A Novel Hierarchical Clustering Approach for Joint Analysis of Multiple Phenotypes Uncovers Obesity Variants Based on ARIC

被引:7
|
作者
Fu, Liwan [1 ,2 ]
Wang, Yuquan [2 ]
Li, Tingting [2 ]
Yang, Siqian [2 ]
Hu, Yue-Qing [2 ,3 ]
机构
[1] Capital Med Univ, Beijing Childrens Hosp, Ctr Noncommunicable Dis Management, Natl Ctr Childrens Hlth, Beijing, Peoples R China
[2] Fudan Univ, Sch Life Sci, Inst Biostat, State Key Lab Genet Engn,Human Phenome Inst, Shanghai, Peoples R China
[3] Fudan Univ, Shanghai Ctr Math Sci, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
GWAS; hierarchical clustering; multiple phenotypes; obesity; bioinformatics; BODY-MASS INDEX; GENOME-WIDE ASSOCIATION; PRINCIPAL-COMPONENTS; COMMON VARIANTS; LOCI; METAANALYSIS; POWER; GENE; HERITABILITY; INDIVIDUALS;
D O I
10.3389/fgene.2022.791920
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Genome-wide association studies (GWASs) have successfully discovered numerous variants underlying various diseases. Generally, one-phenotype one-variant association study in GWASs is not efficient in identifying variants with weak effects, indicating that more signals have not been identified yet. Nowadays, jointly analyzing multiple phenotypes has been recognized as an important approach to elevate the statistical power for identifying weak genetic variants on complex diseases, shedding new light on potential biological mechanisms. Therefore, hierarchical clustering based on different methods for calculating correlation coefficients (HCDC) is developed to synchronously analyze multiple phenotypes in association studies. There are two steps involved in HCDC. First, a clustering approach based on the similarity matrix between two groups of phenotypes is applied to choose a representative phenotype in each cluster. Then, we use existing methods to estimate the genetic associations with the representative phenotypes rather than the individual phenotypes in every cluster. A variety of simulations are conducted to demonstrate the capacity of HCDC for boosting power. As a consequence, existing methods embedding HCDC are either more powerful or comparable with those of without embedding HCDC in most scenarios. Additionally, the application of obesity-related phenotypes from Atherosclerosis Risk in Communities via existing methods with HCDC uncovered several associated variants. Among these, UQCC1-rs1570004 is reported as a significant obesity signal for the first time, whose differential expression in subcutaneous fat, visceral fat, and muscle tissue is worthy of further functional studies.
引用
收藏
页数:13
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