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An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data.

ABSTRACT: Genomic studies have been a major approach to elucidating disease etiology and to exploring potential targets for treatments of many complex diseases. Statistical analyses in these studies often face the challenges of multiplicity, weak signals, and the nature of dependence among genetic markers. This situation becomes even more complicated when multi-omics data are available. To integrate the data from different platforms, various integrative analyses have been adopted, ranging from the direct union or intersection operation on sets derived from different single-platform analysis to complex hierarchical multi-level models. The former ignores the biological relationship between molecules while the latter can be hard to interpret. We propose in this study an integrative approach that combines both single nucleotide variants (SNVs) and copy number variations (CNVs) in the same genomic unit to co-localize the concurrent effect and to deal with the sparsity due to rare variants. This approach is illustrated with simulation studies to evaluate its performance and is applied to low-density lipoprotein cholesterol and triglyceride measurements from Taiwan Biobank. The results show that the proposed method can more effectively detect the collective effect from both SNVs and CNVs compared to traditional methods. For the biobank analysis, the identified genetic regions including the gene VNN2 could be novel and deserve further investigation.

SUBMITTER: Yu QY

PROVIDER: S-EPMC8456116 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:A pervasive pest of stored leguminous products, the bean beetle Callosobruchus maculatus (Coleoptera: Chrysomelidae) associates with a simple bacterial community during adulthood. Despite its economic importance, little is known about the compositional stability, heritability, localization, and metabolic potential of the bacterial symbionts of C. maculatus. In this study, we applied community profiling using 16S rRNA gene sequencing to reveal a highly conserved bacterial assembly shared between larvae and adults. Dominated by Firmicutes and Proteobacteria, this community is localized extracellularly along the epithelial lining of the bean beetle's digestive tract. Our analysis revealed that only one species, Staphylococcus gallinarum (phylum Firmicutes), is shared across all developmental stages. Isolation and whole-genome sequencing of S. gallinarum from the beetle gut yielded a circular chromosome (2.8 Mb) and one plasmid (45 kb). The strain encodes complete biosynthetic pathways for the production of B vitamins and amino acids, including tyrosine, which is increasingly recognized as an important symbiont-supplemented precursor for cuticle biosynthesis in beetles. A carbohydrate-active enzyme search revealed that the genome codes for a number of digestive enzymes, reflecting the nutritional ecology of C. maculatus. The ontogenic conservation of the gut microbiota in the bean beetle, featuring a "core" community composed of S. gallinarum, may be indicative of an adaptive role for the host. In clarifying symbiont localization and metabolic potential, we further our understanding and study of a costly pest of stored products. IMPORTANCE From supplementing essential nutrients to detoxifying plant secondary metabolites and insecticides, bacterial symbionts are a key source of adaptations for herbivorous insect pests. Despite the pervasiveness and geographical range of the bean beetle Callosobruchus maculatus, the role of microbial symbioses in its natural history remains understudied. Here, we demonstrate that the bean beetle harbors a simple gut bacterial community that is stable throughout development. This community localizes along the insect's digestive tract and is largely dominated by Staphylococcus gallinarum. In elucidating symbiont metabolic potential, we highlight its possible adaptive significance for a widespread agricultural pest.

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An Integrative Co-localization (INCO) Analysis for SNV and CNV Genomic Features With an Application to Taiwan Biobank Data.

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