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Hardy-Weinberg Principle

Hardy-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.

Genome-wide Association Studies-GWAS

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...

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

Distributions to Estimate Population Parameter

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...

Mutation, Gene Flow, and Genetic Drift

Mutation, Gene Flow, and Genetic Drift

In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.

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Updated: Jun 22, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Estimating Haplotype Structure and Frequencies: A Bayesian Approach to Unknown Design in Pooled Genomic Data.

Yuexuan Wang¹, Ritabrata Dutta², Andreas Futschik¹

¹Department of Applied Statistics, Johannes Kepler University, Linz, Austria.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|July 3, 2024

Summary

This study introduces a Bayesian hierarchical model for reconstructing haplotype structure and frequencies from pooled sequencing data, offering uncertainty quantification and ensuring identifiability for genomic analysis.

Keywords:

gibbs sampling markov chain monte carlo shrinkage unknown design matrix

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Area of Science:

Genomics
Computational Biology
Statistical Genetics

Background:

Haplotype structure and frequencies are vital for understanding genome composition.
Existing methods often focus on single individuals or provide point estimates for pooled samples.
Reconstructing haplotypes from pooled sequencing data presents unique challenges.

Purpose of the Study:

To develop a Bayesian hierarchical model for estimating haplotype structure and frequencies from pooled sequencing data.
To address the challenge of identifiability caused by permutation ambiguity in pooled samples.
To quantify the uncertainty associated with haplotype reconstruction.

Main Methods:

Proposed a Bayesian hierarchical model incorporating an order-preserving shrinkage prior.
Introduced a blocked Gibbs sampler for efficient inference under permutation constraints.
Validated the method using simulation studies and real-world pooled sequencing data.

Main Results:

The Bayesian model successfully reconstructs haplotype structure and frequencies from pooled samples.
The proposed prior and sampler effectively handle permutation identifiability issues.
The method quantifies uncertainty in haplotype estimations, providing a more comprehensive analysis.

Conclusions:

The developed Bayesian approach offers a robust and informative method for haplotype reconstruction from pooled sequencing data.
This technique enhances genomic analysis by providing reliable haplotype estimates with uncertainty quantification.
The model demonstrates strong performance in complex, high-dimensional genomic datasets.