Tandem repeats (TRs) occupy a significant portion of the human genome and are a source of polymorphisms due to variations in sizes and motif compositions. Some of these variations have been associated with various neuropathological disorders, highlighting the clinical importance of assessing the motif structure of TRs. Moreover, assessing the TR motif variation can offer valuable insights into evolutionary dynamics and population structure. Previously, characterizations of TRs were limited by short-read sequencing technology, which lacks the ability to accurately capture the full TR sequences. As long-read sequencing becomes more accessible and can capture the full complexity of TRs, there is now also a need for tools to characterize and analyze TRs using long-read data across multiple samples. In this study, we present MotifScope, a novel algorithm for the characterization and visualization of TRs based on a de novo k-mer approach for motif discovery. Comparative analysis against established tools reveals that MotifScope can identify a greater number of motifs and more accurately represent the underlying repeat sequences. Moreover, MotifScope has been specifically designed to enable motif composition comparisons across assemblies of different individuals, as well as across long-read sequencing reads within an individual, through combined motif discovery and sequence alignment. We showcase potential applications of MotifScope in diverse fields, including population genetics, clinical settings, and forensic analyses.

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.

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.