Genetic associations studied in metabolic-syndrome research

Behavioral Response

Metabolic syndrome is a research and clinical cluster of risk factors including abdominal obesity, high blood pressure, abnormal lipids, and elevated blood glucose.

GWAS work has identified many variants linked to its individual components, with overlap across diabetes, lipid, and blood pressure traits.

This result reflects research-level variation across the metabolic syndrome trait cluster and is not a clinical diagnosis.

Diet, physical activity, weight, sleep, smoking, alcohol, and medications all strongly affect actual metabolic syndrome status.

All traits Compare samples

Selected sample: Demo Report
Report markers: 3 SNP entries
Genes: 1
Research: 1 study

Important Medical Disclaimer

This report is intended for educational and informational purposes only. It is not intended to provide medical advice or be used solely by the customer in the diagnosis, cure, mitigation, treatment, or prevention of any disease or health condition. If you have serious medical conditions, including but not limited to weight-related issues, diabetes, or heart disease, you should not make changes to your diet or exercise routine without consulting your healthcare provider. Under no circumstances should you modify your medication or medical care without professional medical guidance. Find more information at this link

Research summary

What this trait studies

Research summary, not an individual result

This trait is presented as population-level genetic research rather than an individual Low/High verdict, because the science behind it is either too preliminary, too easily misread as a diagnosis, or too sensitive to responsibly reduce to a single badge.

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Explore the evidence

Behind this report

Review the genes, tested markers and supporting research.

Genes analyzed

Gene summaries include genomic locations and associated markers. A detected genotype does not by itself indicate a health condition.

Understanding the data

SNP: A specific genetic marker relevant to this trait.
Genotype: The DNA letters reported at a marker location.
Variant allele: The alternative DNA sequence at the SNP site.
Variant allele frequency: The reported percentage of the reference population carrying this variant.
Variant found: Whether the variant was detected in your DNA file.

1 of 1 genes have detected genotypes.

APOA5

The protein encoded by this gene is an apolipoprotein that plays an important role in regulating the plasma triglyceride levels, a major risk factor for coronary artery disease. It is a component of high density lipoprotein and is highly similar to a rat protein that is upregulated in response to liver injury. Mutations in this gene have been associated with hypertriglyceridemia and hyperlipoproteinemia type 5. This gene is located proximal to the apolipoprotein gene cluster on chromosome 11q23. Alternatively spliced transcript variants encoding the same protein have been identified. [provided by RefSeq, Oct 2009]

GeneCards

Chromosome: 11
Start: 116,789,367
End: 116,792,420
Build: HG19

Associated SNPs 1 / 1 detected

Associated markers for APOA5
SNP	Genotype	Ref. allele	Variant allele	Frequency	Status
rs662799	AG	G	A	83.71%	Detected

SNPs tested

These are the unique genetic markers analyzed for this trait. Genotypes showing "--" were not identified in your DNA file. Report marker totals can include repeated study entries; this table shows each SNP once.

Genotypes and reported variant frequencies
SNP	Chromosome	Genotype	Variant allele	Frequency
rs662799	11	AG	A	83.71%

DNA Genics reports genotypes based on the 'positive' strand of the human genome reference sequence (build 37/HG19). Other companies may use the opposite strand, so genotypes may need conversion for comparison.

Research studies

The following peer-reviewed scientific studies support the genetic associations analyzed in this report.

Effects of APOA5 -1131T>C (rs662799) on fasting plasma lipids and risk of metabolic syndrome: evidence from a case-control study in China and a meta-analysis

Evidence

Statistical significance

2013

Authors: Xu C, Bai R, Zhang D, Li Z, Zhu H, Lai M, Zhu Y