AI Analysis of a Copy Number Variant Database Identifies a Genetic Factor for a Murine Model of the Metabolic Syndrome
Copy number variants (CNVs) are a major source of genetic diversity and could contain some of the missing heritability for mouse models of human disease. However, mouse CNVs have not been comprehensively characterized because they are difficult to resolve in repeat-rich, segmentally duplicated or reference sequence-absent regions of the genome. Here we analyzed long range sequence (LRS) data for…
An in-depth examination of copy number variants (CNVs) within a database of 40 inbred mouse strains has unveiled a genetic factor potentially linked to the metabolic syndrome in a murine model. This discovery was made possible through the application of advanced artificial intelligence (AI) techniques to long-range sequence (LRS) data.
The study, which utilized pangenome graph-based methods and a C57BL/6J telomere-to-telomere (T2T) genome reference sequence, revealed 1,594 high-confidence CNVs. These variants frequently overlapped with tandem repeats, segmental duplications, and pericentromeric regions of the genome. Notably, 131 of these CNVs were confirmed to be specific to the T2T sequence.
The analysis impacted 384 protein-coding genes, which encompassed a wide array of vital functional classes. The pangenome map, expanded from 2.29 to 3.32 Gb through the inclusion of wild-derived strains, provided a more comprehensive view of the mouse genome.
Among these findings, a 29-kb deletion CNV within the Nlrp1b locus of KK mice emerged as particularly significant. This variant was found to contribute to the development of the metabolic syndrome observed in these mice. Interestingly, corresponding human NLRP1 alleles have also been associated with metabolic syndrome features in human populations.
The application of AI analysis to this extensive T2T pangenome-based resource holds the potential to uncover additional missing heritability factors for mouse models of human diseases and biomedical traits. This breakthrough underscores the importance of comprehensive genomic characterization and the role of AI in identifying genetic factors that could contribute to complex diseases.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
