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Principles of Pharmacogenetics: Types of Genetic Variants

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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...

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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...

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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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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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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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Variations in predicted risks in personal genome testing for common complex diseases.

Rachel R J Kalf¹, Raluca Mihaescu¹, Suman Kundu¹

¹Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.

Genetics in Medicine : Official Journal of the American College of Medical Genetics

|June 29, 2013

Summary

This summary is machine-generated.

Direct-to-consumer genetic testing companies show varying accuracy in predicting disease risk using single nucleotide polymorphisms. Understanding these differences is key for future personalized genomics advancements.

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Area of Science:

Genomics
Personalized Medicine
Biostatistics

Background:

Personalized genomics holds promise for predicting risks of complex diseases.
Direct-to-consumer (DTC) genetic testing companies offer personal genome testing services.
The accuracy of these services relies on predicting genetic risks from single nucleotide polymorphisms (SNPs).

Purpose of the Study:

To examine and compare the predictive methods of three DTC genetic testing companies: 23andMe, deCODEme, and Navigenics.
To assess the predictive ability of these companies for six common complex diseases using Area Under the Curve (AUC).

Main Methods:

Simulated genotype data for 100,000 individuals based on published frequencies.
Predicted disease risks using the proprietary methods of each company.
Assessed predictive performance using AUC for six distinct diseases.

Main Results:

AUC values varied significantly across diseases and companies.
Highest predictive accuracy observed for age-related macular degeneration, celiac disease, and Crohn disease.
Substantial differences in predicted risks were attributed to variations in SNP selection, population risk data, and risk calculation formulas.

Conclusions:

The predictive algorithms employed by early DTC genetic testing companies have varying strengths and limitations.
Understanding these differences is crucial for the development of future predictive models in complex disease genomics.
This study highlights the need for transparency and standardization in DTC genetic risk prediction.