- Unveiling genetic complexity in rats through pangenome graphs and Genome-Phenome analysis
- 2023 Update on GeneNetwork.org
- Recombination between heterologous human acrocentric chromosomes
- Pangenome graphs in pathogen genomics for public health
- Building a Pangenome Alignment Index via Recursive Prefix-Free Parsing
- MemPanG25 details
Flavia Villani, PhD student, University of Tennessee Health Science Center
The HXB/BXH (HXB) family of recombinant inbred strains of rats has an unrivaled phenome with quantitative molecular and organismal phenotypes that have been systematically accumulated for over 25 years. We generated a pangenome for the HXB family of rats and used this resource to evaluate the functional impact of both known and new genetic variants. We sequenced both parents and all 30 progeny strains using linked-read libraries at ~40x coverage. We used PGGB to construct the family pangenome graph and to compute the frequency of major classes of DNA variants. Validation of SNVs on Chr 12 by Sanger sequencing underscored pangenome’s potential for uncovering novel genetic variants, especially those residing in complex regions (repetitive regions and p-arm regions). Functional annotation of these variants highlighted their potential roles in gene regulation and molecular mechanisms. We focused on 12 validated genetic markers exclusively identified via the pangenome graph. We conducted a Phenome-Wide Association Study (PheWAS) to explore the broader significance of these novel variants. Intriguingly, we identified associations between specific markers and phenotypic traits, including a link between a lncRNA-encoding region and insulin concentration. Another association was found between a marker at 18.8 Mb and the insulin/glucose ratio in line with previous research. Specifically, our results align with a previously reported QTL highlighting a genomic region significantly linked to insulin resistance at T 30 min at 19.6 Mb. The investigation of structural variants (SVs) in Chr12 further confirmed the pangenome’s reliability in detecting variations. We identified 2,481 high-confidence SVs, underscoring their presence in the SHR/OlaIpcv sample, 27 of which are predicted to have high impact. We validated 13 novel SVs with long-read sequencing using Nanopore technology. Out of 13, two insertions and four deletions consistently observed in the SHR/OlaIpcv sample and spanning multiple individuals were successfully validated. One deletion, that is preceded by simple repeats, was consistently present in all samples and situated within the Lmtk2 gene. Mutations in the Lmtk2 gene have been linked to multiple neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and schizophrenia. These results demonstrate that pangenomes constructed from linked-reads can provide valuable information about genetic variation, making it a useful tool for the study of complex traits.
Pjotr Prins, Associate Professor, University of Tennessee Health Science Center
Our GeneNetwork web-service (GN – formerly WebQTL) was developed for the needs of the mouse community, and represents over 25 years of research, data and software. Today we are growing support for other species, including synteny and pangenome-driven genotyping. In this talk we will discuss reorganization of the informatics of biomedical research and how that can go a long way towards addressing two problematic areas common to both human and model organism analysis: access to massive and complex ‘omics’ data, and the need for robust but accessible systems for analysis of joint statistical/causal models that produce useful predictions. We greatly improved search facilities and we are adding flexible data end-points exposing data and metadata. Combining and integrating datasets from old and new research, or de-siloing, will lead to novel connections and ideas followed by insight. Rather than having researchers creating their own silos we promote interaction and sharing of both data and tools and turn discovery into health. That also means we actively invite new communities to contribute their data to GeneNetwork and we can organize workshops to introduce data submission and analysis options. Finally we’ll announce the GeneNetwork consortium that will allow us to write a paper on more than 25 years of GeneNetwork related efforts!
Andrea Guarracino, Postdoctoral Scholar, University of Tennessee Health Science Center
The evolution of repetitive sequences and their role in chromosomal rearrangements have long been of interest to evolutionary biologists. In this context, the homologous regions shared by the short arms of human acrocentric chromosomes (SAACs) 13, 14, 15, 21, and 22 have been a subject of long-standing interest. While the first complete assembly of these regions in the Telomere-to-Telomere consortium’s human reference genome (T2T-CHM13) provided a model of their homology, it remained unclear whether these patterns were ancestral or maintained by ongoing recombination exchange. Here we demonstrate that the SAACs contain pseudo-homologous regions (PHRs) indicative of recombination between non-homologous chromosomes. Considering an all-to-all alignment of the high-quality human pangenome from the Human Pangenome Reference Consortium (HPRC), we find that contigs from all the SAACs form a community similar to that formed by the sex chromosome pair. Untangling a variation graph constructed from centromere-spanning acrocentric contigs, we highlight regions where most contigs appear as recombinants of heterologous T2T-CHM13 acrocentrics. On chromosomes 13, 14, 21, and 22, we observe lower levels of linkage disequilibrium in the PHRs than in their short and long arms, indicating higher rates of recombination. Of note, the PHRs include sequences previously shown to lie at the breakpoint of Robertsonian translocations and their arrangement is compatible with crossover in inverted duplications on chromosomes 13, 14, and 21. Our analysis suggests that recombination between heterologous acrocentric chromosomes is a basic feature of human genome architecture, providing sequence and population-based confirmation of hypotheses first developed cytogenetically fifty years ago.
Joep de Ligt PhD, Senior Science Lead Bioinformatics & Genomics
Whole genome sequencing has revolutionized infectious disease surveillance for tracking and monitoring the spread and evolution of pathogens. However, using a linear reference genome for genomic analyses may introduce biases, especially when studies are conducted on highly variable bacterial genomes of the same species. Pangenome graphs provide an efficient model for representing and analyzing multiple genomes and their variants as a graph structure that includes all types of variations. In this study, we present a practical bioinformatics pipeline that employs the PanGenome Graph Builder and the Variation Graph toolkit to build pangenomes from assembled genomes, align whole genome sequencing data and call variants against a graph reference. The pangenome graph enables the identification of structural variants, rearrangements, and small variants (e.g., single nucleotide polymorphisms and insertions/deletions) simultaneously. We demonstrate that using a pangenome graph, instead of a single linear reference genome, improves mapping rates and variant calling for both simulated and real datasets of the pathogen Neisseria meningitidis. Overall, pangenome graphs offer a promising approach for comparative genomics and comprehensive genetic variation analysis in infectious disease.
Christina Boucher, Associate Professor, University of Florida
Emerging pangenomic methods include VG, Giraffe, and Moni, where the first two methods build an index a variation graph from the multiple alignment of the sequences, and Moni simply indexes all the sequences in a manner that takes the repetition of the sequences into account. Moni uses a preprocessing technique called prefix-free parsing to build a dictionary and parse from the input—these, in turn, are used to build the main run-length encoded BWT, and suffix array of the input. This is accomplished in linear space in the size of the dictionary and parse. Therein lies the open problem that we tackle in this paper. Although the dictionary scales nicely (sub-linear) with the size of the input, the parse becomes orders of magnitude larger than the dictionary. To scale the construction of Moni, we need to remove the parse from the construction of the RLBWT and suffix array. We accomplish this, in this paper by applying prefix-free parsing recursively on the parse. Although conceptually simple, this leads to an algorithmic challenge of constructing the RLBWT and suffix array without access to the parse. We solve this problem, implement it, and demonstrate that this improves the construction time by a factor of 8.9 the running time and by a factor of 2.7 the memory required.
This is joint work with Marco Oliva (PhD candidate at the University of Florida) and Travis Gagie at Dalhousie University.