Linked Life Data

20377463

Source:http://linkedlifedata.com/resource/pubmed/id/20377463

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-4-9
pubmed:abstractText
Genome-wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely on the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected in the study provided that they are present in a reference data set which contains both SNPs collected in the study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to previous methods, our method is up to ten times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods, and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1557-8666
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
547-60
pubmed:dateRevised
2011-11-1
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
EMINIM: an adaptive and memory-efficient algorithm for genotype imputation.
pubmed:affiliation
Biostatistics Department, University of Michigan, Ann Arbor, Ann Arbor, Michigan, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural