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Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Related Experiment Video

Updated: Mar 30, 2026

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Efficient genotype compression and analysis of large genetic-variation data sets.

Ryan M Layer¹, Neil Kindlon¹, Konrad J Karczewski²

¹Department of Human Genetics, University of Utah, Salt Lake City, Utah, USA.

|November 10, 2015

Summary

Genotype Query Tools (GQT) offers a novel indexing strategy to accelerate genome-variation analysis. This method significantly enhances performance for large-scale genomic datasets, improving with larger cohort sizes.

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Published on: August 21, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Analyzing large-scale genome-variation data is computationally intensive.
Existing methods struggle with efficiency as dataset size increases.

Purpose of the Study:

To introduce Genotype Query Tools (GQT), an indexing strategy for expedited genome-variation data analysis.
To demonstrate GQT's performance improvements over existing methods, especially for large cohorts.

Main Methods:

Developed a compressed genotype index to minimize decompression overhead.
Designed GQT to leverage sample genotypes, phenotypes, and relationships for efficient querying.

Main Results:

Achieved substantial performance gains, up to 443-fold, compared to existing methods.
Demonstrated improved performance with increasing cohort size.
Showcased GQT's utility for exploring massive genomic datasets.

Conclusions:

GQT provides a significant advancement in analyzing large-scale genome-variation data.
The strategy is scalable and particularly effective for cohorts ranging from thousands to millions of genomes.
GQT is publicly available for broader research application.