Last year, I came up with a list containg 10 tips on how to search for genetic conditions. Now, after weeks of tagging and browsing, I’d like to improve that list with some new tips. But this time, I’d like to show you databases dedicated not only to genetic conditions, but gene-disease associations and human genome epidemiology as well.
- Human Genome Epidemiology Network (HuGENet™ ):
A global collaboration of individuals & organizations committed to the assessment of the impact of human genome variation on population health & how genetic information can be used to improve health & prevent disease.
It provides access to a continuously updated knowledge base in human genome epidemiology, including information on population prevalence of genetic variants, gene-disease associations, gene-gene and gene- environment interactions, and evaluation of genetic tests.
GAIN is taking the next step in the search to understand the genetic factors influencing risk for complex diseases. Through a series of whole genome association studies, using samples from existing case-control studies of patients with common diseases, GAIN will contribute to the identification of genetic pathways that make us more susceptible to these diseases and thus facilitate discovery of new molecular targets for prevention, diagnosis, and treatment.
It archives and distributes the results of studies that have investigated the interaction of genotype and phenotype. Such studies include genome-wide association studies, medical sequencing, molecular diagnostic assays, as well as association between genotype and non-clinical traits.
A website which assigns molecular functional effects of non-synonymous SNPs based on structure and sequence analysis. You should also check out the Disease-Gene mapping tool.
It will conduct genome wide association studies and analyses in several large NHLBI Cohort studies to identify genes underlying cardiovascular and lung disease and other disorders like osteoporosis and diabetes.
Let’s finish with a great idea, the human disease network published at PNAS.
A network of disorders and disease genes linked by known disorder–gene associations offers a platform to explore in a single graph-theoretic framework all known phenotype and disease gene associations, indicating the common genetic origin of many diseases. Genes associated with similar disorders show both higher likelihood of physical interactions between their products and higher expression profiling similarity for their transcripts, supporting the existence of distinct disease-specific functional modules.
Let me know please if you happen to know more useful databases and tools.