Linked Life Data

21068042

Source:http://linkedlifedata.com/resource/pubmed/id/21068042

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1711
pubmed:dateCreated
2011-4-7
pubmed:abstractText
Detecting regions of the human genome that are, or have been, influenced by natural selection remains an important goal for geneticists. Many methods are used to infer selection, but there is a general reliance on an accurate understanding of how mutation and recombination events are distributed, and the well-known link between these processes and their evolutionary transience introduces uncertainty into inferences. Here, we present and apply two new, independent approaches; one based on single nucleotide polymorphisms (SNPs) that exploits geographical patterns in how humans lost variability as we colonized the world, the other based on the relationship between microsatellite repeat number and heterozygosity. We show that the two methods give concordant results. Of these, the SNP-based method is both widely applicable and detects selection over a well-defined time interval, the last 50 000 years. Analysis of all human genes by their Gene Ontology codes reveals how accelerated and decelerated loss of variability are both preferentially associated with immune genes. Applied to 168 immune genes used as the focus of a previous study, we show that members of the same gene family tend to yield similar indices of selection, even when located on different chromosomes. We hope our approach will provide a useful tool with which to infer where selection has acted to shape the human genome.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
1471-2954
pubmed:author
pubmed:issnType
Electronic
pubmed:day
22
pubmed:volume
278
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1587-94
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Using human demographic history to infer natural selection reveals contrasting patterns on different families of immune genes.
pubmed:affiliation
Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK. w.amos@zoo.cam.ac.uk
pubmed:publicationType
Journal Article