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Human Molecular Genetics Advance Access originally published online on August 30, 2007
Human Molecular Genetics 2007 16(23):2844-2853; doi:10.1093/hmg/ddm240
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Association of haplotypic variants in DRD2, ANKK1, TTC12 and NCAM1 to alcohol dependence in independent case–control and family samples

Bao-Zhu Yang1,7, Henry R. Kranzler8, Hongyu Zhao2,3, Jeffrey R. Gruen3,4,6, Xingguang Luo1,7 and Joel Gelernter1,3,5,7,*

1 Division of Human Genetics, Department of Psychiatry, 2 Department of Epidemiology and Public Health, 3 Department of Genetics, 4 Department of Investigative Medicine, 5 Department of Neurobiology and 6 Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA 7 VA Connecticut Healthcare Center, West Haven, CT, USA and 8 Alcohol Research Center, Department of Psychiatry, University of Connecticut, School of Medicine, Farmington, CT, USA

* To whom correspondence should be addressed at. Division of Human Genetics, Department of Psychiatry, Yale University School of Medicine, VA CT 116A2, 950 Campbell Avenue, West Haven, CT 06516, USA. Tel: +1 2039325711/ext 3590; Fax: +1 2039374741; Email: joel.gelernter{at}yale.edu

Received July 6, 2007; Revised August 17, 2007; Accepted August 21, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
There have been many conflicting reports concerning the association of the DRD2 locus with alcohol dependence (AD). To investigate whether these findings could be reconciled by considering the genomic region of DRD2 in greater detail, we conducted two separate association studies of AD in 1220 European-American subjects using family-based (488 subjects) and case–control (318 cases and 414 controls) designs, and 43 single nucleotide polymorphisms mapped to the gene cluster of NCAM1, TTC12, ANKK1 and DRD2. We used a generalized linear model and haplotype score tests for the case–control sample, and the family-based association test for the family sample. Haplotype associations centered on TTC12 exon 3 [rs1893699–rs723077; optimal individual haplotype simulated P-value (Poihs) = 0.00021] in both independent samples (family and case–control). Additional AD-associated haplotypes centered around NCAM1 exon 12 in the family sample (Poihs = 0.0032), and at exons 2 and 5 of ANKK1 in the case–control sample (Poihs = 0.00058). LD contrasts between cases and controls support selection at TTC12 exon 3 and ANKK1 exon 2. The armadillo repeat domains encoded by TTC12 and dopamine interact in the Wnt pathway and may have effects on dopamine cell development in the ventral midbrain. We conclude that risk for AD is attributable in part to variants in four regions within this cluster: exon 3 of TTC12, exon 12/intron13 of NCAM1 and exons 2 and 5 of ANKK1. The complexity of these relationships, many of which replicate between our independent samples, may explain prior inconsistent results.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Alcohol dependence (AD) is linked to a wide range of adverse physical, mental and social effects. In the United States, during a 1-year period, 17.8 million adults were estimated to meet criteria for an alcohol use disorder (1). The adverse consequences associated with these disorders resulted in an estimated annual cost of nearly $184.6 billion in the United States in 1998 (2).

AD is a complex and multi-factorial disorder arising from interactions between genetic and environmental risk factors. Evidence from twin, adoption and family studies shows that AD is genetically influenced, with a heritability of 50–60% (3). The inherited vulnerability to AD corresponds in part to interindividual differences in pharmacokinetics (i.e. alcohol absorption, distribution and metabolism) and the pharmacodynamic response to alcohol (4). This vulnerability arises from the concurrent impact of functional variation at numerous risk genes, in the context of the molecular complexity of the nervous system, leading to considerable genetic heterogeneity (5). Candidate gene studies have shown that genes conferring either a predisposition to AD or protective effects fall into different classes, including those encoding proteins regulating the metabolism of alcohol [e.g. alcohol or aldehyde dehydrogenase genes (6–18)] and those that encode functional receptors or receptor subunits that interact with alcohol or related systems [e.g. GABRA2 (19–22)].

Four genes, neural cell adhesion molecule 1 (NCAM1), tetratricopeptide repeat domain 12 (TTC12), ankyrin repeat and kinase domain containing 1 (ANKK1) and the D2 dopamine receptor (DRD2), occur in a ~542 kilobase (kb) cluster on chromosome 11q (23). In this gene cluster, DRD2 maps molecularly close to NCAM1, and both are functional candidates for AD risk. ANKK1 and TTC12 map between DRD2 and NCAM1. We recently reported a strong association between a haplotype spanning TTC12 and ANKK1 and nicotine dependence (ND) (23). All four of these genes may thus now reasonably be considered candidate loci for substance dependence (SD).

The D2 dopamine receptor gene, DRD2 (OMIM no. 126450 [OMIM] ), encodes a G protein-coupled receptor located on post-synaptic dopaminergic neurons, which plays a central role in reward-mediating mesocorticolimbic pathways (24). DRD2 has been widely studied as a risk gene for AD and other SD traits (25–27). ANKK1 rs1800497, previously called ‘DRD2 TaqI ‘A’’, is one of the most frequently studied polymorphisms in DRD2 genetic studies (28); however, the evidence associating this variant with AD, first reported more than 15 years ago (29), remains ambiguous and controversial. Neville et al. (24) determined that the DRD2 TaqI ‘A’ is actually located not within DRD2, but rather within a protein-coding region, exon 8, of the adjacent ANKK1 gene. ANKK1 maps <10 kb downstream of DRD2, spans ~13 kb, contains 8 exons and encodes a deduced 765-amino acid protein involved in signal transduction pathways (24).

NCAM1 is part of the immunoglobulin gene superfamily; it maps about 120 kb downstream of DRD2 (NCBI Build 35.1, http://www.ncbi.nlm.nih.gov/), spans ~316 kb and contains at least 19 exons. NCAM, the protein encoded by this gene, is involved in various neural functions including mediating adhesion among neurons and between neurons and muscle, structural linkages to extracellular and intracellular proteins, synaptic stability and plasticity, neural development, signal transduction, long-term potentiation, neurogenesis, memory consolidation and learning (30–35). Dysregulation of NCAM isoforms in brain may contribute to the pathophysiology of neuropsychiatric disorders (35), and association of NCAM1 polymorphisms with bipolar disorder was observed in a Japanese population (36).

Tetratricopeptide repeat domain 12 gene (TTC12) is located between the NCAM1 and ANKK1 genes, spans 58.75 kb (NCBI build 35) and consists of 22 exons, encoding a 705-amino-acid protein with tetratricopeptide repeat (TPR) and armadillo repeat (ARM) domains (37). Its physiological function is not understood thoroughly, but it appears likely to interact with dopamine pathways.

In this study, we examined the association of AD with variants that map to each of these four genes using a large clinical case–control sample and an independent family sample, in which we genotyped a panel of 43 SNPs (Table 1) that map to this region, including well-known functional variants (23).


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  		Table 1. Characteristics for the 43 SNPs

 

   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Gene and marker characteristics
The relative location of the four genes and the selected SNPs is shown in Figure 1. ANKK1 is the smallest gene (~13 kb) among the four; the lengths of NCAM1 (~316 kb), TTC12 (~58 kb) and DRD2 (~65 kb) are 25, 4.6 and 5.2 times the length of ANKK1, respectively. The average intermarker distance was 12 kb. Linkage disequilibrium (LD) coefficients (D') and haplotype blocks are plotted in Figure 2 for all subjects of the case–control and family samples. Minor allele frequencies (MAFs) estimated from the case–control and the founders of family samples were shown in Table 1.


Figure 1
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  		Figure 1. Map of the NCAM1, TTC12, ANKK1 and DRD2 gene region. (A) Relative gene locations. (B) Distribution of 43 SNPs studied.

 


Figure 2
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  		Figure 2. LD map structure and locations of 43 SNPs in the gene cluster of NCAM1, TTC12, ANKK1 and DRD2. The LD maps for the family and case–control samples are shown, respectively, above and below the gene structure diagram. The measure of LD (D') among all possible pairs of SNPs (identified by number; Table 1) is shown graphically according to the shade of red, where white represents very low D' and dark red represents very high D'.

 
Single SNP association analysis
Single SNP allelic association screening for susceptibility loci from both samples identified two SNPs in each sample that were nominally significant (Supplementary Material, Table S1), i.e. no longer significant after correction for multiple testing. For the case–control sample, SNP T7, located in intron 7 of TTC12 (P = 0.0095), and SNP D2 in intron 2 of DRD2 (P = 0.030), were nominally significantly associated with AD, when adjusted for age and sex; for the family sample, SNP N8 and SNP N9, located, respectively, in exon 12 and intron 13 of NCAM1 (P = 0.036 and 0.022, respectively, from FBAT tests without adjusting for age and sex) were nominally significantly associated with AD.

Global haplotype association analysis
We defined ‘exhaustive search’ as a sliding window of three, four or five SNPs in consecutive positional order for construction of haplotypes, and the ‘starting SNP’ in a haplotype as the first SNP in the direction from centromere to telomere, i.e. from NCAM1 to DRD2. We employed an exhaustive search of haplotypic effects using the ‘haplo.score.slide’ function in the Haplo.stats package, and the analogous approach in FBAT. Age and sex were covaried in these tests for the case–control sample. Significant haplotypic effects emerged (Fig. 3A; P-values for all tests, Supplementary Material, Table S1). Among 3-SNP haplotype tests for the case–control sample, sliding haplotypes mostly in TTC12, starting from T3–T5 to T10—A2, except T7–T9, were associated to AD (simulated global P (Psg) = 0.037–0.0004), as were three other non-consecutive haplotypes, A3–A5 (entirely within ANKK1), D1–D3 (entirely within DRD2) and D5–D7 (upstream of the 5'-UTR of DRD2). For the 4-SNP haplotypes, there was evidence of association extending from T2–T5 in TTC12 to A3–A6 in ANKK1 (Psg = 0.031–0.0011), excluding a marginally significant haplotype, T5–T8. Haplotypes in the 3' regions of ANKK1 and DRD2—A7–D3 (Psg = 0.022)—and the 5' region of DRD2—D4–D7 (Psg = 0.026)—were also associated with AD. These are one-SNP extensions of the associated 3-SNP haplotypes. When the haplotype window was extended one additional SNP, the 5-SNP haplotypic effects included all of the SNPs tested in TTC12 and ANKK1 and into DRD2, bounded on the telomeric side by A6–D3 (Psg = 0.042–0.0002).


Figure 3
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  		Figure 3. Results of single SNP and haplotype association analyses for the case–control and family samples. P-values are shown. For haplotype analyses, the value at each SNP location represents the haplotype with SNPs starting from the current location downward from the NCAM1 direction to DRD2. The horizontal dashed lines correspond to type I error rate of 0.05. The three vertical lines from left to right indicate the positions of SNPs N19, T10 and A7, respectively.

 
In the completely independent family sample, significant haplotypic associations to AD were replicated in T3–T5, T1–T4, T2–T5 and T1–T5 in TTC12 (Psg = 0.018–0.007). One haplotype extending across ANKK1 to DRD2, A7–D3, was also associated (Psg = 0.032) in the family sample.

Also in the family sample, the exhaustive search of haplotypic effects uncovered another associated region, not identified in the case–control sample, around exon 12 of NCAM1 (Fig. 3B). The 3-SNP haplotypes N6–N8, N8–N10 and N9–N11; the 4-SNP haplotypes N6–N9, N8–N11 and N9–N12; and the 5-SNP haplotypes N5–N9, N7–N11, N8–N12 and N9–N13 were all associated with AD (Psg = 0.049–0.008). The results for all of the global haplotype association analyses are summarized in Supplementary Material, Table S1.

Specific haplotype association analysis
The associated haplotype sets described earlier were further investigated, to identify specific risk or protective haplotypic effects. Table 2 lists the specific associated haplotypes. Viewed graphically, these haplotypic variants display a mosaic of nucleotide patterns indicative of positive or negative association to AD. For the family sample, the cluster around exon 12 of NCAM1 had a protective effect shown by nucleotides T-C-A of N8–N10, located in exon 12 to intron 13, spanning 6950 bp. In contrast, nucleotides G-A-A at the same SNP positions, with haplotype frequency ~12% (i.e. approximately half of the haplotype frequency of most of the other associated haplotypes for the family sample, Table 2), conveyed a risk effect. It is noteworthy that the ‘C’ allele at SNP N9 appeared in all of the associated protective haplotypes (Table 2).


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  		Table 2. Summary results of significant individual haplotypes associated with AD

 
For the family sample, the haplotypic risk effect in TTC12 corresponds to nucleotides G-G-A of the haplotype T3–T5 (Table 2), located in intron 1 to exon 3, spanning 7392 bp. In the case–control sample, the highest peaks of transformed P-values for the sliding 3-SNP haplotype tests (Fig. 3A) were mainly from the G-A-A nucleotides at T4–T6, centering around exon 3 of TTC12, in a range of 8185 bp. We observed two additional 5-SNP risk haplotypes sharing the common nucleotide pattern T-C-G-A in T4–T7 associated to AD—this is the complement of G-A-A-G (Table 2) [a situation denoted ‘yin yang haplotypes’ (38), i.e. the allele at each corresponding SNP position is different]. These associated haplotypes relevant to T4–T5 (mapping to TTC12) shared a common G-A (or T-C, in some haplotypes observed in the case–control and family samples) risk motif. There were two additional significant protective haplotypes starting from T7 to T10 and T10 to A3 sharing G-G-C in T8–T10 and C-A-A in T10–A2, located in the upstream portion and extending to exon 2 of ANKK1, and a stand-alone risk haplotype of C-G-C-C-A in T10 to A4 originating from C-A-C in A3–A5 centering on A4 in exon 5 of ANKK1. These three haplotypes all have frequencies around 5%, which is relatively low compared with all the other associated haplotypes, which had frequencies of ≥10%.

LD comparisons between cases and controls were accomplished by use of LD Contrast Test software. Two regions, one around TTC12 exon 3 (SNPs T2–T5) and another around ANKK1 exon 2 (SNPs T10–A3), were identified as differing in the standardized composite LD (both P = 0.000005). The LD difference gradually disappeared as the number of included SNPs was increased. The differences in pairwise LD matrices are displayed by ellipse plots (Fig. 4) in which the shape of an ellipse indicates the magnitude of LD and the direction shows the sign of the disequilibrium. The more circular shape of an ellipse reflects a low degree of LD, and 45°-oriented ellipses suggest a positive value of LD. This finding from the LD contrast test further supports our association results and provides evidence of selection around TTC12 exon3 and ANKK1 exon2 in affected subjects. Such selection would reflect the presence of a functional variant.


Figure 4
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  		Figure 4. Ellipse plots for the results of the LD contrast tests. (A) Region around TTC12 exon 3. (B) Region around ANKK1 exon 2. For each ellipse plot, the pairwise LD matrices are displayed for the contrast of cases and controls, respectively, above and below the diagonal line. The shape of an ellipse indicates the magnitude of LD and the direction shows the sign of the disequilibrium. The more circular shape of an ellipse reflects a low degree of LD, and 45°-oriented ellipses suggest a positive value of LD.

 
Population stratification was tested using a panel of 37 short tandem repeat (STR) markers that we designed to differentiate major US populations (39). The average ancestry proportion for EAs was 96.4% in ADs and 95.9% in controls. In the absence of evidence of population stratification in this study sample, it was ruled out as a source of spurious association in the case–control sample. The family-based analyses are not considered subject to stratification artifact.

In summary, a susceptibility region near exon 3 of TTC12 (marked by SNPs T4 and T5) was a significant risk locus that was evident in both the case–control and the family samples. This is the most important risk region, judging by the level of statistical significance. There was an additional region near exon 12 to intron 13 of NCAM1 marked by SNPs N8, N9 and N10 affecting risk or protection against AD for the nucleotides of G-A-A or T-C-A carriers in the family sample. Another region around exon 2 (SNP A2) and exon 5 (SNP A4) of ANKK1 also affects AD risk but shows relatively low haplotype frequencies (e.g. the haplotype frequency for nucleotides C-G-C-C-A of haplotype T10–A4 is around 7.3% for cases and 3.8% for controls and 5.3% for pooled cases and controls).


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
We present here, in two independent EA samples, strong evidence for association of markers mapped to different locations within a four-gene cluster on chromosome 11q with AD. The key finding of the haplotype association analyses was that exon 3 of TTC12 is, or is close to, a risk locus for AD, and was evident in both samples, where the minimal individual haplotype simulated P = 0.00021. Further, the LD contrast test showed different LD in TTC12 exon 3 between the cases with AD, in contrast to controls, supporting the hypothesis that this region has undergone selection; this is additional independent evidence supporting an association. This is the first study to find association between TTC12 and AD. Although little is known that is specific about its biological action, more is known about the action of the armadillo repeat domain in the protein TTC12 encodes. An armadillo repeat domain can bind to other proteins, with function related to signal transduction, regulation of desmosome assembly (important for cell adhesion), neurogenesis and post development synaptogenesis. Beta-catenin, a prototypical armadillo domain containing protein, has been implicated as an integral component in the Wnt signaling pathway. Beta-catenin and dopamine interact in the Wnt pathway, which has effects on dopamine cell development in the ventral midbrain (40). Beta-catenin was identified as a possible susceptibility locus for bipolar affective disorder in a recent whole genome association study (41). In addition, an armadillo repeat gene (ARVCF) is deleted in velocardiofacial syndrome and is a candidate gene for the psychiatric manifestations of that disorder (42). Thus, there is considerable circumstantial evidence that ties the function of TTC12 to both DRD2 and NCAM1, supporting our speculation that these genes are related functionally.

Alcohol increases synaptic dopamine, which reinforces self-administration. Thus, most prior investigations of this region have focused on DRD2 only, which, because it encodes a dopamine receptor, bears a potential relationship to these phenotypes. We extended our investigation to include the other three genes in this regional cluster in an attempt to provide an explanation for inconsistently-reported associations with DRD2, which seemed unlikely to be explicable on the basis of functional variation at DRD2 alone. In a prior study [which included family-based association tests (FBATs) in two populations—completely non-overlapping with the present study] (23), we found evidence for association of ND to markers in a region spanning ANKK1 and TTC12, as noted earlier. In the present study, the results are consistent with these previously reported results, but the present study has greater power; two different study designs were used; of these, the case–control study had greater power than the family study, and has allowed a more detailed examination of the relationships between variants and haplotypes in this region and AD. A more complicated picture now emerges, with several associated regions that map to at least three of these loci, and perhaps all four. This supports the presence of multiple risk variants, and is consistent with our previous hypothesis that this set of four genes may have related functions, albeit in a way that is not yet understood.

The role of variants in exon 3 of TTC12 for AD
When the LD for AD between markers around exon 3 of TTC12 (T4 and T5, separated by 1644 bp) and all of the other 41 markers was further investigated, the following values of D' were of note (1): 0.66 (T4 versus A6), 0.72 (T5 versus A6), 0.71 (T5 versus D4) from the family sample. A6 resides in the same exon 8 as A7 (the variant formerly known as DRD2 Taq I‘A’) and D4 is in the putative promoter region of DRD2 (2). 0.59 (T4 versus A4), 0.52 (T4 versus A6) from the case–control sample. A4 is on ANKK1 exon 5, a risk locus found in the case–control sample. Although the LDs, to some degree, link the association to DRD2 (in terms of correlation of genotypes), DRD2 itself only showed globally significant associations, and was not significantly associated with AD for any specific haplotypes in the primary analysis. The associated specific risk region for DRD2 variants resided at the region where the two 3' ends of DRD2 and ANKK1 meet, including D1 (DRD2^C957T), a variant affecting mRNA stability (43). These results raise an important question: what are the relative roles in AD played by (i) the variants at exon 3 of TTC12 (i.e. which map in a gene with unclear biological function, where we observed evidence of selection, and strong statistical significance that was not explicable by LD to DRD2) and (ii) variants at the boundary of ANKK1 and DRD2 (which are easier to explain biologically, based on existing understanding of gene function, but show weaker statistical significance)? Variation in exon 3 of TTC12 might in actuality represent a stronger risk effect than variation at the juncture of DRD2 and ANKK1, since exon 3 of TTC12 showed association to AD but the juncture of DRD2 and ANKK1 only showed global association. Further, we cannot exclude effects attributable to a DRD2 regulatory element, or elements, that map in these nearby genes.

Comparison of the analyses for the case–control and the family samples
All association analyses for the case–control sample were adjusted for age and sex, while the family-based association analyses using FBAT were not. This is due to a technical limitation of the FBAT software, but additionally, the design renders such correction much less important because of the family-based context. Further, the FBAT approach tends to be conservative statistically. Because of the conservative family-based design and the smaller sample size, the global P-values were not as significant as those obtained in the case–control design.

The findings in the case–control and family samples were similar except that the findings in NCAM1 were exclusive to the family sample for AD. Note that SNP N9, 1911 bp downstream of SNP N8 in exon 12 of NCAM1, is located in intron 13, and was found to be associated with bipolar affective disorder in a Japanese sample for a haplotype block centered on this SNP (36); it was also in an associated haplotype block (N8–N9–N10) in the current study. The differences we observed between the case–control and family samples might also originate from heterogeneity between AD samples, ascertainment bias and differing statistical power.

Our recent work (23) used the same set of markers to map risk variants for ND in two distinct populations (both completely non-overlapping with the present study) in EA and AA subjects by the family-based association method. The results showed relatively weak evidence for association of the flanking DRD2 and NCAM1 markers to ND, but very strong evidence of association of multiple SNPs at TTC12 and ANKK1 in both populations and in the pooled sample, as well as strong evidence for highly significant association of a single haplotype spanning TTC12 and ANKK1 to ND in the pooled sample (P = 0.0000001). We concluded that a risk locus for ND, important both in AAs and EAs, maps to a region that spans TTC12 and ANKK1. A limitation of the present study is that we did not have the ND diagnostic information for the current study samples, so we cannot address the issue of the importance of comorbid ND in driving these associations directly.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
We conclude that (i) exon 3 of TTC12 is a risk locus for AD, (ii) the region flanking exon 12 of NCAM1 contains protective or risk variants for AD, (iii) in view of the finding that a region spanning TTC12 and ANKK1 is a risk locus for ND (23), the findings reported here for AD underscore the importance of this cluster of genes on chromosome 11q for determining risk of SD. Replication of the findings in other populations by other investigators is necessary to validate these findings, and functional analysis within these regions is warranted.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
Subject recruitment and assessment
The study protocol was approved by all relevant institutional review boards. All subjects provided written informed consent and were paid for their participation.

Case–control sample
Seven hundred and thirty-two European-American subjects (EAs) were recruited at either the University of Connecticut Health Center or the VA Connecticut Healthcare System, West Haven campus. Among them, 318 were affected with AD (including 243 males and 75 females; mean age: 40.8 years) and 414 were healthy controls (of whom there were 167 males and 247 females; mean age: 27.9 years). Affected subjects met lifetime DSM-III-R (44) or DSM-IV (45) diagnostic criteria for AD. Healthy controls were recruited by advertisement from the general population, and screened to exclude individuals with major Axis I mental disorders, including alcohol or drug dependence, mood disorders, major anxiety disorders and psychotic disorders. They were screened using the Structured Clinical Interview for DSM-III-R (SCID-III-R) or DSM-IV (SCID-IV), or the Computerized Diagnostic Interview Schedule for DSM-III-R (C-DIS-R). Overall, the control sample was younger and more likely to be female than the patient sample (Table 3).


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  		Table 3. Demographic and clinical characteristics of the European-American sample

 
Family sample
Four hundred and eighty-eight EAs from 143 nuclear families were recruited at the same centers, with 136 families assessed using the SCID and seven by the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA), as described elsewhere (46,47). Among them, there were 257 parents and 231 siblings; average family size was 3.4 members. The average age of probands was 35.0 years, of AD-affected siblings, 36.1 years, of AD-unaffected siblings, 33.5 years and of parents, 62.5 years (Table 3).

SNP selection
We selected SNPs based on prior published association reports, information content, MAF, functional potential, LD structure and validation evidence (23). NCAM1 and DRD2 were targeted initially; ANKK1 was added as it was reported to be adjacent to the 3' end of DRD2, and TTC12 was added to complete the survey of genes mapping from NCAM1 to DRD2. As shown in Table 1, 43 SNPs were selected: 17 mapped to NCAM1 (designated N1–N17), 7 to TTC12 (T3–T9), 7 to ANKK1 (A1–A7) and 4 to DRD2 (D1–D4); 8 SNPs were intergenic.

Marker genotyping
SNPs were genotyped as described previously (23). Genotyping failure rate for the 43 selected SNPs was 4.2% for the family and 6.4% for the case–control samples. In addition, 37 ancestry informative markers (AIMs) described by Yang et al. (39) were genotyped in all case and control subjects for population admixture analysis.

Family genotype data were error-checked via PedCheck (48) to detect Mendelian inconsistencies, with 12 detected. These families were re-genotyped, with resolution of all inconsistencies. For the case–control data, two SNPs were not in Hardy–Weinberg equilibrium (HWE): SNP N4 (P = 7.6 x 10–5) among the control samples; and SNP A4 (P = 1.7 x 10–6) among the case samples. We checked the genotypes for these SNPs via the method of departures from HWE developed by Wittke-Thompson et al. (49) and excluded genotyping errors as a likely cause of the disequilibrium.


   STATISTICAL ANALYSIS
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 
LD, haplotype blocks and tagging SNPs
LD, common haplotype patterns and allele frequencies were estimated using the software program Haploview 3.32 (http://www.broad.mit.edu/mpg/haploview/) (50). The LD plot summarizes the standardized LD coefficients, D'. Haplotype blocks were defined according to the criteria of Gabriel et al. (51). Tagging SNPs (tSNPs) were also identified to restrict haplotype analysis in the subsequent association tests and reduce the risk of Type 1 error. We applied Haploview for ‘aggressive’ tagging with 2- or 3-marker haplotypes having pairwise correlation coefficient >0.8. Conformance with HWE expectations was ascertained separately for cases and controls using an exact test in Haploview.

Association analysis for the case–control design
Single-marker allelic association with AD was screened via a generalized linear model (GLM in R) adjusting for sex and age covariates, and coding genotypes for genotypic additive effects. Haplotype-based association analysis was implemented via the Haplo.stats Package (http://mayoresearch.mayo.edu/mayo/research/biostat/schaid.cfm) in the R environment (R Project for Statistical Computing, http://www.r-project.org/). A GLM was used to model AD with haplotypes of additive effect and covariates of sex and age with a logit link function (52). A most frequent haplotype was chosen as the baseline haplotype in the GLM model, and a multivariate Wald test was then conducted. The P-values for single marker allelic associations were assessed by the {chi}2 test on analysis of deviance (‘anova’ function in R), while the P-values for the global haplotype tests were computed empirically, through simulation, to circumvent the cumulative Type I error rates that would otherwise result from multiple testing. The significance level of the haplotype-specific tests within each haplotype set was adjusted by Bonferroni correction, i.e. the conventional {alpha} = 0.05 was divided by the number of major haplotypes with frequency greater than 5.0%.

LD contrast test
For true risk loci, LD is generally stronger among cases than controls due to shared disease alleles and haplotypes in a region of genetic association and the effects of selection (53). The LD contrast test, proposed by Zaykin et al. (54), was used to compare the standardized composite LD measures between cases and controls via a composite disequilibrium approach without HWE assumption. The permutation parameter was set at 200 000 iterations.

Population structure analysis
We used the program Structure 2.1 (55) based on the genotype data for the 37 AIMs (39,56,57) to assess population stratification in the case and control subjects by estimating the ancestry proportion for each EA subject. Two hundred and sixty-six African-American (AA) subjects (previously ascertained and genotyped) were included in each STRUCTURE run to set AA ancestral allele frequencies, thereby increasing the accuracy when inferring admixture. Simulation parameters were set to 100 000 burn-ins followed by 100 000 iterations and K was set to 2.

Association analysis for the family design
Allelic association of individual SNPs was examined using the FBAT method (58,59) (http://www.biostat.harvard.edu/~fbat/), assuming an additive genetic model under the null hypothesis of no linkage and no association, biallelic mode, minimum number of informative families of 20 for each analysis and offset of zero. Haplotypic association was also calculated by FBAT with the ‘hbat’ command, with an assumption of a biallelic mode for haplotype-specific association and multi-allelic mode for global association, testing all haplotypes as a whole. Under the null hypothesis of no linkage and no association, all P-values derived from ‘hbat’ in this study were computed using a permutation test (100 000 iterations). The significance level of haplotype-specific tests was adjusted using a Bonferroni correction (i.e. division by the number of major haplotypes with frequency greater than 5.0%) and ranged from 0.01 to 0.00625.


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


   FUNDING
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
REFERENCES
 
National Institutes of Health (R01 AA11330, P50 AA12870, K08 AA13732, K24 AA13736, R01 AA016015, R01 DA12690, R01 DA12849, K24 DA15105, M01 RR06192); the US Department of Veterans Affairs [the VA Connecticut–Massachusetts Mental Illness Research, Education and Clinical Center (MIRECC)]. Funding for the open access charge is: 50% NIAAA-AA 11330, 30% NIDA-DA 15105, 10% NIDA-DA 12849, 10% NIDA-DA 12690.


   ACKNOWLEDGEMENTS
 
We thank the families and individuals who volunteered to participate in this research study. Ann Marie Lacobelle, Lisa Naito and Yakov Lozovatsky provided excellent technical assistance.

Conflict of Interest statement. None declared.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 RESULTS
 DISCUSSION
 CONCLUSIONS
 MATERIALS AND METHODS
 STATISTICAL ANALYSIS
 SUPPLEMENTARY MATERIAL
 FUNDING
 REFERENCES
 

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