Background
Genome-wide association studies (GWAS) linked TMEM106B variants to susceptibility for neurodegenerative diseases, but the causal genetic elements remain unclear.

Method
We used genotyping data from 5,792 Alzheimer disease cases and controls, and applied COJO to identify haplotypes in the TMEM106B locus that independently associated with AD. Then, we used long-read sequencing data from 513 individuals to annotate these haplotypes with structural variations that map into them.

Results
Analysis of the genotyping data revealed that the TMEM106B locus consists of four major haplotypes: HA/Ha (covering the coding region), and HB/Hb (covering the upstream regulatory region). These combine into four combinations with varying population-frequencies: HAB (57%), HaB (34%), Hab (9%), and HAb (<1%). Long-read sequencing of 513 individuals showed that HA haplotypes (marked by 185-Threonine) carry unique methylated CpG sites and an AluYb8-retrotransposon in the 3' UTR, while the Ha haplotypes are marked by the 185-Serine allele. Hb haplotypes carry several structural variants (SVs) in nearby distal enhancers, including a 19 Kbp rearrangement, absent in all other haplotypes. Joint association models revealed that the HAB combination (AluYb8+185-Threonine) is risk-increasing, while Hab (SVs+185-Serine) confers the protective effect. HaB (185-Serine only) is neutral, while HAb was too rare to assess. Relative to middle-aged non-demented controls, cognitively healthy centenarians were more enriched with Hab (OR=1.49, padj=2.18×10-2) than with HaB (OR=1.23, padj=5.06×10-2). Proteomic analysis of temporal cortex tissues (n = 182) indicated that relative to the neutral HaB combination, the protective Hab is associated with 1.1-fold lower TMEM106B C-terminal peptide abundance, while the risk-increasing HAB is associated with 1.16-fold higher abundance.

Conclusion
Our data indicates that the genetic structure underlying the association of the TMEM106B locus with neurodegenerative diseases is driven by the effect of multiple haplotypes.

Background
Many types of dementia have high heritability, which creates opportunities for DNA diagnostics. Clinicians sporadically test for causal genetic variants. However, in addition to causal genetic mutations, an increasing number of both common and rare risk factors are being identified, especially for Alzheimer’s disease (AD). Here, we describe and evaluate diagnostic performance of combining genetic risk factors for AD to assist memory clinic clinicians.

Methods
A retrospective analysis of 998 consecutive patients (mean age 62.1, 40.3% females, 63.3% dementia) was conducted over 2.5 years in a Dutch memory clinic. The patients underwent a complete genetic risk assessment, including whole-exome sequencing and array genotyping. We examined known pathogenic genetic variants for all dementia types and their correlation with clinical diagnoses. We evaluated a combined genetic score (GS) based on all genetic risk factors for AD - namely APOE genotypes, candidate risk rare variants in 11 genes, and a polygenic risk score (PRS) based on 82 common variants. Then, we analyzed the discriminatory characteristics of the GS.

Results
Causal pathogenic variants were rare, present in 3.4% of individuals, but genetic testing would have altered the diagnosis in over half of the carriers. Candidate rare risk variants were more common, identified in 31.6% of patients. Both APOE genotypes and the PRS were independently associated with AD, and gene-specific interaction was found between TREM2 and AD-PRS (β = -1.16, p = 0.015). Patients with a high GS were 7 times more likely receive an AD diagnosis compared to those with a low GS (p = 2.5E-07).

Conclusion
Overall, this study highlights the potential of integrating genetic risk factors into clinical practice to enhance AD diagnosis, though the improvement in diagnostic accuracy was moderate. The findings underscore the importance of genetic testing in diagnosis while also recognizing its limitations.

Alzheimer’s disease (AD) is heterogenous at the molecular level. Understanding this heterogeneity is critical for AD drug development. Here we define AD molecular subtypes using mass spectrometry proteomics in cerebrospinal fluid, based on 1,058 proteins, with different levels in individuals with AD (n = 419) compared to controls (n = 187). These AD subtypes had alterations in protein levels that were associated with distinct molecular processes: subtype 1 was characterized by proteins related to neuronal hyperplasticity; subtype 2 by innate immune activation; subtype 3 by RNA dysregulation; subtype 4 by choroid plexus dysfunction; and subtype 5 by blood–brain barrier impairment. Each subtype was related to specific AD genetic risk variants, for example, subtype 1 was enriched with TREM2 R47H. Subtypes also differed in clinical outcomes, survival times and anatomical patterns of brain atrophy. These results indicate molecular heterogeneity in AD and highlight the need for personalized medicine.

Background
Genome-Wide Association Studies (GWAS) have identified 86 SNPs associated with Alzheimer’s disease (AD). GWAS-SNPs are markers of genetic variation in linkage disequilibrium (LD), which may drive the association with AD. One major class of genetic variation are Structural Variants (SVs), which can regulate transcription and translation of nearby genes. Here, we explored the interplay between large SVs (>50 bp) and AD-associated SNPs.

Method
We performed long-read whole-genome sequencing of 214 individuals representing the extreme ends of the cognitive spectrum: N = 93 AD patients (age 67.2∓8.5), and N = 121 cognitively healthy centenarians (age 101.2∓1.8). We identified SVs using sniffles2 genome-wide, further characterised them through (local) de novo assembly, and annotated tandem repeats (TR) and transposable elements (TE). Next, we estimated LD between SVs and 86 AD-associated GWAS-SNPs, Finally, we compared SNP frequencies and SV sizes between AD cases and centenarians using logistic regression models.

Result
Across all 214 individuals, we found >27,404 SVs (>50 bp), with a Minor Allele Frequency (MAF) ≥ 5%. Most SVs were TRs (63%), followed by TEs (37%). We found that 37 AD GWAS-SNPs paired with 90 SVs in low to strong LD (R2 > 0.1), 56 of which involved SNP-TR pairs (62%) and 34 involved SNP-TE pairs (38%). In six AD loci, the SVs associated more strongly with AD risk than the leading GWAS-SNP (p < 0.05). This includes ADAM10, CTSH, SLC2A4RG, CD2AP, SPI1, and IDUA, which were encompassed by complex haplotypes harbouring multiple TRs and TEs.

Conclusion
Our findings provide the first support for SVs as candidate-drivers of the association between respective GWAS loci with AD risk. Hence, SVs may lead to increased effect sizes.

Background
The TMEM106B protein is critical for proper functioning of the endolysomal system, which is utilised by all cells to traffic and degrade molecular cargo. Genome-wide association studies identified a haplotype in the TMEM106B gene that is associated with increased risk for Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal lobar degeneration with TAR DNA binding protein inclusions (FTLD-TDP). However, the causal variant that drives the association has thus far remained elusive.

Methods
We generated long-read whole-genome sequencing data of 256 individuals, primarily from Dutch descent. We characterized SNPs and larger structural variants in the TMEM106B locus using de novo genome assembly.

Results
We identified an insertion of an AluYb8 retrotransposon in the 3’ UTR of TMEM106B gene, that was in complete linkage with the TMEM106B risk-SNP. AluYb8 retrotransposons have the propensity to propagate through our genomes by utilising a ‘copy-paste’ mechanism, and once integrated can disrupt transcription and translation of nearby genes. However, propagation of retrotransposons can be suppressed by methylation of the insert and its surrounding regions. Indeed, we observed that the risk haplotype with the AluYb8 insertion, but not the protective haplotype, accumulated CpG islands over evolutionary time. Notably, we observed similar retrotransposon insertions in the 3’ UTR of TMEM106B orthologs in non-primate species. This suggests a survival advantage, which may be explained by recent findings that TMEM106B is an entry-receptor for specific viruses in lung-tissue, such that SINE-mediated downregulation of TMEM106B may limit viral infection-load across species.

Conclusions
We speculate that AluYb8-mediated downregulation of TMEM106B may be protective at younger ages in lung tissues, but that at advanced ages its downregulation in the brain may contribute to increased risk of neurodegenerative diseases. Furthermore, next to the supression of AluYb8 activation by DNA methylation, it may also be suppressed by TDP-43, in its role in post-translational RNA-processing. This leads us to further speculate that age-related demethylation and age-related dysregulation of TDP-43 may result in a negative feedback loop that ultimately reduces the endolysosomal activity in cells. We argue that such a mechanism would explain why increased age is among the strongest risk factors of neurodegenerative diseases.

Tandem repeats (TRs) play important roles in genomic variation and disease risk in humans. Long-read sequencing allows for the accurate characterization of TRs; however, the underlying bioinformatics perspectives remain challenging. We present otter and TREAT: otter is a fast targeted local assembler, cross-compatible across different sequencing platforms. It is integrated in TREAT, an end-to-end workflow for TR characterization, visualization, and analysis across multiple genomes. In a comparison with existing tools based on long-read sequencing data from both Oxford Nanopore Technology (ONT, Simplex and Duplex) and Pacific Bioscience (PacBio, Sequel II and Revio), otter and TREAT achieve state-of-the-art genotyping and motif characterization accuracy. Applied to clinically relevant TRs, TREAT/otter significantly identify individuals with pathogenic TR expansions. When applied to a case-control setting, we replicate previously reported associations of TRs with Alzheimer's disease, including those near or within APOC1 (P = 2.63 × 10−9), SPI1 (P = 6.5 × 10−3), and ABCA7 (P = 0.04) genes. Finally, we use TREAT/otter to systematically evaluate potential biases when genotyping TRs using diverse ONT and PacBio long-read sequencing data sets. We show that, in rare cases (0.06%), long-read sequencing from coverage drops in TRs, including the disease-associated TRs in ABCA7 and RFC1 genes. Such coverage drops can lead to TR misgenotyping, hampering the accurate characterization of TR alleles. Taken together, our tools can accurately genotype TRs across different sequencing technologies and with minimal requirements, allowing end-to-end analysis and comparisons of TRs in human genomes, with broad applications in research and clinical fields.