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Genetics of Obesity Traits: A Bivariate Genome-Wide Association Analysis

Overview of attention for article published in Frontiers in Genetics, May 2018
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Title
Genetics of Obesity Traits: A Bivariate Genome-Wide Association Analysis
Published in
Frontiers in Genetics, May 2018
DOI 10.3389/fgene.2018.00179
Pubmed ID
Authors

Yili Wu, Haiping Duan, Xiaocao Tian, Chunsheng Xu, Weijing Wang, Wenjie Jiang, Zengchang Pang, Dongfeng Zhang, Qihua Tan

Abstract

Previous genome-wide association studies on anthropometric measurements have identified more than 100 related loci, but only a small portion of heritability in obesity was explained. Here we present a bivariate twin study to look for the genetic variants associated with body mass index and waist-hip ratio, and to explore the obesity-related pathways in Northern Han Chinese. Cholesky decomposition model for 242 monozygotic and 140 dizygotic twin pairs indicated a moderate genetic correlation (r = 0.53, 95%CI: 0.42-0.64) between body mass index and waist-hip ratio. Bivariate genome-wide association analysis in 139 dizygotic twin pairs identified 26 associated SNPs with p < 10-5. Further gene-based analysis found 291 nominally associated genes (P < 0.05), including F12, HCRTR1, PHOSPHO1, DOCK2, DOCK6, DGKB, GLP1R, TRHR, MMP1, GPR55, CCK, and OR2AK2, as well as 6 enriched gene-sets with FDR < 0.05. Expression quantitative trait loci analysis identified rs2242044 as a significant cis-eQTL in both the normal adipose-subcutaneous (P = 1.7 × 10-9) and adipose-visceral (P = 4.4 × 10-15) tissue. These findings may provide an important entry point to unravel genetic pleiotropy in obesity traits.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 111 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 14%
Student > Bachelor 11 10%
Researcher 9 8%
Student > Ph. D. Student 8 7%
Other 5 5%
Other 11 10%
Unknown 51 46%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 16%
Medicine and Dentistry 10 9%
Nursing and Health Professions 5 5%
Agricultural and Biological Sciences 5 5%
Immunology and Microbiology 4 4%
Other 12 11%
Unknown 57 51%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 June 2018.
All research outputs
#13,233,951
of 23,323,574 outputs
Outputs from Frontiers in Genetics
#2,821
of 12,335 outputs
Outputs of similar age
#159,745
of 328,495 outputs
Outputs of similar age from Frontiers in Genetics
#46
of 126 outputs
Altmetric has tracked 23,323,574 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 12,335 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 75% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 328,495 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.