PMID- 36606461
OWN - NLM
STAT- MEDLINE
DCOM- 20230214
LR  - 20230216
IS  - 1832-4274 (Print)
IS  - 1832-4274 (Linking)
VI  - 25
IP  - 6
DP  - 2022 Dec
TI  - Exploring the Genetic Association between Obesity and Serum Lipid Levels Using 
      Bivariate Methods.
PG  - 234-244
LID - 10.1017/thg.2022.39 [doi]
AB  - It is crucial to understand the genetic mechanisms and biological pathways 
      underlying the relationship between obesity and serum lipid levels. Structural 
      equation models (SEMs) were constructed to calculate heritability for body mass 
      index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein 
      cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic 
      connections between BMI and the four classes of lipids using 1197 pairs of twins 
      from the Chinese National Twin Registry (CNTR). Bivariate genomewide association 
      studies (GWAS) were performed to identify genetic variants associated with BMI 
      and lipids using the records of 457 individuals, and the results were further 
      validated in 289 individuals. The genetic background affecting BMI may differ by 
      gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 
      39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG 
      and LDL-C in phenotypic and genetic correlation, while negatively correlated with 
      HDL-C. There were gender differences in the correlation between BMI and lipids. 
      Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, 
      LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to 
      BMI and lipid levels. The significant biological pathways were the regulation of 
      cholesterol reverse transport and the regulation of high-density lipoprotein 
      particle clearance (p < .001). BMI and blood lipid levels were affected by 
      genetic factors, and they were genetically correlated. There might be gender 
      differences in their genetic correlation. Bivariate GWAS analysis found MIR144 
      gene and its related biological pathways may influence obesity and lipid levels.
FAU - Ke, Ji
AU  - Ke J
AUID- ORCID: 0000-0002-2114-1481
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Gao, Wenjing
AU  - Gao W
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Wang, Biqi
AU  - Wang B
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Cao, Weihua
AU  - Cao W
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Lv, Jun
AU  - Lv J
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Yu, Canqing
AU  - Yu C
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Huang, Tao
AU  - Huang T
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Sun, Dianjianyi
AU  - Sun D
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Liao, Chunxiao
AU  - Liao C
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Pang, Yuanjie
AU  - Pang Y
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
FAU - Pang, Zengchang
AU  - Pang Z
AD  - Qingdao Municipal Center for Disease Control and Prevention, Qingdao, China.
FAU - Cong, Liming
AU  - Cong L
AD  - Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.
FAU - Wang, Hua
AU  - Wang H
AD  - Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
FAU - Wu, Xianping
AU  - Wu X
AD  - Sichuan Center for Disease Control and Prevention, Chengdu, China.
FAU - Liu, Yu
AU  - Liu Y
AD  - Heilongjiang Provincial Center for Disease Control and Prevention, Harbin, China.
FAU - Li, Liming
AU  - Li L
AD  - Department of Epidemiology and Biostatistics, School of Public Health, Peking 
      University, Beijing, China.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20230106
PL  - England
TA  - Twin Res Hum Genet
JT  - Twin research and human genetics : the official journal of the International 
      Society for Twin Studies
JID - 101244624
RN  - 0 (Cholesterol, HDL)
RN  - 0 (Cholesterol, LDL)
RN  - 0 (Lipids)
RN  - EC 2.7.1.- (MELK protein, human)
RN  - EC 2.7.11.1 (Protein Serine-Threonine Kinases)
RN  - 0 (Triglycerides)
RN  - 0 (MIRN144 microRNA, human)
SB  - IM
MH  - Female
MH  - Humans
MH  - Male
MH  - Body Mass Index
MH  - Cholesterol, HDL/genetics
MH  - Cholesterol, LDL/genetics
MH  - *Lipids/genetics
MH  - *Obesity/genetics
MH  - Protein Serine-Threonine Kinases
MH  - Triglycerides/genetics
OTO - NOTNLM
OT  - BMI
OT  - Twins
OT  - genome-wide association study
OT  - lipids
OT  - structural equation model
EDAT- 2023/01/07 06:00
MHDA- 2023/02/14 06:00
CRDT- 2023/01/06 04:32
PHST- 2023/01/07 06:00 [pubmed]
PHST- 2023/02/14 06:00 [medline]
PHST- 2023/01/06 04:32 [entrez]
AID - S1832427422000391 [pii]
AID - 10.1017/thg.2022.39 [doi]
PST - ppublish
SO  - Twin Res Hum Genet. 2022 Dec;25(6):234-244. doi: 10.1017/thg.2022.39. Epub 2023 
      Jan 6.