Late Onset Alzheimer’s Disease Risk Variants in Cognitive Decline: The PATH Through Life Study

Abstract

Recent genome wide association studies have identified a number of single nucleotide polymorphisms associated with late onset Alzheimer’s disease (LOAD). We examined the associations of 24 LOAD risk loci, individually and collectively as a genetic risk score, with cognitive function. We used data from 1,626 non-demented older Australians of European ancestry who were examined up to four times over 12 years on tests assessing episodic memory, working memory, vocabulary, and information processing speed. Linear mixed models were generated to examine associations between genetic factors and cognitive performance. Twelve SNPs were significantly associated with baseline cognitive performance (ABCA7, MS4A4E, SORL1), linear rate of change (APOE, ABCA7, INPP5D, ZCWPW1, CELF1), or quadratic rate of change (APOE, CLU, EPHA1, HLA-DRB5, INPP5D, FERMT2). In addition, a weighted genetic risk score was associated with linear rate of change in episodic memory and information processing speed. Our results suggest that a minority of AD related SNPs may be associated with non-clinical cognitive decline. Further research is required to verify these results and to examine the effect of preclinical AD in genetic association studies of cognitive decline. The identification of LOAD risk loci associated with non-clinical cognitive performance may help in screening for individuals at greater risk of cognitive decline.

Keywords

Alzheimer’s disease cognitive aging genetic epidemiology genetic risk scores longitudinal studies single nucleotide polymorphisms

INTRODUCTION

Late onset Alzheimer’s disease (LOAD), in which patients show clinical symptoms >65 years of age, is the most common form of dementia and the number of individuals with LOAD is expected to triple by 2050 [1]. LOAD has a long preclinical phase that commences decades before the onset of clinical symptoms, which are characterized by progressive degeneration of brain structure and chemistry resulting in gradual cognitive and functional decline [2]. The neuropathological hallmarks of LOAD are aggregation and accumulation of extracellular amyloid-β (Aβ) peptides into amyloid plaques and accumulation of intraneuronal hyperphosphorylated and misfolded tau into neurofibrillary tangles. Accumulation of amyloid plaques and neurofibrillary tangles prompt the pathogenesis of AD by promoting alterations in lipid metabolism, neuroinflammation, endocytosis, and synaptic dysfunction and loss that ultimately leads to neuronal cell death [3, 4].

LOAD has a large genetic component, with the heritability estimated to be 60–80% [5]. Apolipoprotein (APOE) epsilon 4 (*ɛ4) was the first common genetic variant to be identified [6] and remains the strongest genetic predictor of LOAD. Beyond APOE, recent genome-wide association studies (GWAS) and a meta-analysis by the International Genomics of Alzheimer’s Project (IGAP) have identified single nucleotide polymorphisms (SNPs) at 23 loci associated with LOAD (ABCA7, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, MS4A4A, MS4A4E, MS4A6A, PICALM, HLA-DRB5, PTK2B, SORL1, SLC24A4-RIN3, DSG2, INPP5D, MEF2C, NME8, ZCWPW1, CELF1, FERMT2, and CASS4; [7 –12]).

The identified LOAD risk loci are clustered in biological pathways that play an important role in disease onset and progression [13] and are involved in the accumulation of the pathological features of LOAD [14]. Furthermore, postmortem analysis suggests that the neuropathological hallmarks of LOAD progress to varying degrees in individuals without dementia and are associated with cognitive status and nonclinical cognitive decline [15, 16]. LOAD risk genes are, therefore, good candidates for investigating potential genetic associations with cognitive performance and decline. How these loci affect normal cognitive function may inform how they influence LOAD onset and progression.

This cross-over effect is exemplified by APOE, which is associated with LOAD and has effects on episodic memory, perceptual speed, executive functioning and global cognitive ability [17, 18] mediated predominantly by Aβ plaques [19]. Association with cognitive decline of the first 11 LOAD risk loci identified by GWAS are inconsistent [20 –27]. Whether the new risk loci identified by IGAP are associated with cognitive decline has yet to be extensively investigated [28 –30].

Here, we report associations of the 24 most significant LOAD risk loci with longitudinal change in cognitive performance (based on four neuropsychological outcomes) over 12 years in 1,626 community dwelling older adults. We investigate whether these loci are associated, either individually or collectively, as genetic risk scores (GRS), with average differences in cognitive performance, rate of cognitive decline, and acceleration of the rate of decline over time.

METHODS

Participants

Participants of this study are community dwelling older adults who were recruited into the Personality and Total Health (PATH) through life project, a longitudinal study of health and wellbeing.Participants in PATH were sampled randomly from the electoral rolls of Canberra and the neighboring town of Queanbeyan into one of three cohorts based on age at baseline, the 20+ (20–24), 40+ (40–44) and 60+ (60–64) cohorts. Participants are assessed at 4-year intervals, and data from 4 waves of assessment are available. The background and test procedures for PATH have been described in detail elsewhere [31]. Written informed consent for participation in the PATH project was obtained from all participants according to the ‘National Statement’ guidelines of the National Health and Medical Research Council of Australia and following a protocol approved by the Human Research Ethics Committee of The Australian National University.

In this study, data for the 60+ cohort were used, with interviews conducted in 2001-2002 (n = 2,551), 2005-2006 (n = 2,222), 2009-2010 (n = 1,973), and 2014-2015 (n = 1,645), for a total of 12 years of follow-up. Individuals were excluded from analysis based on the following criteria: attendance at only 1 wave (n = 309); no available genomic DNA (n = 185); APOE ɛ2/ɛ4 genotype (n = 60, to avoid conflation of the ɛ2 protective and ɛ4 risk affect); non-European ancestry (n = 110); probable dementia at any wave (Mini-Mental State Examination (MMSE) <27 was used due to the high educational level in PATH [32]; n = 269); self-reported medical history of epilepsy, brain tumors or infections, stroke, and transient ischemic attacks (n = 450). Missing values in “Education” (total number of years of education, n = 128) were imputed using the ‘missForest’ package in R [33]. This left a final sample of 1,626 individuals at baseline.

Cognitive assessment

All participants were assessed at baseline and at each subsequent interview for the following four cognitive abilities: perceptual speed was assessed using the Symbol Digit Modalities Test, which asks the participant to substitute as many digits for symbols as possible in 90 seconds [34]; episodic memory was assessed using the Immediate Recall of the first trial of the California Verbal Learning Test, which involves recalling a list of 16 nouns [35]; working memory was assessed using the Digit Span Backward from the Wechsler Memory Scale, which presents participants with series of digits increasing in length at the rate of one digit per second and asks them to repeat the digits backwards [36]; and vocabulary was assessed with the Spot-the-Word Test, which asks participants to choose the real words from 60 pairs of words and nonsense words [37]. Raw cognitive test scores at each wave and Pearson’s correlation between test scores are presented in Supplementary Tables 1 & 2.

Genotyping

For this study, we used genotype data for 25 SNPs that have been associated with LOAD (Table 1). Genotyping of 11 GWAS LOAD risk SNPs (in the following loci: ABCA7, BIN1, CD2AP, CD33, CLU, CR1, EPHA1, MS4A4A, MS4A4E, MS4A6A, and PICALM) using TaqMan OpenArray assays has been reported previously [20]. In this study 16 SNPs were selected for genotyping. These included the 12 LOAD GWAS SNPs, which were identified in a meta-analysis of the previous GWAS studies performed by IGAP (in the following loci: HLA-DRB5, PTK2B, SORL1, SLC24A4-RIN3, DSG2, INPP5D, MEF2C, NME8, ZCWPW1, CELF1, FERMT2, and CASS4;). Three were associated with general cognitive function (MIR2113-rs10457441, AKAP6-rs17522122, TOMM40-rs10119; [28]). One was associated as a haplotype with LOAD (FRMD4A-rs2446581; [38]). We used proxy SNPs that were in LD with four (HLA-DRB5/HLA-DRB1-rs9271192 [r² = 1], MEF2C-rs190982 [r² = 0.89], CELF1-rs10838725 [r² = 0.99], and CASS4-rs7274581 [r² = 0.99]) of the SNPs reported by IGAP, as TaqMan assays were unavailable [12].

Genomic DNA was extracted from cheek swabs (n = 2,192) using Qiagen DNA kits or from peripheral blood leukocytes (n = 101) using QIAamp 96 DNA blood kits. Pre-amplification of the targeted loci was performed using the TaqMan PreAmp Master Mix Kit (Life Technologies). Each reaction included 2.5μl TaqMan PreAmp Master Mix (2x), 1.25μl Pre-amplification Assay Pool, 0.5μl H₂O, and 1.2μl genomic DNA. These reactions were incubated in a Biorad thermocycler for 10 min at 95°C, followed by 12 cycles of 95°C for 15 s and 60°C for 4 min, and then incubated at 99.9°C for 10 min. The PreAmplified products were then held at 4°C until they were diluted 1 : 20 in 1x Tris-EDTA buffer and then stored at –20°C until use.

Post-PreAmplification, samples were genotyped using the TaqMan OpenArray System. 2μl diluted pre-amplified product was mixed with 2μl TaqMan OpenArray Master Mix. The resulting samples were dispensed using the OpenArray^® AccuFill^TM System onto Format 32 OpenArray plates with each plate containing 96 samples and 16 SNP assays per sample. The QuantStudio^TM 12 K Flex instrument (Applied Biosystems, Carlsbad, California) was used to perform the real-time PCR reactions on the loaded OpenArray plates. The fluorescence emission results were read using the OpenArray^® SNP Genotyping Analysis software v1 (Applied Biosystems) and the genotyping analysis was performed using TaqMan^® Genotyper v1.3, using the autocalling feature. Manual calls were made on selected genotype calls based on the proximity to the nearest cluster and HapMap positive controls.

Participant-specific quality controls included filters for genotype success rate (>90%) and sample provenance error assessed via pairwise comparisons of genotype calls between all samples to identify samples with >90% similarity. Analysis of samples that were flagged in the initial quality control checks were repeated. Those samples that still failed quality control were excluded. SNP-specific filters included genotype call rate (>90%) and Hardy-Weinberg equilibrium (p > 0.05) assessed using an exact test.

The two SNPs defining the APOE alleles were genotyped separately using TaqMan assays as previously described [39]. All SNPs were in Hardy-Weinberg equilibrium and genotype frequencies are reported in Supplementary Table 3.

Data preparation and statistical analysis

All analyses were performed in the R 3.2.3 Statistical computing environment [40]. Cognitive tests at all 4 waves were transformed into z-scores (Mean = 0, SD = 1) using the means and SD at baseline to facilitate comparisons between cognitive tests. A higher score on all tests indicates better cognitive performance.

Genetic dominance was assumed for the previously reported risk allele for all SNPs, except SORL1, DSG2, and CASS4 where a recessive model of inheritance was assumed due to the low frequencies of the non-risk allele. APOE alleles were coded as the number of APOE * ɛ4 alleles (0,1,2). Participants with the APOE * ɛ2/ɛ4 allele were excluded to avoid conflation between the APOE * ɛ2 protective and APOE * ɛ4 risk effects.

Three genetic risk scores were constructed [41]: (1) a simple count genetic risk score (SC-GRS) of the number of risk alleles where SC_GRS = $\sum_{i = 1}^{I} G_{ij}$ ; (2) an odds ratio weighted genetic risk score (OR-GRS) where OR_GRS = $\sum_{i = 1}^{I} log ({OR}_{ij}) \times G_{ij}$ ; and (3) an explained variance genetic risk score (EV-GRS) weighted by minor allele frequency and odds ratios where EV_GRS = $\sum_{i = 1}^{I}$ $(log ({OR}_{ij}) \sqrt{2 {MAF}_{ij} (1 - {MAF}_{ij})}) \times G_{ij}$ . For the above formulae, risk scores are calculated for theith patient, where log(OR_i)= the odds ratio forthe jth SNP; MAF_ij= the minor allele frequencyfor the jth SNP; and G_ij= the number of risk alleles for jth SNP. SNPs were weighted by their previously reported OR for LOAD and by the minor allele frequency (MAF) reported by the International HapMap project for the CEU reference population (Table 1). Participants missing genetic data for any SNP were excluded from GRS analysis (n = 121). All three GRS were transformed into z-scores to facilitate comparison between them. A higher score indicates greater genetic risk.

Linear mixed effects models (LMMs) with maximum likelihood estimation and subject-specific random intercepts and slopes were used to evaluate the effect of individual SNPs or GRS on longitudinal cognitive performance. Longitudinal change was modelled as a quadratic growth curve, where age centered on baseline was used as an indicator of time; linear rate of change (age) is estimated from the slope of the line tangential to the curve at the intercept and quadratic rate of change (age²) is estimated from the acceleration/deceleration in the curve over time. Quadratic growth curves were represented as orthogonal polynomials to avoid collinearity problems and facilitate estimation of the models [42]. Covariates included in the models were gender, total years of education and, for individual SNP models, the number of APOE * ɛ4 alleles. LMMs were estimated using the R package ‘lme4’ [43]. Statistical significance of the fixed effects was determined using a Kenward-Roger approximation for F-tests, where a full model, containing all fixed effects, is compared to a reduced model that excludes an individual fixed effect (R package ‘afex’ [44]). Because 24 loci (APOE + 23 LOAD GWAS SNPs) and three GRS were tested, p < 0.0017 were considered to be study-wide significant after Bonferroni correction. p < 0.05 and >0. 0017 were nominally significant. Conditional R² ( $R_{c}^{2}$ ), the variance explained by fixed and random effects (i.e., the entire model), and marginal R² ( $R_{m}^{2}$ ), the variance explained by the fixed effects were calculated using the R package ‘MuMIn’ [45 –47] by comparing a full model containing the predictor of interest to a reduced model excluding thepredictor.

Power curves were calculated to assess the effect size that could be detected at a given power for our sample size using the R package ‘simr’ [48]. The power calculations are based on Monte Carlo simulations (n = 1000) of linear mixed effectsmodels for each of the cognitive outcomes considered, where the effect sizes for the baseline, linear and quadratic coefficients were altered in the base model in increments of 0.2 and 0.5 for baseline coefficients and linear/quadratic coefficients respectively. Supplemental Figure 1 shows the results of the power calculations.

RESULTS

Population characteristics of the PATH cohort

Demographic characteristics of the PATH cohort are presented in Table 2. LMMs (Supplementary Table 4) showed that all the cognitive tests were associated with significant linear and quadratic rates of change except for the Digits Span Backwards test. Immediate Recall was associated with linear (β= –22.31; SE = 0.71; p = <0.0001) and quadratic rate of change (β=–7.68; SE = 0.69; p≤0.0001), with Immediate Recall scores declining with age, and with the decline accelerating over time. Digits Span Backwards Test was associated with linear (β= 1.64; SE = 0.71; p = 0.02) but not quadratic (β= –0.31; SE = 0.66; p = 0.64) rate of change, with Digits Span Backwards test scores increasing with age. Spot-the-Word was associated with linear (β= 4.35; SE = 0.36; p = <0.0001) and quadratic (β= –1.58; SE = 0.32; p = <0.0001) rate of change, with Spot-the-Word scores increasing with age, and with the rate of change decelerating over time. Symbol Digits Modalities Test was associated with linear (β= –14.56; SE = 0.57; p = <0.0001) and quadratic (β= –1.88; SE = 0.5; p = <0.0001) rate of change, with Symbol Digits Modalities Test scores declining with age, and with the decline accelerating over time.

Linear rate of change explained 53% – 78% of outcome variation for the entire model, with quadratic rate of change explaining an additional 1.2% – 4% of outcome variation. Introducing the covariates into the models explained an additional 3.7% – 19.1% of the variation in the fixed effects (Supplementary Table 5).

Main effects of LOAD GWAS SNPs

Associations between single SNPs and cognitive outcomes did not withstand corrections for multiple testing and we report the results that were nominally significant. Introduction of the 24 LOAD GWAS risk loci individually into the LMMs (Table 3; for full models including fixed and random effects see Supplementary Tables 6–29) identified 12 loci (APOE, ABCA7, BIN1, CLU, EPHA1, MS4A4E, SORL1, DSG2, INPP5D, ZCWPW1, CELF1, and FERMT2) that were significantly associated with cognitive performance. The remaining 12 loci (CD2AP, CD33, CR1, MS4A4A, MS4A6A, PICALM, HLA-DRB5, PTK2B, SLC24A4-RIN3, MEF2C, NME8, and CASS4) were not significantly associated with cognitive performance.

APOE * ɛ4 allele was associated with a greater rate of decline in Immediate Recall and Symbol Digit Modalities Tests scores. ABCA7-rs3764650-G was associated with a lower initial status at baseline in Immediate Recall Test scores and a reduced rate of decline in Symbol Digit Modalities Test scores. BIN1-rs744373-G was associated with a lower initial status at baseline in Immediate Recall Test scores. CLU-rs11136000-C was associated with quadratic rate of change in Digits Span Backwards test scores showing an accelerating positive slope. EPHA1-rs11767557-T was associated with a faster rate of decline in Digits Span Backwards test scores. MS4A4E-rs670139-T was associated with increased initial status at baseline in Spot-the-word test scores. SORL1-rs11218343-T was associated with a lower initial status at baseline in Symbol Digits Modalities Test scores. DSG2-rs8093731-C was associated with an improvement in Spot-the-Word test scores. INPP5D-rs35349669-T was associated with a reduced rate of decline in Immediate Recall Test scores and a greater rate of decline Symbol Digits Modalities Test scores. ZCWPW1-rs1476679-T was associated with an increased rate of growth in Spot-the-word test scores. CELF1-rs7933019-C was associated with reduced rate of decline in Immediate Recall test scores. FERMT2-rs17125944-C was associated with a quadratic rate of change in Symbol Digits Modalities Test scores.

Comparisons in the R² statistics between covariate only models and the SNPs showed that there was a negligible increase in marginal R² statistics and no increase in conditional R² statistics (Supplementary Tables 6–29).

Main effects of LOAD GRS

We evaluated the association of three genetic risk scores with cognitive performance (Table 3; Supplementary Tables 30–32). Mean and SD for the raw GRS at baseline are presented in Table 2. The SC-GRS was not associated with cognitive performance. Higher OR- and EV-GRS were associated with a greater rate of decline in Immediate Recall and for the EV-GRS, a greater rate of decline in Symbol Digit Modalities Test scores.

Comparisons in the R² statistics between covariates-only models and the GRS models showed that there was a negligible increase in marginal R² statistics and no increase in conditional R² statistics (Supplementary Tables 30–32). OR- and EV-GRS were not associated with cognitive performance when APOE was excluded from the GRS (Supplementary Tables 33–35).

DISCUSSION

In this study, we investigated the association of the 23 most significant LOAD GWAS risk loci with cognitive performance in episodic memory, vocabulary, working memory and processing speed. We identified 11 SNPs as associated with baseline cognitive performance (ABCA7, BIN1, MS4A4E, SORL1), linear rate of change (APOE, ABCA7, EPHA1, DSG2, INPP5D, ZCWPW1, CELF1), or quadratic rate of change (CLU, FERMT2). GRS, weighted by OR and by OR plus MAF, were both associated with a linear rate of change in episodic memory and processing speed. When APOE was excluded from these scores neither GRS were significantly associated with cognitive performance indicating that the association was driven by the dominant effect of the APOE *ɛ4 allele. It should be noted, however, that the effect sizes for the observed associations are small, with an increase in marginal R² statistics ranging from 0.1–0.2% after inclusion of the genetic predictors. In comparison, inclusion of the covariate education in the model increases the marginal R² statistic around 4.3–19.8%.

Previous studies of associations between the initial GWAS LOAD risk loci and the limited number of studies that have examined the role of the IGAP LOAD risk loci and cognitive performance are characterized by a lack of consistent findings[21–30 , 49–57].

In univariate analysis, SNPs from 7 of the 23 non-APOE GWAS loci have been associated with cognitive performance. ABCA7 with declines in the MMSE score in women [30]; BIN1 with decline in MMSE score [26]; CD2AP with a composite episodic memory [50]; CD33 with a composite executive function score [50] and decline in MMSE in women [30]; CLU with baseline episodic memory [21], baseline and decline in a composite cognitive score [27, 55], and decline in 3MS [58]; CR1 with declines in verbal fluency [26], global cognition [25, 54], episodic memory, perceptual speed, semantic memory [56], and attention [58]; PICALM with a composite cognitive score [55] and decline in global cognition [24]; and NME8 with declines in Clinical Dementia Rating Scale Sum of Boxes Scores [57].

Aggregating SNP variation across genomic regions in a ‘gene based’ approach, has identified additional AD risk loci as associated with cognitive performance. In a meta-analysis of 31 studies (n = 53,949), PICALM, MEF2C, and SLC24A4-RIN3 gene regions were associated with general cognitive function (p≤0.05). In single sex cohorts, BIN1, CD33, CELF1, CR1, HLA cluster, and MEF2C gene regions were associated with decline in MMSE in an all-female cohort and ABCA7, HLA cluster, MS4A6E, PICALM, PTK2B, SLC24A4, and SORL1 gene regions were associated with decline in 3MS in an all-male cohort.

Genetic risk scores can have greater predictive power than individual variants as they are based on the cumulative effect of many variants that individually may have effects that are too small to be reliably detected in a univariate analysis. GRS composed of LOAD risk SNPs identified in the initial LOAD GWAS have been associated with baseline general cognition [50], episodic memory [21], visual memory and MMSE [26], and with decline in episodic memory [21], verbal fluency, visual memory, andMMSE [26]. However, these associations were no longer statistically significant when APOE was excluded from the GRS. GRS that include the IGAP risk loci have been associated with a decline in MMSE in participants with mild cognitive impairment (MCI) when APOE was excluded [29], and with memory performance at baseline and a faster rate of decline that accelerated with age, though only linear rate of change remained significant after APOE was excluded [59].

Genome-wide significant IGAP LOAD risk loci only explain only 30.62% of the genetic variance of LOAD [60]. Thus, an alternative approach is to construct a genome-wide polygenic score composed of all nominally associated variants at a given significance level. The first study to use this method did not find an association with cognitive ability or cognitive change [61]. A more recent study using data collected from the UK Biobank (n = 112 151) found that an AD GRS constructed from 20,437 SNPs that were associated with AD at a threshold of p < 0.05 in the IGAP study was significantly associated with lower verbal-numerical reasoning, memory, and educational attainment [62].

Several factors may explain the lack of consistent findings across studies. First, the failure to replicate positive results between studies could result from differences in participant characteristics (e.g., baseline education, mean age, gender, and ethnicity) and methodologies (e.g., sample size, duration of the study, number of follow-ups, non-linear time, population stratification, variation in classification, and cognitive measures) [63]. In particular, studies that did not exclude cognitively impaired individuals from the analysis could bias the observed results in favor of a positive association [28, 64].

Selectively removing individuals who develop cognitive impairment during the study from the analysis, as was done in this study, may not resolve the issue because of inadvertent inclusion of participants with preclinical dementia. Inclusion of individuals who are cognitively normal but have biomarker and neuroimaging evidence of preclinical AD greatly exaggerates age-related cognitive decline across multiple cognitive domains [65]. This suggests that AD-related genes may be associated with cognitive decline in participants who are in the preclinical stages of AD. This has been observed in cognitively normal APOE * ɛ4 carriers who had low levels of PET Aβ and who remained cognitively stable, in comparison to APOE * ɛ4 carriers with high PET Aβ who experienced faster rates of cognitive decline. This suggests that declines in cognitive function observed in APOE *ɛ4 carriers reflects the effect of APOE exacerbating Aβ-related cognitive decline rather than an independent APOE effect [66]. This effect is further indicated by previous studies showing that ABCA7, EPAH1, and CLU were associated with cognitive decline in participants classified as cognitively impaired or demented, but not in those who remained cognitively normal [20 , 53].

Second, the rationale for including LOAD risk loci in the analysis is that they may be associated with biological processes, such as neuritic plaque or neurofibrillary tangle burden, that affect both LOAD and general cognitive performance. However, of the 23 loci identified in the IGAP study, only 11 have been associated with neuritic plaque (ABCA7, BIN1, CASS4, MEF2C, PICALM, MS4A6A, CD33, and CR1) or neurofibrillary tangle (ABCA7, BIN1, CASS4, MEF2C, PICALM, CLU, SORL1, and ZCWPW1) burdens in AD case/control autopsies [54, 67]. In a longitudinal study, only BIN1 and CASS4 were associated with amyloid accumulation [68]. In contrast, in subjects with MCI, none of the LOAD risk loci were associated with levels of Aβ in cerebrospinal fluid and only SORL1 was associated with levels of cerebrospinal fluid tau and phosphorylated tau (components of neurofibrillary tangles). Furthermore, neuritic plaques and neurofibrillary tangles only explain 30% of the variation in cognitive decline, with cerebrovascular and Lewy body disease neuropatholgies explaining an additional 10% of variation [69]. This highlights that while LOAD pathology is an important factor in cognitive decline, it occurs in conjunction with other pathological features.

Finally, the pathogenesis of LOAD spans decades, clinically progressing through the preclinical, MCI, and dementia stages. As such, where and when a risk locus is involved in the LOAD pathogenesis cascade may influence whether it is associated with processes that predispose, initiate, or propagate cognitive decline. Associations have been reported between CD2AP, CLU, MS4A6A, and INPP5D and progression from normal cognition to dementia [70]; CLU, CR1, and NME8 and progression from MCI to dementia [70 –72]; INPP5D, MEFC2, EPHA1, PT2KB, FERMT2, CASS4, and rate of progression in AD [73]; and PICALM and MS4A6A and progression to MCI/Dementia from normal cognitionnormal [21].

The present findings need to be interpreted with an understanding of their limitations. First, the PATH cohort is better educated then the population it was drawn from. As higher education is associated with a reduced risk of cognitive decline and incident dementia, this may limit our ability to detect an association between genetic factors and cognitive performance. Second, the subjects in this study were of European ancestry, and thus the results presented may not be generalizable to other populations. Finally, there may have been differential attrition from the PATH study of individuals who later became severely impaired and demented, which may have biased results because these individuals would not be excluded from our analysis and are more likely to experience faster rates of cognitive decline [74].

Despite these limitations, this study has a number of strengths. It was performed in a large community-based cohort that has been followed for a period of 12 years with four waves of data assessing four separate cognitive domains. This allows for robust statistical modelling of the association of genetic factors with non-linear declines across a broad spectrum of cognition functions. Additionally, the narrow age range of this cohort reduces the influence of age differences on the results.

In conclusion, our results suggest that a subset of AD-risk loci are associated with non-clinical cognitive decline, although the effect size of each locus is small. Further, when demographic and lifestyle factors are taken into account, neither individual SNPs nor GRS explain a significant proportion of the variance in cognitive decline in our sample. Further investigation of the association of LOAD risk loci with cognitive function needs to account for the inclusion of participants with preclinical AD. The use of neuroimaging and cerebrospinal fluid biomarkers to determine preclinical AD status will allow for a more robust analysis of the role of LOAD risk loci in cognitive aging.

Footnotes

ACKNOWLEDGMENTS

We thank the participants of the PATH study, Peter Butterworth, Andrew Mackinnon, Anthony Jorm, Bryan Rodgers, Helen Christensen, Nicolas Cherbuin, Patricia Jacomb, and Karen Maxwell. The study was supported by the National Health and Medical Research Council (NHMRC) grants 973302, 179805, and 1002160, the NHMRC Dementia Collaborative Research Centres Grant CE110001029 from the Australian Research Council. DD is funded by NHMRC Project Grant No. 1043256. KJA is funded by NHMRC Research Fellowship No. 1002560.

Authors’ disclosures available online ().

Appendix

References

Alzheimer’s Association (2015) 2015 Alzheimer’s disease facts and figures. Alzheimers Dement 11, 332–384.

Sperling

, Aisen

, Beckett

, Bennett

, Craft

, Fagan

, Iwatsubo

, Jack

, Kaye

, Montine

, Park

, Reiman

, Rowe

, Siemers

, Stern

, Yaffe

, Carrillo

, Thies

, Morrison-Bogorad

, Wagster

, Phelps

(2011) Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7, 280–292.

Huang

, Mucke

(2012) Alzheimer mechanisms and therapeutic strategies.. Cell 148, 1204–1222.

Holtzman

, Morris

, Goate

(2011) Alzheimer’s disease: The challenge of the second century. Sci Transl Med 3, 77sr1.

Gatz

, Reynolds

, Fratiglioni

, Johansson

, Mortimer

, Berg

, Fiske

, Pedersen

(2006) Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry 63, 168–174.

Corder

, Saunders

, Strittmatter

, Schmechel

, Gaskell

, Small

, Roses

, Haines

, Pericak-Vance

(1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921–923.

Harold

, Abraham

, Hollingworth

, Sims

, Gerrish

, Hamshere

, Pahwa

, Moskvina

, Dowzell

, Williams

, Jones

, Thomas

, Stretton

, Morgan

, Lovestone

, Powell

, Proitsi

, Lupton

, Brayne

, Rubinsztein

, Gill

, Lawlor

, Lynch

, Morgan

, Brown

, Passmore

, Craig

, McGuinness

, Todd

, Holmes

, Mann

, Smith

, Love

, Kehoe

, Hardy

, Mead

, Fox

, Rossor

, Collinge

, Maier

, Jessen

, Schurmann

, van den Bussche

, Heuser

, Kornhuber

, Wiltfang

, Dichgans

, Frolich

, Hampel

, Hull

, Rujescu

, Goate

, Kauwe

JSK

, Cruchaga

, Nowotny

, Morris

, Mayo

, Sleegers

, Bettens

, Engelborghs

, De yn

, Van Broeckhoven

, Livingston

, Bass

, Gurling

, McQuillin

, Gwilliam

, Deloukas

, Al-Chalabi

, Shaw

, Tsolaki

, Singleton

, Guerreiro

, Muhleisen

, Nothen

, Moebus

, Jockel

K-H

, Klopp

, Wichmann

, Carrasquillo

, Pankratz

, Younkin

, Holmans

, O’Donovan

, Owen

, Williams

(2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat Genet 41, 1088–1093.

Hollingworth

, Harold

, Sims

, Gerrish

, Lambert

J-C

, Carrasquillo

, Abraham

, Hamshere

, Pahwa

, Moskvina

, Dowzell

, Jones

, Stretton

, Thomas

, Richards

, Ivanov

, Widdowson

, Chapman

, Lovestone

, Powell

, Proitsi

, Lupton

, Brayne

, Rubinsztein

, Gill

, Lawlor

, Lynch

, Brown

, Passmore

, Craig

, McGuinness

, Todd

, Holmes

, Mann

, Smith

, Beaumont

, Warden

, Wilcock

, Love

, Kehoe

, Hooper

, Vardy

ERLC

, Hardy

, Mead

, Fox

, Rossor

, Collinge

, Maier

, Jessen

, Ruther

, Schurmann

, Heun

, Kolsch

, van den Bussche

, Heuser

, Kornhuber

, Wiltfang

, Dichgans

, Frolich

, Hampel

, Gallacher

, Hull

, Rujescu

, Giegling

, Goate

, Kauwe

JSK

, Cruchaga

, Nowotny

, Morris

, Mayo

, Sleegers

, Bettens

, Engelborghs

, De yn

, Van Broeckhoven

, Livingston

, Bass

, Gurling

, McQuillin

, Gwilliam

, Deloukas

, Al-Chalabi

, Shaw

, Tsolaki

, Singleton

, Guerreiro

, Muhleisen

, Nothen

, Moebus

, Jockel

K-H

, Klopp

, Wichmann

, Pankratz

, Sando

, Aasly

, Barcikowska

, Wszolek

, Dickson

, Graff-Radford

, Petersen

, van Duijn

, Breteler

MMB

, Ikram

, DeStefano

, Fitzpatrick

, Lopez

, Launer

, Seshadri

, Berr

, Campion

, Epelbaum

, Dartigues

J-F

, Tzourio

, Alperovitch

, Lathrop

, Feulner

, Friedrich

, Riehle

, Krawczak

, Schreiber

, Mayhaus

, Nicolhaus

, Wagenpfeil

, Steinberg

, Stefansson

, Snaedal

, Bjornsson

, Jonsson

, Chouraki

, Genier-Boley

, Hiltunen

, Soininen

, Combarros

, Zelenika

, Delepine

, Bullido

, Pasquier

, Mateo

, Frank-García

, Porcellini

, Hanon

, Coto

, Alvarez

, Bosco

, Siciliano

, Mancuso

, Panza

, Solfrizzi

, Nacmias

, Sorbi

, Bossu

, Piccardi

, Arosio

, Annoni

, Seripa

, Pilotto

, Scarpini

, Galimberti

, Brice

, Hannequin

, Licastro

, Jones

, Holmans

, Jonsson

, Riemenschneider

, Morgan

, Younkin

, Owen

, O’Donovan

, Amouyel

, Williams

(2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43, 429–435.

Naj

, Jun

, Beecham

, Wang

L-S

, Vardarajan

, Buros

, Gallins

, Buxbaum

, Jarvik

, Crane

, Larson

, Bird

, Boeve

, Graff-Radford

, De Jager

, Evans

, Schneider

, Carrasquillo

, Ertekin-Taner

, Younkin

, Cruchaga

, Kauwe

JSK

, Nowotny

, Kramer

, Hardy

, Huentelman

, Myers

, Barmada

, Demirci

, Baldwin

, Green

, Rogaeva

, George-Hyslop

, Arnold

, Barber

, Beach

, Bigio

, Bowen

, Boxer

, Burke

, Cairns

, Carlson

, Carney

, Carroll

, Chui

, Clark

, Corneveaux

, Cotman

, Cummings

, DeCarli

, DeKosky

, Diaz-Arrastia

, Dick

, Dickson

, Ellis

, Faber

, Fallon

, Farlow

, Ferris

, Frosch

, Galasko

, Ganguli

, Gearing

, Geschwind

, Ghetti

, Gilbert

, Gilman

, Giordani

, Glass

, Growdon

, Hamilton

, Harrell

, Head

, Honig

, Hulette

, Hyman

, Jicha

, Jin

L-W

, Johnson

, Karlawish

, Karydas

, Kaye

, Kim

, Koo

, Kowall

, Lah

, Levey

, Lieberman

, Lopez

, Mack

, Marson

, Martiniuk

, Mash

, Masliah

, McCormick

, McCurry

, McDavid

, McKee

, Mesulam

, Miller

, Parisi

, Perl

, Peskind

, Petersen

, Poon

, Quinn

, Rajbhandary

, Raskind

, Reisberg

, Ringman

, Roberson

, Rosenberg

, Sano

, Schneider

, Seeley

, Shelanski

, Slifer

, Smith

, Sonnen

, Spina

, Stern

, Tanzi

, Trojanowski

, Troncoso

, Van Deerlin

, Vinters

, Vonsattel

, Weintraub

, Welsh-Bohmer

, Williamson

, Woltjer

, Cantwell

, Dombroski

, Beekly

, Lunetta

, Martin

, Kamboh

, Saykin

, Reiman

, Bennett

, Morris

, Montine

, Goate

, Blacker

, Tsuang

, Hakonarson

, Kukull

, Foroud

, Haines

, Mayeux

, Pericak-Vance

, Farrer

, Schellenberg

(2011) Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer’s disease. Nat Genet 43, 436–441.

10.

Seshadri

, Fitzpatrick

, Ikram

, DeStefano

, Gudnason

, Boada

, Bis

, Smith

, Carassquillo

, Lambert

J-C

, Harold

, Schrijvers

EMC

, Ramírez-Lorca

, Debette

, Longstreth

, Janssens

ACJW

, Pankratz

, Dartigues

J-F

, Hollingworth

, Aspelund

, Hernández

, Beiser

, Kuller

, Koudstaal

, Dickson

, Tzourio

, Abraham

, Antunez

, Du

, Rotter

, Aulchenko

, Harris

, Petersen

, Berr

, Owen

, Lopez-Arrieta

, Varadarajan

, Becker

, Rivadeneira

, Nalls

, Graff-Radford

, Campion

, Auerbach

, Rice

, Hofman

, Jonsson

, Schmidt

, Lathrop

, Mosley

, Au

, Psaty

, Uitterlinden

, Farrer

, Lumley

, Ruiz

, Williams

, Amouyel

, Younkin

, Wolf

, Launer

, López

, van Duijn

, Breteler

MMB

, CHARGE Consortium, GERAD1 Consortium, EADI1 Consortium (2010) Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA 303, 1832–1840.

11.

Lambert

J-C

, Heath

, Even

, Campion

, Sleegers

, Hiltunen

, Combarros

, Zelenika

, Bullido

, Tavernier

, Letenneur

, Bettens

, Berr

, Pasquier

, Fievet

, Barberger-Gateau

, Engelborghs

, De

yn P

, Mateo

, Franck

, Helisalmi

, Porcellini

, Hanon

, de Pancorbo

, Lendon

, Dufouil

, Jaillard

, Leveillard

, Alvarez

, Bosco

, Mancuso

, Panza

, Nacmias

, Bossu

, Piccardi

, Annoni

, Seripa

, Galimberti

, Hannequin

, Licastro

, Soininen

, Ritchie

, Blanche

, Dartigues

J-F

, Tzourio

, Gut

, Van Broeckhoven

, Alperovitch

, Lathrop

, Amouyel

(2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat Genet 41, 1094–1099.

12.

Lambert

, Ibrahim-Verbaas

, Harold

, Naj

, Sims

, Bellenguez

, DeStafano

, Bis

, Beecham

, Grenier-Boley

, Russo

, Thorton-Wells

, Jones

, Smith

, Chouraki

, Thomas

, Ikram

, Zelenika

, Vardarajan

, Kamatani

, Lin

, Gerrish

, Schmidt

, Kunkle

, Dunstan

, Ruiz

, Bihoreau

, Choi

, Reitz

, Pasquier

, Cruchaga

, Craig

, Amin

, Berr

, Lopez

, De Jager

, Deramecourt

, Johnston

, Evans

, Lovestone

, Letenneur

, Moron

, Rubinsztein

, Eiriksdottir

, Sleegers

, Goate

, Fievet

, Huentelman

, Gill

, Brown

, Kamboh

, Keller

, Barberger-Gateau

, McGuiness

, Larson

, Green

, Myers

, Dufouil

, Todd

, Wallon

, Love

, Rogaeva

, Gallacher

, St George-Hyslop

, Clarimon

, Lleo

, Bayer

, Tsuang

, Yu

, Tsolaki

, Bossu

, Spalletta

, Proitsi

, Collinge

, Sorbi

, Sanchez-Garcia

, Fox

, Hardy

, Deniz Naranjo

, Bosco

, Clarke

, Brayne

, Galimberti

, Mancuso

, Matthews

, Moebus

, Mecocci

, Del Zompo

, Maier

, Hampel

, Pilotto

, Bullido

, Panza

, Caffarra

, Nacmias

, Gilbert

, Mayhaus

, Lannefelt

, Hakonarson

, Pichler

, Carrasquillo

, Ingelsson

, Beekly

, Alvarez

, Zou

, Valladares

, Younkin

, Coto

, Hamilton-Nelson

, Gu

, Razquin

, Pastor

, Mateo

, Owen

, Faber

, Jonsson

, Combarros

, O’Donovan

, Cantwell

, Soininen

, Blacker

, Mead

, Mosley

THJ

, Bennett

, Harris

, Fratiglioni

, Holmes

, de Bruijn

, Passmore

, Montine

, Bettens

, Rotter

, Brice

, Morgan

, Foroud

, Kukull

, Hannequin

, Powell

, Nalls

, Ritchie

, Lunetta

, Kauwe

, Boerwinkle

, Riemenschneider

, Boada

, Hiltuenen

, Martin

, Schmidt

, Rujescu

, Wang

, Dartigues

, Mayeux

, Tzourio

, Hofman

, Nothen

, Graff

, Psaty

, Jones

, Haines

, Holmans

, Lathrop

, Pericak-Vance

, Launer

, Farrer

, van Duijn

, Van Broeckhoven

, Moskvina

, Seshadri

, Williams

, Schellenberg

, Amouyel

(2013) Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet 45, 1452–1458.

13.

Karch

, Goate

(2015) Alzheimer’s disease risk genes and mechanisms of disease pathogenesis. Biol Psychiatry 77, 43–51.

14.

Campion

, Pottier

, Nicolas

, Le Guennec

, Rovelet-Lecrux

(2016) Alzheimer disease: Modeling an Aβ-centered biological network. Mol Psychiatry 21, 861–871.

15.

Boyle

, Yu

, Wilson

, Schneider

, Bennett

(2013) Relation of neuropathology with cognitive decline among older persons without dementia. Front Aging Neurosci 5, 50.

16.

Nelson

, Alafuzoff

, Bigio

, Bouras

, Braak

, Cairns

, Castellani

, Crain

, Davies

, Del Tredici

, Duyckaerts

, Frosch

, Haroutunian

, Hof

, Hulette

, Hyman

, Iwatsubo

, Jellinger

, Jicha

, Kovari

, Kukull

, Leverenz

, Love

, Mackenzie

, Mann

, Masliah

, McKee

, Montine

, Morris

, Schneider

, Sonnen

, Thal

, Trojanowski

, Troncoso

, Wisniewski

, Woltjer

, Beach

(2012) Correlation of Alzheimer disease neuropathologic changes with cognitive status: A review of the literature. J Neuropathol Exp Neurol 71, 362–381.

17.

Small

, Rosnick

, Fratiglioni

, Bäckman

(2004) Apolipoprotein E and cognitive performance: A meta-analysis. Psychol Aging 19, 592–600.

18.

Wisdom

, Callahan

, Hawkins

(2011) The effects of apolipoprotein E on non-impaired cognitive functioning: A meta-analysis. Neurobiol Aging 32, 63–74.

19.

, Boyle

, Leurgans

, Schneider

, Bennett

(2014) Disentangling the effects of age and APOE on neuropathology and late life cognitive decline. Neurobiol Aging 35, 819–826.

20.

Andrews

, Das

, Cherbuin

, Anstey

, Easteal

(2016) Association of genetic risk factors with cognitive decline: The PATH through life project. Neurobiol Aging 41, 150–158.

21.

Carrasquillo

, Crook

, Pedraza

, Thomas

, Pankratz

, Allen

, Nguyen

, Malphrus

, Ma

, Bisceglio

, Roberts

, Lucas

, Smith

, Ivnik

, Machulda

, Graff-Radford

, Petersen

, Younkin

, Ertekin-Taner

(2015) Late-onset Alzheimer’s risk variants in memory decline, incident mild cognitive impairment, and Alzheimer’s disease. Neurobiol Aging 36, 60–67.

22.

Engelman

, Koscik

, Jonaitis

, Okonkwo

, Hermann

, La Rue

, Sager

(2013) Interaction between two cholesterol metabolism genes influences memory: Findings from the Wisconsin Registry for Alzheimer’s Prevention. J Alzheimers Dis 36, 749–757.

23.

Davies

, Harris

, Reynolds

, Payton

, Knight

, Liewald

, Lopez

, Luciano

, Gow

, Corley

, Henderson

, Murray

, Pattie

, Fox

, Redmond

, Lutz

, Chiba-Falek

, Linnertz

, Saith

, Haggarty

, McNeill

, Ke

, Ollier

, Horan

, Roses

, Ponting

, Porteous

, Tenesa

, Pickles

, Starr

, Whalley

, Pedersen

, Pendleton

, Visscher

, Deary

(2014) A genome-wide association study implicates the APOE locus in nonpathological cognitive ageing. Mol Psychiatry 19, 76–87.

24.

Zhang

, Pierce

(2014) Genetic susceptibility to accelerated cognitive decline in the US Health and Retirement Study. Neurobiol Aging 35, 1512.e11–8.

25.

De Jager

, Shulman

, Chibnik

, Keenan

, Raj

, Wilson

, Yu

, Leurgans

, Tran

, Aubin

, Anderson

, Biffi

, Corneveaux

, Huentelman

, Rosand

, Daly

, Myers

, Reiman

, Bennett

, Evans

(2012) A genome-wide scan for common variants affecting the rate of age-related cognitive decline. Neurobiol Aging 33, 1017.e1–1017.e15.

26.

Vivot

, Glymour

, Tzourio

, Amouyel

, Chene

, Dufouil

(2015) Association of Alzheimer’s related genotypes with cognitive decline in multiple domains: Results from the Three-City Dijon study. Mol Psychiatry 20, 1173–1178.

27.

Mengel-From

, Thinggaard

, Lindahl-Jacobsen

, McGue

, Christensen

, Christiansen

(2013) CLU genetic variants and cognitive decline among elderly and oldest old. PLoS One 8, e79105.

28.

Davies

, Armstrong

, Bis

, Bressler

, Chouraki

, Giddaluru

, Hofer

, Ibrahim-Verbaas

, Kirin

, Lahti

, van der Lee

, Le Hellard

, Liu

, Marioni

, Oldmeadow

, Postmus

, Smith

, Thalamuthu

, Thomson

, Vitart

, Wang

, Yu

, Zgaga

, Zhao

, Boxall

, Harris

, Hill

, Liewald

, Luciano

, Adams

, Ames

, Amin

, Amouyel

, Assareh

, Au

, Becker

, Beiser

, Berr

, Bertram

, Boerwinkle

, Buckley

, Campbell

, Corley

, De Jager

, Dufouil

, Eriksson

, Espeseth

, Faul

, Ford

, Scotland

, Gottesman

, Griswold

, Gudnason

, Harris

, Heiss

, Hofman

, Holliday

, Huffman

, Kardia

SLR

, Kochan

, Knopman

, Kwok

, Lambert

, Lee

, Li

S-C

, Loitfelder

, Lopez

, Lundervold

, Lundqvist

, Mather

, Mirza

, Nyberg

, Oostra

, Palotie

, Papenberg

, Pattie

, Petrovic

, Polasek

, Psaty

, Redmond

, Reppermund

, Rotter

, Schmidt

, Schuur

, Schofield

, Scott

, Steen

, Stott

, van Swieten

, Taylor

, Trollor

, Trompet

, Uitterlinden

, Weinstein

, Widen

, Windham

, Jukema

, Wright

, Yang

, Amieva

, Attia

, Bennett

, Brodaty

, de Craen

AJM

, Hayward

, Ikram

, Lindenberger

, Nilsson

L-G

, Porteous

, Raikkonen

, Reinvang

, Rudan

, Sachdev

, Schmidt

, Schofield

, Srikanth

, Starr

, Turner

, Weir

, Wilson

, van Duijn

, Launer

, Fitzpatrick

, Seshadri

, Mosley

THJ

, Deary

(2015) Genetic contributions to variation in general cognitive function: A meta-analysis of genome-wide association studies in the CHARGE consortium (N=53 949). Mol Psychiatry 20, 183–192.

29.

Louwersheimer

, Wolfsgruber

, Espinosa

, Lacour

, Heilmann-Heimbach

, Alegret

, Hernández

, Rosende-Roca

, Tárraga

, Boada

, Kornhuber

, Peters

, Frolich

, Hull

, Ruther

, Wiltfang

, Scherer

, Riedel-Heller

, Jessen

, Nothen

, Maier

, Koene

, Scheltens

, Holstege

, Wagner

, Ruiz

, van der Flier

, Becker

, Ramirez

(2016) Alzheimer’s disease risk variants modulate endophenotypes in mild cognitive impairment. Alzheimers Dement 12, 872–881.

30.

Nettiksimmons

, Tranah

, Evans

, Yokoyama

, Yaffe

(2016) Gene-based aggregate SNP associations between candidate AD genes and cognitive decline. AGE 38, 41.

31.

Anstey

, Christensen

, Butterworth

, Easteal

, Mackinnon

, Jacomb

, Maxwell

, Rodgers

, Windsor

, Cherbuin

, Jorm

(2012) Cohort profile: The PATH through life project. Int J Epidemiol 41, 951–960.

32.

Spering

, Hobson

, Lucas

, Menon

, Hall

, O’Bryant

(2012) Diagnostic accuracy of the MMSE in detecting probable and possible Alzheimer’s disease in ethnically diverse highly educated individuals: An analysis of the NACC database. J Gerontol A Biol Sci Med Sci 67, 890–896.

33.

Stekhoven

, Bühlmann

(2012) MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118.

34.

Smith

(2002) Symbol digit modalities test: Manual, Western Psychological Corporation, Los Angeles.

35.

Delis

, Kramer

, Kaplan

, Ober

(1987) California Verbal Learning Test, Psychological Corporation, San Antonio.

36.

Wechsler

(1945) A standardized memory scale for clinical use. J Psychol 19, 87–95.

37.

Baddeley

, Emslie

, Nimmo-Smith

(1993) The Spot-the-Word test: A robust estimate of verbal intelligence based on lexical decision. Br J Clin Psychol 32, 55–65.

38.

Lambert

, Grenier-Boley

, Harold

, Zelenika

, Chouraki

, Kamatani

, Sleegers

, Ikram

, Hiltunen

, Reitz

, Mateo

, Feulner

, Bullido

, Galimberti

, Concari

, Alvarez

, Sims

, Gerrish

, Chapman

, Deniz-Naranjo

, Solfrizzi

, Sorbi

, Arosio

, Spalletta

, Siciliano

, Epelbaum

, Hannequin

, Dartigues

, Tzourio

, Berr

, Schrijvers

EMC

, Rogers

, Tosto

, Pasquier

, Bettens

, Van Cauwenberghe

, Fratiglioni

, Graff

, Delepine

, Ferri

, Reynolds

, Lannfelt

, Ingelsson

, Prince

, Chillotti

, Pilotto

, Seripa

, Boland

, Mancuso

, Bossu

, Annoni

, Nacmias

, Bosco

, Panza

, Sanchez-Garcia

, Del Zompo

, Coto

, Owen

, O’Donovan

, Valdivieso

, Caffara

, Scarpini

, Combarros

, Buee

, Campion

, Soininen

, Breteler

, Riemenschneider

, Van Broeckhoven

, Alperovitch

, Lathrop

, Tregouet

, Williams

, Amouyel

(2013) Genome-wide haplotype association study identifies the FRMD4A gene as a risk locus for Alzheimer’s disease. Mol Psychiatry 18, 461–470.

39.

Jorm

, Mather

, Butterworth

, Anstey

, Christensen

, Easteal

(2007) APOE genotype and cognitive functioning in a large age-stratified population sample. Neuropsychology 21, 1–8.

40.

Ihaka

, Gentleman

(1996) R: A language for data analysis and graphics. J Comput Graph Stat 5, 299–314.

41.

Che

, Motsinger-Reif

(2013) Evaluation of genetic risk score models in the presence of interaction and linkage disequilibrium. Front Genet 4, 138.

42.

Donald

, Robert

(2006) Longitudinal Data Analysis, John Wiley & Sons, Inc., Hoboken, NJ, USA.

43.

Bates

, Eigen

, Rcpp

(2014) Package “lme4.”.

44.

Singmann

, Bolker

, Westfall

(2015) Afex: Analysis of factorial experiments.

45.

Barton

(2013) Package “MuMIn.”.

46.

Nakagawa

, Schielzeth

(2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4, 133–142.

47.

Johnson

(2014) Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol Evol 5, 944–946.

48.

Green

, MacLeod

(2016) SIMR: An R package for power analysis of generalized linear mixed models by simulation. Methods Ecol Evol 7, 493–498.

49.

Gui

, Jiang

, Cherny

, Sham

, Xu

, Liu

, Jin

, Zhu

, Zhang

, Thomas

, Cheng

, Lam

(2014) Influence of Alzheimer’s disease genes on cognitive decline: The Guangzhou Biobank Cohort Study. Neurobiol Aging 35, 2422. e3–8.

50.

Verhaaren

BFJ

, Vernooij

, Koudstaal

, Uitterlinden

, van Duijn

, Hofman

, Breteler

MMB

, Ikram

(2013) Alzheimer’s disease genes and cognition in the nondemented general population. Biol Psychiatry 73, 429–434.

51.

Hamilton

, Harris

, Davies

, Liewald

, Tenesa

, Starr

, Porteous

, Deary

(2011) Alzheimer’s disease genes are associated with measures of cognitive ageing in the lothian birth cohorts of 1921 and 1936. Int J Alzheimers Dis 2011, 505984.

52.

Barral

, Bird

, Goate

, Farlow

, Diaz-Arrastia

, Bennett

, Graff-Radford

, Boeve

, Sweet

, Stern

, Wilson

, Foroud

, Ott

, Mayeux

(2012) Genotype patterns at PICALM, CR1, BIN1, CLU, and APOE genes are associated with episodic memory. Neurology 78, 1464–1471.

53.

Thambisetty

, Beason-Held

, An

, Kraut

, Nalls

, Hernandez

, Singleton

, Zonderman

, Ferrucci

, Lovestone

, Resnick

(2013) Alzheimer risk variant CLU and brain function during aging. Biol Psychiatry 73, 399–405.

54.

Chibnik

, Shulman

, Leurgans

, Schneider

, Wilson

, Tran

, Aubin

, Buchman

, Heward

, Myers

, Hardy

, Huentelman

, Corneveaux

, Reiman

, Evans

, Bennett

, De Jager

(2011) CR1 is associated with amyloid plaque burden and age-related cognitive decline. Ann Neurol 69, 560–569.

55.

Mengel-From

, Christensen

, McGue

, Christiansen

(2011) Genetic variations in the CLU and PICALM genes are associated with cognitive function in the oldest old, Neurobiol Aging 32, 554. e7–11.

56.

Keenan

, Shulman

, Chibnik

, Raj

, Tran

, Sabuncu

, Alzheimer’s Disease Neuroimaging Initiative, Allen

, Corneveaux

, Hardy

, Huentelman

, Lemere

, Myers

, Nicholson-Weller

, Reiman

, Evans

, Bennett

, De Jager

(2012) A coding variant in CR1 interacts with APOE-ɛ4 to influence cognitive decline. Hum Mol Gen 21, 2377–2388.

57.

Liu

, Yu

J-T

, Wang

H-F

, Hao

X-K

, Yang

Y-F

, Jiang

, Zhu

X-C

, Cao

, Zhang

D-Q

, Tan

(2014) Association between NME8 locus polymorphism and cognitive decline, cerebrospinal fluid and neuroimaging biomarkers in Alzheimer’s disease. PLoS One 9, e114777.

58.

Sweet

, Seltman

, Emanuel

, López

, Becker

, Bis

, Weamer

, DeMichele-Sweet

MAA

, Kuller

(2012) Effect of Alzheimer’s disease risk genes on trajectories of cognitive function in the Cardiovascular Health Study. Am J Psychiatry 169, 954–962.

59.

Marden

, Mayeda

, Walter

, Vivot

, Tchetgen Tchetgen

, Kawachi

, Glymour

(2016) Using an Alzheimer disease polygenic risk score to predict memory decline in black and white Americans over 14 years of follow-up. Alzheimer Dis Assoc Disord 30, 195–202.

60.

Ridge

, Hoyt

, Boehme

, Mukherjee

, Crane

, Haines

, Mayeux

, Farrer

, Pericak-Vance

, Schellenberg

, Kauwe

JSK

, Alzheimer’s Disease Genetics Consortium (ADGC) (2016) Assessment of the genetic variance of late-onset Alzheimer’s disease. Neurobiol Aging 41, 200.e13–20.

61.

Harris

, Davies

, Luciano

, Payton

, Fox

, Haggarty

, Ollier

, Horan

, Porteous

, Starr

, Whalley

, Pendleton

, Deary

(2014) Polygenic risk for Alzheimer’s Disease is not associated with cognitive ability or cognitive aging in non-demented older people. J Alzheimers Dis 39, 565–574.

62.

Hagenaars

, Harris

, Davies

, Hill

, Liewald

, Ritchie

, Marioni

, Fawns-Ritchie

, Cullen

, Malik

, METASTROKE Consortium, International Con-sortium for Blood PressureGWAS, SpiroMeta Consortium, CHARGE Consortium Pulmonary Group, CHARGE Con-sortium Aging and Longevity Group, Worrall

, Sudlow

, Wardlaw

, Gallacher

, Pell

, McIntosh

, Smith

, Gale

, Deary

(2016) Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (N=112 151) and 24 GWAS consortia. Mol Psychiatry 21, 1624–1632.

63.

Payton

(2009) The impact of genetic research on our understanding of normal cognitive ageing: 1995 to 2009. Neuropsychol Rev 19, 451–477.

64.

Knight

, Tsui

HSL

, Abraham

, Skeaff

, McMahon

, Cutfield

(2014) Lack of effect of the apolipoprotein E epsilon4 genotype on cognition during healthy aging. J Clin Exp Neuropsychol 36, 742–750.

65.

Hassenstab

, Chasse

, Grabow

, Benzinger

TLS

, Fagan

, Xiong

, Jasielec

, Grant

, Morris

(2016) Certified normal: Alzheimer’s disease biomarkers and normative estimates of cognitive functioning. Neurobiol Aging 43, 23–33.

66.

Lim

, Laws

, Villemagne

, Pietrzak

, Porter

, Ames

, Fowler

, Rainey-Smith

, Snyder

, Martins

, Salvado

, Bourgeat

, Rowe

, Masters

, Maruff

(2016) Aβ-related memory decline in APOE ɛ4 noncarriers: Implications for Alzheimer disease. Neurology 86, 1635–1642.

67.

Beecham

, Hamilton

, Naj

, Martin

, Huentelman

, Myers

, Corneveaux

, Hardy

, Vonsattel

, Younkin

, Bennett

, De Jager

, Larson

, Crane

, Kamboh

, Kofler

, Mash

, Duque

, Gilbert

, Gwirtsman

, Buxbaum

, Kramer

, Dickson

, Farrer

, Frosch

, Ghetti

, Haines

, Hyman

, Kukull

, Mayeux

, Pericak-Vance

, Schneider

, Trojanowski

, Reiman

, the Alzheimer’s Disease Genetics Consortium (ADGC), Schellenberg

, Montine

(2014) Genome-wide association meta-analysis of neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet 10, e1004606.

68.

Ramanan

, Risacher

, Nho

, Kim

, Shen

, McDonald

, Yoder

, Hutchins

, West

, Tallman

, Gao

, Foroud

, Farlow

, De Jager

, Bennett

, Aisen

, Petersen

, Jack

CRJ

, Toga

, Green

, Jagust

, Weiner

, Saykin

(2015) GWAS of longitudinal amyloid accumulation on 18F-florbetapir PET in Alzheimer’s disease implicates microglial activation gene IL1RAP. Brain 138, 3076–3088.

69.

Boyle

, Wilson

, Yu

, Barr

, Honer

, Schneider

, Bennett

(2013) Much of late life cognitive decline is not due to common neurodegenerative pathologies. Ann Neurol 74, 478–489.

70.

Adams

HHH

, de Bruijn

RFAG

, Hofman

, Uitterlinden

, van Duijn

, Vernooij

, Koudstaal

, Ikram

(2015) Genetic risk of neurodegenerative diseases is associated with mild cognitive impairment and conversion to dementia. Alzheimers Dement 11, 1277–1285.

71.

Rodríguez-Rodríguez

, Sánchez-Juan

, Vázquez-Higuera

, Mateo

, Pozueta

, Berciano

, Cervantes

, Alcolea

, Martínez-Lage

, Clarimón

, Lleó

, Pastor

, Combarros

(2012) Genetic risk score predicting accelerated progression from mild cognitive impairment to Alzheimer’s disease. J Neural Transm 120, 1–6.

72.

Lacour

, Espinosa

, Louwersheimer

, Heilmann

, Hernandez

, Wolfsgruber

, Fernandez

, Wagner

, Rosende-Roca

, Mauleon

, Moreno-Grau

, Vargas

, Pijnenburg

YAL

, Koene

, Rodriguez-Gomez

, Ortega

, Ruiz

, Holstege

, Sotolongo-Grau

, Kornhuber

, Peters

, Frolich

, Hull

, Ruther

, Wiltfang

, Scherer

, Riedel-Heller

, Alegret

, Nothen

, Scheltens

, Wagner

, Tarraga

, Jessen

, Boada

, Maier

, van der Flier

, Becker

, Ramirez

, Ruiz

(2017) Genome-wide significant risk factors for Alzheimer’s disease: Role in progression to dementia due to Alzheimer’s disease among subjects with mild cognitive impairment. Mol Psychiatry 22, 153–160.

73.

Wang

, López

, Sweet

, Becker

, DeKosky

, Barmada

, Demirci

, Kamboh

(2015) Genetic determinants of disease progression in Alzheimer’s disease. J Alzheimers Dis 43, 649–655.

74.

, Tchetgen Tchetgen

, Osypuk

, White

, Mujahid

, Maria Glymour

(2013) Combining directand proxy assessments to reduce attrition bias in a longitudinal study. Alzheimer Dis Assoc Disord 27, 207–212.