Linked Life Data

20718041

Source:http://linkedlifedata.com/resource/pubmed/id/20718041

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2010-9-6
pubmed:abstractText
We propose a new approach for the analysis of copy number variants (CNVs)for genome-wide association studies in family-based designs. Our new overall association test combines the between-family component and the within-family component of the family-based data so that the new test statistic is fully efficient and, at the same time, maintains robustness against population-admixture and stratification, like classical family-based association tests that are based only on the within-family component. Although all data are incorporated into the test statistic, an adjustment for genetic confounding is not needed, even for the between-family component. The new test statistic is valid for testing either quantitative or dichotomous phenotypes. If external CNV data are available, the approach can also be applied to completely ascertained samples. Similar to the approach by Ionita-Laza et al. ([2008]. Genet Epidemiol 32:273-284), the proposed test statistic does not require a CNV-calling algorithm and is based directly on the CNV probe intensities. We show, via simulation studies, that our methodology increases the power of the FBAT statistic to levels comparable to those of population-based designs. The advantages of the approach in practice are demonstrated by an application to a genome-wide association study for body mass index.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1098-2272
pubmed:author
pubmed:copyrightInfo
(c) 2010 Wiley-Liss, Inc.
pubmed:issnType
Electronic
pubmed:volume
34
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
582-90
pubmed:dateRevised
2011-5-18
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
On the genome-wide analysis of copy number variants in family-based designs: methods for combining family-based and population-based information for testing dichotomous or quantitative traits, or completely ascertained samples.
pubmed:affiliation
Channing Laboratory, Brigham and Women's Hospital, Boston, Massachusetts, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural