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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
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Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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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Multiple Allele Traits

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Multiple Allele Traits01:49

Multiple Allele Traits

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LocusPackRat: an R package to support prioritizing candidate genes from large GWAS intervals with standardized evidence aggregation.

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Related Experiment Video

Updated: May 28, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

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Published on: October 11, 2018

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.