Fine-Mapping of the TMEM106B Locus Reveals Four Haplotypes That are Differentially Associated With Risk for Neurodegeneration

Fine-mapping of the TMEM106B locus identifies four haplotypes with differential associations to neurodegeneration | 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 Fine-mapping of the TMEM106B locus identifies four haplotypes with differential associations to neurodegeneration View ORCID Profile Alex Salazar , Niccolò Tesi , Lydian Knoop , Martijn Huisman , Natasja M. van Schoor , View ORCID Profile Betty Tijms , Jort Vijverberg , View ORCID Profile Sven van der Lee , Sanduni Wijesekera , Jana Krizova , Mikko Hiltunen , Markus Damme , Leonard Petrucelli , View ORCID Profile Marcel Reinders , August B. Smit , Anke A. Dijkstra , Marc Hulsman , ADGC , Bonn , CHARGE , EADB , EADI , FinnGen , GERAD , GR@ACE/DEGESCO , PGC-ALZ , View ORCID Profile Henne Holstege doi: https://doi.org/10.1101/2025.02.02.25321330 Alex Salazar 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alex Salazar Niccolò Tesi 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands 2 Delft Bioinformatics Lab, Delft University of Technology , Delft, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lydian Knoop 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Martijn Huisman 3 Department of Epidemiology and Data Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam , The Netherlands 4 Amsterdam Public Health Research Institute, Aging & Later Life , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Natasja M. van Schoor 3 Department of Epidemiology and Data Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam , The Netherlands 4 Amsterdam Public Health Research Institute, Aging & Later Life , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Betty Tijms 5 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam , Amsterdam UMC location VUmc, Amsterdam, The Netherlands 6 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Betty Tijms Jort Vijverberg 5 Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam , Amsterdam UMC location VUmc, Amsterdam, The Netherlands 6 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Sven van der Lee 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands 6 Amsterdam Neuroscience, Neurodegeneration , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sven van der Lee Sanduni Wijesekera 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jana Krizova 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Mikko Hiltunen 7 Institute of Biomedicine, University of Eastern Finland. Kuopio , Finland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Markus Damme 8 Institute of Biochemistry, Christian-Albrechts-University Kiel , Otto-Hahn-Platz 9, Kiel, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Leonard Petrucelli 9 Department of Neuroscience, Mayo Clinic , Jacksonville, FL, USA 10 Neuroscience Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences , Jacksonville, FL, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marcel Reinders 2 Delft Bioinformatics Lab, Delft University of Technology , Delft, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marcel Reinders August B. Smit 11 Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam , Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Anke A. Dijkstra 12 Department of Pathology, Amsterdam UMC, Amsterdam Neuroscience , Amsterdam, Netherlands 13 Swammerdam Institute for Life Sciences, University of Amsterdam , Amsterdam, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marc Hulsman 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Henne Holstege 1 Section Genomics of Neurodegenerative Diseases and Aging, Department of Clinical Genetics, Vrije Universiteit Amsterdam , Amsterdam UMC, Amsterdam, The Netherlands 3 Department of Epidemiology and Data Sciences, Amsterdam UMC location Vrije Universiteit Amsterdam , The Netherlands 4 Amsterdam Public Health Research Institute, Aging & Later Life , Amsterdam, The Netherlands 14 VIB Center for Brain and Disease Research , Leuven, Belgium 15 Department of Neurosciences, Leuven Brain Institute , KU Leuven, Leuven, Belgium Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Henne Holstege For correspondence: h.holstege{at}amsterdamumc.nl Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Genetic variation at the TMEM106B locus has been associated with Alzheimer’s disease (AD) and related neurodegenerative phenotypes, yet its haplotypic architecture remains incompletely resolved. We refined the genetic landscape of TMEM106B by integrating AD-GWAS summary statistics (∼978K individuals) with haplotype analyses in 5,873 Dutch genomes including AD-cases, age-matched controls, and cognitively healthy centenarians. In addition to the known linkage disequilibrium (LD) block defining risk and protective haplotypes characterized by the amino acid substitution at residue 185 (Threonine/Serine), we identify a second conditionally independent LD block. Together these form four haplotypes (T1-T4), with T3 strongly enriched in centenarians. Long-read whole-genome sequencing in 493 individuals reveals haplotype-specific methylation patterns and structural variation, including a ∼19 Kbp genomic rearrangement carried by T3. These findings indicate that the TMEM106B locus comprises multiple haplotypes defined by methylation patterns and structural variants, offering insights into GWAS associations with AD risk and cognitive longevity. INTRODUCTION TMEM106B plays an important role in maintaining cognitive health during aging. Genome-wide association studies (GWAS) have repeatedly identified variants at the TMEM106B locus that influence risk for multiple neurodegenerative diseases [ 1 – 3 ]. These variants are in complete linkage disequilibrium (LD), forming an LD-block that defines two haplotypes. The risk haplotype has a frequency of ∼59% in European populations, and has been associated with various neurodegenerative diseases, including Alzheimer’s disease [ 3 ] and frontotemporal dementia [ 1 , 2 ]. The reciprocal protective haplotype carries the alleles associated with protection against these traits, and occurs at a frequency of approximately ∼41%. We recently observed that the protective haplotype is among the most enriched AD-associated loci in cognitively healthy centenarians [ 4 ]. This enrichment suggests that TMEM106B may contribute to preserved cognitive function into extreme old age. Importantly, recent neuropathological analyses indicate that TMEM106B variants are primarily associated with TDP-43-related pathologies (including LATE-NC and HS) [ 5 ]; tau tangles [ 5 , 6 ]; and aggregation of TMEM106B fibrils [ 7 – 9 ]. This raises the possibility that the enrichment of the protective haplotype in centenarians reflects protection against neurodegeneration mediated by these proteinopathies. Consistent with this, a subset of centenarians from the 100-plus cohort exhibited TDP-43 aggregates in the brain, and TDP-43 positivity was associated with reduced cognitive performance [ 10 ]. The specific causal variants underlying the effects in the TMEM106B haplotypes remain unresolved [ 11 , 12 ]. One candidate is the missense variant rs3173615 (C>T), which results in a Thr185Ser substitution. The risk-associated rs3173615-C allele encodes threonine at position 185 and has been hypothesized to alter glycosylation, potentially influencing protein stability or aggregation propensity [ 13 ]. Indeed, carriers of the risk haplotype exhibit higher levels of TMEM106B C-terminal aggregates compared to carriers of the rs3173615-G allele (185-Ser) [ 7 – 9 ]. However, knock-in of the homologous rs3173615-G allele in mice did not confer protection [ 12 ], suggesting that this variant alone may not explain disease risk. The risk haplotype contains multiple additional variants in complete LD with rs3173615-C that could contribute to functional differences. These include an AluYb8 insertion in the 3′ untranslated region (3′ UTR) [ 14 – 16 ] and a variant affecting CTCF binding near rs1990620 [ 17 ], both of which may influence transcriptional or post-transcriptional regulation of TMEM106B . Despite consistent GWAS associations, the genetic architecture underlying the TMEM106B locus remains incompletely resolved. In particular, variants within the locus are typically inherited in strong linkage disequilibrium, complicating efforts to disentangle independent signals and identify functional alleles. Moreover, structural variants (SVs), including genomic rearrangements, tandem repeats, and transposable elements, are largely invisible to conventional imputation-based approaches and may contribute to haplotype-specific effects that remain unexplored. Here, we refine the TMEM106B locus by integrating long-read sequencing and large-scale GWAS summary statistics with haplotype analyses in cognitively healthy centenarians, AD cases, and age-matched controls. METHODS Study population Samples used in this study included a total 5,873 genomes: 443 centenarians from the 100-plus Study [ 18 ]; 2,321 Alzheimer disease (AD) patients from the Amsterdam Dementia Cohort [ 19 ] and the Netherlands Brain Bank [ 20 ]; and 3,109 non-demented controls age-matched with the AD patients previously described [ 4 ], including 1,614 from LASA cohort [ 21 ]. The Medical Ethics Committee of the Amsterdam University Medical Center approved all studies. All participants and/or their legal representatives provided written informed consent for participation in clinical and genetic studies. Genotyping and AD-GWAS summary statistics Array-based genotyping and haplotype-imputations of the above cohorts were previously described [ 4 ]. We analyzed GWAS variants within 100 Kbp upstream and downstream of TMEM106B coding sequence (chr7:12111294-12343367), using the latest AD-GWAS summary statistics [ 22 ]. Pairwise linkage disequilibrium (LD) values (r 2 ) were calculated with PLINK (v2.00a6LM) [ 23 ] using the centenarian, AD-cases, and control cohorts described above, and variants were clumped using a conservative r 2 threshold of 0.15. We retained the sentinel SNPs of LD blocks with genome-wide significance in the AD-GWAS (p-value < 5.5×10 −8 ). We define an LD block as a set of variants that were in strong LD (r 2 ≥0.8) with the sentinel SNPs, resulting in two blocks: Block 1 (marked by rs5011439) and Block 2 (rs13237415). To avoid overestimation in downstream analysis, we additionally performed pairwise LD between all variants within each LD block, and identified a representative SNP for each block via hierarchical clustering, resulting in two medoid-based marker SNPs for Block 1 (rs12699338) and Block 2 (rs12699361). Joint effect analyses using GCTA-COJO (v1.94.1) [ 24 ] were performed separately for (i) sentinel SNPs from each block; and (ii) the corresponding medoid SNPs, confirming that both sets show independent associations. Differential enrichment of TMEM106B haplotype in centenarians Using the array-imputed haplotypes of the 5,873 genomes (11,746 total haplotypes), we used the sentinel SNPs to classify TMEM106B haplotypes (T1-T4), as detailed in Table S1. To determine whether individual haplotypes and their genotypes were enriched in centenarians, we used logistic regression models implemented in R (v4.5.2) with the glm function by separately comparing centenarians with non-demented controls and AD-cases. All models were adjusted for APOE genotype (modeled as categorical factors) and population structure using the first five genetic principal components calculated with PLINK. For the enrichment analysis of individual haplotypes, we applied a weighted sum-to-zero (effect) encoding in a joint model to test the independent enrichments of the T1-T4 haplotype. This approach enables estimation of the independent effect of each haplotype relative to the frequency-weighted average effect across all haplotypes, allowing effect sizes (β), standard errors, 95% confidence intervals, and p-values to be interpreted on a common scale. Briefly, we fitted a joint logistic regression model with centenarian phenotype as the response variable, Centenarian ∼ T2 + T3 + T4 + APOE + PC 1-5 , using T1 as the reference level. We then used the estimated β’s and their variance-covariance matrix to compute the frequency-weighted average effect across all haplotypes, including T1, where weights were defined by haplotype frequencies in 5,873 array-imputed genomes. We re-expressed the haplotype-specific β’s under a weighted sum-to-zero constraint, and applied Wald tests to evaluate whether each haplotype significantly deviated from the weighted average effect. To determine specific genotypes of the TMEM106B haplotypes, we constructed a categorical variable representing all observed genotypes (e.g., T1/T1, T1/T2, T2/T3, etc.). We then fitted a joint logistic regression model using T1/T1 as the reference genotype, adjusting for APOE genotype and genetic PC 1-5 . Due to the relative low frequency of T4, we adjusted for but not interpreted estimated effects T4-containing genotypes. Odds ratios and 95% confidence intervals were calculated from the regression coefficients. To account for multiple testing, we defined two families of tests: (i) enrichment of individual haplotypes and (ii) enrichment of genotypes. We controlled the false discovery rate (FDR) within each family using the Benjamini–Hochberg procedure (family i: 8 tests; family ii: 10 tests). FDR-adjusted q-values <0.1 were considered statistically significant. Adjusted q-values are reported alongside nominal p-values. Long-read sequencing of 493 Dutch genomes Peripheral blood tissue of a subset of 245 CHCs and 248 ADCs were long-read whole-genome sequenced on Pacific Biosciences (PacBio) Sequel IIe System. For 10 centenarians, we long-read whole-genome sequenced temporal brain tissue. All samples were sequenced at the Department of Clinical Genetics at the Amsterdam University Medical Center (AUMC) and at the Radboud Technology Genome Center and Radboud University Medical Center (RUMC), using 2 hours of pre-extension time and 30 hours of collection time. Overall, the AD and centenarian genomes were sequenced at a median coverage of 18.2x, with median read-lengths of 14.8 Kbp. See extended details in Supplemental Methods . After sequencing, raw reads were collected and analyzed through an in-house pipeline (freely available at https://github.com/holstegelab/snakemake_pipeline ; Supplemental Methods). In addition, we used publicly available HiFi-based 47 whole-genomes from the Human Pangenome Research Consortium, which were similarly processed through our in-house pipeline ( Supplemental Methods ). Variant and methylation profiles using long-read sequencing data Single nucleotide polymorphisms (SNPs) were identified in both the ADC and CHC long-read sequencing data using deepvariant (v1.1.0) [ 25 ], and joint-called using GLnexus (v1.4.1-0-g68e25e5) using ‘--config Deepvariant’ parameter [ 26 ]. Structural variants (SVs) in AD cases, centenarians, and HPRC genomes were identified and genotyped using an in-house local assembly pipeline based on Sniffles2 (v2.0.6) [ 27 ] and Otter (v1.0) [ 28 ] ( Supplemental Methods) . The pipeline comprises three steps. First, we performed local assembly of allele sequences for each candidate SV in each genome. Second, we conducted joint genotyping across genomes using a fast sequence-based clustering procedure implemented in Otter ( Supplemental Results ), enabling direct comparison and harmonization of SV allele sequences across >1 million SV regions and large collections of long-read genomes. This step generated an SV-based gVCF for the centenarian and AD genomes. Third, we recalibrated SV genotypes by probabilistically inferring the most likely genotype based on supporting long-read alignments, together with a corresponding genotype quality (GQ) score, using the svg function in Haplr (v0.1.0) ( https://github.com/holstegelab/haplr ), enabling consistent genotyping across both high-coverage and low-coverage samples. Additionally, this procedure also quantifies CpG methylation within each SV-allele. The corresponding allele-sequence of all unique genotypes of all SVs were annotated using DFAM (release 3.7) [ 29 ] to annotate transposable elements (TEs), while tandem repeats (TRs) were annotated by overlapping TR-annotation tracks from the UCSC Genome Browser ( http://genome.ucsc.edu ) [ 30 ]. To evaluate haplotype-specific structural variants (SVs) and CpG methylation, reads were phased using the svg and mtp functions in Haplr. GWAS variants defined in ( Table S1 ), enabling assignment of SV alleles and CpG methylation to specific haplotypes. For methylation analyses, we focused on CpG sites within the start/end coordinates from the union of both LD blocks (chr7:12146976-12247317). CpG sites were retained if they had a minimum per-sample coverage of 4 reads and were observed in at least 10 independent individuals. Methylation levels were calculated per individual and haplotype as the fraction of methylated reads over total reads. CpG methylation was summarised separately for each pairwise haplotype-comparison (T1 vs T2; T1 vs T3; and T2 vs T3) across individuals, and sites with at least 10% median methylation between haplotypes (|Δ methylation| ≥ 0.1) were considered differentially methylated CpG sites, and those with a 90% difference (|Δ methylation| ≥ 0.9) were considered haplotype-specific. Regulatory annotation was incorporated using candidate cis-regulatory element (cCRE) tracks from ENCODE (dataset ENCFF924IMH) [ 31 ]. RESULTS Two independent LD blocks in the TMEM106B locus are associated with Alzheimer’s disease (AD) To explore whether additional haplotypes, next to the known risk and protective haplotypes also associated with AD-risk, we used linkage disequilibrium (LD) profiles from 5,873 Dutch genomes comprising of cognitively healthy centenarians, AD-cases, and age-matched controls to analyze the summary statistics of the most recent AD-GWAS [ 22 ] (∼978K participants; Figure 1A ). We restricted analyses to common variants that (i) surpassed genome-wide significance in their associations with AD (p≤5.5×10 −8 ), (ii) had minor allele frequency ≥1%, and (iii) clustered within multi-SNP LD blocks defined by r 2 ≥0.15 (see Methods ). Download figure Open in new tab Figure 1. Four separate TMEM106B haplotypes are differentially associated with risk for Alzheimer’s disease (AD) and extreme cognitive preservation. (A) LocusZoom plot of the TMEM106B locus in the latest AD-GWAS [ 22 ] highlighting conditionally independent LD blocks. Top: Block 1 encompassing the known risk and protective haplotypes which harbour alleles previously associated with risk to various neurodegenerative traits and pathologies, marked by rs5011439. Bottom: Block 2 encompassing novel risk and protective haplotypes marked by rs13237415 with similar effects as Block 1. (B) The alleles from both blocks are inherited in four specific haplotypes, T1-T4, as observed in 5,873 Dutch genomes. (C-D) Enrichment analysis of the haplotypes when comparing centenarians with controls or AD-cases. FDR-adjusted p (10% threshold): *** < 0.01; ** < 0.05; * < 0.1. (C) At the individual haplotype level, T3 is significantly enriched in centenarians, while T1 is significantly depleted. (D) Relative to T1/T1 homozygous carriers, the T2/T3 genotype is significantly enriched in centenarians. Two independent LD-blocks at TMEM106B reached genome-wide significance ( Figure 1A ). Block 1 (marker SNP rs5011439-C; AF=43%, OR=0.96, p=1.9×10 −13 ) overlaps the TMEM106B coding region and corresponds to known risk and protective haplotypes (risk: rs5011439-G; protective: rs5011439-C). Block 2, marked by SNP rs13237415-T (AF=9%, OR=0.94, p=7.7×10 −9 ), spans ∼65 kb upstream to ∼25 kb downstream of TMEM106B ( Figure 1A ). The blocks are largely independent (r 2 =0.11), and each remained significant in joint/conditional GCTA-COJO analyses (OR=0.96 for both; p=6.4×10 −9 and 3.3×10 −4 ), even when extending to other marker SNPs in their respective LD blocks ( Methods ). All SNPs in Block 2 are in strong LD (r 2 ≥0.8; Table S1 ), defining two additional haplotypes with rs13237415-G associated with risk and rs13246024-T with protection. Together, these findings establish that the TMEM106B locus comprises two conditionally independent association signals, each defined by distinct haplotypes. Four TMEM106B haplotypes are differentially enriched in cognitively healthy centenarians The allele combination of the sentinel SNPs of blocks 1 and 2 define four haplotypes across all genomes (n=11,746; Figure 1B and Table S1 ). We refer to these haplotypes as T1-T4 in descending frequency: T1 is the most common combination (57%, n=6,701) and contains the risk-associated alleles of both blocks. T2 follows (33%, n=3,919) and carries the protective alleles of block 1 and risk alleles of block 2. T3 (9%, n=1,020) carries protective alleles from both blocks, and T4 (∼1%, n=106) carries risk alleles from block 1 and protective alleles from block 2. We found that, compared with non-demented controls, carriers of T3 were significantly associated with a 1.42-fold higher odds of being a centenarian (OR=1.42, 95% CI=[1.14,1.76]; p=0.002; q=0.007). In contrast, T3 was significantly depleted in centenarians, with a 0.9-fold lower odds (OR=0.90, CI=[0.84,0.96]; p=7.43×10 −4 ; q=0.006). T2 had modest significant enrichment (OR=1.10, CI=[0.99,1.21]; p=0.070; q=0.093), while enrichment for T4 was imprecise due to its relative low frequency (OR=1.03, CI=[0.49,2.16]; p=0.944; q=0.944). To further disentangle the contribution of the 185-Serine-encoding haplotypes, we repeated the analysis using T2mthe most frequent serine-containing haplotype, as the reference. Under this model, T1 remained depleted (OR=0.82, CI=[0.70,0.96]; p=0.012), while T3 remained enriched (OR=1.29, CI=[1.00,1.66]; p=0.048), indicating that the enrichment of T3 cannot be explained by the serine allele alone and may reflect additional protective features. Similar patterns were observed when comparing centenarians with AD cases, although effect sizes were attenuated ( Figure 1C ) . Analogous to the genotype-specific effects observed for ε4/ε4, ε4/ε3, and ε4/ε2 at APOE , we next examined the TMEM106B genotypes in centenarians. To do this, we fitted a logistic regression model relative to T1/T1 homozygous carriers, the genotype with the most depleted haplotype. This analysis reinforced the prominent role of T3 ( Figure 1D ): the heterozygous T2/T3 genotype had a 2.10-fold significantly higher odds of becoming a centenarian (OR=2.10, CI=[1.39,3.20]; p=4.82×10 −4 ; q=0.005), and significantly increased to 2.92 for the homozygous T3/T3 although with wide confidence intervals (OR=2.92, CI=[1.01,8.40]; p=0.047; q=0.084). Other genotypes, including T1/T2, T1/T3, and T2/T2, showed more modest but significant enrichments ( Figure 1D ). In contrast, estimates for genotypes containing T4 were imprecise due to its low frequency. Similar patterns were observed when comparing centenarians with AD cases, although effect sizes were attenuated ( Figure 1D ). All associations were adjusted for APOE genotype status. Long-read fine-mapping reveals haplotype-specific structural variants in TMEM106B To fine-map the genetic basis of the differential associations observed for the T1-T4 TMEM106B haplotype, we performed long-read whole-genome sequencing of 493 individuals, including 245 AD-cases from the ADC cohort and 248 cognitively healthy centenarians from the 100-plus Study ( Methods ). We used these data to characterize haplotype-resolved structural variation and DNA methylation patterns of the different haplotypes spanning TMEM106B . We found eight total common structural variants (SVs, ≥25 bp, ≥1% frequency). The SVs ranged from 35 bp to ∼19 Kbp ( Figure 2A ), with allele frequencies spanning ∼10–90%, similar to those of the two LD blocks ( Table S1 ). In addition to the previously reported AluYb8 insertion in the 3′UTR, we identified seven novel SVs. The largest was a ∼19 Kbp genomic rearrangement located ∼51 Kbp upstream of the TMEM106B transcription start site, likely mediated by an AluYh3 element ( Figure 2B ). Among the identified SVs, a multi-allelic tandem repeat (TR_12157495) located near the rearrangement directly overlapped a known distal enhancer (EH38E2534480), suggesting potential regulatory relevance. Download figure Open in new tab Figure 2. The TMEM106B haplotypes are characterized by large structural variation (SVs). (A) Landscape of SVs in the TMEM106B locus using 493 long-read sequenced genomes. (B) The largest SV was a ∼19 Kbp genomic rearrangement likely mediated by an AluYh3 element. (C) LD values (r 2 ) between the SVs and the SNP-markers for the Block 1 and Block 2. (D) Long-read phasing and CpG methylation of allele-sequences for SVs in moderate LD (r 2 ≥0.6) with either Block 1 or Block 2. (E) These analyses highlight that SVs distinctly characterize the T1-T4 haplotypes of TMEM106B . Both LD-based analyses and long-read phasing independently supported haplotype-specific associations for four SVs ( Figure 2C and 2D ): the AluYb8 insertion was linked to T1 and T4; and the ∼19 Kbp rearrangement, along with contractions in TR_12157495 and expansions TR_12159254, were linked to T3 and T4 ( Figure 2C ). The AluYb8 insertion and the ∼19 Kbp rearrangement were both highly methylated ( Figure 2D ), highlighting possible regulatory effects. We note that while haplotypes T1-T4 are predominantly present in European (Dutch) ancestry, we observed additional haplotypes with distinct allelic combinations in genomes with African ancestry ( Supplemental Results ), consistent with prior work highlighting differences in LD structure in the TMEM106B locus between genomes with European and African ancestry [ 32 ]. This includes a distinct haplotype with the risk-associated 185-Threonine, and the protective-associated absence of the AluYb8 element ( Figure S1 ). These findings indicate that although both T2 and T3 carry the 185-serine allele, T3 uniquely harbors multiple SVs located ∼51 Kbp upstream of TMEM106B ( Figure 2E ). These haplotype-specific SVs may contribute to the preferential enrichment of T3 observed in centenarians. Functional studies will be required to determine the biological consequences of these variants. The TMEM106B haplotypes have distinct methylation profiles We next investigated whether TMEM106B haplotypes harbor distinct DNA methylation profiles using CpG methylation frequencies derived from haplotype-resolved long-read sequencing data. Pairwise comparisons of the common haplotypes (T1, T2, and T3) identified 146 unique CpG sites that were differentially methylated in at least one comparison (median methylation difference ≥10%), of which 129 showed near-complete haplotype-specific methylation (median difference ≥90%) ( Figure 3A and Table S2 ). Many of these differences were driven by SNPs that created or disrupted CpG dinucleotides, indicating haplotype-specific CpG content ( Table S2 ). This included CpGs overlapping known distal enhancers: two linked to T1, three linked to T2, and four linked to T3. Notably, these differentially methylated CpG sites led to higher methylation in the intronic regions (introns 3 and 4) and the 3’ UTR in T1, including the AluYb8 element; along with low methylation in T3 in the region overlapping the known rearrangement ( Figure 3A ). Download figure Open in new tab Figure 3. The TMEM106B haplotypes are also characterized by distinct CpG DNA methylation profiles. (A-B) CpG methylation profiles derived from phased long-read sequencing of blood samples from 493 individuals. Differentially methylated CpG sites are defined as sites where the fraction of methylated reads is at least 10% between haplotypes. (A) Using differentially methylated CpG sites (n=146), spline regression of median methylation values per common haplotype (T1-T3) along with standard errors (colour shades), with the bottom plot illustrating the canonical transcript of TMEM106B . (B) For a subset of 10 individuals, long-read whole-genome sequencing was also performed on matched brain tissue (temporal cortex). Shown are per-sample methylation differences between brain and blood for CpG sites exhibiting an absolute methylation difference ≥10% (n=301), stratified by haplotype. Homozygous haplotype-genotypes were evaluated as a single measurement. As the long-read sequencing data were derived from blood, we examined whether similar methylation patterns were observed in temporal cortex tissue by performing long-read sequencing on paired brain samples from 10 centenarians ( Figure 3B ). Across 15 distinct haplotypes, we identified 301 unique CpG sites that were differentially methylated between brain and blood (absolute median methylation difference ≥10%). Tissue-specific patterns were consistent across haplotypes: methylation levels were higher in the brain at the transcription start site, whereas blood showed higher methylation across introns 3 through the 3′UTR. These results indicate that haplotype-associated methylation patterns in TMEM106B likely differ by tissues. DISCUSSION In this study, we refine the genetic architecture of the TMEM106B locus and demonstrate that two conditionally independent GWAS LD blocks generate four common haplotypes (T1-T4) with differential associations with Alzheimer’s disease and extreme cognitive preservation. Among these haplotypes, T3 shows the strongest enrichment in cognitively healthy centenarians, while T1 is depleted. These haplotypes are further distinguished by structural and epigenetic features, including large structural variants (SVs) and distinct DNA methylation patterns. This includes a ∼19 Kbp genomic rearrangement ∼51 Kbp upstream of the transcription start site of TMEM106B that are near and/or overlap known distal enhancers, strongly linked to T3 and T4. In addition, T1 is characterized by highly methylated CpG sites within the intronic region and 3′UTR of TMEM106B . Together, these findings extend previous GWAS observations and reveal a more nuanced haplotypic structure underlying TMEM106B -associated risk. Our results do not pinpoint the exact causal variants underlying the associations at the TMEM106B locus, but instead indicate that multiple genetic mechanisms likely contribute beyond the previously emphasized amino acid substitution at residue 185 (Threonine/Serine). In particular, the role of the Serine allele and its protective associations with neurodegenerative traits has been actively debated [ 11 , 12 ]. Although both T2 and T3 harbor the serine allele, T3 is more strongly enriched than T2 in centenarians, suggesting that additional protective mechanisms may be encoded within the T3 haplotype. The large structural variants strongly linked to T3 represent plausible candidates for such effects; however, we note that multiple other variants, including CpG sites in regulatory elements, are also in strong linkage disequilibrium with T3 and may contribute to the observed associations. TMEM106B has been implicated in TMEM106B fibril pathology, TDP-43-related neurodegeneration, and tau pathology [ 5 – 9 ]. The presence of multiple TMEM106B haplotypes raises the possibility that they may differentially influence specific pathological pathways, or exert overlapping effects across these processes. The strong enrichment of T3 in cognitively healthy centenarians suggests that this haplotype may contribute to preserving cognitive function [ 33 , 34 ], and potentially confer protection across multiple proteinopathies. Dissecting pathology-specific effects of individual haplotypes will require neuropathologically characterized cohorts and lies beyond the scope of the present study. Several limitations warrant consideration. First, our structural variant analysis focused on common SVs (allele frequency ≥1%), and mainly in genomes with European ancestry. Rarer variants, and non-European haplotypes may also contribute to locus heterogeneity [ 32 ]. Indeed, we observe additional haplotypes with distinct permutations of the 185-Threonine/Serine amino acids along with the SVs including the AluYb8 element and rearrangement. Evaluating the effects of these novel haplotypes on disease risk is beyond the scope of this study. Second, the T4 haplotype is relatively rare, limiting power to estimate its independent effects with precision. Third, although we identify haplotype-specific structural and methylation differences, functional consequences remain to be experimentally validated. The regulatory impact of the upstream rearrangement, tandem repeats, and AluYb8 elements will require targeted functional assays. Despite these limitations, our findings provide new insight into the genetic basis of TMEM106B -associated risk and resilience. By leveraging extreme longevity as an informative phenotype and combining haplotype-aware GWAS interpretation with long-read sequencing, we uncover structural and epigenetic features that refine the locus beyond single-marker associations. These results underscore the importance of haplotype-resolved analyses and structural variation in interpreting complex disease loci and suggest that similar hidden architectures may underlie other GWAS signals in neurodegeneration. SUPPORTING INFORMATION Supplemental results, methods, figures, and tables can be found in the companion Supplemental Materials and Tables. Consortia members and affiliations can be found in Supplemental Materials. DATA AND MATERIALS AVAILABILITY Long-read sequencing data generated with PacBio Sequel 2 for the AD patients cognitively healthy centenarians is available upon submission of a research proposal to the Alzheimer Genetics Hub (AGH, https://alzheimergenetics.org/ ). FUNDING Part of the work in this manuscript was carried out on the Cartesius supercomputer, which is embedded in the Dutch national e-infrastructure with the support of SURF Cooperative. Computing hours were granted to H. H. by the Dutch Research Council (‘100plus’: project# vuh15226, 15318, 17232, and 2020.030; ‘Role of VNTRs in AD’; project# 2022.31, ‘Alzheimer’s Genetics Hub’ project# 2022.38). This work is supported by a VIDI grant from the Dutch Scientific Counsel (#NWO 09150172010083) and a public-private partnership with TU Delft and PacBIo, receiving funding from ZonMW and Health∼Holland, Topsector Life Sciences & Health (PPP-allowance), and by Alzheimer Nederland WE.03-2018-07. H.H., S.L., are recipients of ABOARD, a public-private partnership receiving funding from ZonMW (#73305095007) and Health∼Holland, Topsector Life Sciences & Health (PPP-allowance; #LSHM20106). S.L. is recipient of ZonMW funding (#733050512). H.H. was supported by the Hans und Ilse Breuer Stiftung (2020), Dioraphte 16020404 (2014) and the HorstingStuit Foundation (2018). Acquisition of the PacBio Sequel II long read sequencing machine was supported by the ADORE Foundation (2022). CONSENT OF STATEMENT The Medical Ethics Committee of the Amsterdam University Medical Center approved all studies. All participants and/or their legal representatives provided written informed consent for participation in clinical and genetic studies. CONFLICT OF INTEREST STATEMENT HH has a collaboration contract with Muna Therapeutics, PacBio, Neurimmune and Alchemab. She serves in the scientific advisory boards of Muna Therapeutics and is an external advisor for Retromer Therapeutics. Research of Alzheimer center Amsterdam is part of the neurodegeneration research program of Amsterdam Neuroscience. Alzheimer Center Amsterdam is supported by Stichting Alzheimer Nederland and Stichting Steun Alzheimercentrum Amsterdam. The clinical database structure was developed with funding from Stichting Dioraphte. ACKNOWLEDGMENTS The authors are grateful to all study participants, their family members, the participating medical staff, general practitioners, pharmacists and all laboratory personnel involved in patient diagnosis, blood collection, blood biobanking, DNA preparation and sequencing. Footnotes This version of the manuscript has been revised to improve clarity, consistency, and interpretation of the genetic architecture at the TMEM106B locus. We refined the description explicitly defining two independent linkage disequilibrium blocks and standardized the naming of haplotypes and genotypes throughout the manuscript. We clarified the statistical framework by separating haplotype-level and genotype-level enrichment analyses in centenarians, and improving the description of regression models and reference groups. We improved the description of structural variant and methylation analyses by clarifying methodological steps and refining terminology. We clarified the analysis of genomes with African ancestry, linking linkage disequilibrium patterns with additional haplotypes not observed in European ancestry genomes. We revised the Introduction and Discussion to better align with the main findings, improve flow, and reduce redundancy. 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Share Fine-mapping of the TMEM106B locus identifies four haplotypes with differential associations to neurodegeneration Alex Salazar , Niccolò Tesi , Lydian Knoop , Martijn Huisman , Natasja M. van Schoor , Betty Tijms , Jort Vijverberg , Sven van der Lee , Sanduni Wijesekera , Jana Krizova , Mikko Hiltunen , Markus Damme , Leonard Petrucelli , Marcel Reinders , August B. Smit , Anke A. Dijkstra , Marc Hulsman , ADGC , Bonn , CHARGE , EADB , EADI , FinnGen , GERAD , GR@ACE/DEGESCO , PGC-ALZ , Henne Holstege medRxiv 2025.02.02.25321330; doi: https://doi.org/10.1101/2025.02.02.25321330 Share This Article: Copy Citation Tools Fine-mapping of the TMEM106B locus identifies four haplotypes with differential associations to neurodegeneration Alex Salazar , Niccolò Tesi , Lydian Knoop , Martijn Huisman , Natasja M. van Schoor , Betty Tijms , Jort Vijverberg , Sven van der Lee , Sanduni Wijesekera , Jana Krizova , Mikko Hiltunen , Markus Damme , Leonard Petrucelli , Marcel Reinders , August B. Smit , Anke A. Dijkstra , Marc Hulsman , ADGC , Bonn , CHARGE , EADB , EADI , FinnGen , GERAD , GR@ACE/DEGESCO , PGC-ALZ , Henne Holstege medRxiv 2025.02.02.25321330; doi: https://doi.org/10.1101/2025.02.02.25321330 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 Genetic and Genomic Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (297) Cardiovascular Medicine (4421) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (606) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15212) Forensic Medicine (30) Gastroenterology (1121) Genetic and Genomic Medicine (6581) Geriatric Medicine (667) Health Economics (996) Health Informatics (4520) Health Policy (1366) Health Systems and Quality Improvement (1611) Hematology (539) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15906) Intensive Care and Critical Care Medicine (1103) Medical Education (620) Medical Ethics (144) Nephrology (667) Neurology (6580) Nursing (345) Nutrition (998) Obstetrics and Gynecology (1141) Occupational and Environmental Health (956) Oncology (3324) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1689) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5433) Public and Global Health (9212) Radiology and Imaging (2193) Rehabilitation Medicine and Physical Therapy (1368) Respiratory Medicine (1194) Rheumatology (593) Sexual and Reproductive Health (709) Sports Medicine (529) Surgery (709) Toxicology (99) Transplantation (288) 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:'9ff4fb9d2ebead07',t:'MTc3OTM4MTIwNg=='};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())}}}})();