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

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

SUBMITTER: Chuffart F 

PROVIDER: S-EPMC4968810 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature