Related Experiment Video
Updated: Aug 13, 2026
07:15
Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
How to quantify information loss due to phase ambiguity in haplotype case-control studies
Hae-Won Uh1, Jeanine J Houwing-Duistermaat, Hein Putter
1Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands. h.uh@lumc.nl
BMC Genetics
|February 3, 2006
Summary
Determining genetic haplotypes in case-control studies is difficult. This study quantifies information loss from missing phase data and shows how parental information can improve accuracy.
Area of Science:
- Genetics
- Bioinformatics
- Statistical genetics
Background:
- Haplotype assignment is crucial for genetic association studies.
- Missing phase information in case-control studies presents a significant analytical challenge.
- Quantifying information loss is essential for understanding study limitations.
Purpose of the Study:
- To propose a method for quantifying information loss due to missing haplotype phase.
- To identify individuals contributing most to this information loss.
- To assess the potential information gain from resolving ambiguous haplotype assignments using parental data.
Main Methods:
- Development of a novel method to calculate information loss from unphased genotypes.
- Identification of informative individuals based on their contribution to phase ambiguity.
- Simulation or analysis of scenarios with added parental data to estimate information gain.
Main Results:
- The proposed method effectively quantifies information loss from missing haplotype phase.
- Specific individuals were identified as major contributors to phase ambiguity.
- Resolving ambiguous individuals with parental information demonstrated a significant potential for information gain.
Conclusions:
- The developed method provides a quantitative measure of haplotype phase information loss.
- Targeted data collection, such as adding parental information for specific individuals, can optimize study design.
- This approach aids in understanding and mitigating the impact of missing phase data in genetic studies.
Related Concept Videos
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:11Genome-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...
GWAS does not require the identification of the target gene involved in...
01:25Strategies for Assessing and Addressing Confounding
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:49Probability Laws
Overview
01:49Hardy-Weinberg Principle
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...