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Related Experiment Video

Updated: May 22, 2026

Assessment of Global DNA Double-Strand End Resection using BrdU-DNA Labeling coupled with Cell Cycle Discrimination Imaging
06:44

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Published on: April 28, 2021

Selecting markers and evaluating coverage.

Matthew A Cleveland1, Nader Deeb

  • 1Genus plc, 100 Bluegrass Commons Boulevard, Suite 2200, Hendersonville, TN 37075, USA. matthew.cleveland@pic.com

Methods in Molecular Biology (Clifton, N.J.)
|May 9, 2012
PubMed
Summary
This summary is machine-generated.

Selecting the right genetic markers is crucial for effective genomic research. Evaluating marker quantity, distribution, and coverage, particularly using linkage disequilibrium, ensures accurate marker-trait association analysis and reliable research outcomes.

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Area of Science:

  • Genomics
  • Quantitative Genetics
  • Bioinformatics

Background:

  • Genetic markers facilitate marker-trait association studies across various genomic scales.
  • Marker selection involves custom approaches or commercial products, with quantity and genomic distribution being key factors.

Purpose of the Study:

  • To outline considerations for selecting appropriate genetic markers for genomic research.
  • To emphasize the importance of evaluating marker coverage for study design and power.
  • To discuss the role of linkage disequilibrium in determining marker number and analysis strategy.

Main Methods:

  • Review of principles for genetic marker selection.
  • Evaluation of marker coverage and its impact on genomic research.
  • Assessment of linkage disequilibrium as a parameter for marker selection.

Main Results:

  • Marker quantity and distribution are critical for effective genomic studies.
  • Marker coverage directly influences the power to detect marker-trait associations.
  • Linkage disequilibrium is a key metric for optimizing marker density and analysis.

Conclusions:

  • Strategic marker selection and coverage evaluation are essential for successful genomic research.
  • Understanding marker distribution and linkage disequilibrium improves the design and interpretation of marker-trait association studies.
  • The choice of statistical and computational methods for association analysis is influenced by marker coverage.