CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Hierarchical clustering analysis of blood plasma lipidomics profiles from mono- and dizygotic twin families
Authors
D.I. Boomsma
H.H.M. Draisma
+4 more
Th. Hankemeier
J.J. Meulman
Th.H. Reijmers
J. van der Greef
Publication date
1 January 2013
Publisher
'Springer Science and Business Media LLC'
Doi
Cite
Abstract
Twin and family studies are typically used to elucidate the relative contribution of genetic and environmental variation to phenotypic variation. Here, we apply a quantitative genetic method based on hierarchical clustering, to blood plasma lipidomics data obtained in a healthy cohort consisting of 37 monozygotic and 28 dizygotic twin pairs, and 52 of their biological nontwin siblings. Such data are informative of the concentrations of a wide range of lipids in the studied blood samples. An important advantage of hierarchical clustering is that it can be applied to a high-dimensional 'omics' type data, whereas the use of many other quantitative genetic methods for analysis of such data is hampered by the large number of correlated variables. For this study we combined two lipidomics data sets, originating from two different measurement blocks, which we corrected for block effects by 'quantile equating'. In the analysis of the combined data, average similarities of lipidomics profiles were highest between monozygotic (MZ) cotwins, and became progressively lower between dizygotic (DZ) cotwins, among sex-matched nontwin siblings and among sex-matched unrelated participants, respectively. Our results suggest that (1) shared genetic background, shared environment, and similar age contribute to similarities in blood plasma lipidomics profiles among individuals; and (2) that the power of quantitative genetic analyses is enhanced by quantile equating and combination of data sets obtained in different measurement blocks. © 2013 Macmillan Publishers Limited. All rights reserved
Similar works
Full text
Available Versions
NARCIS
See this paper in CORE
Go to the repository landing page
Download from data provider
Last time updated on 14/10/2017
VU Research Portal
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:research.vu.nl:publication...
Last time updated on 18/04/2020
NARCIS
See this paper in CORE
Go to the repository landing page
Download from data provider
Last time updated on 03/09/2017