Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects

• DOI: 10.1371/journal.pgen.1006213
• Publication Date: 2016-08-01T17:29:39Z
• Authors: Florent Chuffart; Magali Richard; Daniel Jost; Claire Burny; Hélène Duplus-Bottin; Yoshikazu Ohya; Gaël Yvert
• Keywords: Genome-wide Association Study; Genetic architecture; Genetic Association

Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping benefit wealth of data produced single-cell technologies. We present here development novel method that allows scan genomes for Probabilistic Trait Loci modify statistical properties cellular-level quantitative traits. Phenotypic values are acquired thousands individual cells, is obtained multivariate analysis matrix Kantorovich distances. No prior assumption required mode action loci involved and, exploiting all values, can reveal non-deterministic effects. Using both simulations yeast experimental datasets, we show it detect linkages missed classical mapping. A effect single SNP cell shape was detected validated. also locus associated with elevated gene expression noise galactose regulon. Our results illustrate how technologies be exploited improve dissection certain available as an open source R package called ptlmapper.