A SNP in the ACT gene associated with astrocytosis and rapid cognitive decline in AD

Neurobiology of Aging 29 (2008) 1167–1176

A SNP in the ACT gene associated with astrocytosis and rapid cognitive decline in AD O. Belbin a , J.L. Dunn b , S. Chappell a , A.E. Ritchie a , Y. Ling a , L. Morgan a , A. Pritchard c,1 , D.R. Warden d , C.L. Lendon c,1 , D.J. Lehmann d , D.M.A. Mann e , A.D. Smith d , N. Kalsheker a , K. Morgan a,∗ a

Division of Clinical Chemistry, Institute of Genetics, Queen’s Medical Centre, University of Nottingham, Nottingham NG7 2UH, UK b School of Physics and Astronomy, University of Nottingham, University Park, Nottingham NG7 2RD, UK c Department of Human Genetics, G Floor CBCRC Building, Queensland Institute of Medical Research, 300 Herston Road, Herston, Brisbane 4006, Australia d OPTIMA, Oxford Centre for Gene Function, Department of Physiology, Anatomy & Genetics, Oxford OX1 3PT, UK e Greater Manchester Neurosciences Centre, University of Manchester, Manchester M6 8HD, UK Received 15 November 2006; received in revised form 19 December 2006; accepted 10 February 2007 Available online 26 March 2007

Abstract There is biochemical and animal model evidence supporting a pathological role of the ACT gene in AD. However, direct genetic evidence remains controversial and has been mostly limited to individual single nucleotide polymorphism (SNP) analysis. To resolve this apparent conflict we have used a high-density ACT SNP map, constructed haplotypes and explored correlations with phenotype. SNPs were identified by sequencing and used to construct haplotypes in 668 AD patients and 419 controls and a case–control association study was performed. Five SNPs, comprising five common haplotypes, represented 93% of ACT gene variation. Although no single SNP or haplotype was associated with AD status, a SNP in intron 2 was associated with later onset and more rapid cognitive decline (P = 0.04). This SNP was both individually associated with severe astrocytosis (P = 0.004) in AD patients and when combined with the signal sequence SNP (P = 0.002). This suggests that astrocytosis may have a protective function for a limited period in some patients. These SNP associations either support a direct role for the ACT gene, in AD pathology or alternatively reflect linkage with polymorphisms in other genes nearby. © 2007 Elsevier Inc. All rights reserved. Keywords: Alpha-1-antichymotrypsin; Alzheimer’s disease; Haplotypes; Single nucleotide polymorphisms

1. Introduction Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by the formation of senile plaques (SP) and neurofibrillary tangles in the brain. SP result from the extracellular deposition of ␤-amyloid protein (A␤). Intracellular A␤1–42 is proteolytically derived from amyloid precursor protein (APP) by ␤- and ␥-secretases (Citron et al., 1992), and induces neuronal cell death (Kienlen-Campard et al., 2002), resulting in severe cogni∗

Corresponding author. Tel.: +44 115 8230724; fax: +44 115 9709167. E-mail address: [email protected] (K. Morgan). 1 Present address: Department of Human Genetics, Queensland Institute of Medical Research, Herston, QLD 4006, Australia. 0197-4580/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.neurobiolaging.2007.02.021

tive impairment. Known mutations in APP (MIM 104760; http://www.ncbi.nlm.nih.gov/Omim) and in the presenilin genes, PSEN1 (MIM 104311) and PSEN2 (MIM 600759), cause familial early-onset AD, accounting for around 5% of AD cases (Bertram and Tanzi, 2004). Remaining cases constitute “sporadic AD” for which genetic associations, other than the apolipoprotein (APOE; MIM 107741) ␧4 allele risk and the ACE indel polymorphism (Lehmann et al., 2001), are currently not fully established. These cases include both early-onset AD (EOAD) and late-onset AD (LOAD) defined by age-at-onset less than or greater than 65 years respectively. Since alpha-1-antichymotrypsin has been detected in abundance in SP, the ACT gene (ACT, SERPINA3; MIM 107280), located on chromosome 14q32.1 (Bao et al., 1987;

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Chandra et al., 1983; Chappell et al., 2006), has become an AD candidate. ACT is an acute-phase reactant that increases up to four-fold during inflammation (Travis and Salvesen, 1983) in response to cytokines (Kordula et al., 2000, 1998). Whilst ACT is synthesised mainly in the liver, it is also produced in the brain by activated astrocytes, which surround the SP (Abraham et al., 1988; Kordula et al., 2000; Pasternack et al., 1989). The exact role of ACT in SP pathology is undetermined. Some studies report that ACT promotes rapid disaggregation of A␤ fibrils and forms part of the inflammatory response to A␤ aggregation (Eriksson et al., 1995; Fraser et al., 1993). However, there is increasing biochemical and pathological evidence that ACT may act as a molecular chaperone, both increasing the neurotoxicity of the A␤ peptide and promoting amyloid filament formation (Janciauskiene et al., 1996, 1998; Ma et al., 1996, 1994). In vitro ACT forms a complex with the putative neurotoxic A␤1–42 in a dosedependent manner (Eriksson et al., 1995; Fraser et al., 1993; Sun et al., 2002) and this has recently been shown to act synergistically to alter gene expression in astrocytes (Baker et al., 2007). Transgenic mouse models confirm that ACT directly inhibits A␤ degradation and promotes A␤ deposition in brain (Mucke et al., 2000; Nilsson et al., 2001a, 2004). Supporting genetic evidence in humans has been difficult to obtain and is controversial. Following the initial observation that a common variant (Ala) in the ACT signal sequence (rs4934, here termed ACT 5) was associated with increased risk of AD (Kamboh et al., 1995), a number of studies refuted this (Corder et al., 1993; Haines et al., 1996; Itabashi et al., 1998; Lamb et al., 1998; Murphy et al., 1997) whilst others were supportive (Ezquerra et al., 1998; Talbot et al., 1996; Thome et al., 1995; Yoshiiwa et al., 1997). Conflictingly, the Thr (A) allele of this SNP has also been implicated in EOAD (Licastro et al., 2000) reducing the amount of mature glycosylated ACT secreted by transfected rat glioma cells (Nilsson et al., 2001b). The Thr allele is in strong linkage disequilibrium (D’ 0.97) with the T-allele of the previously reported ACT promoter SNP (Morgan et al., 2001) (rs1884082, here termed ACT 4), which is associated with a 22% mean higher serum ACT concentration, increased functional activity (∼30%) in vitro and a more rapid cognitive decline (Licastro et al., 2005). To try and resolve this apparent conflict regarding ACT SNPs and AD, and to verify earlier observations implicating the ACT gene in EOAD and cognitive decline, we have used a high density SNP mapping approach combined with haplotype analysis in a large case–control association study (N = 1087). Since AD can often present with variable phenotypes, even a large sample size such as this may not achieve adequate statistical power for a case–control haplotype association study. Considering the most severe cases only, those with EOAD, may improve statistical power by increasing the phenotypic homogeneity, but at the expense of a reduction in sample size. For this reason, analyses of the correlation

of ACT haplotypes with pathological and cognitive indices, as performed here, may be a more sensitive test of genetic effects.

2. Materials and methods 2.1. Patient samples Details of patient samples are shown in Fig. 1. Informed consent was obtained for all samples, which was granted approval by the local Ethics Committee. For the case–control haplotype association study, the 1087 samples were obtained from 4 UK centres; University of Nottingham Brain Bank, Oxford Project To Investigate Memory and Ageing (OPTIMA), University of Manchester Neurosciences Centre, and University of Birmingham Institute of Biomedical Research (no controls from this centre). As there was no evidence for population stratification of this gene in a large (N = 2000) study comprising seven centres within Western Europe including England and Scotland (Chappell et al., 2006), or in this study, the samples were pooled. Samples were histopathologically confirmed as definite disease (AD) (N = 420) or control (N = 419) using CERAD criteria (Mirra et al., 1993). Probable AD patients (N = 248, Manchester and Oxford) were also included since there was 100% concordance between patients diagnosed with probable AD and confirmation of the disease post-mortem (N = 34) (Nagy et al., 1998). All patients with evidence of an autosomal dominant AD trait, or where a first degree relative had been diagnosed with familial AD, were excluded. In order to determine any effect of age-at-onset, the AD samples from Manchester, Birmingham and Oxford (centres where age-at-onset was known), were subsequently divided into LOAD and EOAD. There was no evidence for an effect of age on ACT SNP allele frequency in the control group and the frequencies in young and old controls were not different, thus all controls were used as a comparator for both LOAD and EOAD. The cognitive decline study comprised 129 AD patients and 156 controls from Oxford for which cognitive scores were available. The pathology association study comprised 123 AD patients from Manchester, for which levels of A␤40, A␤42 and total A␤ as well as tau and microglial cell load and degree of astrocytic activity within frontal cortex were measured at death. 2.2. Phase 1. SNP identification Forty-four control samples were used to identify the SNPs within the ACT gene by sequencing known regulatory regions (promoter (Morgan et al., 2001) and −13 kb enhancer (Kordula et al., 1998)), all coding regions (exons 1–5) and 1 kb of 5 and 3 flanking sequence. This approach has 99% power for the detection of polymorphisms present at a frequency of ≥5%. Polymorphic sites were identified by multiple sequence alignment using ClustalW soft-

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Fig. 1. A flow-chart of the patient samples used for each phase of this study.

ware (http://searchlauncher.bcm.tmc.edu/multi-align/multialign.html) and confirmed by repeat PCR and sequencing from the same or opposite strand. Polymorphisms with a minor allele frequency (MAF) of ≥5% were then genotyped in a further 225 control samples to obtain a more accurate estimate of allelic frequency. 2.2.1. PCR and automated sequencing Genomic DNA was extracted from whole blood or brain tissue using the QIAamp DNA blood mini kit (Qiagen, Crawley, West Sussex, UK). The amplification and sequencing protocols for all ACT regions can be found in the supplementary information. 2.3. Phase 2. Case–control haplotype association study 2.3.1. Genotyping of polymorphisms All 1087 samples were genotyped at five sites using fluorescently labelled TaqMan probes (Vic or Fam) by Geneservice (Cambridge, UK); details are available in the supplementary information. Fifteen percent of the samples assayed were of known genotype, determined by sequencing, which were unknown to Geneservice, but known at source and 10% were genotyped in duplicate as a quality assurance measure. The data were only accepted when there was 100% concordance between duplicate samples. 2.3.2. Haplotype analyses The genotyped SNPs were used to estimate the haplotype frequencies in controls and AD patients using the haplo.em function of haplo.stats v1.2.2 (http://mayoresearch.mayo.

edu/mayo/research/biostat/schaid.cfm). This method utilises a maximum likelihood analysis approach, which we have previously used to describe the variability of the alpha-1antitrypsin gene (Chappell et al., 2004). Samples that failed at >50% of the sites were not included in the analyses. The haplo.stats software predicts missing genotypes with a high probability of inferring the correct genotype. In an ‘in-house’ simulation, with and without this estimation, no significant difference in haplotype frequency was observed, thus validating the approach. For each of the SNPs the failure rates were as follows; ACT 1: 0.7%, ACT 4: 4.1%, ACT 5: 11.5%, ACT 6: 9.8% and ACT 7: 1.7%. χ2 -Tests of individual SNPs with disease status were performed using Statistical Package for Social Sciences (SPSS) v12.0.1. This was followed by a case–control haplotype association study using haplo.stats for all SNP combinations. We used the haplo.score function of haplo.stats to calculate global and haplotype-specific score statistics (Schaid et al., 2002) (and corresponding P-values). The global score statistic tests for an overall association of haplotypes (occurring at a frequency of >5%) comprising information from all SNPs in any particular combination with disease status. The haplotype-specific score statistic compares the frequency of each individual haplotype compared to the most frequent haplotype. Rather than correcting for multiple testing, simulated P-values were computed for the global and haplotype-specific scores by repeatedly permuting the genotypes (10,000 iterations) among the subjects. The simulated P-value is calculated as the number of times the simulated score statistic exceeds the observed, divided by the total number of simulations.

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In order to validate the findings, we replicated the haplotype association study using a second software package, UNPHASED, which uses the same approach to generate haplotypes (Dudbridge, 2003). 2.3.3. Cognitive decline analysis Cognitive scores were obtained using the CAMCOG score system (Roth et al., 1988). In a previous study using samples from the same OPTIMA collection, the CAMCOG score data could be fitted using a non-linear mixed effects model (Martins et al., 2005). Consequently, analysis of the CAMCOG data in this study was performed using the same methods. Briefly, we used a three-parameter logistic (S-shaped) function: CAMCOG = asymptote/[1 + exp([age − xmid]/scale)]. The asymptotic score was set at a CAMCOG score of 95, in common with the previous work. The xmid parameter is the age at which patients reach 50% of the asymptotic score (CAMCOG = 47.5) and scale is the time taken to fall from three-fourths to half the asymptotic score. We first modelled the interactive effects of age with APOE allelic status (fixed effect) upon the xmid and scale parameters. A random effect was also included for xmid, recognising that different patients will obtain a score of 50% of the asymptotic value at different ages. We then modelled ACT single SNP genotypes using a similar model. The model was implemented using the non-linear mixedeffects function ‘nlme’ of the nlme library v3.1-77 (Pinheiro, 2000).

astrocytosis. Of the quantitative variables, A␤40, tau and microglia did not follow a gaussian distribution (assumed by haplo.score and Pearson correlations). This was adjusted for by computing the square root of these values and the transformed variables were used in all subsequent analyses. Pearson correlations were used to test association between individual SNPs and each pathological feature using SPSS. Quantitative trait analysis within the haplo.score function of haplo stats was used to determine any association between these pathological features and all SNP combinations. Confounding variables were age-at-death (calculated as age-at-onset + duration of illness), duration of illness, APOE ␧4 status and gender and the score statistics were adjusted using the x.ma function of haplo.score. For haplotypes showing a significant association with a particular trait, the haplo.glm function of haplo stats was used to perform regression in a general linear model. This provides P-values for all haplotype frequencies compared to the baseline haplotype. The effects of haplotypes can be modelled as additive (homozygotes for a particular haplotype have a larger effect than heterozygotes), dominant (heterozygotes and homozygotes have equivalent effects), or recessive (homozygotes have an alternative effect on the trait). All P-values were adjusted for all confounding variables.

3. Results 3.1. Phase 1. Identification of SNPs

2.3.4. Pathology association analyses The percentage area of frontal cortex tissue occupied by amyloid (total A␤, A␤40 and A␤42), tau and microglial cells was determined by computer image analysis as described elsewhere (Iwatsubo et al., 1994; Thaker et al., 2003; Zhang et al., 2004). The degree of astrocytosis within frontal cortex was rated in GFAP immunostained sections on a scale from 0 to 2, where 0 = absent/mild, 1 = moderate, 2 = severe

In addition to the ACT signal sequence polymorphism (ACT 5), a total of twelve other SNPs (ACT 1–4 and 6–13) were identified (Fig. 2). The adjacent sequences for these SNPs are available in the supplementary information. Eight of these polymorphisms had been described previously (ACT 2 and 7 (HapMap, 2005), 4 (Morgan et al., 2001), 5 and 6 (Poller et al., 1993), 8–10 (Wang et al., 2002)) and five

Fig. 2. Schematic representation of the ACT gene showing (A) location of exons and (B) locations of SNPs identified during this study. The previously identified functional promoter SNP is indicated by a star (*), and the signal sequence polymorphism by a hash (#). The scale bar represents the number of base pairs from the start of chromosome 14.

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Table 1 Frequencies (%) of the five common haplotypes of the ACT gene in 419 controls from Nottingham, Oxford and Manchester Haplotype

Nottingham controls

ACT 1

ACT 4

ACT 5

ACT 6

ACT 7

G G G G T

G G T T G

G G A A G

A A A A G

C G C G G

Oxford controls

Manchester controls

P-value

23 (10.3%) 43 (19.2%) 95 (42.4%) 19 (8.5%) 24 (10.7%)

57 (13.8%) 102 (24.7%) 158 (38.2%) 34 (8.2%) 35 (8.4%)

23 (11.4%) 53 (26.4%) 79 (39.5%) 13 (6.7%) 21 (10.5%)

0.43 0.24 0.50 0.56 0.54

20 (8.9%) 224

28 (6.7%) 414

11 (5.5%) 200

0.66

All other haplotypes Total chromosomes

Global P-value

0.69

Table 2 Single SNP analyses, frequency of minor allele (less frequent allele in controls), performed for AD (N = 668), LOAD (N = 251) and EOAD (N = 234) vs. all controls (N = 419) SNP

Allele

Controls

AD

P-value

LOAD

P-value

EOAD

P-value

ACT 1 ACT 4 ACT 5 ACT 6 ACT 7

T G G G G

84 (10.0%) 398 (49.1%) 381 (49.0%) 88 (11.0%) 362 (43.7%)

141 (10.7%) 624 (49.0%) 570 (49.7%) 124 (10.7%) 518 (39.5%)

0.70 0.98 0.78 0.90 0.06

53 (10.6%) 221 (46.2%) 221 (48.3%) 50 (10.3%) 189 (38.9%)

0.80 0.34 0.85 0.77 0.10

52 (11.3%) 228 (52.5%) 171 (53.1%) 43 (12.4%) 184 (39.7%)

0.56 0.28 0.24 0.54 0.17

were novel (ACT 1,3, 11–13). Of these 13 SNPs, seven had a MAF of >5% during the mapping of 88 alleles (data available in supplementary information). TaqMan assays were designed for six SNPs (ACT 1, 4, 5, 6, 7 and 8); one site (ACT 2) was excluded because a successful TaqMan assay could not be designed. Genotyping was performed in a total of 225 individuals, and this information was used to reestimate the MAFs. The MAF of ACT 8 decreased from 6% to 4% resulting in its exclusion. The remaining 5 SNPs, of which ACT 4 and ACT 5 were in 88% linkage disequilibrium (LD), were used for haplotype analyses in the case–control study of 1087 samples. All polymorphisms were in Hardy–Weinberg equilibrium in controls, LOAD and EOAD patients.

3.2. Case–control haplotype association analyses Using these five SNPs, five common haplotypes were identified, which accounted for over 93% ACT variation in controls (Table 1). A χ2 -test of the distribution of these haplotypes between the three control sample collections found no significant difference in their frequencies (P = 0.69); hence they were pooled. Table 2 shows the P-values for the χ2 -tests of each individual SNP versus disease status. The lowest P-value for a single SNP association was for ACT 7 (P = 0.06 in total dataset, P = 0.10 in LOAD and P = 0.17 in EOAD). The global P-values obtained for each multiple SNP combination are shown in Fig. 3. The lowest global P-value was obtained with ACT 1 and ACT 7 in the

Fig. 3. Global P-values for case–control association with all SNP combinations in the total (solid line), LOAD (dashed line) and EOAD (dotted line) datasets. Y-axis is shown on a log scale, intercept crosses y-axis at P = 0.05.

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Fig. 4. Non-linear model of cognitive decline for 129 AD patients. (a)–(e) interactive effects of individual ACT genotype on cognitive decline; (f) effect of age on cognitive decline (old refers to patients >73.2 years and young to those ≤73.2 years).

total (global P = 0.08, global simulated P = 0.06 (computed over 10,000 iterations), maximum score statistic = 0.06) and LOAD datasets (global P = 0.07, global simulated P = 0.06, maximum score statistic = 0.13). In EOAD, the lowest global P-value was for ACT 4, 5 and 7 (global P = 0.05, global simulated P = 0.05, maximum score statistic P = 0.02). These results were consistent with those obtained using the COCAPHASE function of UNPHASED (data not shown). 3.2.1. Effects of ACT genotypes and APOE allelic status on cognitive decline in AD The effects of APOE allelic status on cognitive decline have been previously described using a larger dataset (N = 218) of the OPTIMA collection (Martins et al., 2005). We performed the same analysis on 129 OPTIMA patients and found the same trends (␧4 allele predicted earlier cognitive decline and ␧2 predicted slower decline), although the findings were not significant in this smaller subset (data not shown). We then investigated the individual ACT genotypes using a similar non-linear model (Fig. 4a–e). The only SNP to show an effect of genotype on cognitive decline was for ACT 7; in comparison with the CC homozygotes, the curve for GG homozygotes showed a significant shift to the left, thus predicting earlier cognitive decline (P = 0.04), whereas the curve for the GC heterozygotes showed no significant shift (P = 0.69; Fig. 4e). Both the GC (scale = 1.65 years) and CC (scale = 1.4 years) genotypes predicted more rapid decline than the GG genotype (scale = 2.03 years) as evidenced by the steeper slopes (P = 0.009 and P < 0.0001, respectively).

The average predicted cognitive decline (xmid parameter) was calculated as 11.6 points per year for GG homozygotes compared with 16.8 points per year for CC homozygotes. We next modelled the effects of age on cognitive decline (Fig. 4f). The 920 measurements in our dataset covering our patients at different ages were divided into two subsets about the mean age at testing (73.2 years) and the cognitive scores for the two subsets were fit to the model separately. There was no significant difference in rate of cognitive decline between the young (age at testing ≤73.2 years, 443 measurements, scale = 1.65 years) and old (age at testing >73.2 years, 477 measurements, scale = 1.54 years) individuals. Therefore, the difference in rate of cognitive decline for ACT 7 genotypes cannot be accounted for by the effect of age. 3.2.2. Analysis of ACT haplotypes with AD pathology The P-values for correlation between individual SNPs and the six pathological parameters are shown in Table 3A. ACT 6 and ACT 7 were both correlated with astrocytosis (P = 0.007 and 0.004, respectively). Haplotype score tests were performed for all SNP combinations and each pathological feature. Global P-values for all 2-SNP combinations are shown in Table 3B. Although no SNP combination showed association with A␤ (total, A␤40 or A␤42), tau load, or proportion of microglial cells a strong association was observed between the SNP combination ACT 5 and 7 and astrocytosis (global P = 0.003, global simulated P = 0.003, maximum score statistic = 0.002). No associations were observed between any 3-, 4- or 5-SNP combination

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Table 3 (A) P-values for single SNP Pearson correlations and pathological features in AD patients (N = 123); (B) Global P-values from haplotype score tests for all 2-SNP combinations vs. pathological features in AD patients (N = 123); (C) Haplotype frequencies of the ACT 5 and 7 combination associated with the degree of astrocytosis SNP

Total A-beta

A-beta 40

A-beta 42

Tau

Microglia

Astrocytosis

(A) P-values for SNP 1 0.26 4 0.44 5 0.85 6 0.35 7 0.10

0.21 0.45 0.32 0.35 0.29

0.49 0.51 0.57 0.51 0.14

0.33 0.65 0.97 0.40 0.54

0.44 0.21 0.09 0.29 0.86

0.03 0.42 0.77 0.007 0.004

(B) Global P-values 1, 4 1, 5 1, 6 1, 7 4, 5 4, 6 4, 7 5, 6 5, 7 6, 7

0.60 0.61 0.43 0.13 0.84 0.51 0.08 0.72 0.10 0.15

0.11 0.36 0.27 0.24 0.14 0.08 0.10 0.25 0.63 0.32

0.66 0.40 0.58 0.96 0.93 0.97 0.92 0.44 0.89 0.97

0.28 0.21 0.80 0.51 0.46 0.29 0.56 0.27 0.37 0.40

0.06 0.23 0.04 0.007 0.40 0.03 0.03 0.04 0.003 0.004

Haplotype ACT 5

0.07 0.34 0.16 0.07 0.14 0.08 0.07 0.32 0.53 0.15 Haplotype score

ACT 7

Haplotype frequency (%)

Haplotype-specific P-value

Absent/Mild astrocytosis

Moderate astrocytosis

Severe astrocytosis

(C) Haplotype frequencies G G −2.2 A G −1.1 A C 0.2 G C 3.4

36.3 8.4 45.6 9.7

30.5 4.0 52.9 12.6

17.1 6.5 44.9 31.5

Number of patients

38

29

36

0.03 0.28 0.87 0.0006

P-values in (B) and (C) are corrected for confounding variables. All P-values <0.01 are shown in bold.

and any pathological feature (data available in supplementary information). Table 3C shows the haplotype frequencies for the ACT 5 and 7 combination in individuals grouped by astrocytosis severity. The haplotype showing the strongest association was ACT 5 G/ACT 7 C (global P = 0.0006 and global simulated P = 0.0004) and was more frequent in patients with severe astrocytosis (31.5%) compared with moderate (12.6%) and absent/mild (9.7%) astrocytosis. This 2-SNP combination was fitted to a dominant effect general linear model with astrocytosis. The P-values for the frequency of all haplotypes compared to ACT 5 A/ACT 7 C in the two groups of astrocytosis severity were as follows; ACT 5 A/ACT 7 G (P = 0.68), ACT 5 G/ACT 7 G (P = 0.42) and ACT 5 G/ACT 7 C (P = 0.003). When fitted as an additive model the association was not as strong (ACT 5 G/ ACT 7 C P = 0.01). There was no evidence for an APOE ␧4 effect on the degree of astrocytosis (OR for possession of at least one ␧4 allele = 1.17 (95% CI .8-1.8 P = 0.48)) or age-at-death and degree of astrocytosis. Analysis of this 2-SNP haplotype and the quantitative pathological traits (all except astrocytosis) was replicated using the QTPHASE function of UNPHASED (data not shown). As with haplo.score, no association was seen with this haplotype and any pathological feature. Since UNPHASED cannot perform analysis on ordinal traits such

as degree of astrocytosis we were unable to accurately replicate this analysis using UNPHASED.

4. Discussion These findings implicate an intronic SNP of the ACT gene (rs8004988, ACT 7) as a modifier of AD clinical and pathological indices. Analysis of cognitive decline in AD patients revealed that possession of one or two copies of the ACT 7 C allele predicted a faster rate of cognitive decline but at a later age than GG homozygotes. Interestingly, APOE allelic status showed no association with cognitive decline in these same patients, although a larger data-set from the same cohort did show an effect of APOE genotype (Martins et al., 2005). Since cognitive decline is directly correlated with neuronal death, the association of the ACT 7 C allele with a later age of onset for cognitive decline possibly indicates a potential protective role of this SNP, albeit that the rate of eventual decline is more rapid. In terms of AD pathology, possession of the ACT 7 C allele was associated with more severe astrocytosis at death in a different subset of our AD patients. Astrocytosis can be used as a direct measure of the brain’s response to A␤ deposition and neuronal death. It is interesting to note that the degree of astro-

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cytosis did not correlate with age-at-death or duration of illness and therefore did not reflect the severity of the disease in this study. Furthermore, the mean age-at-death of AD patients in our astrocytosis study was 72.7 (±10.1 years) and according to our non-linear model, patients of this age possessing at least one copy of the C allele will still be in the early stages of cognitive decline. It is possible that the higher degree of astrocytosis reflects a protective mechanism in some patients. Interestingly, the association with severe astrocytosis was strongest in individuals possessing a G allele at the ACT signal sequence polymorphism, ACT 5 (rs4934), and the C allele at ACT 7. Pertinently, a potential function of this 2SNP haplotype can be inferred, since the G-allele at ACT 5 correlates with an increased secretion of mature glycosylated ACT (Nilsson et al., 2001b); it is possible that possession of the ACT 5 G/ACT 7 C haplotype could result in an increased secretion of mature glycosylated ACT. The ACT 7 SNP has no known function to-date. Evidence from animal models has shown that elevated ACT in the brain may play a detrimental role in AD pathophysiology. This could be one explanation why the cognitive decline in patients possessing one or two copies of the ACT 7 C allele was more rapid than GG homozygotes; increased astrocyte secretion of ACT in the brain of AD patients may eventually lead to a more aggressive disease. An alternative explanation could be that one or both of these ACT SNPs are in LD with disease-associated polymorphisms in other genes nearby, rather than the ACT gene, and that these SNPs are simply acting as ‘proxies’ for this association. In many previous studies, the potential association between ACT SNPs and AD has centred on single SNP analyses. However, looking at single SNPs, additive and/or opposing associations with other SNPs are being ignored and may in part explain some of the conflicting data reported in the past. Such additive or opposing actions may be accounted for by haplotype analysis. This study, based on 1087 samples, represents the most comprehensive haplotype analysis of ACT in AD to date. This approach has 80% power to detect a genotype relative risk of 1.5–2.1 associated with alleles or haplotypes with a frequency ≥5% at a level of statistical significance of ≤0.01. Since we do not find any single SNP or haplotype associations with disease in the whole AD dataset, or separately in EOAD and LOAD, it could be concluded that ACT gene variation has little or no association with AD. It is also possible that complex genetic diseases, such as AD, are characterised by multiple gene haplotypes each having a subtle, but significant effect, of a magnitude similar to that observed in this study. However, collectively, they may demonstrate synergy. It is important to validate our findings in other large cohorts using similar approaches.

Disclosure statement None of the authors report any conflict of interest and all appropriate approval and procedures were in place for the use of human DNA samples.

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