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. Author manuscript; available in PMC: 2018 Aug 1.
Published in final edited form as: Neurobiol Aging. 2017 May 3;56:150–158. doi: 10.1016/j.neurobiolaging.2017.04.018

Candidate gene analysis for Alzheimer’s disease in adults with Down syndrome

Joseph H Lee a,b,c,*, Annie J Lee a, Lam-Ha Dang a,c, Deborah Pang d, Sergey Kisselev e, Sharon J Krinsky-McHale d, Warren B Zigman d, José A Luchsinger f, Wayne Silverman g, Benjamin Tycko e, Lorraine N Clark b,e, Nicole Schupf a,b,c
PMCID: PMC5603247  NIHMSID: NIHMS873653  PMID: 28554490

Abstract

Individuals with Down syndrome (DS) overexpress many genes on chromosome 21 due to trisomy and have high risk of dementia due to the Alzheimer’s disease (AD) neuropathology. However, there is a wide range of phenotypic differences (e.g., age at onset of AD, amyloid β levels) among adults with DS, suggesting the importance of factors that modify risk within this particularly vulnerable population, including genotypic variability. Previous genetic studies in the general population have identified multiple genes that are associated with AD. This study examined the contribution of polymorphisms in these genes to the risk of AD in adults with DS ranging from 30 to 78 years of age at study entry (N = 320). We used multiple logistic regressions to estimate the likelihood of AD using single-nucleotide polymorphisms (SNPs) in candidate genes, adjusting for age, sex, race/ethnicity, level of intellectual disability and APOE genotype. This study identified multiple SNPs in APP and CST3 that were associated with AD at a gene-wise level empirical p-value of 0.05, with odds ratios in the range of 1.5–2. SNPs in MARK4 were marginally associated with AD. CST3 and MARK4 may contribute to our understanding of potential mechanisms where CST3 may contribute to the amyloid pathway by inhibiting plaque formation, and MARK4 may contribute to the regulation of the transition between stable and dynamic microtubules.

Keywords: Down syndrome, Alzheimer disease, Dementia, Gene mapping, Candidate genes, APP, CST3, MARK4

1. Introduction

Adults with Down syndrome (DS) are at high risk of developing Alzheimer’s disease (AD) (Schupf, 2002; Zigman, 2013; Zigman and Lott, 2007), and many, but not all, will develop dementia by the end of their seventh decade of life (Lai and Williams, 1989; Zigman, 2013). The neuropathological manifestations of AD in DS have been attributed, at least in part, to triplication and overexpression of the gene for amyloid precursor protein (APP) located on chromosome 21 (Rumble et al., 1989), leading to an increased substrate for production of amyloid β (Aβ) peptides (Mehta et al., 1998; Schupf et al., 2001; Tokuda et al., 1997). Of the two major species of Aβ peptides—Aβ40 and Aβ42—generated by sequential proteolytic cleavage by β and γ secretases of the APP (Selkoe, 2001), lower levels of Aβ42 or the Aβ42/Aβ40 ratio in cerebrospinal fluid along with high levels of tau are associated with high risk of AD (Blennow and Hampel, 2003; Jack et al., 2013). However, even among individuals with full trisomy 21, age at onset of AD varies widely, and levels of Aβ40 and Aβ42 and Aβ42/Aβ40 ratio also vary widely even among individuals who are of comparable age (Coppus et al., 2008; Head et al., 2012; Holland et al., 2000; Lai and Williams, 1989; Schupf, 2002; Zigman et al., 2007).

Genetic as well as environmental factors may contribute to the observed variation in age at onset. Multiple genome-wide association studies (GWAS) and meta-analyses have identified at least 20 genes that are significantly associated with AD in the general population (Bertram et al., 2007; Hollingworth et al., 2011; Lambert et al., 2009, 2013; Lee et al., 2011; Naj et al., 2011; Wijsman et al., 2011). To date, however, only 1 genome-wide study of age at the onset of AD in DS based 67 autopsy samples has been reported (Jones et al., 2013). Several studies have examined the relation between single nucleotide polymorphisms (SNPs) and dementia in adults with DS using a candidate gene approach (Jones et al., 2013; Lee et al., 2007b; Liu et al., 2008; Margallo-Lana et al., 2004; Mok et al., 2014; Patel et al., 2011). In addition, mouse models of DS have identified genes that are differentially expressed between AD and controls (Chrast et al., 2000; Cook et al., 2005; Lyle et al., 2004; Prandini et al., 2007). Compared with individuals without DS, triplication and overexpression of genes that are located on chromosome 21, including APP and others, may contribute to AD risk or more general atypical aging in adults with DS. Some of these genes have also been implicated in AD pathogenesis. These include beta amyloid converting enzyme-2 (BACE2), superoxide dismutase (SOD1), and the astrocyte-derived neurotrophic factor S100 beta (S100β). In the present study, we examined SNPs in candidate genes on chromosome 21 as well as a subset of autosomes and chromosome X to determine their contribution to variation in risk for dementia due to AD in a large longitudinal cohort of adults with DS (refer to Supplement Table 1 for a complete list of candidate genes).

2. Materials and methods

2.1. Study participants

We examined 93 individuals with dementia and 227 without dementia for a total of 320 community-residing adults with confirmed DS (Table 1). All individuals were 30 years of age and older at the time of their study enrollment (range 31–78) and resided in New York, Connecticut, New Jersey, or eastern Pennsylvania. Participants were recruited with the help of state and voluntary service provider agencies and were eligible for inclusion in the present study if: (1) a family member or correspondent provided informed consent; (2) he or she either provided consent or assent indicating willingness to participate; and (3) he or she was willing and able to provide blood samples. Recruitment, informed consent, and study procedures were approved by the Institutional Review Boards of the New York State Institute for Basic Research in Developmental Disabilities, Columbia University Medical Center, and the Johns Hopkins University School of Medicine.

Table 1.

Characteristics of the study participants

Characteristics Combined Dementia No dementia
Number of individuals 320 93 227
Mean age at baseline (SD) 49.9 (7.58) 55.4 (7.28) 47.7 (6.48)
Level of intellectual disability (n, %)
 Mild/moderate 186 (58.1) 47 (50.5) 139 (61.2)
 Severe/profound 134 (42.9) 46 (49.5) 88 (38.8)
Ethnicity (n, %)
 White 294 (91.9) 87 (93.5) 207 (91.2)
 Non-White 26 (8.1) 6 (6.5) 20 (8.8)
APOE allele frequencya
 ε2 0.077 0.065 0.082
 ε3 0.807 0.801 0.809
 ε4 0.116 0.134 0.109
Sex (n, %)
 Female 235 (73.4) 65 (69.9) 170 (74.9)
 Male 85 (26.6) 28 (30.1) 57 (25.1)
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a

Two subjects missing APOE status.

2.2. Clinical assessment

Assessments were conducted at the time of study entry and were repeated at intervals of approximately 18 months for up to 5 cycles of follow-up (mean duration of follow-up of 4.5 years; SD = 1.89). Assessments included evaluations of cognition and functional abilities, behavioral/psychiatric conditions, and an examination of medical records for information on health status and medication usage. Cognitive function was evaluated with a test battery designed for use with individuals varying widely in their initial levels of intellectual functioning, as previously described (Silverman et al., 2004). Structured interviews were conducted with caregivers to collect information on adaptive behavior and neuropsychiatric conditions. Past and current medical records were reviewed for all participants.

For diagnostic classification of dementia, recommendations of the AAMR-IASSID Working Group for the Establishment of Criteria for the Diagnosis of Dementia in Individuals with Developmental Disability were followed (Aylward et al., 1997; Burt and Aylward, 2000). After each assessment cycle, dementia classification was made based on consensus case conferences relying on empirical evidence of stability or decline in performance profiles over time (Silverman et al., 2004). Each individual was classified as: (1) no dementia, indicating with reasonable certainty that significant impairment was absent; (2) MCI-DS, indicating that there was evidence of mild cognitive or functional decline, but importantly, the observed change did not meet dementia criteria; (3) possible dementia, indicating that some signs and symptoms of dementia were present but declines over time was not entirely convincing; and (4) definite dementia, indicating with reasonable confidence that dementia was present based on substantial decline over time.

2.3. Selection of candidate genes

Candidate genes (see Supplement Table 1) were selected based on previous reports of positive associations with AD or dementia, either in adults with DS or the general population. These genes included: (1) SNPs that were found to be significant in other genetic studies of DS; (2) the top candidate genes from the ALZGENE database when the customized SNP chips were being developed for this study between 2012 and 2013; and (3) additional positional candidate genes from published genome-wide linkage and association studies. Due to the limited capacity of the Illumina’s GoldenGate platform, only a subset of candidate genes was examined. For candidate regions from genome-wide linkage or association studies where precise genes have not been identified, we used SNAP (http://www.broadinstitute.org/mpg/snap/ldsearch.php) to identify genes within the candidate regions. This process generated 6 candidates on chromosome 21 and 41 genes on other chromosomes. Candidate genes on chromosome 21 included the genes for amyloid precursor protein (APP), β amyloid converting enzyme-2 (BACE2), the DS critical region-1 (DSCR1; also known as RCAN1), runt-related transcription factor 1 (RUNX1), the astrocyte-derived neurotrophic factor S100β, and CU/Zn superoxide dismutase (SOD-1). Additional candidate genes were on chromosomes 1, 2, 6–11, 15, 17, 19, 20, and X (see Supplement Table 1 for the full list of genes). Fig. 1 provides an overview of SNP selection and SNP analysis performed in this 2-stage candidate gene study.

Fig. 1.

Fig. 1

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Flow chart for a 2-stage candidate gene study of Alzheimer’s disease.

2.4. SNP selection

We genotyped each gene with a sufficient number of SNPs to provide relatively dense coverage (r2 ~ 0.8), and selected SNPs that had a relatively high minor allele frequency (>0.15) to increase the information content of each SNP, thereby enhancing statistical power. From these SNPs, we used the TAGGER program (de Bakker, 2009) to identify tag SNPs using the Caucasian samples from the HapMap data set (http://hapmap.ncbi.nlm.nih.gov). To ensure that coverage of the gene was relatively complete, we used SNAP (http://www.broadinstitute.org/mpg/snap/ldsearch.php) to check LD patterns across the genic region. For chromosome 21, 231 SNPS from the 6 genes had a median inter-marker distance of 2185 base pairs. For chromosomes other than 21, we identified 1114 SNPs from 41 genes with a median inter-marker distance of 2552 base pairs. In this article, we present top strands from the Illumina-customized platform.

2.5. SNP genotyping: customized SNP array in trisomic samples

Genomic DNA was genotyped using an Illumina GoldenGate custom array. Clustering and genotype calling of Chromosome 21 SNPs and non-Chr21 SNPs was performed using GenomeStudio genotyping module v1.8 which supports polyploidy loci. For SNPs on chromosome 21, the custom cluster option in GenomeStudio genotyping module v1.8 was used to specify 4 clusters and the custom GType was used to display genotype calls for polyploidy loci (AAA, AAB, ABB, or BBB). All genotype calls were then inspected manually by viewing SNP graph cluster plots (Schupf et al., 2015).

2.6. Quality control (QC) assessment

We included SNPs in the allelic association analysis when the Gencall value, a quality score, exceeded 0.25. This quality score was determined from allele cluster definitions for each SNP as determined by the Illumina GenomeStudio Genotyping Module version 3.0. For chromosome 21, we studied SNPs that produced call rate ≥90% (average call rate 98%) and dropped SNPs with call rate <90% (n = 23) or produced no genotypes (n = 9). For chromosomes other than 21, we dropped SNPs with call rates <98% (n = 11; average call rate: 99%). In addition, we randomly selected 15 samples and genotyped in duplicate. The concordance rates for genotyped SNPs in these samples ranged from 91.8% to 100% for chromosome 21 SNPs and from 95.2 to 99.6 for nonchromosome 21 SNPs. Further QC assessments using PLINK (Purcell et al., 2007) excluded 15 additional SNPs with the following characteristics: missing genotyping rate >5%; minimum allele frequency <1%; Hardy-Weinberg Equilibrium test at a p-value <1E-6. Eventually, we analyzed 231 SNPs on chromosome 21 and 1099 SNPs from chromosomes other than 21.

2.7. Population stratification and covariates

We applied the multidimensional scaling method as implemented in PLINK to adjust for population stratification. Using all available SNPs that survived the QC process, genetic similarity across individuals was estimated by computing identity by state. In addition to our own samples, we included Whites (n = 165), Africans (n = 165), and Asians (170) from the Hapmap database (www.hapmap.org) to ensure proper classification of ethnicity. This analysis generated 3 ethno-racial clusters. These clusters, akin to principal components, were included in the multivariate model as covariates. For multivariable models, we adjusted for the following potential confounders: age, sex, level of intellectual disability (mild to moderate [IQ 35–70] vs. severe to profound [IQ <35]), ethno-racial clusters from population stratification analysis, and the presence or absence of an APOE ε4 allele.

2.8. Statistical analyses

To minimize type-1 error rate from multiple testing, we conducted a 2-stage analysis (Fig. 1). In stage 1, we selected tag SNPs to achieve an r2 of 0.3 or below (variance inflation factor of 1.43) using the PLINK algorithm. We then applied a multivariable logistic regression model to examine the association between an SNP and AD, adjusting for confounders. An additive dosage model was used where we compared the risk associated with having none versus 1 versus 2 copies of the SNP. SNP-wise empirical p-value was estimated based on 10,000 replicates. In stage 2, to fine map the genes that had at least one SNP with SNP-wise empirical p-value <0.05, we then used all genotyped SNPs within the gene for further evaluation using the same logistic regression model. To take into account multiple SNP testing within any given gene, we applied the false discovery rate approach proposed by Benjamini and Yekutieli (Benjamini and Yekutieli, 2001) and computed gene-wise empirical p-value. The rationale for the false discovery rate approach was that: (1) the main goal of the present study is to confirm candidate genes for AD or dementia in adults with DS; thus, multiple testing correction at the level of each gene is reasonable and (2) even though this is the largest fine mapping study in DS to date, sample size is still relatively small. For the two most promising non-chromosome 21 genes (the loci that had more than one SNP with marginal significance [p < 0.05]), we performed sliding window haplotype analysis using 3 SNPs at a time to identify haplotypes that may contain functional variants within a gene. R statistical package (http://www.r-project.org/) was used for analysis.

3. Results

3.1. Demographic and clinical characteristics

The average age of the 320 study participants at the time of baseline was 49.9 year old (SD = 7.6), and the mean age of adults at the baseline for adults with dementia was 7 years older than those without (Table 1). The majority of the individuals had mild to moderate intellectual disability. Ethnicity for over 90% of the study participants was reported in the medical charts as non-Hispanic White, and the allele frequency of the APOE ε4 (11.6%) was comparable to other populations of Caucasian ancestry (Table 1).

3.2. Stage 1 screening analysis

In stage 1, we screened genes on chromosome 21 using a set of tag SNPs. Because these tag SNPs represent a group of SNPs in the chromosomal region with high linkage disequilibrium, they reduced the burden of multiple testing. Only APP and RUNX1 had SNPs that had empirical p-values <0.05 (Table 2). rs17588612 in APP had an SNP-wise empirical p-value of 0.0126, and rs4816501 and rs13046934 in RUNX1 had SNP-wise empirical p-values of 0.0308 and 0.0081, respectively. In stage 1, screening of genes on chromosomes other than 21, the following genes had one or more SNPs with a significant association at empirical p < 0.05: MSRA (empirical p = 0.0230), DAPK1 (empirical p = 0.0474), PITRM1 (empirical p = 0.0498), SORCS1 (0.0230 < empirical p < 0.0469), SORL1 (empirical p = 0.0387), TNK1 (empirical p = 0.0477), LDLR (empirical p = 0.0442), ZNF224 (empirical p = 0.0275), MARK4 (empirical p < 0.0298), and CST3 (empirical p = 0.0094) (Table 3).

Table 2.

Chromosome 21 SNPs that are associated with Alzheimer’s disease at empirical p < 0.05

Chr Gene SNPa BP (Hg19) Risk allele MAF OR OR_L95 OR_U95 Empirical p (pointwise)b BH adjusted empirical p (genewise)
21 APP rs3991 27,428,256 T 0.236 1.893 1.384 2.618 0.0001 0.0031
21 APP rs2830031 27,429,317 C 0.314 1.702 1.244 2.356 0.0010 0.0122
21 APP rs2830033 27,430,925 A 0.314 1.763 1.280 2.458 0.0004 0.0081
21 APP rs2830036 27,435,525 A 0.182 0.558 0.365 0.829 0.0050 0.0235
21 APP rs1041420 27,443,650 A 0.221 0.554 0.371 0.804 0.0028 0.0203
21 APP rs2830048 27,459,674 C 0.339 0.673 0.490 0.910 0.0135 0.0484
21 APP rs2070654 27,462,727 T 0.298 2.044 1.439 2.948 0.0000 0.0000
21 APP rs2830050 27,464,270 T 0.252 1.590 1.157 2.198 0.0046 0.0234
21 APP rs2830054 27,476,104 G 0.407 1.564 1.156 2.137 0.0030 0.0203
21 APP rs2830066 27,494,202 C 0.496 1.488 1.122 1.993 0.0063 0.0275
21 APP rs2830076 27,502,468 A 0.357 1.541 1.143 2.093 0.0044 0.0234
21 APP rs2830086 27,512,956 T 0.325 1.612 1.194 2.193 0.0018 0.0183
21 APP rs2830088 27,514,740 C 0.487 1.433 1.085 1.909 0.0118 0.0480
21 APPc rs17588612 27,517,203 C 0.082 1.766 1.124 2.794 0.0126 0.0480
21 APP rs17588612 27,517,203 C 0.082 1.766 1.124 2.794 0.0126 0.0480
21 APP rs13049230 27,521,417 G 0.318 1.550 1.149 2.104 0.0039 0.0234
21 APP rs2830099 27,530,610 C 0.319 1.586 1.174 2.159 0.0023 0.0200
21 APP rs2830100 27,533,329 T 0.334 1.658 1.228 2.261 0.0010 0.0122
21 RUNX1 rs4816501 36,294,539 A 0.251 1.369 1.028 1.825 0.0308 0.4980
21 RUNX1 rs13046934 36,371,207 T 0.215 1.592 1.134 2.245 0.0081 0.4980
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Key: MAF, minor allele frequency; OR, odds ratio; SNPs, single nucleotide polymorphisms.

a

SNPs with gene-wise FDR-adjusted empirical p-value <0.05 are in boldface.

b

For empirical p-value, 10,000 replicates were generated.

c

Positive SNPs from stage 1 are highlighted in gray. SNPs without the gray highlight were added in stage 2 for fine mapping.

Table 3.

Nonchromosome 21 SNPs that are associated with Alzheimer’s disease at empirical p < 0.05

Chr Gene SNPa BP (Hg19) Risk allele Allele freq OR (L95-U95) Empirical p (pointwise)b BH adjusted empirical p (genewise)
8 MSRAc rs17692624 10,070,368 C 0.233 0.589 0.374 0.927 0.0230 0.8470
9 DAPK1 rs2058882 90,114,746 G 0.2 1.579 1.003 2.486 0.0474 0.9415
10 PITRM1 rs9423705 3,200,155 G 0.339 1.440 1.002 2.070 0.0498 0.4050
10 SORCS1 rs10748921 108,390,100 C 0.333 0.648 0.424 0.990 0.0469 0.5532
10 SORCS1 rs1538417 108,583,599 A 0.283 1.536 1.025 2.301 0.0361 0.5532
10 SORCS1 rs10787011 108,863,228 G 0.359 1.482 1.022 2.150 0.0371 0.5532
10 SORCS1 rs7897974 108,893,522 A 0.458 0.685 0.475 0.988 0.0426 0.5532
10 SORCS1 rs7099998 108,928,558 G 0.2 0.566 0.345 0.929 0.0230 0.5532
11 SORL1 rs11605969 121,430,872 A 0.153 1.683 1.032 2.747 0.0387 0.8816
17 TNK1 rs7219773 7,283,144 A 0.42 1.549 1.065 2.251 0.0257 0.0596
17 TNK1 rs12948090 7,285,104 A 0.416 1.458 1.013 2.098 0.0442 0.0596
17 TNK1 rs1554948 7,286,326 A 0.413 1.604 1.106 2.327 0.0135 0.0596
17 TNK1 rs3744549 7,293,715 G 0.251 0.655 0.429 0.998 0.0477 0.0596
19 LDLR rs2738466 11,242,765 G 0.423 1.537 1.010 2.341 0.0442 0.2063
19 ZNF224 rs4803675 44,589,716 G 0.388 1.522 1.054 2.198 0.0275 0.1925
19 MARK4 rs12976518 45,759,344 G 0.475 1.636 1.141 2.347 0.0089 0.0534
19 MARK4 rs2377324 45,768,946 G 0.282 0.551 0.355 0.854 0.0089 0.0534
19 MARK4 rs2306660 45,802,863 G 0.245 0.599 0.378 0.948 0.0298 0.0715
20 CST3 rs35610040 23,616,469 G 0.179 1.774 1.106 2.844 0.0165 0.0297
20 CST3 rs3787498 23,616,781 A 0.172 1.909 1.180 3.086 0.0085 0.0211
20 CST3 rs3827142 23,617,007 A 0.179 1.907 1.182 3.078 0.0079 0.0211
20 CST3 rs5030707 23,618,656 C 0.167 1.974 1.230 3.167 0.0049 0.0211
20 CST3 rs3827143 23,619,617 G 0.226 1.748 1.141 2.677 0.0094 0.0211
20 CST3 rs2254635 23,622,758 A 0.203 1.655 1.043 2.626 0.0316 0.0406
20 CST3 rs2405367 23,622,880 A 0.189 1.703 1.080 2.686 0.0200 0.0300
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a

SNPs with gene-wise FDR adjusted empirical p-value <0.05 are in boldface.

b

For empirical p-value, 10,000 replicates were generated.

c

Positive SNPs from stage 1 are highlighted in gray. SNPs without the gray highlight were added in stage 2 for fine mapping.

3.3. Stage 2 fine mapping analysis

For the genes that were significant in stage 1, we fine mapped the genes using all genotyped SNPs to better localize SNPs that are associated with AD (Tables 2 and 3). In the stage 2 analysis of chromosome 21, we computed a gene-wise empirical p-value to correct for multiple SNPs within the gene (Table 2). When this more rigorous correction was applied in stage 2, 18 SNPs in the APP gene, located within 100kb, had gene-wise empirical p-values that reached a threshold of p ≤ 0.05, and odds ratios (ORs) for minor allele for these SNPs ranged from 2.04 (rs2070654) to 1.49 (rs2830066). Three minor alleles for the associated SNPs, namely rs2830036, rs1041420, rs2830048, were protective (OR <1). For genes on chromosomes other than 21, we identified 5 SNPs in the CST3 gene that had gene-wise empirical p-value <0.05 (Table 3), and ORs for those SNPs ranged from 1.97 to 1.75. On the other hand, for the MARK4 gene, 3 SNPs barely missed gene-wise empirical p-value of 0.05 (rs12976518, p = 0.0534; rs2377324, p = 0.0534; rs2306660, p = 0.0715). The minor allele for rs12976518 was putative (OR = 1.64), whereas the minor alleles for rs2377324 (OR = 0.55) and rs2306660 (OR = 0.60) were protective.

3.4. Haplotype analysis

To better localize the SNP signals and to potentially guide our future sequencing efforts for the 2 candidate genes that are located on chromosomes other than 21, we performed a 3-mer sliding window haplotype analysis for the two most promising candidate genes: CST3 and MARK4 (Tables 4 and 5; linkage disequilibrium patterns for SNPs in CST3 and MARK4 are shown in Supplement Fig. 1). For CST3, we examined the region containing rs2424577 to rs2405367 and found the strongest evidence in a contiguous 3-mer haplotype G-G-G for rs3787498-rs3827142-rs5030707 (p = 0.00281) to 3-mer haplotype G-A-A for rs2424582-rs2254635-rs2405367 (p = 0.00884). All associated haplotypes in this region were risk haplotypes. For MARK4, the strongest evidence was observed for haplotype A-A-G for rs12976518-rs10445572-rs2377324 (p = 0.00796) and haplotype A-G-A for rs10445572-rs2377324-rs2240672 (p = 0.00924). For APP on chromosome 21, we are currently working to develop an algorithm to generate robust haplotypes for trisomy.

Table 4.

Haplotypes analysis for CST3 and Alzheimer’s disease: 3-SNP window

SNP seta Omnibus Haplotype
1 2 3 STAT p Haplotype Frequency OR STAT p
rs2424577 rs35610040 rs3787498 7.170 0.02780 G G A 0.172 1.960 7.500 0.00617
G A G 0.196 0.758 1.290 0.25600
A A G 0.625 0.815 1.200 0.27300
rs35610040 rs3787498 rs3827142 6.330 0.01190 G A A 0.172 1.960 7.500 0.00617
A G G 0.818 0.544 6.330 0.01190
rs3787498 rs3827142 rs5030707 9.100 0.02800 A A C 0.154 1.910 6.490 0.01080
G G C 0.012 2.820 1.540 0.21500
A A G 0.018 1.640 0.622 0.43000
G G G 0.809 0.493 8.930 0.00281
rs3827142 rs5030707 rs3827143 8.960 0.06220 A C G 0.152 2.010 7.590 0.00587
A G G 0.020 1.840 0.815 0.36700
G G G 0.052 0.928 0.040 0.84100
G C A 0.010 1.820 0.408 0.52300
G G A 0.757 0.576 6.640 0.00997
rs5030707 rs3827143 rs2424582 10.100 0.03820 C G G 0.152 2.010 7.590 0.00587
G G G 0.020 1.020 0.001 0.97400
G A G 0.019 0.212 1.950 0.16200
G G A 0.052 1.090 0.058 0.80900
G A A 0.742 0.613 5.260 0.02180
rs3827143 rs2424582 rs2254635 9.000 0.06120 G G A 0.170 2.120 9.070 0.00260
A G A 0.019 0.207 1.990 0.15900
A A A 0.014 0.714 0.130 0.71900
G A C 0.053 1.130 0.121 0.72800
A A C 0.737 0.661 3.890 0.04860
rs2424582 rs2254635 rs2405367 4.820 0.18600 G A A 0.175 1.900 6.850 0.00884
A A A 0.014 0.874 0.024 0.87600
G A G 0.014 0.717 0.141 0.70700
A C G 0.790 0.627 3.970 0.04640
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Key: SNP, single nucleotide polymorphism.

a

SNP(s) most strongly associated with AD are bolded and italicized, and the most significant haplotype is highlighted in gray.

Table 5.

Haplotypes analysis for MARK4 and Alzheimer’s disease: 3-SNP window

SNP seta Omnibus Haplotype
1 2 3 STAT p Haplotype Frequency OR STAT p
rs12976518 rs10445572 rs2377324 10.400 0.01530 A A G 0.278 0.548 7.040 0.00796
G G A 0.386 1.610 6.520 0.01070
G A A 0.086 1.510 1.660 0.19800
A A A 0.241 0.816 0.860 0.35400
rs10445572 rs2377324 rs2240672 9.780 0.02050 A G A 0.274 0.552 6.780 0.00924
G A A 0.048 1.120 0.070 0.79200
G A G 0.344 1.610 6.530 0.01060
A A G 0.324 0.992 0.002 0.96800
rs2377324 rs2240672 rs345409 8.370 0.03900 G A G 0.268 0.559 6.390 0.01150
A A G 0.050 1.070 0.029 0.86500
A G G 0.138 1.420 2.010 0.15600
A G A 0.530 1.300 2.100 0.14700
rs2240672 rs345409 rs11883302 7.410 0.19200 A G G 0.246 0.584 4.920 0.02660
G G G 0.096 1.100 0.096 0.75600
G A G 0.019 1.720 0.637 0.42500
A G A 0.073 0.788 0.387 0.53400
G G A 0.047 1.700 1.780 0.18200
G A A 0.513 1.270 1.650 0.19900
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Key: SNP, single nucleotide polymorphism.

a

SNP(s) most strongly associated with AD are bolded and italicized, and the most significant haplotype is highlighted in gray.

3.4.1. Comparison with the general population

When we compared allelic association of the significant candidate SNPs from our study of DS against those in the general population, we observed 3 SNPs—rs2830066 (p = 0.003) and rs2830088 (p = 0.033) in the APP gene, and rs2377324 in the MARK4 gene (p = 0.023)—that had p-values <0.05 in a large GWAS study of adults without DS (Lambert et al., 2013). In addition, rs9423705 (p = 0.074) in the PITRM1 gene and rs2306660 (p = 0.096) in the MARK4 were weakly associated with AD. For this purpose, we used the first stage meta-GWAS data (Lambert et al., 2013) (n = 54,167).

4. Discussion

The present study confirmed that SNPs in APP and CST3 were significantly associated with AD risk for adults with DS, whereas those in MARK4 were suggestively associated; further, 2 SNPs in APP and 1 SNP in MARK4 were also found to be associated with AD in a large-scale GWAS of adults without DS. Our results extend previous findings of a relationship between SNPs in candidate genes located on chromosome 21 and chromosomes other than 21 and risk of AD in adults with DS (Jones et al., 2013; Lee et al., 2007a; Liu et al., 2008; Margallo-Lana et al., 2004; Mok et al., 2014; Patel et al., 2011; Wegiel et al., 2008, 2011). Our analysis revealed that multiple SNPs in APP within a 100kb region on 21q21.3 were associated with AD in adults with DS at gene-wise level. Some of the minor alleles were risk alleles, whereas others were protective alleles. Beyond APP, this study reports significant allelic association for SNPs in CST3, suggesting potential contributions through vascular factors in adults with DS; and also reports suggestive allelic association for SNPs in MARK4, suggesting interaction between excess levels of Aβ peptides and tau.

To date, several studies have examined the role of genes on AD or age at onset of AD in adults with DS. Among genes on chromosome 21, a tetratnucleotide repeat in intron 7 on APP (Jones et al., 2010), SNPs on BACE2 (Mok et al., 2014), and an SNP on RUNX1 (Patel et al., 2011) have been associated with earlier age at the onset of AD, whereas among nonchromosome 21 candidate genes, SNPs in APOE, SORL1, BACE1, ALDH18A1 (Lai et al., 1999; Lee et al., 2007b; Patel et al., 2011; Prasher et al., 2008; Schupf et al., 1998) and PIC-ALM (Jones et al., 2013) have also been associated with earlier age at onset of AD. Subsequently, Patel et al. (2014) examined the relation of 9 GWAS-derived SNPs with risk of AD in DS, including SNPs in CR1, BIN1, CD2AP, EPHA1, CLU MS4A6A/4A, PICALM, ABCA7, and CD33 but found no significant relationship to dementia in adults with DS. Below we discuss 3 genes (APP, CST3, and MARK4) that had the most promising signals for genetic association in adults with DS, plus PICALM, which was previously reported to be associated with age at the onset in the only genome-wide study in autopsy samples from individuals with DS.

4.1. APP

Even though mutations in APP are among mutations in 3 genes that are known to cause AD, along with PSEN1 and PSEN2, only a few variants have been implicated in population-based studies of late onset AD (LOAD; Hindorff et al., 2014; Welter et al., 2014). Guyant-Marechal et al. (2007) found and replicated 1 variant rs463946 located at 27,546,187 bp that are somewhat proximal to the SNP signals in the present study, whereas Margallo-Lana et al. (2004) reported that individuals with 3 tetranucleotide repeats on intron 7 of the APP gene had significantly earlier age at the onset than those who did not, independent of APOE. Subsequently, Jones et al. (2010) confirmed the finding by Margallo-Lana et al. in a larger cohort of adults with DS. In 2002, Athan et al. (2002) reported +37G/C polymorphisms in the promotor region of the APP gene were associated with increased risk of AD. Jonsson et al. (2012) reported that rs63750847, a rare Icelandic mutation with allele frequency <1%, protects against age-related cognitive losses, but this finding has not yet been confirmed in other independent studies. To evaluate the relevance of these 2 previously reported loci, we will need additional genotyping of the 2 loci; however, it is unlikely that the present study of primarily US Caucasians will have sufficient number of carriers, since 2 large publicly available data sets (i.e., ADGC and IGAP) did not observe any individual with this coding mutation.

4.2. CST3

This gene codes for the cystatin c protein and colocalizes with Aβ in vascular walls and in senile plaque cores in the brains of individuals with AD as well as in adults with DS. Even though cystatin c protein binds with Aβ, it is reported to prevent oligomerization, and formation of fibrils in vitro (Kaur and Levy, 2012). Cystatin c protein plays the role of an inhibitor by suppressing the production of cathepsins B and D, where low levels of cathepsins were associated with reduced neuronal damages. Thus, cystatic c may rescue degenerating neurons (Kaur and Levy, 2012; Kaur et al., 2010). This idea was supported by an in vivo knockout mice experiment that showed deletion of cystatin c in knockout mice resulted in an elevated cathepsin B activity, leading to greater neuronal damages (Sun et al., 2008). Some have suggested that low levels of cystatin c protein may be a risk factor for AD (Gauthier et al., 2011).

The findings from genetic studies have been inconsistent in that allelic association between candidate SNPs in CST3 and AD differs by ethnicity. A meta-analysis of SNPs in CST3 using German, US, and European cohorts showed that the minor allele in rs5030707 was associated with an increased risk for AD (OR = 1.28) (Dodel et al., 2002; Finckh et al., 2000). In a separate study, a homozygous missense varint (G allele) in CST3 was associated with both AD and ALS. Among Asian cohorts, however, the relationship between AD and SNPs in CST3 was equivocal (Chuo et al., 2007; Maruyama et al., 2001; Wang et al., 2008). Further studies are needed to better understand the differences in allelic association for SNPs in CST3 among different ethnic groups as these ethnic groups will differ in their genetic background as well as the distribution of environmental risk factors.

4.3. MARK4

Studies have reported that MARK4 may be involved in early tau phosphorylation and/or may play a critical role for the PAR1/MARK-tau axis in mediating the toxic effects of Aβ on synapses and dendritic spines (Lund et al., 2014; Yu et al., 2012). The observed association between SNPs on MARK4 and AD risk in adults with DS supports the possibility that a variant in MARK4 may influence this process. However, the potential mechanism underlying this 2-hit model is not well studied. Interestingly, the GWAS by Naj et al. (2011) observed association between AD and SNPs located in the EXOC2L3, and MARK4 region, but they dismissed the association because it no longer was observed when adjustment was made for APOE ε4. However, it is possible that this gene is a significant modifier only in the presence of elevated levels of Aβ, as in the case of APOE ε4 or in adults with DS with trisomy, consistent with the finding of the association between SNPs in MARK4 and AD for adults with DS after adjusting for APOE ε4.

4.4. PICALM

We examined PICALM, implicated in a GWAS employing autopsy samples of adults with DS (Jones et al., 2013) and in large-scale genome studies (Harold et al., 2009; Lambert et al., 2013; Naj et al., 2011), using 40 SNPs. Based on our more conservative gene-wise empirical p-values, the SNPs (rs2888903, rs7941541, rs10751134 and rs561655) that were associated with AD in the general population, and an earlier age at onset in the DS population (Jones et al., 2013) were not significantly associated with AD in the present study of adults with DS.

4.5. Adults with versus without DS

Of the genes identified from the large-scale international genome-wide association studies in the general population (Lambert et al., 2013), we examined 9, but did not observe significant association.

Our study has several limitations. First, even though this is the largest gene mapping study of AD in adults with DS to date, this study is limited by a relatively small sample size, and lacked sufficient power for SNPs with low allele frequencies and those with small effect sizes. Despite this limitation, this study identified multiple SNPs with modest risk ratios at the gene-wise level, and most likely, the positive findings may be owed to studying a high-risk group that is known for early onset of AD and high levels of Aβ peptides. Second, this study examined the relation between AD and intronic SNPs, which serve as genetic markers to better localize the chromosomal location. Thus, further sequencing of these loci is needed to identify genetic variants that contribute to physiological alterations. Third, we interrogated known AD genes in adults with DS. Therefore, this candidate gene study was not designed to identify novel genes/variants, but rather, to better characterize the role of the SNPs in genes that are already shown to be associated with increased AD risk in adults with DS or in individuals with high levels of Aβ. Finally, study participants without AD were, on average, 7.7 years younger than those who had AD. It is possible that the effect sizes associated with the significant SNPs might be reduced if unaffected individuals were followed for a longer period of time. However, given that our multivariate model had adjusted for age and other potential confounders, the effect of age difference on allelic association would be limited.

In short, the present study reports SNPs in APP, CST3, and MARK4 that are associated with elevated risk of AD in adults with DS. This study illustrates that genetic factors that contribute to AD in the general population are likely to play a similar role in adults with DS, and a genetic study of AD in adults with DS can provide additional insight into the mechanisms for AD as these 2 genes may provide a new understanding for the role of cardiovascular factors and tau in AD. For these genes, further studies of adults with DS are needed to identify functionally relevant variants through extensive sequencing of exons and introns, and to evaluate how these variants may influence levels of gene expression.

Supplementary Material

1
2

Acknowledgments

This study is supported by grants R01AG014673 (Schupf) and P01HD035897 and U54 HD079123 (Silverman) from NIA and NICHD, respectively, and by NYS through its Office for People with Developmental Disabilities. The authors thank the study participants and participating agencies from the tristate area that made this study possible.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging.2017.04.018.

Footnotes

Disclosure statement

The authors have no actual or potential conflicts of interest.

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