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Artikel ; Online: High-dimensional phenotyping to define the genetic basis of cellular morphology

Matthew Tegtmeyer / Jatin Arora / Samira Asgari / Beth A. Cimini / Ajay Nadig / Emily Peirent / Dhara Liyanage / Gregory P. Way / Erin Weisbart / Aparna Nathan / Tiffany Amariuta / Kevin Eggan / Marzieh Haghighi / Steven A. McCarroll / Luke O’Connor / Anne E. Carpenter / Shantanu Singh / Ralda Nehme / Soumya Raychaudhuri

Nature Communications, Vol 15, Iss 1, Pp 1-

2024  Band 12

Abstract: Abstract The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combine ... ...

Abstract Abstract The morphology of cells is dynamic and mediated by genetic and environmental factors. Characterizing how genetic variation impacts cell morphology can provide an important link between disease association and cellular function. Here, we combine genomic sequencing and high-content imaging approaches on iPSCs from 297 unique donors to investigate the relationship between genetic variants and cellular morphology to map what we term cell morphological quantitative trait loci (cmQTLs). We identify novel associations between rare protein altering variants in WASF2, TSPAN15, and PRLR with several morphological traits related to cell shape, nucleic granularity, and mitochondrial distribution. Knockdown of these genes by CRISPRi confirms their role in cell morphology. Analysis of common variants yields one significant association and nominate over 300 variants with suggestive evidence (P < 10−6) of association with one or more morphology traits. We then use these data to make predictions about sample size requirements for increasing discovery in cellular genetic studies. We conclude that, similar to molecular phenotypes, morphological profiling can yield insight about the function of genes and variants.
Schlagwörter Science ; Q
Thema/Rubrik (Code) 500
Sprache Englisch
Erscheinungsdatum 2024-01-01T00:00:00Z
Verlag Nature Portfolio
Dokumenttyp Artikel ; Online
Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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