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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...
What is Population Genetics?01:25

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 Parameter01:26

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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...
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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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Genomics02:02

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Related Experiment Video

Updated: May 21, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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Published on: December 7, 2021

Blockwise HMM computation for large-scale population genomic inference.

Joshua S Paul1, Yun S Song

  • 1Computer Science Division and Department of Statistics, University of California, Berkeley, CA 94720, USA.

Bioinformatics (Oxford, England)
|May 30, 2012
PubMed
Summary
This summary is machine-generated.

Algorithmic improvements to hidden Markov models (HMMs) accelerate population genomic inference using the conditional sampling distribution (CSD). These advancements enable large-scale genomic analyses previously considered intractable.

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Area of Science:

  • Genomics
  • Computational Biology
  • Population Genetics

Background:

  • Conditional sampling distribution (CSD) methods approximate DNA sequence sampling probabilities in population genomics.
  • CSD applications include imputation, recombination rate estimation, and ancestry analysis.
  • Current CSD implementations using hidden Markov models (HMMs) are computationally intractable for large genomic datasets.

Purpose of the Study:

  • To develop algorithmic improvements for exact HMM computation in population genomic inference.
  • To enhance the efficiency of CSD-based methods for large-scale genomic data.

Main Methods:

  • Exploiting the specific structure of the CSD.
  • Leveraging typical characteristics of genomic data.
  • Implementing algorithmic optimizations for exact HMM computations.

Main Results:

  • Achieved speedups of several orders of magnitude for large datasets.
  • Demonstrated that speedup increases with the number of sequences.
  • Enabled previously impracticable large-scale genomic analyses.

Conclusions:

  • Optimized algorithms significantly enhance the computational efficiency of CSD-based population genomic inference.
  • These improvements make large-scale genomic analyses feasible.
  • The enhanced methods support a wider range of applications in population genetics.