A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape
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Published version
Author(s)
Type
Journal Article
Abstract
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits,
one trait at a time. We examined whether genetic variants affect body shape as a composite
phenotype that is represented by a combination of anthropometric traits. We developed an
approach that calculates averaged PCs (AvPCs) representing body shape derived from
six anthropometric traits (body mass index, height, weight, waist and hip circumference,
waist-to-hip ratio). The first four AvPCs explain
4
99% of the variability, are heritable, and
associate with cardiometabolic outcomes. We performed genome-wide association analyses
for each body shape composite phenotype across 65 studies and meta-analysed summary
statistics. We identify six novel loci:
LEMD2
and
CD47
for AvPC1,
RPS6KA5
/
C14orf159
and
GANAB
for AvPC3, and
ARL15
and
ANP32
for AvPC4. Our findings highlight the value of
using multiple traits to define complex phenotypes for discovery, which are not captured by
single-trait analyses, and may shed light onto new pathways.
one trait at a time. We examined whether genetic variants affect body shape as a composite
phenotype that is represented by a combination of anthropometric traits. We developed an
approach that calculates averaged PCs (AvPCs) representing body shape derived from
six anthropometric traits (body mass index, height, weight, waist and hip circumference,
waist-to-hip ratio). The first four AvPCs explain
4
99% of the variability, are heritable, and
associate with cardiometabolic outcomes. We performed genome-wide association analyses
for each body shape composite phenotype across 65 studies and meta-analysed summary
statistics. We identify six novel loci:
LEMD2
and
CD47
for AvPC1,
RPS6KA5
/
C14orf159
and
GANAB
for AvPC3, and
ARL15
and
ANP32
for AvPC4. Our findings highlight the value of
using multiple traits to define complex phenotypes for discovery, which are not captured by
single-trait analyses, and may shed light onto new pathways.
Date Issued
2016-11-23
Date Acceptance
2016-09-21
Citation
Nature Communications, 2016, 7
ISSN
2041-1723
Publisher
Nature Publishing Group: Nature Communications
Journal / Book Title
Nature Communications
Volume
7
Copyright Statement
© The Author(s) 2016. This work is licensed under a Creative Commons Attribution 4.0
International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise
in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
International License. The images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise
in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material.
To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Identifier
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Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
GENOME-WIDE ASSOCIATION
DIET-INDUCED OBESITY
MASS INDEX
DISEASE ASSOCIATIONS
FAT DISTRIBUTION
EARLY-ONSET
HIP RATIO
RESOURCE
BIOLOGY
HEIGHT
Publication Status
Published
Article Number
ARTN 13357