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Related Concept Videos

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
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Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects.

Florent Chuffart1, Magali Richard1, Daniel Jost2,3

  • 1Laboratoire de Biologie et de Modélisation de la Cellule, Ecole Normale Supérieure de Lyon, CNRS, Université de Lyon, Lyon, France.

Plos Genetics
|August 2, 2016
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Summary
This summary is machine-generated.

This study introduces a new genetic mapping method using single-cell data to find subtle genetic influences on traits. The ptlmapper tool detects linkages missed by traditional methods, improving genetic dissection of common traits.

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Area of Science:

  • Genetics
  • Genomics
  • Computational Biology

Background:

  • Genome-wide association studies (GWAS) struggle to detect genetic variants with small effects due to statistical power limitations.
  • Incomplete penetrance and probabilistic molecular effects can obscure the contribution of certain genetic polymorphisms.
  • Single-cell technologies offer rich data that can potentially overcome limitations in traditional genetic mapping.

Purpose of the Study:

  • To develop a novel genetic mapping method to identify single-cell Probabilistic Trait Loci (ptl).
  • To enable the detection of genetic loci that modify statistical properties of cellular-level quantitative traits.
  • To leverage single-cell data for improved genetic dissection of complex traits.

Main Methods:

  • Developed a new genetic mapping method analyzing single-cell data to identify probabilistic trait loci.
  • Utilized multivariate analysis of Kantorovich distances on phenotypic values from thousands of individual cells.
  • Applied the method to simulations and yeast experimental datasets, including single nucleotide polymorphism (SNP) analysis.

Main Results:

  • The novel method successfully detected genetic linkages missed by classical genetic mapping approaches.
  • Identified a probabilistic effect of a single SNP on yeast cell shape, which was experimentally validated.
  • Discovered a novel locus associated with increased gene expression noise in the yeast galactose regulon.

Conclusions:

  • Single-cell technologies can significantly enhance the genetic dissection of complex traits by revealing non-deterministic genetic effects.
  • The developed method, ptlmapper, effectively identifies subtle genetic influences and novel loci.
  • This approach offers a powerful new tool for understanding the genetic basis of common traits, particularly those with probabilistic components.