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Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

Pedigree Analysis

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...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

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...

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.In the early 20th century,...

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Related Experiment Video

Updated: Jun 6, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Finding most likely haplotypes in general pedigrees through parallel search with dynamic load balancing.

Lars Otten¹, Rina Dechter

¹Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA. lotten@ics.uci.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 2, 2010

Summary

This study introduces a parallelized Branch and Bound algorithm for efficiently solving complex genetic pedigree problems. The method uses likelihood functions to manage parallel nodes, successfully tackling difficult instances on commodity hardware.

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Published on: June 21, 2018

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Last Updated: Jun 6, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

Computational Biology
Bioinformatics
Genetics

Background:

General pedigrees can be represented as Bayesian networks.
The Most Probable Explanation (MPE) query in these networks aims to find the most likely haplotype configuration.

Purpose of the Study:

To introduce a grid parallelization strategy for a state-of-the-art Branch and Bound algorithm designed for MPE queries in genetic pedigrees.
To enhance the efficiency and scalability of solving complex haplotype configuration problems.

Main Methods:

A Branch and Bound master node manages independent worker nodes that concurrently solve subproblems.
Likelihood functions are employed to predict subproblem complexity and automate the parallelization process.
The algorithm is evaluated on up to 20 parallel nodes using commodity hardware.

Main Results:

The parallelization strategy demonstrates effectiveness in solving several very hard problem instances.
Experimental evaluations show very promising results, indicating the scheme's success.
The system achieves efficient automation of the parallelization process through complexity prediction.

Conclusions:

The proposed grid parallelization strategy significantly enhances the performance of Branch and Bound algorithms for MPE queries in genetic pedigrees.
The approach is scalable and deployable on loosely coupled commodity hardware, paving the way for larger-scale applications.