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Related Concept Videos

Single Nucleotide Polymorphisms-SNPs01:05

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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Frequency-dependent Selection01:21

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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A hair follicle or HF is a small part of the skin that produces the hair shaft. Paul Gerson Unna was the first to observe a bulge in the human hair follicle's outer root sheath (ORS). The bulge is present between the sebaceous gland and the arrector pili muscle and is the niche for hair follicle stem cells (HFSCs). The bulge is also a niche for melanocyte stem cells, and their loss results in graying of hair. The HFSCs express Sox9 and Lhx2, which help them maintain stemness and prevent...
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Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.
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Related Experiment Video

Updated: May 4, 2026

A Noninvasive Hair Sampling Technique to Obtain High Quality DNA from Elusive Small Mammals
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Gradient Boosting as a SNP Filter: an Evaluation Using Simulated and Hair Morphology Data.

Gh Lubke1, C Laurin2, R Walters2

  • 1Department of Psychology, University of Notre Dame, Notre Dame, IN, USA ; Department of Biological Psychology, VU University Amsterdam, Amsterdam Netherlands.

Journal of Data Mining in Genomics & Proteomics
|January 10, 2014
PubMed
Summary
This summary is machine-generated.

Gradient Boosting Machine (GBM) offers a sensitive, two-step filtering approach for genome-wide association studies (GWAS). This method efficiently identifies complex genetic interactions, reducing the number of SNPs for subsequent analysis.

Keywords:
BoostingGCTAGWAS

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Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Genome-wide association studies (GWAS) typically use simple additive models for single nucleotide polymorphisms (SNPs).
  • The computational burden of testing all possible genetic models (e.g., recessive, dominant, SNP-SNP, SNP-environment interactions) is prohibitive for genome-wide data.
  • Existing GWAS methods struggle to detect complex interactions, especially when one interacting SNP has no main effect.

Purpose of the Study:

  • To propose and evaluate a two-step approach for GWAS using a sensitive filtering method.
  • To assess the utility of Gradient Boosting Machine (GBM) as a filter for detecting various SNP effects and interactions.
  • To enable more feasible in-depth analysis of selected SNPs by reducing the overall number of variants.

Main Methods:

  • Utilized Gradient Boosting Machine (GBM), a machine learning algorithm, as a primary filtering step.
  • GBM was evaluated for its ability to detect SNP main effects and various interaction types without pre-specified genetic models.
  • Included a large number of covariates to explore gene-environment (GxE) interactions.
  • Performed simulations to compare GBM performance against standard additive regression models.
  • Analyzed empirical data on hair morphology.

Main Results:

  • GBM demonstrated strong performance, even in scenarios favorable to standard GWAS additive models.
  • GBM effectively detected interaction effects, including those where one interacting SNP had a zero main effect, which are missed by traditional GWAS.
  • The analysis of hair morphology data indicated that selecting 10K-20K top-ranked SNPs in the first step is sufficient for explaining phenotypic variance.
  • GBM facilitates the exploration of multiple GxE interactions, overcoming limitations of parametric GWAS frameworks.

Conclusions:

  • A two-step GWAS approach employing GBM as a filter is a powerful strategy for efficiently identifying significant SNPs and complex genetic interactions.
  • GBM offers a computationally feasible method for exploring diverse genetic effects and interactions in large-scale genomic datasets.
  • This approach enhances the power of GWAS to detect biologically relevant genetic architectures, including complex GxE interactions.