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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.
Simple randomization
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Single Nucleotide Polymorphisms-SNPs01:05

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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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Mismatch Repair01:20

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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Genetic Screens02:46

Genetic Screens

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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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Related Experiment Video

Updated: Jul 12, 2025

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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MendelianRandomization v0.9.0: updates to an R package for performing Mendelian randomization analyses using

Ashish Patel1, Ting Ye2, Haoran Xue3,4

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, England, CB2 0SR, UK.

Wellcome Open Research
|November 2, 2023
PubMed
Summary

The MendelianRandomization R package now offers robust methods to address bias from weak instruments and pleiotropy. It also includes new functions for Mendelian randomization with correlated variants and instrument strength calculations.

Keywords:
Mendelian randomizationcausal inferencegenetic associationsgenetic epidemiologyinstrumental variablepost-GWAS analysissummarized data

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Area of Science:

  • Biostatistics
  • Genetic Epidemiology
  • Statistical Genetics

Background:

  • Mendelian randomization (MR) is a powerful method for causal inference using genetic variants as instrumental variables.
  • Summarized data analysis in MR is efficient but requires robust methods to handle biases.
  • Previous versions of the MendelianRandomization package provided foundational tools for MR.

Purpose of the Study:

  • To describe enhancements and new functionalities in the MendelianRandomization R package since version 0.5.0.
  • To introduce robust methods for addressing bias in Mendelian randomization analyses.
  • To provide tools for handling correlated genetic variants and assessing instrument strength.

Main Methods:

  • Implementation of new robust Mendelian randomization methods to mitigate bias from weak instruments, winner's curse, and pleiotropic variants.
  • Development of dimension reduction techniques for Mendelian randomization with large numbers of correlated genetic variants.
  • Inclusion of functions for calculating first-stage F statistics (instrument strength) in univariable and multivariable MR with both correlated and uncorrelated variants.

Main Results:

  • The updated package offers improved robustness against common sources of bias in Mendelian randomization.
  • New methods facilitate the application of Mendelian randomization in the presence of correlated genetic variants.
  • Enhanced functions provide comprehensive assessment of instrument strength in various MR settings.

Conclusions:

  • The enhanced MendelianRandomization package provides advanced, robust tools for causal inference using summarized genetic data.
  • The new functionalities improve the reliability and applicability of Mendelian randomization studies, particularly with complex genetic data.
  • The package supports researchers in conducting rigorous causal inference by addressing key methodological challenges.