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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
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...
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...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Related Experiment Video

Updated: May 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Estimating uncertainty in family-based GWAS.

Xinyi Miao1, Michael D Edge2, Arbel Harpak1,3

  • 1Department of Integrative Biology, University of Texas at Austin, Austin, TX.

Biorxiv : the Preprint Server for Biology
|May 25, 2026
PubMed
Summary

Sibling-based genome-wide association studies (sib-GWAS) offer robust genetic insights but face estimation uncertainty. A novel resampling method accurately quantifies this uncertainty, outperforming existing approaches, especially in smaller samples.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Related Experiment Videos

Last Updated: May 26, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Standard genome-wide association studies (GWASs) are susceptible to confounding factors like stratification and dynastic effects.
  • Sibling-based GWAS (sib-GWAS) are increasingly used to isolate direct genetic effects but yield more variable estimates due to smaller sample sizes.
  • Accurate quantification of uncertainty in sib-GWAS is crucial for applications like polygenic scoring and causal inference.

Purpose of the Study:

  • To investigate sources of uncertainty in sib-GWAS allelic effect estimators.
  • To evaluate the bias of existing uncertainty measurement methods and propose a new, robust approach.
  • To understand the impact of effect heterogeneity and heteroskedasticity on sib-GWAS uncertainty estimation.

Main Methods:

  • Theoretical investigation of uncertainty sources in sib-GWAS.
  • Bias assessment of common uncertainty measurement methods and a novel resampling-based method.
  • Simulations and empirical analysis using UK Biobank data to validate findings.

Main Results:

  • Heterogeneity in allelic effects and heteroskedasticity can bias existing uncertainty estimation methods, particularly in small samples and for rare variants.
  • The proposed resampling-based approach demonstrates approximate unbiasedness across various scenarios.
  • Empirical validation confirms the theoretical predictions regarding effect heterogeneity and heteroskedasticity.

Conclusions:

  • Existing methods for quantifying uncertainty in sib-GWAS can be biased by effect heterogeneity and heteroskedasticity.
  • The novel resampling-based method provides a robust and approximately unbiased estimation of uncertainty in sib-GWAS.
  • This study enhances understanding of uncertainty in family-based genetic association studies and offers a reliable estimation tool.