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Related Concept Videos

Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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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.
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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
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Related Experiment Video

Updated: Aug 2, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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matchRanges: generating null hypothesis genomic ranges via covariate-matched sampling.

Eric S Davis1, Wancen Mu2, Stuart Lee3

  • 1Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

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Summary

Generating null genomic loci for biological insights is challenging due to complex feature distributions. The matchRanges method efficiently creates matched null ranges, improving genomic data analysis.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Deriving biological insights from genomic data often involves comparing selected genomic loci to a null set.
  • Selecting appropriate null sets is complex due to non-uniform distributions of genomic features like genes and enhancers.
  • Existing propensity score methods are not optimized for genomic data classes and can be slow for large datasets.

Purpose of the Study:

  • To develop an efficient and convenient method for generating matched null genomic ranges.
  • To address the limitations of existing covariate matching methods for genomic data analysis.

Main Methods:

  • Developed matchRanges, a propensity score-based covariate matching method.
  • Implemented within the Bioconductor framework for seamless integration with genomic workflows.
  • Designed for efficient generation of null ranges from background ranges while controlling for covariates.

Main Results:

  • matchRanges provides efficient and convenient generation of matched null ranges.
  • The method is suitable for large genomic datasets, overcoming previous performance limitations.
  • Facilitates the comparison of genomic loci attributes against well-controlled null sets.

Conclusions:

  • matchRanges enhances the process of deriving biological insights from genomic data.
  • The tool simplifies the creation of appropriate null sets for genomic analyses.
  • Improves the utility of propensity score matching in bioinformatics.