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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...

Incomplete Dominance

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.

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...

Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

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.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Related Experiment Video

Updated: Sep 25, 2025

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

A complete pedigree-based graph workflow for rare candidate variant analysis.

Charles Markello¹, Charles Huang², Alex Rodriguez²

¹UC Santa Cruz Genomics Institute, Santa Cruz, California 95060, USA.

Genome Research

|April 28, 2022

Summary

This summary is machine-generated.

VG-Pedigree improves genome sequencing accuracy for rare disease diagnosis by reducing reference bias. This new workflow enhances the detection of disease-causing genetic variants in families.

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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

Area of Science:

Genomics
Clinical Genetics
Bioinformatics

Background:

Linear genome references introduce bias in sequencing data analysis, reducing genotyping accuracy in certain regions.
Pangenome graphs reduce mapping bias but offer room for improvement in clinical genetics.
Accurate variant detection is crucial for diagnosing rare diseases.

Purpose of the Study:

To develop and validate VG-Pedigree, a novel pedigree-aware workflow for enhanced genome sequencing data analysis.
To improve the accuracy of single-nucleotide variant (SNV) and insertion/deletion (indel) variant calling.
To streamline the detection of deleterious variants (DVs) for rare disease diagnosis.

Main Methods:

Introduced VG-Pedigree, integrating the Giraffe pangenome mapper and DeepTrio variant caller with a specialized model.
Compared VG-Pedigree's mapping and variant calling performance against BWA-MEM and standard Giraffe.
Adapted and upgraded deleterious variant detection methods into a unified workflow.

Main Results:

VG-Pedigree demonstrated improved mapping and variant calling for both SNVs and indels compared to existing methods.
The workflow successfully recapitulated previously diagnosed deleterious variants in Undiagnosed Diseases Program families.
A slightly higher absolute count of deleterious variants was observed in probands compared to unaffected siblings.

Conclusions:

VG-Pedigree offers enhanced accuracy for genome sequencing analysis, particularly in clinical genetics for rare diseases.
The workflow effectively identifies candidate deleterious variants within family structures.
This approach aids in resolving challenging cases and advancing rare disease diagnostics.