Mendelian Randomization
Mendelian Randomization - Related Videos
11:14
Primer-Free Aptamer Selection Using A Random DNA Library
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Cited by 20 •
2010
SELEX protocols comprise multiple rounds of selection, each of which require regeneration of bound ligands, which in turn require fixed primer sequences flanking the random library regions. These fixed primer sequences can interfere with the selection process (false positives and negatives). Here we present a primer-free protocol.
01:09
Random Variables
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2023
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. Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number. For example, let X = the...
01:13
Randomized Experiments
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2023
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 Simple...
01:04
Random Error
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2024
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
01:20
Random and Systematic Errors
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2023
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
FAQs
Three conditions are central: the inherited variant must be strongly related to the exposure, independent of factors that could confound the exposure-outcome relationship, and connected to the health outcome only through that exposure. If any condition is doubtful, the genetic variant may not function as a valid instrumental variable, weakening the causal interpretation of the findings.
Alleles are randomly assigned during conception, so genetic differences can provide a natural source of exposure variation that is less shaped by later environmental or behavioral factors. This supports causal analysis when randomized trials are difficult, costly, or unethical. The approach does not remove the need to evaluate whether the selected variants satisfy the instrumental-variable assumptions.
Interpretation becomes problematic if a variant influences the outcome through a pathway other than the exposure being studied. That violates the requirement that the variant affect the outcome only through the exposure. Researchers therefore need to consider whether the observed genetic association reflects the proposed mechanism or an alternative biological route.
A typical analysis begins by identifying inherited variants associated with the exposure. Researchers then assess whether those variants are strongly related to the exposure, independent of confounding influences, and restricted to the proposed exposure pathway. Finally, they use the variants as instrumental variables to estimate whether differences in the exposure produce differences in the health outcome.
The method is valuable when a randomized trial would be difficult, expensive, or unethical to perform. It can examine whether biomarkers, lifestyle factors, diseases, or potential treatment targets have causal effects on health outcomes. Results may help distinguish causal relationships from associations and guide decisions about which questions deserve further clinical investigation.
By testing whether genetically influenced differences in an exposure correspond to differences in a health outcome, the method can provide evidence about the exposure's causal role in disease. This can help researchers move beyond observing that a biomarker or lifestyle factor is associated with illness, supporting more focused investigation of underlying disease mechanisms.
Evidence from the method can help prioritize potential drug targets by indicating whether influencing a biological exposure might affect a disease outcome. It may also highlight possible benefits or risks associated with that target before clinical research proceeds. In this way, genetic evidence can support decisions about which therapeutic hypotheses merit additional study.