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 We present here the development of a novel genetic (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx

Natural gene expression variation studies in yeast

“…Single‐cell molecular expression profiling of isogenic cells demonstrated that noise in gene expression (refered as the stochastic variation of mRNA abundance) is subjected to genetic control, and reproducible differences in noise were reported between divergent genetic backgrounds (Ansel et al ., ; Chuffart et al ., ; Fehrmann et al ., ). In this sense, recent advances in the field are able to predict, localize and validate single SNPs affecting a trait (Chuffart et al ., ). Given the differences owing to environmental perturbations even between cells within isogenic cultures, advances in single‐cell technologies could be greatly exploited to help understand the consequences of noise and natural expression variation.…”

“…Moreover, environmental conditions may not be the sole source of fluctuations in gene expression. Single‐cell molecular expression profiling of isogenic cells demonstrated that noise in gene expression (refered as the stochastic variation of mRNA abundance) is subjected to genetic control, and reproducible differences in noise were reported between divergent genetic backgrounds (Ansel et al ., ; Chuffart et al ., ; Fehrmann et al ., ). In this sense, recent advances in the field are able to predict, localize and validate single SNPs affecting a trait (Chuffart et al ., ).…”

Assigning function to natural allelic variation via dynamic modeling of gene network induction

“…1). All strains of the panel also harboured a P GAL1 -GFP reporter of network activity, where the promoter of the GAL1 gene controlled the expression of a GFP fluorescent protein destabilized by a degradation signal 27,28 . GAL1 is a paralogous gene of GAL3 29 and transcription at its promoter is commonly used as a proxy of GAL network activity 15,20,22 .…”

Cell-to-cell expression dispersion of B-cell surface proteins displays genetic variation among humans

“…We previously described a statistical method (ptlmapper) that can use the full distribution of single-cell traits to specifically identify scPTL 7 . This method looks for scPTL that may affect any property of the single-cell expression distribution, and not only specific pre-defined features such as mean, variability or dispersion.…”

“…it has a probabilistic effect, but only apparently because, although they are not known, compensatory deterministic factors exist. In addition, using model organisms and single-cell measurements, we and others have shown that genetic variants can also have effects that are inherently probabilistic: even in a controlled context, mutations can sometimes affect only some cells or individuals [1][2][3][4][5][6][7] . Consequently, we previously introduced the concept of single-cell Probabilistic Trait Loci (scPTL) 8 .…”