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. 2022 Dec;54(12):1786-1794.
doi: 10.1038/s41588-022-01208-7. Epub 2022 Nov 21.

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's disease

Henne Holstege #  1   2   3   4 , Marc Hulsman #  5   6   7   8 , Camille Charbonnier #  9 , Benjamin Grenier-Boley  10 , Olivier Quenez  9 , Detelina Grozeva  11 , Jeroen G J van Rooij  12   13 , Rebecca Sims  11 , Shahzad Ahmad  14   15 , Najaf Amin  14   16 , Penny J Norsworthy  17 , Oriol Dols-Icardo  18   19 , Holger Hummerich  17 , Amit Kawalia  20 , Philippe Amouyel  10 , Gary W Beecham  21 , Claudine Berr  22 , Joshua C Bis  23 , Anne Boland  24 , Paola Bossù  25 , Femke Bouwman  26   27 , Jose Bras  28   29 , Dominique Campion  9 , J Nicholas Cochran  30 , Antonio Daniele  31 , Jean-François Dartigues  32 , Stéphanie Debette  32   33 , Jean-François Deleuze  24 , Nicola Denning  34 , Anita L DeStefano  35   36   37 , Lindsay A Farrer  35   37   38   39 , Maria Victoria Fernández  40   41   42 , Nick C Fox  43 , Daniela Galimberti  44   45 , Emmanuelle Genin  46 , Johan J P Gille  47 , Yann Le Guen  48 , Rita Guerreiro  28   29 , Jonathan L Haines  49 , Clive Holmes  50 , M Arfan Ikram  14 , M Kamran Ikram  14 , Iris E Jansen  26   27   51 , Robert Kraaij  13 , Marc Lathrop  52 , Afina W Lemstra  26   27 , Alberto Lleó  18   19 , Lauren Luckcuck  11 , Marcel M A M Mannens  53 , Rachel Marshall  11 , Eden R Martin  21   54 , Carlo Masullo  55 , Richard Mayeux  56   57 , Patrizia Mecocci  58 , Alun Meggy  34 , Merel O Mol  12 , Kevin Morgan  59 , Richard M Myers  30 , Benedetta Nacmias  60   61 , Adam C Naj  62   63 , Valerio Napolioni  48   64 , Florence Pasquier  65 , Pau Pastor  66   67 , Margaret A Pericak-Vance  21   54 , Rachel Raybould  34 , Richard Redon  68 , Marcel J T Reinders  69 , Anne-Claire Richard  9 , Steffi G Riedel-Heller  70 , Fernando Rivadeneira  13 , Stéphane Rousseau  9 , Natalie S Ryan  43 , Salha Saad  11 , Pascual Sanchez-Juan  19   71 , Gerard D Schellenberg  63 , Philip Scheltens  26   27 , Jonathan M Schott  43 , Davide Seripa  72 , Sudha Seshadri  36   37   73 , Daoud Sie  47 , Erik A Sistermans  47 , Sandro Sorbi  60   61 , Resie van Spaendonk  47 , Gianfranco Spalletta  74 , Niccolo' Tesi  75   26   27   69 , Betty Tijms  26 , André G Uitterlinden  13 , Sven J van der Lee  75   26   27   69 , Pieter Jelle Visser  26 , Michael Wagner  76   77 , David Wallon  78 , Li-San Wang  63 , Aline Zarea  78 , Jordi Clarimon  18   19 , John C van Swieten  12 , Michael D Greicius  48 , Jennifer S Yokoyama  79 , Carlos Cruchaga  40   41   42 , John Hardy  80 , Alfredo Ramirez  20   73   76   77   81 , Simon Mead  17 , Wiesje M van der Flier  26   27 , Cornelia M van Duijn  14   16 , Julie Williams  11 , Gaël Nicolas #  82 , Céline Bellenguez #  10 , Jean-Charles Lambert #  83
Affiliations

Exome sequencing identifies rare damaging variants in ATP8B4 and ABCA1 as risk factors for Alzheimer's disease

Henne Holstege et al. Nat Genet. 2022 Dec.

Abstract

Alzheimer's disease (AD), the leading cause of dementia, has an estimated heritability of approximately 70%1. The genetic component of AD has been mainly assessed using genome-wide association studies, which do not capture the risk contributed by rare variants2. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals-16,036 AD cases and 16,522 controls. Next to variants in TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Additionally, the rare-variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential drivers of respective AD-genome-wide association study loci. Variants associated with the strongest effect on AD risk, in particular loss-of-function variants, are enriched in early-onset AD cases. Our results provide additional evidence for a major role for amyloid-β precursor protein processing, amyloid-β aggregation, lipid metabolism and microglial function in AD.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Study setup and power.
a, Schematic of the study setup. The AD association of genes identified in stage 1 was confirmed in stage 2 and significance was determined by meta-analysis. Variant characteristics were investigated in a merged mega-sample rather than the meta-sample, allowing more accurate variant effect size estimates for variant categories/age-at-onset bins. The mega-sample (without exome extracts) was also used for the GWAS gene burden analysis. MTC, multiple testing correction. b, Top, number of genes (y axis) with at least a certain cumulative carrier frequency of prioritized variants (x axis), prioritized according to different deleteriousness thresholds. White box, genes with a cMAC ≥ 10 (cumulative minor allele count of ≥10 prioritized alleles identified across the 12,652 cases and 8,693 controls in the stage 1 sample) were considered to have sufficient carrier frequency to allow burden analysis. The SORL1, TREM2 and ABCA7 genes are indicated, revealing that carriers of rare damaging variants in these genes are relatively common, allowing identification in smaller sample sizes–. Bottom, power analysis for stage 1, to attain a P < 1 × 10−6, at the same scale as the top figure. For comparison, we indicate 80% power thresholds for sample sizes of 1,000 and 5,000 individuals (subsampled from stage 1). Cumulative carrier frequency and estimated effect size ranges are indicated for common variants identified to associate with AD by GWAS (green), rare-variant burdens in SORL1, TREM2 and ABCA7 identified using sequencing studies– (grey/blue), and for rare variants observed in autosomal dominant AD (magenta). Common variants with high effect sizes (red) are not expected to exist. Genes with cMAC < 10 were not analyzed (pink). Power calculations show that aggregating more cases and controls might allow for the identification of rare-variants that have a large effect on AD but for which only few carriers are observed, or for variants that have a modest/average effect on AD, for which many carriers are observed (power calculations shown in Supplementary Table 6). c, Quantile–quantile plot of P values determined in the stage 1 discovery analysis based on an ordinal logistic burden test. For each of 13,222 genes, we tested the burden of variants adhering to four variant deleteriousness thresholds, conditional on having a cMAC ≥ 10 (n = 31,204 tests). Threshold for multiple testing correction: FDR < 0.1, P value inflation, 1.046. Gene names in black indicate the deleteriousness threshold of the most significant burden test in that gene.
Fig. 2
Fig. 2. Characterization of gene-specific variant features based on the mega-sample.
For all variant features, we considered the deleteriousness threshold that provides the most evidence for AD association in the meta-analysis. Variant features were investigated in a merged mega-sample (n = 31,905) instead of the meta-sample because this allows for increased accuracy for estimations of variant effect sizes for each variant category/age-at-onset bin (Table 3, refined burden). a, Carrier frequency according to age at onset. A carrier carries at least one damaging variant in the considered gene. b, ORs according to age at onset. The effect size significantly decreased with age at onset for SORL1, TREM2, ABCA7, ABCA1 and ADAM10 (after multiple testing correction; Supplementary Table 9). c, ORs according to variant frequency. The rareness of variants in SORL1 was significantly associated with the effect size (Supplementary Table 11). d, cMAC by variant frequency: the stacked total number of cases (dark) and controls (light) that carry gene variants with allele frequencies as observed in the mega-sample. The numbers above the bars indicate the number of contributing variants. Whiskers: 95% CI. Genes in black: genes identified to significantly associate with AD in the meta-analysis; gray: genes not significantly associated with AD in the meta-analysis; blue: genes identified by the targeted GWAS analysis, these were not significantly associated with AD in the meta-analysis. Source data
Fig. 3
Fig. 3. ORs according to age at onset and variant pathogenicity.
ORs for LOF (red) and missense (yellow) variants as observed in the mega-sample (n = 31,905). Case/control OR (square, 95% CI), EOAD OR (triangle pointing upward), LOAD OR (triangle pointing downward). Missense variants in the considered gene appertained to the variant deleteriousness threshold that provides the most evidence for its AD association (Table 3, refined). The LOF burden effect size was significantly larger than the missense burden effect size in the SORL1 and we observed similar trends in ABCA7 and ABCA1 (Supplementary Table11). Of note, for ZCWPW1 only the burden of the LOF variants was significantly associated with AD; missense variants are shown for reference purposes (REVEL > 25).Grey: gene was not significantly associated with AD in the meta-analysis. Source data
Extended Data Fig. 1
Extended Data Fig. 1. Age, gender, APOE genotype distribution.
Age, gender and APOE genotype distribution of all samples, stratified by case/control status.
Extended Data Fig. 2
Extended Data Fig. 2. PCA: Sample population compared to 1,000 G population samples.
Sample population compared to 1,000 G population samples. First two PCA components of the study samples used for the Stage 1 and Stage 2 analysis, shown in context of the 1000 Genomes samples for reference (see Supplementary Note section 1.3.4). Samples in red are considered population outliers. Samples with only exome-extracts were not included in this analysis.
Extended Data Fig. 3
Extended Data Fig. 3. P value inflation in Stage-2 analysis.
P value inflation in Stage-2 analysis: Quantile-quantile plot for Stage-2 (without exome-extract samples), based on a ordinal logistic burden test (see Methods). Results are shown for all burden tests (n = 20,681) for which at least 10 damaging alleles were present in this dataset (based on 4 different variant deleteriousness thresholds per gene). While not used in this analysis, the threshold for multiple testing correction based on FDR < 0.1 is shown for reference. The genomic p-value inflation was 1.016. Note that causative mutations were not separately removed in Stage-2, as we focused on a specific set of genes.
Extended Data Fig. 4
Extended Data Fig. 4. P value inflation in the mega-analysis dataset.
P value inflation in the mega-analysis dataset: Quantile-quantile plot for the mega-analysis dataset (without exome-extract samples) based on a ordinal logistic burden test (see Methods). Results are shown for all burden tests (n = 37,710) for which at least 10 damaging alleles were present in this dataset (based on 4 different variant deleteriousness thresholds per gene). For reference, the threshold for multiple testing correction based on a false discovery rate threshold of 0.1 is shown. P values for the mega-analysis are shown in Supplementary Table 15. The genomic p-value inflation was 1.025.
Extended Data Fig. 5
Extended Data Fig. 5. Variant carrier frequency in controls by age last seen.
Variant carrier frequency in controls by age last seen: Carrier frequency in controls by age last seen for the variant selection threshold with the strongest association, as observed in the mega-analysis (n = 31,905 unique individuals); RIN3, CLU, ZCWPW1, ACE (n = 29,727 unique individuals; that is without exome-extracts) (Table 3, refined). Black: genes significant in the meta-analysis. Grey: genes not significant in meta-analysis. Blue: genes detected in the GWAS targeted analysis.
Extended Data Fig. 6
Extended Data Fig. 6. Age-at-onset by variant deleteriousness category.
Age-at-onset by variant deleteriousness category: Age-at-onset (median and IQR) in the mega-analysis (n = 31,905 unique individuals); RIN3, CLU, ZCWPW1, ACE (n = 29,727 unique individuals; that is without exome-extracts). Samples in variant deleteriousness categories with <10 samples are shown individually. The median age at onset and IQR for the complete mega-analysis dataset is shown on the right. Black: genes significant in the meta-analysis. Grey: genes not significant in meta-analysis. Blue: genes detected in the GWAS targeted analysis.
Extended Data Fig. 7
Extended Data Fig. 7. Attributable fraction per gene and age-at-onset category.
Attributable fraction per gene and age-at-onset category: Attributable fractions as derived based on the mega-analysis in the mega-analysis (n = 31,905 unique individuals); RIN3, CLU, ZCWPW1, ACE (n = 29,727 unique individuals; that is without exome-extracts). The attributable fraction of a gene is an estimate of the fraction of AD cases in a specific age group that have become part of this dataset due to carrying a rare damaging variant in the respective gene (Methods). This estimate accounts only for variants in the burden selection. Black: genes significant in the meta-analysis. Grey: genes not significant in meta-analysis. Blue: genes detected in the GWAS targeted analysis.
Extended Data Fig. 8
Extended Data Fig. 8. Sensitivity Analysis: AD vs Age association.
AD vs Age association: Sensitivity analysis of the gene burden tests (for the most significant deleteriousness thresholds, Table 2) for the mega-analysis dataset (RIN3, CLU, ZCWPW1, ACE: without exome-extracts) (respectively n = 31,905 and n = 29,727 unique individuals). Comparison of the case/control odds ratio of an age-matched and a non-age-matched analysis. Age-matching was performed as described in the methods. Based on the confidence intervals, we cannot exclude that the signals in ACE, ADAM10 and ZCWPW1 are affected by other age-related conditions. Note however, that the signals in ADAM10 and ZCWPW1 are based on very few variants, such that confidence intervals are expected to be wide.
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