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Genome-wide Association Studies-GWAS

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Updated: May 28, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Published on: June 21, 2018

Optimizing strain selection for association studies under hard cost constraints.

Christoph D Rau1, Patrick H Bradley2

  • 1Department of Genetics and Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA.

Genetics
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Selecting the most cost-effective strains maximizes statistical power in quantitative genetics studies. Prioritizing sample size over genetic diversity, especially with unequal strain costs, is key for efficient research design.

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Area of Science:

  • Quantitative genetics
  • Genotype-phenotype association studies
  • Biomedical research

Background:

  • Quantitative genetics methods link genotype to phenotype, valuable in model organisms.
  • Phenotyping large strain collections is costly, necessitating efficient subset selection.

Purpose of the Study:

  • Evaluate strain selection methods to maximize statistical power within budget constraints.
  • Compare approaches considering cost, genetic diversity, or both.

Main Methods:

  • Simulations using bacterial isolates and mouse strains.
  • Analysis of real cardiovascular phenotype data from the Hybrid Mouse Diversity Panel (HMDP).
  • Evaluation of various selection objectives including MinCost, MaxMin, MaxSum, MaxMAF, and p-Median.

Main Results:

  • Ignoring genetic diversity (MinCost) often yielded the highest statistical power.
  • Diversity-focused methods generally had lower power due to smaller sample sizes.
  • Novel objectives like MaxSum and MaxMAF, and a cost-aware p-Median approach, showed high power.
  • MaxMin objective surprisingly had the lowest power.

Conclusions:

  • Maximizing sample size under budget is generally more effective than prioritizing genetic diversity.
  • Some methods balancing cost and diversity can be powerful.
  • Cost-aware selection strategies are crucial for efficient genetic studies.