Washington University School of Medicine researchers consider factors influencing allele-specific expression (ASE) with RNA sequencing and other analyses on mice with different genetic backgrounds and diets, comparing male and female animals in each group. Based on ASE profiles for nearly 2,900 genes assessed in three tissues from mice in several environmental contexts, the team saw tissue and environmental effects on both parent-of-origin-dependent and sequence-dependent ASE events. "Our ASE genes are often enriched in [quantitative trait loci] for metabolic and musculoskeletal traits, highlighting how this orthogonal approach can prioritize candidate genes," the authors write. "Together, our results provide novel insights into how genetic, epigenetic, and environmental mechanisms govern allele-specific expression, which is an essential step towards deciphering the genotype to phenotype map."
Using deep genome sequencing and variant allele frequency patterns, a team from Osaka University and other centers in Japan reconstructs de novo mutation accumulation and related embryonic cell lineage patterns in a mouse model. With their direct method for translating mosaic mutation patterns into embryonic cell lineage clues with deep sequence data generated for multiple mouse tissues, the researchers put together lineage trees leading to germ cells or somatic cells in the adult mice — results they confirmed with genotyping on embryos developed using nuclear transfer experiments. "Our results show a new framework to assess embryonic lineages," they write, noting that the results so far "suggest an evolutionary strategy for preserving heterogeneity owing to postzygotic mutations in offspring."
Investigators with the University of California, Santa Cruz, the National Institutes of Health, and Google outline a pedigree-informed approach for finding rare variants. The team's VG-Pedigree workflow is designed to map and call single nucleotide changes and small insertions and deletions — including deleterious variants — with the help of existing pangenome-mapping and trained variant calling methods. Along with analyses of 1000 Genomes Project data, the authors demonstrate that VG-Pedigree could pick up deleterious variants with data from more than a dozen parent-child sets from the Undiagnosed Diseases Program. "All candidate [deleterious variants] that were previously diagnosed using the Mendelian models covered by the previously published methods were recapitulated by these workflows," they report, adding that "results of these experiments indicate that a slightly greater absolute count of [deleterious variants] are detected in the proband population than in their matched unaffected siblings."