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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Classifying Matter by Composition03:35

Classifying Matter by Composition

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Matter: Pure Substances and Mixtures
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures. 
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated. 
A mixture is composed of two or...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
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Improper Integrals: Infinite Intervals01:29

Improper Integrals: Infinite Intervals

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An integral is classified as improper due to an infinite interval when at least one of its limits of integration extends to positive or negative infinity. In such cases, the region under the curve is unbounded, and standard techniques for evaluating definite integrals are not directly applicable. Instead, the improper integral is defined through a limiting process that allows one to determine whether the accumulated area remains finite despite the infinite domain.Application to Exponential...
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Interval Level of Measurement00:55

Interval Level of Measurement

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For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...
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Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
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β-composite Interval Mapping for robust QTL analysis.

Md Mamun Monir1,2, Mita Khatun2, Md Nurul Haque Mollah1

  • 1Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh.

Plos One
|December 4, 2018
PubMed
Summary
This summary is machine-generated.

Robust quantitative trait locus (QTL) mapping using Beta-Composite Interval Mapping (BetaCIM) improves detection of genetic architecture, especially with outlier data. BetaCIM outperforms traditional methods in identifying true QTLs and estimating effects when data is noisy.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) mapping is crucial for understanding the genetic basis of traits and diseases.
  • Composite Interval Mapping (CIM) is a widely used QTL mapping method but is sensitive to phenotypic outliers.
  • Outliers can hinder the accurate detection of true QTL positions and effect sizes.

Purpose of the Study:

  • To evaluate the performance of β-Composite Interval Mapping (BetaCIM) for robust QTL detection.
  • To assess BetaCIM's ability to identify both linked and unlinked QTLs in the presence of outliers.
  • To develop and implement a robust QTL analysis approach and an associated R-package.

Main Methods:

  • Developed and formulated the β-Composite Interval Mapping (BetaCIM) approach, a robust extension of CIM.
  • Described and formulated a cross-validation procedure for selecting the optimal tuning parameter β.
  • Applied BetaCIM, CIM, and Interval Mapping (IM) to simulated and real mouse kidney weight data.

Main Results:

  • BetaCIM demonstrated superior performance in detecting true QTLs and estimating effects in the presence of phenotypic outliers.
  • In simulated data and mouse kidney weight analysis, BetaCIM identified significant QTLs where CIM and IM failed due to outliers.
  • BetaCIM achieved comparable results to CIM and IM when no outliers were present, indicating its versatility.

Conclusions:

  • BetaCIM offers a robust alternative to traditional interval mapping methods for QTL analysis.
  • The developed R-package 'BetaCIM' provides a practical tool for researchers to implement robust QTL mapping.
  • BetaCIM significantly improves the accuracy of genetic architecture discovery in datasets with potential outliers.