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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Uncertainty: Overview00:59

Uncertainty: Overview

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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
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Chi-square Analysis

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Related Experiment Video

Updated: Jun 18, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
10:36

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Published on: August 3, 2018

Quantifying uncertainty in genotype calls.

Benilton S Carvalho1, Thomas A Louis, Rafael A Irizarry

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.

Bioinformatics (Oxford, England)
|November 13, 2009
PubMed
Summary
This summary is machine-generated.

We developed CRLMM version 2, a new genotype calling algorithm for genome-wide association studies (GWAS). This method improves accuracy by accounting for batch variability and identifying low-quality data, enhancing GWAS findings.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Genome-wide association studies (GWAS) are crucial for identifying genes linked to complex diseases.
  • Microarrays are widely used in GWAS for simultaneous analysis of millions of single nucleotide polymorphisms (SNPs).
  • Accurate genotype calling from raw microarray data is essential for reliable GWAS results, but influenced by data quality variations.

Purpose of the Study:

  • To develop an improved genotype calling algorithm for GWAS that accounts for data quality variations.
  • To enhance the accuracy of genotype calls in the presence of variability across SNPs, arrays, and sample batches.
  • To provide a method for identifying low-quality data that can impact GWAS findings.

Main Methods:

  • Developed an enhanced multi-level model (CRLMM version 2) building upon previous methods.
  • Incorporated accounting for batch variability and improved call-specific assessment.
  • Implemented quality metrics for SNPs, samples, and sample batches.

Main Results:

  • CRLMM version 2 demonstrated superior performance compared to CRLMM version 1 and Affymetrix Birdseed across three independent datasets.
  • The new model effectively identifies low-quality SNPs, samples, and batches.
  • Improved accuracy in genotype calling, leading to potentially more reliable GWAS outcomes.

Conclusions:

  • CRLMM version 2 offers a robust solution for genotype calling in GWAS, addressing limitations of existing methods.
  • The ability to identify and flag low-quality data enhances the integrity of GWAS.
  • The developed method and software are freely available, promoting wider adoption and improved genetic research.