ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers

ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers View ORCID Profile Andrés Peña-Tauber , View ORCID Profile Ricardo Hernández Arriaza , View ORCID Profile Yann Le Guen , View ORCID Profile Junyoung Park , View ORCID Profile Michael D. Greicius doi: https://doi.org/10.1101/2025.01.24.25321105 Andrés Peña-Tauber 1 Department of Neurology and Neurological Sciences, Stanford University , Stanford, CA, USA BA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Andrés Peña-Tauber Ricardo Hernández Arriaza 2 Department of Chemistry, Stanford University , Stanford, CA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ricardo Hernández Arriaza Yann Le Guen 3 Quantitative Sciences Unit, Department of Medicine, Stanford University , Stanford, CA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yann Le Guen Junyoung Park 1 Department of Neurology and Neurological Sciences, Stanford University , Stanford, CA, USA PhD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Junyoung Park Michael D. Greicius 1 Department of Neurology and Neurological Sciences, Stanford University , Stanford, CA, USA MD Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Michael D. Greicius For correspondence: greicius{at}stanford.edu Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background and Objectives ABCA1 has been associated with Alzheimer’s Disease (AD) via large-scale genetic studies, but the mechanisms by which it impacts disease risk are unknown. ABCA1 catalyzes apolipoprotein lipidation in the central nervous system and is known to interact molecularly with ApoE. We explored whether variants altering ABCA1 activity depend on the presence of different APOE isoforms to modify AD risk. Methods We meta-analyzed European- and African-ancestry whole-genome and whole-exome sequencing datasets from the Alzheimer’s Disease Sequencing Project and UK Biobank. We used Cox hazards regression to assess the impact of rare (MAF < 1%) predicted damaging (loss-of-function, or missense with a REVEL score ≥ 0.75) variants in ABCA1 on AD risk in APOE subgroups and performed APOE interaction analyses considering all APOE genotypes. We then leveraged plasma HDL levels, a phenotype known to be impacted by ABCA1 variants, as a measure of ABCA1 activity and examined whether cumulative effects on HDL levels by ABCA1 missense variants predict AD risk. Results Rare damaging (LOF + REVEL ≥ 75) variants on ABCA1 increased AD risk in APOE ε2/ε3 and ε3/ε3 but not ε3/ε4 or ε4/ε4 cohorts. Interaction analyses indicated that damaging ABCA1 variants increase AD risk considering all APOE isoforms (hazard ratio [HR] = 1.48; 95% confidence interval [CI] = 1.22, 1.79; p = 5.80E-05) and interact with both APOE ε2 (HR = 1.99; 95% CI = 1.30, 3.05; p = 0.002) and ε4 (HR = 0.76; 95% CI = 0.61, 0.95; p = 0.014). Predicted ABCA1 activity based on a weighted sum of HDL-associated ABCA1 missense variants was protective against AD (HR = 0.10; 95% CI = 0.04, 0.27; p = 2.83E-06) and interacted with APOE ε4 to counteract this protective effect (HR = 11.66; 95% CI = 3.81, 35.66; p = 1.67E-05), while there was no significant interaction with ε2. Conclusions ABCA1 activity is protective against AD risk in APOE ε4 non-carriers. Distinct interactions between ABCA1 and the main APOE isoforms suggest the proteins may work together to affect AD risk and motivate the development and testing of ABCA1 therapeutics in specific APOE subpopulations. Introduction Lipid metabolism has received attention as a potentially important pathway in the development of multiple neurodegenerative disorders. 1 , 2 While many lipid-related genes including APOE , ABCA7 , and ABCA1 have now been associated with Alzheimer’s Disease (AD), the most prevalent cause of dementia, the exact role of lipid metabolism in neurodegeneration has remained elusive. 3 – 6 ATP-binding cassette transporter A1 (ABCA1) is a membrane-bound lipid transporter that has been linked with AD via both common, low-effect and rare, high-effect genetic variants. 7 – 9 The function of ABCA1 in peripheral lipid metabolism has been well characterized, as it is essential to produce high-density lipoprotein (HDL) particles in blood by catalyzing the efflux of phospholipids and cholesterol onto apolipoprotein A1 (ApoA1). 10 Loss-of-function (LoF) and certain missense variants on ABCA1 lead to decreased peripheral HDL levels, with one copy of such a variant causing familial hypoalphalipoproteinemia, and two copies producing Tangier disease, characterized by extremely low plasma HDL and an increased risk of atherosclerosis. 11 In the central nervous system (CNS), ABCA1 appears to be important in forming “HDL-like” lipoparticles that contain apolipoprotein E (ApoE) via a process that may share molecular features of ApoA1-based lipoprotein formation. 12 , 13 Lipoparticles formed by the three main isoforms of ApoE—ε2, ε3, and ε4—exhibit a stepwise decrease in lipid content mirroring their increase in risk for AD. 14 , 15 This has led some to propose that the pathogenicity of each ApoE isoform in AD may be partially linked to its relative depletion in lipids and to suggest the reversal of this status as a therapeutic strategy. 16 – 18 One such approach, employing a peptide that agonizes ABCA1 activity via mimicking the C-terminal end of ApoE, has been shown to increase the lipidation of ApoE particles and reverse ApoE ε4-associated pathologies in cell and animal studies. 19 – 22 However, based on in vitro experiments, differences in ABCA1-driven lipoparticle formation containing each ApoE isoform remain controversial, often depending on the model system used. 19 , 23 – 26 Further, no human data has yet characterized the relationship between ABCA1 and APOE in the context of AD. Understanding this interaction at a disease level could shed light on the role of these genes, and of lipid metabolism more generally, in AD pathogenesis. Building on the uncovered link between ABCA1 variants and AD risk, here we examine the possibility of a genetic interaction between ABCA1 and APOE by characterizing the effect of APOE isoforms on the risk for AD conferred by variants affecting ABCA1 activity. Methods Genetic sequencing and AD phenotyping We analyzed individuals in the Alzheimer’s Disease Sequencing Project (ADSP) whole genome sequencing (WGS) and whole-exome sequencing (WES) 27 and UK Biobank (UKB) WES 28 datasets. Variants were extracted from original VCF or PLINK files and quality controlled for missingness ( 10 - 8 ), differential missingness between cases and controls (p-value > 10 - 5 ), and in ADSP, for association with sequencing platform (p-value > 10 - 5 in Fisher’s exact test) due to the inclusion of multiple cohorts. In ADSP, we included AD cases and cognitively unimpaired individuals (denoted as healthy controls), whose phenotyping included age at onset and last known healthy control age as described previously. 27 In UKB, AD case status was set based on having a value in Date of Alzheimer’s Disease report (field 42020), and age at onset was obtained from this field minus participant year and month of birth (fields 34 and 52). All subjects without an AD report were considered healthy controls. The last known healthy age for controls was obtained from the latest censoring date in UKB (November 30, 2022) minus participant date of birth. Subjects were classified into one of five continental superpopulations—South Asian (SAS), East Asian (EAS), Amerindian (AMR), African (AFR), and European (EUR)—if they had an ancestry percentage greater than 60% as calculated by SNPWeights version 2 29 using reference populations from the 1000 Genomes Consortium 30 . Samples were excluded if they had a genotype missingness greater than 10%. ABCA1 variant annotation Variants were extracted in the ABCA1 region (hg38 coordinates: chr9:104781002-104928246) and annotated using Ensembl Variant Effect Predictor 31 release 107.0 with Loss-of-Function Transcript Effect Estimator (LOFTEE) 32 and Rare Exome Variant Ensemble Learner (REVEL) 33 plugins. Annotations were filtered to those on the canonical transcript (ENST00000374736) of ABCA1 . AD association analysis We divided each dataset (ADSP WGS, ADSP WES, and UKB WES) into ancestry cohorts and performed analyses separately in each cohort, then meta-analyzed results for high-confidence effect estimates using fixed-effects inverse-weighted variance via metafor 34 v4.6.0 in R 35 v4.3.3. Only cohorts with sufficient sample size for analysis (greater than 4,000 individuals) were included. We considered a result statistically significant if it had a p-value < 0.05 in the meta-analysis. We performed Cox proportional hazards regression using lifelines 36 v0.27.8, using age at onset or last known control age as the time to event and case/control status as the right-censoring variable, to assess for association with AD. We included as covariates ten principal components produced by PC-AiR in Genesis 37 v2.32.0, sex, and in ADSP, sequencing platform. All AD cases and healthy controls were included in ADSP. In UKB, all AD cases were included, but to reduce computational burden, controls were sampled randomly at a 10:1 ratio with cases. Related pairs of individuals up to 3 rd degree relatedness (kinship > 0.0442) were identified using the --make-king-table command in PLINK 2.0. 38 , 39 One individual from each related pair was removed, favoring first the sample that was genotyped through WGS rather than WES, then the sample with an age at onset or last healthy control age available, and at random otherwise. ABCA1 rare variant burden test We considered a set of rare variants on ABCA1 likely to reduce gene function and which were previously associated with AD, comprised of LoF variants and missense variants with a REVEL score ≥ 0.75 (denoted LoF + REVEL ≥ 75). 9 Variants were considered rare if they had an allele frequency (AF) below 1% in their respective cohort and all gnomAD 32 exome and genome non-bottlenecked populations (AFR, AMR, EAS, NFE, and SAS). Variants were considered LoF if they had an IMPACT annotation of “HIGH” and were “high confidence” LoF by LOFTEE. We summed the number of alleles across selected variants for each individual (denoted “variant burden”) and performed Cox regression. APOE interaction analysis To assess for differences in effect of ABCA1 variants depending on APOE genotype, we calculated and compared separate effect estimates in APOE genotype subgroups. To robustly test for an interaction, we then examined all APOE genotype groups together, including four additional covariates: APOE ε2 dosage; APOE ε4 dosage; APOE ε2 dosage x variant burden; and APOE ε4 dosage x variant burden. In this analysis, we excluded individuals with an APOE ε2/ε4 genotype to avoid confounding by potentially conflicting effects of ε2 and ε4. HDL-weighted burden test Computational tools to predict missense variant deleteriousness, such as REVEL, can be useful in genome-wide scans of gene association, but they can be noisy at an individual gene and variant level. 33 , 40 We thus sought to use a more empirical method to relate estimated ABCA1 activity with AD risk, leveraging the well-described connection between ABCA1 variants and plasma HDL. To do so, we predicted the effect of each ABCA1 missense variant on plasma HDL values (field 30760) for participants in UKB WES and used these values to construct a score of predicted ABCA1 activity for each subject. To maximize power while still accounting for population structure and relatedness, we ran BOLT-LMM 41 v2.4.1 on log 2 -transformed HDL values in 404,545 EUR participants in UKB with both SNP array and WES data available. SNP array data was collected as described previously. 42 We obtained SNP array non-imputed data from all autosomes except chromosome 9 to use in the mixed model and combined them with ABCA1 WES data for association testing, additionally covarying for age at assessment (field 21003), sex (field 31), APOE ε2 and ε4 dosage, use of cholesterol-lowering medication (fields 6177 for males and 6153 for females), and 40 principal components provided by UKB (field 22009). We ran BOLT-LMM with the --predBetasFile option, which includes all variants in a locus in a single model, to provide additional, more accurate estimates of single-variant beta values accounting for linkage disequilibrium. We extracted association statistics from ABCA1 variants and kept all missense variants with an allele count of at least 10. Variants significantly affecting HDL were identified using a Bonferroni-corrected p-value < 0.05. We then used beta values from the polygenic prediction model for significant missense variants to perform a weighted burden test in AD datasets. This was done identically to the rare variant burden test described above, except the burden variable was defined as: where z j is the burden variable for a subject j, i is each HDL-associated variant, m is the total number of HDL variants, c ij is variant dosage for the given subject, and i i is the signed beta value from HDL regression. Variants in this test were not otherwise filtered for AF. While AF filtering is necessary in the unweighted burden test to prevent the predominance of a single or few higher-frequency variants in the resulting association, in the HDL-weighted burden test, variant weight tended to be inversely correlated with AF, preventing this issue [Supplementary Figure 2] . Results We included four cohorts in our analysis: ADSP WGS (EUR), ADSP WGS (AFR), ADSP WES (EUR), and UKB WES (EUR), for a total of 62,908 individuals passing quality control [ Table 1 ] . We first examined the effects on risk of AD onset of rare LoF + REVEL ≥ 75 variants in ABCA1 in groups defined by APOE genotype [Methods] . Through the Cox regression burden test, we replicated the finding that LoF + REVEL ≥ 75 variants on ABCA1 increase risk for AD when considering all APOE genotypes together (hazard ratio [HR] = 1.30; 95% confidence interval [CI] = 1.15, 1.48; p-value [p] = 3.85E-05). Stratifying by APOE genotype, LoF + REVEL ≥ 75 variants increased risk in ε2/ε3 (HR = 2.40; 95% CI = 1.48, 3.91; p = 4.23E-04) and ε3/ε3 (HR = 1.62; 95% CI = 1.33, 1.97; p = 1.09E-06) groups but not in ε3/ε4 (HR = 0.99; 95% CI = 0.80, 1.21; p = 0.886) or ε4/ε4 (HR = 1.03; 95% CI = 0.67, 1.58; p = 0.882) groups. It was especially striking that APOE ε2/ε3 individuals saw a significant effect of ABCA1 variants, whereas ε3/ε4 did not, despite a larger sample size in ε3/ε4 (n total = 18,978; n variant carriers = 213) than ε2/ε3 (n total = 7,334; n variant carriers = 79) [ Figure 1a ] . While we do not report effect estimates for ε2/ε2, as there were only four variant carriers in this group across all cohorts, three of these four variant carriers were diagnosed with AD, with ages at onset 57, 79, and 78, below the median age at AD onset in ε2/ε2 individuals across all cohorts (81, N = 350). Further, a log-rank test combining all cohorts indicated a significant difference in risk of AD onset in LoF + REVEL ≥ 75 ABCA1 variant carriers compared with other ε2/ε2 subjects (p = 7.93E-09) [Supplementary Figure 1]. View this table: View inline View popup Download powerpoint Table 1. Demographic breakdown for the four cohorts analyzed in the AD burden test. Download figure Open in new tab Figure 1. Association of burden of LoF + REVEL ≥ 75 variants in ABCA1 with AD. Bottom axes denote hazard ratios with 95% confidence intervals from Cox regression; colored lines are individual datasets, and black diamonds are the meta-analysis. (a) Results for analyses stratified by APOE genotype group. (b) Results for analysis including all APOE groups, with ABCA1 variant dosage x APOE isoform dosage interaction terms. These findings indicated that the AD risk conferred by rare, LoF + REVEL ≥ 75 variants in ABCA1 may depend on the presence of APOE isoforms, with ε2 potentially increasing the magnitude of effect and ε4 reducing it. To test this hypothesis formally, we re-ran the all- APOE analysis in each cohort, including ABCA1 variant burden x APOE isoform dosage interaction terms in the model [Methods] . In this analysis, we saw an AD risk effect of ABCA1 variants by themselves (HR = 1.48; 95% CI = 1.22, 1.79; p = 5.80E-05). Further, ABCA1 variant burden and APOE ε2 dosage interacted to increase AD risk (HR = 1.99; 95% CI = 1.30, 3.05; p = 0.002), and ABCA1 variant burden and APOE ε4 interacted to decrease AD risk (HR = 0.76; 95% CI = 0.61, 0.95; p = 0.014), supporting the observation from the stratified analyses that both ε2 and ε4 modify the risk conferred by LoF + REVEL ≥ 75 ABCA1 variants, in opposite directions [ Figure 1b ] . We sought to construct a more empirically defined measure of ABCA1 activity in each subject, based on the effect of ABCA1 missense variants on plasma HDL, to then relate ABCA1 activity to AD risk [Methods] . Through BOLT-LMM, we identified 35 ABCA1 missense variants that were significantly associated with HDL levels, most of which (29) were associated with decreased HDL [ Table 2 ] . We constructed a weighted sum of predicted HDL effects of HDL-associated variants in each individual, meant to capture the difference in their predicted ABCA1 activity from wild-type on a log-2 scale. Distributions of predicted ABCA1 activity were centered around zero (median = 0 – 0.014 in each cohort), and more subjects had increased (positive value, 28.7 – 84.7% in each cohort) than decreased (negative value, 0.44 – 3.1% in each cohort) predicted ABCA1 activity from wild-type. Interestingly, the African ancestry cohort had more than double the percentage of subjects with increased ABCA1 activity than any of the European cohorts [ Figure 2 ] . Download figure Open in new tab Figure 2. Values for weighted sums of HDL-associated ABCA1 missense variants in individuals in each cohort. View this table: View inline View popup Table 2. ABCA1 missense variants associated with plasma HDL levels in UKB. Variant positions are listed for human reference genome GRCh38. We then performed Cox regression to test the association of this measure of predicted ABCA1 activity against AD risk. Here, the resulting HR reflects the proportional change in AD risk for each 2-fold change in predicted ABCA1 activity from wild-type. Predicted ABCA1 activity was associated with reduced AD risk when considering all APOE genotypes together (HR = 0.49; 95% CI = 0.26, 0.92; p = 0.028). Stratifying by APOE subgroups, the association was only detected in APOE ε3/ε3 individuals, in whom predicted ABCA1 activity was protective (HR = 0.06; 95% CI = 0.02, 0.15; p = 2.91E-09. Predicted ABCA1 activity was not significantly associated with AD risk in APOE ε2/ε3 (HR = 0.43; 95% CI = 0.03, 5.47; p = 0.515), ε3/ε4 (HR = 2.68; 95% CI = 0.97, 7.40; p = 0.058), or ε4/ε4 (HR = 1.58; 95% CI = 0.15, 16.44; p = 0.703) groups [ Figure 3a ] . These results suggested there may be APOE isoform differences in the effects on AD risk conferred by ABCA1 activity. Testing these differences more robustly, an APOE isoform dosage interaction model in the all- APOE group again showed that predicted ABCA1 activity was associated with reduced risk of AD (HR = 0.10; 95% CI = 0.04, 0.27; p = 2.83E-06) and that the interaction between ABCA1 activity and APOE ε4 dosage was associated with increased AD risk (HR = 11.66; 95% CI = 3.81, 35.66; p = 1.67E-05). We saw no significant interaction effect of ABCA1 activity with APOE ε2 dosage (HR = 2.12; 95% CI = 0.17, 25.82; p = 0.556) [ Figure 3b ] . Overall, these results suggested that ABCA1 activity as predicted by HDL-associated missense variants is associated with reduced AD risk in APOE ε2 and ε3 carriers, but that this association is weaker or nonexistent in APOE ε4 carriers. Download figure Open in new tab Figure 3. Association of predicted ABCA1 activity as reflected by a weighted sum of HDL-associated ABCA1 missense variants with AD. Bottom axes denote hazard ratios with 95% confidence intervals; colored lines are individual datasets, and black diamonds are the meta-analysis. (a) Results for analyses stratified by APOE genotype group. (b) Results for analysis including all APOE groups, with ABCA1 activity x APOE isoform dosage interaction terms. We performed sensitivity analyses to validate the robustness and generalizability of the association between predicted ABCA1 activity and AD. First, we tested the impact of including only missense variants increasing (n = 6) or decreasing (n = 29) HDL levels from wild-type in the weighted sum. Weighted sums of both HDL-increasing and HDL-decreasing ABCA1 variants showed the same patterns of association as the weighted sum including all variants, suggesting the association was being driven by individuals with both increased and decreased ABCA1 activity compared to wild-type, despite there being many more individuals in each cohort with increased predicted ABCA1 activity. Second, we excluded the UKB WES dataset, as some of these individuals were used to learn HDL values, potentially introducing bias. Predicted ABCA1 activity was still associated with AD risk (HR = 0.11; 95% CI = 0.03, 0.33; p = 1.26E-04) and interacted with APOE ε4 dosage (HR = 11.31; 95% CI = 3.02, 42.35; p = 3.16E-04) after including only the three ADSP datasets. Third, we conducted unweighted burden tests to assess the impact of variant weights on the AD association. A burden test using simple counts of HDL-associated variants was not significantly associated with AD risk in any model, even when including only HDL-increasing or HDL-decreasing variants, supporting the weighting of variants based on their HDL effects as an important predictor of AD risk [ Supplementary Table 1 ] . In a final analysis, we explored the contribution of each variant to the association of predicted ABCA1 activity with AD in order to identify important variants for further study. For each HDL-associated missense variant, we removed the variant from the weighted sum (assigning it a weight of zero) and re-ran the association, observing changes in p-values for the weighted sum and APOE interaction variables. No removal of a single variant ablated the association of the weighted sum by itself or its APOE ε4 interaction with AD, and the removal of most variants (n = 20 out of 35) at least marginally weakened the association of the weighted sum with AD, showing that most variants included in the weighted sum are important to the association [ Figure 4a ] . We highlight five missense variants that had a large impact on the association of the weighted sum of HDL-associated variants with AD. Four missense variants led to the largest weakening of the association of predicted ABCA1 activity with AD when they were removed, by a similar amount: R587W (chr9:104831058:G:A, p = 3.00E-05 vs. 2.83E-06); N1800H (chr9:104794495:T:G, p = 2.93E-05 vs 2.83E-06); R1342W (chr9:104812600:G:A, p = 2.90E-05 vs. 2.83E-06); and E1172D (chr9:104817351:C:G, p = 2.62E-05 vs. 2.83E-06). Two of these variants can be assessed reliably by single variant statistics due to having more than 10 variant carriers across all cohorts. The variant N1800H significantly increased AD risk (HR = 2.24; 95% CI = 1.29, 3.90; p = 0.004) and interacted with APOE ε2 dosage (HR = 5.09; 95% CI = 1.95, 13.29; p = 8.99E-04), while it did not interact significantly with ε4 dosage but had an opposite effect direction as the interaction with ε2 (HR = 0.61; 95% CI = 0.30, 1.25; p = 0.176). This variant is the most highly associated with HDL out of all missense variants due to a large decrease in HDL levels (marginal log 2 fold change = −0.36; 95% CI = −0.40, −0.32; p = 4.5E-87). Meanwhile, the variant E1172D was associated with decreased AD risk (HR = 0.89; 95% CI = 0.82, 0.97; p = 0.010) and interacted with APOE ε4 dosage (HR = 1.11; 95% CI = 1.01, 1.21; p = 0.027) but not with ε2 (HR = 1.09; 95% CI = 0.87, 1.36; p = 0.474). This variant is predicted to increase HDL levels (marginal log 2 fold change = 0.019; 95% CI = 0.015, 0.023; p = 5.3E-23). Both N1800H and E1172D variants lead to the expected effect directions on AD risk given their effects on HDL and have significant interactions with an APOE isoform, thus appearing to be representative of the association we identified linking ABCA1 activity, APOE isoforms, and AD risk. On the other hand, the variant V771M (chr9:104826974:C:T) led to the greatest improvement in the association of predicted ABCA1 activity with AD when it was removed (p = 1.99E-08 vs. 2.83E-06). This variant is the second most highly associated with HDL out of all missense variants due to an increase in HDL levels (marginal log 2 fold change = 0.039; 95% CI = 0.035, 0.043; p = 4.3E-86). Despite this, V771M was not associated with AD risk (HR = 1.05; 95% CI = 0.95, 1.15; p = 0.365), nor did it interact with APOE ε2 (HR = 1.10; 95% CI = 0.84, 1.45; p = 0.488) or ε4 (HR = 1.06; 95% CI = 0.95, 1.17; p = 0.291) dosage. Thus, this variant may act as an outlier in terms of the association between ABCA1 activity, APOE isoforms, and AD risk [ Figure 4b ]. Download figure Open in new tab Figure 4. Missense variants included in the HDL-weighted burden test. (a) Contribution of each ABCA1 missense variant to the association of predicted ABCA1 activity and its interaction with APOE isoforms with AD. Dashed lines reflect association results for weighted sum of all variants; solid lines denote association results for weighted sum with a single variant removed. Bottom axis denotes the variant that was removed from the weighted sum. Variants are ordered according to their effects on the significance level of the weighted sum univariate term with AD. (b) AD association for three individual missense variants that had a large impact on the association between predicted ABCA1 activity and AD. Bottom axes denote hazard ratios with 95% confidence intervals from Cox regression; colored lines are individual datasets, and black diamonds are the meta-analysis. Discussion The exact roles of ABCA1 and APOE in AD remain unclear. While genome-wide scans such as those which identified variants in ABCA1 as risk factors for AD are useful in nominating candidate risk genes, they do not necessarily elucidate the mechanisms by which these genes play a role in disease. 8 , 9 Understanding these mechanisms requires follow-up analyses that place the risk genes within disease-relevant biological pathways and clarify the specific alterations in gene activity that lead to disease risk. In this analysis, we identified an interaction between ABCA1 activity and APOE isoforms that could lend insight into the mechanisms of ABCA1 and APOE in AD. First, we found a significant interaction between rare pathogenic variants in ABCA1 (LoF + REVEL ≥ 75) and APOE isoforms in conferring AD risk. We found that the risk gain from these variants is strongest in ε2/ε3 individuals, who saw a 2-3 fold increase in AD risk, followed by ε3/ε3 individuals, who saw a 1-2 fold increase in AD risk, and we could not detect an effect in any group carrying an ε4 allele. An interaction model confirmed these APOE subgroup differences, suggesting that rare pathogenic variants in ABCA1 are most damaging in the presence of APOE ε2, moderately damaging in the presence of APOE ε3, and least damaging in the presence of APOE ε4. An additional analysis predicting ABCA1 activity based on a weighted sum of HDL effects of ABCA1 missense variants supported the observations from rare pathogenic ABCA1 variants. This genetic measure of predicted ABCA1 activity was protective against AD considering all APOE genotypes together and in APOE ε3/ε3 individuals, and it appeared to have no effect in ε2/ε3, ε3/ε4, or ε4/ε4 individuals. A model estimating the interaction of ABCA1 activity with APOE genotype suggested a difference in the AD risk effect of ABCA1 activity only in APOE ε4 carriers, while APOE ε2 carriers saw no significant difference compared to ε3/ε3. Thus, this final model indicated that APOE ε2 and ε3 carriers experienced an overall 4-20 fold decrease in risk for every doubling in predicted ABCA1 activity, which was largely ablated in the presence of APOE ε4. Importantly, while we used HDL levels to predict the effect of missense variants on ABCA1 activity, these results are not likely to mean that HDL levels themselves are causal for AD risk. Some groups have observed a correlation between HDL levels and AD or dementia risk, but Mendelian randomization analyses have been unable to support a general association between HDL-associated genetic factors and AD, making it unlikely that HDL levels themselves play a causal role. 43 – 47 Instead, by including only ABCA1 missense variants in our analysis, we refined our test to reflect the activity of a specific protein that is known to be relevant to the CNS and AD. We also ruled out the possibility of data circularity by performing a sensitivity analysis excluding UKB, which was used to estimate HDL effects. Thus, the association we identified between ABCA1- regulated plasma HDL levels and AD is likely an indicator that ABCA1 lipid transport activity in the CNS is related to the risk of AD, rather than plasma HDL levels themselves. However, we do not discount the possibility that some proportion of the effects we observed are related to the impacts of systemic vascular risk factors. From both our analyses, it thus appears that ABCA1 activity is inversely correlated with AD risk—with decreased ABCA1 activity due to rare LoF variants or reduced-function missense variants increasing AD risk, and increased ABCA1 activity due to activating missense variants decreasing AD risk—exclusively, or most strongly, in APOE ε4 non-carriers. The genetic interactions we identified point to a potential interplay of ABCA1 and APOE in AD pathogenesis. Given that alterations in ABCA1 activity seem to be impactful mainly in APOE ε2 and ε3 carriers without an ε4 allele, with some evidence that ε2 carriers are most affected, the protective functions of ε2 and ε3—relative to ε4—may depend on ABCA1 activity levels. These results generally support the idea that, at least partially, the AD risk effects of ApoE isoforms are related to the lipid content of the lipoparticles they form. On the other hand, AD risk in APOE ε4 carriers seems to be partially or wholly independent of ABCA1 activity levels. This could be explained by an intrinsic dysfunction in the ability of the ε4 isoform of ApoE to interact with ABCA1, such that increased or decreased ABCA1 activity will have a lesser or negligible effect on AD risk compared with ε4 non-carriers. This is consistent with a finding we have previously reported, in which the missense variant R251G in the lipid binding domain of ApoE protects against ε4-mediated AD risk, potentially by altering its lipid efflux capacity via interaction with ABCA1. 48 Alternatively, the pathogenic effects of APOE ε4 may occur downstream of ABCA1, independently of lipid transport via this transmembrane protein. Further studies in cell and animal models will be required to understand the exact mechanisms driving this candidate pathogenic pathway. Nonetheless, our results motivate the further development of therapeutics targeting the ABCA1 gene in AD and suggest it may be important to design ABCA1 -targeted trials with attention to potential APOE subgroup effects. While most ABCA1 missense variants in our analysis contributed to the identified link between ABCA1 activity, APOE isoforms, and AD risk, we call attention to three particular missense variants that had a large effect on our results and that could provide an opportunity to study the biochemical correlates of this genetic mechanism. The protein variants N1800H and E1172D led to opposite effects in terms of both apparent ABCA1 activity and AD risk. While N1800H reduced apparent ABCA1 activity and increased AD risk, E1172D increased ABCA1 activity and decreased AD risk. Further, they both interacted with APOE isoforms. These mutations could be studied in terms of their effects on ABCA1 localization, cholesterol efflux activity, and interactions with ApoE in diverse cell types in order to identify potential biochemical effects that lead to AD risk or protective effects. On the other hand, the protein variant V771M provides a potentially useful counterexample, given that it increases apparent ABCA1 activity and yet does not have a meaningful effect on AD risk by itself or via interacting with APOE isoforms. While this could be related to linkage disequilibrium confounding the HDL or AD association results, which may be confirmed by biochemical studies, it could also point to a pleiotropic protein-level effect that can be contrasted with other activating missense variants to aid in identifying the relevant pathways for ABCA1 and ApoE in AD. We also provide a catalog of HDL-associated ABCA1 missense variants, which could be used to estimate AD risk in human carriers of each APOE genotype. Collectively, our results show the potential relevance of genetic epistasis between ABCA1 and APOE in driving AD risk. They underscore the importance of harnessing large genetic datasets to explore gene-gene interactions in loci that have been studied extensively, which along with structural variants and epigenetics could explain a portion of the “missing heritability” of AD and other common diseases. 49 View this table: View inline View popup Download powerpoint Supplementary Table 1. Sensitivity analyses of association of predicted ABCA1 activity with AD. Supplementary Figures Supplementary Figure 1. Kaplan-Meier survival curves for ABCA1 LoF + REVEL ≥ 75 variants in APOE subgroups, combined across all cohorts. Supplementary Figure 2. HDL effects versus allele frequencies for HDL-associated ABCA1 missense variants. Data Availability Data will be made available after the paper is accepted by a peer-reviewed journal. Footnotes Abstract: "Greater than" sign changed for "greater than or equal to" sign for consistency. Table 2: AFs corrected; abbreviation added for AF. Figure 1b: Typo fixed ("variant butden" -> "variant burden"). References 1. ↵ Di Paolo G , Kim TW . Linking lipids to Alzheimer’s disease: cholesterol and beyond . Nat Rev Neurosci . 2011 ; 12 ( 5 ): 284 – 296 . doi: 10.1038/nrn3012 OpenUrl CrossRef PubMed 2. ↵ Galper J , Dean NJ , Pickford R , et al. Lipid pathway dysfunction is prevalent in patients with Parkinson’s disease . Brain . 2022 ; 145 ( 10 ): 3472 – 3487 . doi: 10.1093/brain/awac176 OpenUrl CrossRef PubMed 3. ↵ Liu CC , Kanekiyo T , Xu H , Bu G . Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy . Nat Rev Neurol . 2013 ; 9 ( 2 ): 106 – 118 . doi: 10.1038/nrneurol.2012.263 OpenUrl CrossRef PubMed 4. Aikawa T , Holm ML , Kanekiyo T . ABCA7 and Pathogenic Pathways of Alzheimer’s Disease . Brain Sciences . 2018 ; 8 ( 2 ): 27 . doi: 10.3390/brainsci8020027 OpenUrl CrossRef 5. ↵ Lewandowski CT , Laham MS , Thatcher GRJ . Remembering your A, B, C’s: Alzheimer’s disease and ABCA1 . Acta Pharmaceutica Sinica B . 2022 ; 12 ( 3 ): 995 – 1018 . doi: 10.1016/j.apsb.2022.01.011 OpenUrl CrossRef PubMed 6. ↵ Association A . 2019 Alzheimer’s disease facts and figures . Alzheimer’s & Dementia . 2019 ; 15 ( 3 ): 321 – 387 . doi: 10.1016/j.jalz.2019.01.010 OpenUrl CrossRef 7. ↵ Segrest JP , Tang C , Song HD , et al. ABCA1 is an extracellular phospholipid translocase . Nat Commun . 2022 ; 13 ( 1 ): 4812 . doi: 10.1038/s41467-022-32437-3 OpenUrl CrossRef PubMed 8. ↵ Bellenguez C , Küçükali F , Jansen IE , et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias . Nat Genet . 2022 ; 54 ( 4 ): 412 – 436 . doi: 10.1038/s41588-022-01024-z OpenUrl CrossRef PubMed 9. ↵ Holstege H , Hulsman M , Charbonnier C , et al. Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer’s disease . Nat Genet . 2022 ; 54 ( 12 ): 1786 – 1794 . doi: 10.1038/s41588-022-01208-7 OpenUrl CrossRef PubMed 10. ↵ Smith JD , Le Goff W , Settle M , et al. ABCA1 mediates concurrent cholesterol and phospholipid efflux to apolipoprotein A-I . J Lipid Res . 2004 ; 45 ( 4 ): 635 – 644 . doi: 10.1194/jlr.M300336-JLR200 OpenUrl Abstract / FREE Full Text 11. ↵ Singaraja RR , Visscher H , James ER , et al. Specific Mutations in ABCA1 Have Discrete Effects on ABCA1 Function and Lipid Phenotypes Both In Vivo and In Vitro . Circulation Research . 2006 ; 99 ( 4 ): 389 – 397 . doi: 10.1161/01.RES.0000237920.70451.ad OpenUrl Abstract / FREE Full Text 12. ↵ Wahrle SE , Jiang H , Parsadanian M , et al. ABCA1 is required for normal central nervous system ApoE levels and for lipidation of astrocyte-secreted apoE . J Biol Chem . 2004 ; 279 ( 39 ): 40987 – 40993 . doi: 10.1074/jbc.M407963200 OpenUrl Abstract / FREE Full Text 13. ↵ Krimbou L , Denis M , Haidar B , Carrier M , Marcil M , Genest J . Molecular interactions between apoE and ABCA1: impact on apoE lipidation . Journal of Lipid Research . 2004 ; 45 ( 5 ): 839 – 848 . doi: 10.1194/jlr.M300418-JLR200 OpenUrl Abstract / FREE Full Text 14. ↵ Hanson AJ , Bayer-Carter JL , Green PS , et al. Effect of apolipoprotein E genotype and diet on apolipoprotein E lipidation and amyloid peptides: randomized clinical trial . JAMA Neurol . 2013 ; 70 ( 8 ): 972 – 980 . doi: 10.1001/jamaneurol.2013.396 OpenUrl CrossRef PubMed Web of Science 15. ↵ Serrano-Pozo A , Das S , Hyman BT . APOE and Alzheimer’s disease: advances in genetics, pathophysiology, and therapeutic approaches . The Lancet Neurology . 2021 ; 20 ( 1 ): 68 – 80 . doi: 10.1016/S1474-4422(20)30412-9 OpenUrl CrossRef PubMed 16. ↵ Lanfranco MF , Ng CA , Rebeck GW . ApoE Lipidation as a Therapeutic Target in Alzheimer’s Disease . International Journal of Molecular Sciences . 2020 ; 21 ( 17 ): 6336 . doi: 10.3390/ijms21176336 OpenUrl CrossRef PubMed 17. Kanekiyo T , Xu H , Bu G . ApoE and Aβ in Alzheimer’s disease: accidental encounters or partners? Neuron . 2014 ; 81 ( 4 ): 740 – 754 . doi: 10.1016/j.neuron.2014.01.045 OpenUrl CrossRef PubMed 18. ↵ Hu J , Liu CC , Chen XF , Zhang YW , Xu H , Bu G . Opposing effects of viral mediated brain expression of apolipoprotein E2 (apoE2) and apoE4 on apoE lipidation and Aβ metabolism in apoE4-targeted replacement mice . Mol Neurodegener . 2015 ; 10 : 6 . doi: 10.1186/s13024-015-0001-3 OpenUrl CrossRef PubMed 19. ↵ Vedhachalam C , Narayanaswami V , Neto N , et al. The C-Terminal Lipid-Binding Domain of Apolipoprotein E Is a Highly Efficient Mediator of ABCA1-Dependent Cholesterol Efflux that Promotes the Assembly of High-Density Lipoproteins . Biochemistry . 2007 ; 46 ( 10 ): 2583 – 2593 . doi: 10.1021/bi602407r OpenUrl CrossRef PubMed 20. Hafiane A , Bielicki JK , Johansson JO , Genest J . Novel Apo E-Derived ABCA1 Agonist Peptide (CS-6253) Promotes Reverse Cholesterol Transport and Induces Formation of preβ-1 HDL In Vitro . PLOS ONE . 2015 ; 10 ( 7 ): e0131997 . doi: 10.1371/journal.pone.0131997 OpenUrl CrossRef PubMed 21. Boehm-Cagan A , Bar R , Liraz O , Bielicki JK , Johansson JO , Michaelson DM . ABCA1 Agonist Reverses the ApoE4-Driven Cognitive and Brain Pathologies . Journal of Alzheimer’s Disease . 2016 ; 54 ( 3 ): 1219 – 1233 . doi: 10.3233/JAD-160467 OpenUrl CrossRef 22. ↵ Noveir SD , Kerman BE , Xian H , et al. Effect of the ABCA1 agonist CS-6253 on amyloid-β and lipoprotein metabolism in cynomolgus monkeys . Alz Res Therapy . 2022 ; 14 ( 1 ): 87 . doi: 10.1186/s13195-022-01028-1 OpenUrl CrossRef 23. ↵ Lindner K , Beckenbauer K , Ek LC van , et al. Isoform- and cell-state-specific lipidation of ApoE in astrocytes . Cell Reports . 2022 ; 38 ( 9 ). doi: 10.1016/j.celrep.2022.110435 OpenUrl CrossRef PubMed 24. Hirsch-Reinshagen V , Zhou S , Burgess BL , et al. Deficiency of ABCA1 Impairs Apolipoprotein E Metabolism in Brain* . Journal of Biological Chemistry . 2004 ; 279 ( 39 ): 41197 – 41207 . doi: 10.1074/jbc.M407962200 OpenUrl Abstract / FREE Full Text 25. Michikawa M , Fan QW , Isobe I , Yanagisawa K . Apolipoprotein E Exhibits Isoform-Specific Promotion of Lipid Efflux from Astrocytes and Neurons in Culture . Journal of Neurochemistry . 2000 ; 74 ( 3 ): 1008 – 1016 . doi: 10.1046/j.1471-4159.2000.0741008.x OpenUrl CrossRef PubMed Web of Science 26. ↵ Smith JD , Miyata M , Ginsberg M , Grigaux C , Shmookler E , Plump AS . Cyclic AMP induces apolipoprotein E binding activity and promotes cholesterol efflux from a macrophage cell line to apolipoprotein acceptors . J Biol Chem . 1996 ; 271 ( 48 ): 30647 – 30655 . doi: 10.1074/jbc.271.48.30647 OpenUrl Abstract / FREE Full Text 27. ↵ Beecham GW , Bis JC , Martin ER , et al. The Alzheimer’s Disease Sequencing Project: Study design and sample selection . Neurology Genetics . 2017 ; 3 ( 5 ): e194 . doi: 10.1212/NXG.0000000000000194 OpenUrl Abstract / FREE Full Text 28. ↵ Van Hout CV , Tachmazidou I , Backman JD , et al. Exome sequencing and characterization of 49,960 individuals in the UK Biobank . Nature . 2020 ; 586 ( 7831 ): 749 – 756 . doi: 10.1038/s41586-020-2853-0 OpenUrl CrossRef PubMed 29. ↵ Chen CY , Pollack S , Hunter DJ , Hirschhorn JN , Kraft P , Price AL . Improved ancestry inference using weights from external reference panels . Bioinformatics . 2013 ; 29 ( 11 ): 1399 – 1406 . doi: 10.1093/bioinformatics/btt144 OpenUrl CrossRef PubMed 30. ↵ A global reference for human genetic variation | Nature . Accessed January 17, 2025 . https://www.nature.com/articles/nature15393 31. ↵ McLaren W , Gil L , Hunt SE , et al. The Ensembl Variant Effect Predictor . Genome Biol . 2016 ; 17 ( 1 ): 122 . doi: 10.1186/s13059-016-0974-4 OpenUrl CrossRef PubMed 32. ↵ Karczewski KJ , Francioli LC , Tiao G , et al. The mutational constraint spectrum quantified from variation in 141,456 humans . Nature . 2020 ; 581 ( 7809 ): 434 – 443 . doi: 10.1038/s41586-020-2308-7 OpenUrl CrossRef PubMed 33. ↵ Ioannidis NM , Rothstein JH , Pejaver V , et al. REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants . The American Journal of Human Genetics . 2016 ; 99 ( 4 ): 877 – 885 . doi: 10.1016/j.ajhg.2016.08.016 OpenUrl CrossRef PubMed 34. ↵ Viechtbauer W . Conducting Meta-Analyses in R with the metafor Package . Journal of Statistical Software . 2010 ; 36 ( 3 ): 1 – 48 . doi: 10.18637/jss.v036.i03 OpenUrl CrossRef PubMed 35. ↵ Ihaka R , Gentleman R . R: A Language for Data Analysis and Graphics . Journal of Computational and Graphical Statistics . 1996 ; 5 ( 3 ): 299 – 314 . doi: 10.1080/10618600.1996.10474713 OpenUrl CrossRef 36. ↵ Davidson-Pilon C . lifelines: survival analysis in Python . Journal of Open Source Software . 2019 ; 4 ( 40 ): 1317 . doi: 10.21105/joss.01317 OpenUrl CrossRef 37. ↵ Genetic association testing using the GENESIS R/Bioconductor package | Bioinformatics | Oxford Academic . Accessed January 17, 2025 . https://academic.oup.com/bioinformatics/article/35/24/5346/5536872 38. ↵ Chang CC , Chow CC , Tellier LC , Vattikuti S , Purcell SM , Lee JJ. Second-generation PLINK: rising to the challenge of larger and richer datasets . GigaScience . 2015 ; 4 ( 1 ): s13742 – 015-0047-0048 . doi: 10.1186/s13742-015-0047-8 OpenUrl CrossRef 39. ↵ Manichaikul A , Mychaleckyj JC , Rich SS , Daly K , Sale M , Chen WM . Robust relationship inference in genome-wide association studies . Bioinformatics . 2010 ; 26 ( 22 ): 2867 – 2873 . doi: 10.1093/bioinformatics/btq559 OpenUrl CrossRef PubMed Web of Science 40. ↵ Cheng J , Novati G , Pan J , et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense . Science . 2023 ; 381 ( 6664 ): eadg7492 . doi: 10.1126/science.adg7492 OpenUrl CrossRef PubMed 41. ↵ Loh PR , Kichaev G , Gazal S , Schoech AP , Price AL . Mixed-model association for biobank-scale datasets . Nat Genet . 2018 ; 50 ( 7 ): 906 – 908 . doi: 10.1038/s41588-018-0144-6 OpenUrl CrossRef PubMed 42. ↵ Bycroft C , Freeman C , Petkova D , et al. The UK Biobank resource with deep phenotyping and genomic data . Nature . 2018 ; 562 ( 7726 ): 203 – 209 . doi: 10.1038/s41586-018-0579-z OpenUrl CrossRef PubMed 43. ↵ Merched A , Xia Y , Visvikis S , Serot JM , Siest G . Decreased high-density lipoprotein cholesterol and serum apolipoprotein AI concentrations are highly correlated with the severity of Alzheimer’s disease⋆ . Neurobiology of Aging . 2000 ; 21 ( 1 ): 27 – 30 . doi: 10.1016/S0197-4580(99)00103-7 OpenUrl CrossRef PubMed Web of Science 44. Reitz C , Tang MX , Schupf N , Manly JJ , Mayeux R , Luchsinger JA . Association of Higher Levels of High-Density Lipoprotein Cholesterol in Elderly Individuals and Lower Risk of Late-Onset Alzheimer Disease . Archives of Neurology . 2010 ; 67 ( 12 ): 1491 – 1497 . doi: 10.1001/archneurol.2010.297 OpenUrl CrossRef PubMed Web of Science 45. Zuliani G , Cavalieri M , Galvani M , et al. Relationship Between Low Levels of High-Density Lipoprotein Cholesterol and Dementia in the Elderly . The InChianti Study. The Journals of Gerontology: Series A . 2010 ; 65A ( 5 ): 559 – 564 . doi: 10.1093/gerona/glq026 OpenUrl CrossRef PubMed 46. Østergaard SD , Mukherjee S , Sharp SJ , et al. Associations between Potentially Modifiable Risk Factors and Alzheimer Disease: A Mendelian Randomization Study . PLOS Medicine . 2015 ; 12 ( 6 ): e1001841 . doi: 10.1371/journal.pmed.1001841 OpenUrl CrossRef 47. ↵ Proitsi P , Lupton MK , Velayudhan L , et al. Genetic Predisposition to Increased Blood Cholesterol and Triglyceride Lipid Levels and Risk of Alzheimer Disease: A Mendelian Randomization Analysis . PLOS Medicine . 2014 ; 11 ( 9 ): e1001713 . doi: 10.1371/journal.pmed.1001713 OpenUrl CrossRef PubMed 48. ↵ Le Guen Y , Belloy ME , Grenier-Boley B , et al. Association of Rare APOE Missense Variants V236E and R251G With Risk of Alzheimer Disease . JAMA Neurology . 2022 ; 79 ( 7 ): 652 – 663 . doi: 10.1001/jamaneurol.2022.1166 OpenUrl CrossRef PubMed 49. ↵ Manolio TA , Collins FS , Cox NJ , et al. Finding the missing heritability of complex diseases . Nature . 2009 ; 461 ( 7265 ): 747 – 753 . doi: 10.1038/nature08494 OpenUrl CrossRef PubMed Web of Science View the discussion thread. Back to top Previous Next Posted January 28, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers Andrés Peña-Tauber , Ricardo Hernández Arriaza , Yann Le Guen , Junyoung Park , Michael D. Greicius medRxiv 2025.01.24.25321105; doi: https://doi.org/10.1101/2025.01.24.25321105 Share This Article: Copy Citation Tools ABCA1 activity is associated with reduced Alzheimer’s Disease risk in APOE ε4 non-carriers Andrés Peña-Tauber , Ricardo Hernández Arriaza , Yann Le Guen , Junyoung Park , Michael D. Greicius medRxiv 2025.01.24.25321105; doi: https://doi.org/10.1101/2025.01.24.25321105 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Neurology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9219) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffa5ac76a56df88',t:'MTc3OTQzNzUzMw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();