The PedCheck program (53) was used to identify any inconsistent Mendelian inheritance, non-paternity or other typing errors. To avoid bias, a total of 130 inconsistencies in the AA sample and 73 in the EA sample (i.e. 0.52% of the global analysis) of 38 703 assays were classified as missing. To verify data quality, we also checked the SNP-typing results for any significant departure from HWE. Pair-wise LD for all SNP markers (except for those monomorphisms) was assessed using the Haploview (v.3.2) program (23), employing the option of determining haplotype blocks based on block definitions proposed by Gabriel et al. (24).

Associations between individual SNP and the ND measures were determined using generalized estimating equations available via the PBAT (v.3.0) program (54). Associations between each ND measure and haplotypes from multiple SNP combinations were examined using the FBAT (v.1.5.5) program (55). Three genetic models (additive, dominant and recessive) were tested, with gender and age as covariates in the AA and EA samples. Analyses based on the three ND measures (SQ, HSI and FTND) were conducted individually. All associations found to be significant were corrected for multiple testing according to the SNPSpD approach (25) for individual SNP analysis, and using Bonferroni correction by dividing the significance level by the number of major haplotypes (>0.05 in frequency) for haplotype-based association analysis. In consideration of high correlations between the three ND measures and among the results under different genetic models, we chose not to correct for testing of the three highly-correlated ND measures or genetic models to avoid being over-conservative. This is because there is no generally accepted methodology capable of handling the problem of highly correlated multiple testing.

To determine the contributions of GABARAP and DLG4 to the detected linkage signal, we carried out the linkage analysis for both the EA and AA samples by modeling all the polymorphic SNPs of GABARAP and of DLG4 as covariates, respectively. As reported in our recent communications on the AA (42) and the EA (Li et al., in preparation) samples, Haseman–Elston regression (56) implemented in the SIBPAL program of the S.A.G.E. (v.5.0) was used in our linkage analysis. In theory, the contribution of a causative gene to the linkage signal can be absorbed by its SNPs adjusted as covariates during the linkage analysis. By comparing the linkage profiles before and after adjustment for the SNPs of the GABARAP and DLG4 genes, we can determine whether they can fully or partially account for the detected linkage signal on chromosome 17.


   ACKNOWLEDGEMENTS
 
This project is funded by NIH Grants DA-12844 and DA-13783 to MDL. We thank the Center for Inherited Disease Research (CIDR) for performing the MSTF genome scan. Detailed information on laboratory methods and markers can be found at http://www.cidr.jhmi.edu. Some of the results of this paper were obtained using the program S.A.G.E. (v. 5.0), which is supported by a US Public Health Service Resource Grant (RR03655) from the National Center for Research Resources.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 MATERIALS AND METHODS
 REFERENCES
 

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