Linked Life Data

21537348

Source:http://linkedlifedata.com/resource/pubmed/id/21537348

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2011-5-31
pubmed:abstractText
Many rare kidney disorders exhibit a monogenic, Mendelian pattern of inheritance. Population-based genetic studies have identified many genetic variants associated with an increased risk of developing common kidney diseases. Strongly associated variants have potential clinical uses as predictive markers and may advance our understanding of disease pathogenesis. These principles are elegantly illustrated by a region within chromosome 22q12 that has a strong association with common forms of kidney disease. Researchers had identified DNA sequence variants in this locus that were highly associated with an increased prevalence of common chronic kidney diseases in people of African ancestry. Initial research concentrated on MYH9 as the most likely candidate gene; however, population-based whole-genome analysis enabled two independent research teams to discover more strongly associated mutations in the neighboring APOL1 gene. The powerful evolutionary selection pressure of an infectious pathogen in West Africa favored the spread of APOL1 variants that protect against a lethal form of African sleeping sickness but are highly associated with an increased risk of kidney disease. We describe the data sources, process of discovery, and reasons for initial misidentification of the candidate gene, as well as the lessons that can be learned for future population genetics research.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1759-507X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
7
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
313-26
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
The population genetics of chronic kidney disease: insights from the MYH9-APOL1 locus.
pubmed:affiliation
Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv 69978, Israel.
pubmed:publicationType
Journal Article, Review, Research Support, Non-U.S. Gov't