Feb 18, 2016

Exploiting single-cell quantitative data to map genetic variants having probabilistic effects

BioRxiv : the Preprint Server for Biology
Florent ChuffartGael Yvert

Abstract

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 to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and va...Continue Reading

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Mentioned in this Paper

Gene Polymorphism
Genome-Wide Association Study
Quantitative Trait Loci
Galactose Measurement
Genome
Yeasts
Gene Expression
Galactose
Sequencing
Molecular Genetic Technique

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