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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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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.
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
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Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
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A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
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MR_predictor: a simulation engine for Mendelian Randomization studies.

Benjamin F Voight1

  • 1Department of Pharmacology and Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19143, USA Department of Pharmacology and Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19143, USA.

Bioinformatics (Oxford, England)
|August 29, 2014
PubMed
Summary
This summary is machine-generated.

MR_predictor is a new simulation engine for Mendelian Randomization (MR) studies. It aids in developing and interpreting statistical tests of causality between traits using genetic data, improving MR analysis.

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Statistical methods are crucial for inferring causality between phenotypes using genetic data.
  • Mendelian Randomization (MR) is a powerful approach for causal inference, but requires robust simulation tools for development and validation.

Purpose of the Study:

  • To introduce MR_predictor, a novel simulation engine for Mendelian Randomization (MR) studies.
  • To provide a flexible framework for modeling complex genetic and phenotypic relationships in MR analyses.

Main Methods:

  • MR_predictor simulates individual or multiple correlated phenotypes influenced by multiple genetic loci.
  • The engine supports various sample generation options and outputs compatible with standard bioinformatics tools like PLINK and R.
  • It incorporates detailed genotypic variability from biallelic loci, linked or unlinked.

Main Results:

  • Benchmarks demonstrate the speed and power of MR_predictor for summary statistic-based MR analyses.
  • Performance is compared against analytical expectations, validating the simulation engine's accuracy.
  • The software facilitates the development and interpretation of statistical tests of causality.

Conclusions:

  • MR_predictor is a valuable tool for researchers conducting Mendelian Randomization studies.
  • It enhances the ability to model complex genetic architectures and their impact on phenotypes.
  • The simulation engine supports robust causal inference in genetic epidemiology.