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Related Concept Videos

Principles of Pharmacogenetics: Types of Genetic Variants01:27

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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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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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.
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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Updated: Mar 13, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic

Stephen Burgess1, Jack Bowden, Tove Fall

  • 1From the aCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; bMedical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Bristol, United Kingdom; and cDepartment of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden.

Epidemiology (Cambridge, Mass.)
|October 18, 2016
PubMed
Summary
This summary is machine-generated.

Mendelian randomization (MR) studies require sensitivity analyses to validate causal inferences. Without assessing instrumental variable assumptions, MR findings with multiple genetic variants are speculative.

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Area of Science:

  • Epidemiology
  • Genetics
  • Biostatistics

Background:

  • Mendelian randomization (MR) studies are increasingly powerful due to large genome-wide association studies (GWAS) and summarized genetic data.
  • Utilizing multiple genetic variants in MR analyses raises concerns about the validity of instrumental variable (IV) assumptions.

Purpose of the Study:

  • To discuss essential sensitivity analyses for validating causal inferences in multi-variant MR studies.
  • To highlight practical, summarized-data-based sensitivity analyses for robust causal conclusions.

Main Methods:

  • Review and discussion of various sensitivity analyses relevant to MR.
  • Focus on methods applicable to summarized GWAS data.

Main Results:

  • A simple instrumental variable analysis is insufficient for causal conclusions when using multiple genetic variants.
  • Sensitivity analyses are crucial for supporting or questioning the validity of causal inferences in MR.

Conclusions:

  • MR analyses with multiple genetic variants require rigorous sensitivity analyses to assess the robustness of findings.
  • MR studies lacking sensitivity analyses for instrumental variable assumption violations should be considered speculative.