Learn to use Genetics Home Reference , or GHR, an extensive collection of data that describe the effects of genetic variability on human health and disease. Everyone from the general public to the experienced researcher can explore the genetic topics covered because GHR is written in lay language and navigation between pages is simple. Summary pages describe hundreds of human genetic diseases, conditions and syndromes, the genes thought to be responsible for them, and all of the human chromosomes. GHR provides many additional resources and tools such as a handbook, search tips, a glossary and links, which allow you to find both more general and more specific information.
You will learn:
This tutorial is a part of the tutorial group Human variations. You might find the other tutorials in the group interesting:
GAD: Genetic Association Database: An archived database associating human genes and polymorphisms with diseases
Madeline 2.0: Human pedigree diagram tools
DrugBank: A chemoinformatics and bioinformatics resource
DGV: Database of Genomic Variants: Database of Genomic Variants, DGV, catalogs and displays structural variation in the human genome
OMIM: Online Mendelian Inheritance in Man (OMIM): A database of human genes, genetic diseases and disorders
CGAP: Characterize the molecular genetic changes that cause a normal cell to become a cancer cell
ENCODE Foundations: ENCyclopedia of DNA Elements
GeneSNPs: An integrated view of gene structure and SNP variations
NIEHS SNPs: National Institute for Environmental Health Sciences Environmental Genome Project (EGP) SNPs
HapMap: HapMap, a database and analysis resource of human variation
dbGaP: A database of genotypes and phenotypes with extensive variation data and clinical details
SeattleSNPs: Human SNPs in genes
dbSNP: NCBI's SNP database
GeneTests: GeneTests, a current, comprehensive genetic testing resource
Variation & Medical : Resources that include information about sequence variation, phenotypes, or medically-relevant conditions.
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Recent BioMed Central research articles citing this resource
Mei Yan et al., For robust big data analyses: a collection of 150 important pro-metastatic genes. Chinese Journal of Cancer (2017) doi:10.1186/s40880-016-0178-z
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