Parent-of-origin effects propagate through networks to shape metabolic traits

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Abstract

Parent-of-origin effects are unexpectedly common in complex traits, including metabolic and neurological traits. Parent-of-origin effects can be modified by the environment, but the architecture of these gene-by-environmental effects on phenotypes remains to be unraveled. Previously, quantitative trait loci (QTL) showing context-specific parent-of-origin effects on metabolic traits were mapped in the F16 generation of an advanced intercross between LG/J and SM/J inbred mice. However, these QTL were not enriched for known imprinted genes, suggesting another mechanism is needed to explain these parent-of-origin effects phenomena. We propose that non-imprinted genes can generate complex parent-of-origin effects on metabolic traits through interactions with imprinted genes. Here, we employ data from mouse populations at different levels of intercrossing (F0, F1, F2, F16) of the LG/J and SM/J inbred mouse lines to test this hypothesis. Using multiple populations and incorporating genetic, genomic, and physiological data, we leverage orthogonal evidence to identify networks of genes through which parent-of-origin effects propagate. We identify a network comprised of 3 imprinted and 6 non-imprinted genes that show parent-of-origin effects. This epistatic network forms a nutritional responsive pathway and the genes comprising it jointly serve cellular functions associated with growth. We focus on 2 genes, Nnat and F2r, whose interaction associates with serum glucose levels across generations in high fat-fed females. Single-cell RNAseq reveals that Nnat expression increases and F2r expression decreases in preadipocytes along an adipogenic trajectory, a result that is consistent with our observations in bulk white adipose tissue.

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APA

Macias-Velasco, J. F., Pierre, C. L. S., Wayhart, J. P., Yin, L., Spears, L., Miranda, M. A., … Lawson, H. A. (2022). Parent-of-origin effects propagate through networks to shape metabolic traits. ELife, 11. https://doi.org/10.7554/eLife.72989

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