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Related Concept Videos

Pedigree Analysis01:35

Pedigree Analysis

Overview
Pedigree Analysis01:35

Pedigree Analysis

Overview
Incomplete Dominance01:43

Incomplete Dominance

Gregor Mendel's work (1822 - 1884) was primarily focused on pea plants. Through his initial experiments, he determined that every gene in a diploid cell has two variants called alleles inherited from each parent. He suggested that amongst these two alleles, one allele is dominant in character and the other recessive. The combination of alleles determines the phenotype of a gene in an organism.
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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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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.
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Multiple Allele Traits

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Updated: Jun 21, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

Efficient haplotype inference from pedigrees with missing data using linear systems with disjoint-set data

Xin Li1, Jing Li

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.

Computational Systems Bioinformatics. Computational Systems Bioinformatics Conference
|August 1, 2009
PubMed
Summary
This summary is machine-generated.

This study presents an efficient algorithm for haplotype inference using pedigree data and the zero recombination assumption. The method significantly improves computational speed and accuracy for genetic marker analysis.

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Published on: February 5, 2014

Area of Science:

  • Computational Biology
  • Genetics
  • Bioinformatics

Background:

  • Haplotype inference from pedigree data is crucial for genetic studies.
  • The zero recombination assumption is valid for tightly linked markers like single nucleotide polymorphisms (SNPs).
  • Existing methods may lack efficiency for complex pedigrees or large datasets.

Purpose of the Study:

  • To develop a mathematically rigorous and efficient algorithm for haplotype inference under the zero recombination model.
  • To improve upon existing algorithms in terms of speed and accuracy.
  • To handle complex pedigree structures, including loops and missing data.

Main Methods:

  • Formulating genotype constraints as a linear system of inheritance variables.
  • Utilizing disjoint-set data structures for efficient constraint detection and consistency checking.
  • Extending the algorithm to accommodate looped pedigrees and missing genotypic data.

Main Results:

  • Achieved nearly linear time complexity O(mn x alpha(n)) for tree pedigrees without missing data.
  • Demonstrated correct identification of all 0-recombinant solutions with high efficiency.
  • Showcased 10 to 10(5)-fold improvements compared to other popular algorithms.

Conclusions:

  • The developed algorithm provides a significant advancement in haplotype inference efficiency and accuracy.
  • The mathematical formulation and use of disjoint-set structures are key to the algorithm's performance.
  • The C++ implementation, to be part of the PedPhase package, is robust for various pedigree types and data completeness.