Evidence map›Paper›PMID 29373829›Full record

ArticleCell2018

Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change.

Richard She, Daniel F Jarosz

Abstract read
In one paragraph

Article in Cell, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.

0numbers the graph read from it
0cells of the map it votes in
58citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

58 citing papers in PubMed.

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  9. AScience signaling · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Richard SheDepartment of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Daniel F JaroszDepartment of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: jarosz@stanford.edu.

Funding

Protein-based Molecular Memories in Gene Regulation, Disease, and DevelopmentDP2GM119140 · NIGMS · STANFORD UNIVERSITY · PI JAROSZ, DANIEL · 2015 to 2015
$2.4M
Quantitative analysis of the evolving genotype-to-phenotype mapR00GM098600 · NIGMS · STANFORD UNIVERSITY · PI JAROSZ, DANIEL · 2013 to 2015
$728k
NIGMS NIH HHS DP2 GM119140NIGMS NIH HHS R00 GM098600
6 · The paper itself

Abstract

Understanding the sequence determinants that give rise to diversity among individuals and species is the central challenge of genetics. However, despite ever greater numbers of sequenced genomes, most genome-wide association studies cannot distinguish causal variants from linked passenger mutations spanning many genes. We report that this inherent challenge can be overcome in model organisms. By pushing the advantages of inbred crossing to its practical limit in Saccharomyces cerevisiae, we improved the statistical resolution of linkage analysis to single nucleotides. This "super-resolution" approach allowed us to map 370 causal variants across 26 quantitative traits. Missense, synonymous, and cis-regulatory mutations collectively gave rise to phenotypic diversity, providing mechanistic insight into the basis of evolutionary divergence. Our data also systematically unmasked complex genetic architectures, revealing that multiple closely linked driver mutations frequently act on the same quantitative trait. Single-nucleotide mapping thus complements traditional deletion and overexpression screening paradigms and opens new frontiers in quantitative genetics.

Indexed as

Genetic LinkageMutationPhenotypePolymorphism, GeneticChromosome MappingGenome-Wide Association StudyQuantitative Trait, HeritableSaccharomyces cerevisiaebiochemical interpretation of genetic variantsfunctional genomicsquantitative genetics

Identifiers

PMID29373829
PMCPMC5788306

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.