Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects
0301 basic medicine
Molecular Biology/Genomics [q-bio.GN]
0303 health sciences
[SDV.BIBS] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Genetic Linkage
Quantitative Trait Loci
1. No poverty
610
Chromosome Mapping
Galactose
Genetic Variation
[SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry
Saccharomyces cerevisiae
QH426-470
[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]
Polymorphism, Single Nucleotide
576
03 medical and health sciences
Phenotype
Genetics
[SDV.BBM.GTP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
Single-Cell Analysis
Research Article
Genome-Wide Association Study
DOI:
10.1371/journal.pgen.1006213
Publication Date:
2016-08-01T17:29:39Z
AUTHORS (7)
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 validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.
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CITATIONS (10)
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