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Journal or Publication Title: | Nature Communications | ||||
Publisher: | NATURE PUBLISHING GROUP | ||||
Place of Publication: | LONDON | ||||
Volume: | 7 | ||||
Number of Issue or Book Chapter: | 1 | ||||
Date: | 2016 | ||||
Institutions: | Medicine > Abteilung für Nephrologie Medicine > Institut für Epidemiologie und Präventivmedizin | ||||
Identification Number: |
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Keywords: | GENOME-WIDE ASSOCIATION; DIET-INDUCED OBESITY; MASS INDEX; DISEASE ASSOCIATIONS; FAT DISTRIBUTION; EARLY-ONSET; HIP RATIO; RESOURCE; BIOLOGY; HEIGHT; | ||||
Dewey Decimal Classification: | 600 Technology > 610 Medical sciences Medicine | ||||
Status: | Published | ||||
Refereed: | Yes, this version has been refereed | ||||
Created at the University of Regensburg: | Yes | ||||
Item ID: | 42804 |

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 ...

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 >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.
Metadata last modified: 16 Mar 2022 10:27