Familial aggregation analysis of gene expressions.
Traditional studies of familial aggregation are aimed at defining the genetic (and non-genetic) causes of a disease from physiological or clinical traits. However, there has been little attempt to use genome-wide gene expressions, the direct phenotypic measures of genes, as the traits to investigate several extended issues regarding the distributions of familially aggregated genes on chromosomes or in functions. In this study we conducted a genome-wide familial aggregation analysis by using the in vitro cell gene expressions of 3300 human autosome genes (Problem 1 data provided to Genetic Analysis Workshop 15) in order to answer three basic genetics questions. First, we investigated how gene expressions aggregate among different types (degrees) of relative pairs. Second, we conducted a bioinformatics analysis of highly familially aggregated genes to see how they are distributed on chromosomes. Third, we performed a gene ontology enrichment test of familially aggregated genes to find evidence to support their functional consensus. The results indicated that 1) gene expressions did aggregate in families, especially between sibs. Of 3300 human genes analyzed, there were a total of 1105 genes with one or more significant (empirical p < 0.05) familial correlation; 2) there were several genomic hot spots where highly familially aggregated genes (e.g., the chromosome 6 HLA genes cluster) were clustered; 3) as we expected, gene ontology enrichment tests revealed that the 1105 genes were aggregating not only in families but also in functional categories.
Rao
Shao-Qi
SQ
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.|Departments of Molecular Cardiology and Cardiovascular Medicine, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.|Biomedical Engineering Institute, Capital University of Medical Sciences, Beijing 100054, People's Republic of China.
Xu
Liang-De
LD
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Zhang
Guang-Mei
GM
The First Clinical College, Harbin Medical University, Harbin 150081, People's Republic of China.
Li
Xia
X
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.|Biomedical Engineering Institute, Capital University of Medical Sciences, Beijing 100054, People's Republic of China.|Department of Computer Science, Harbin Institute of Technology, Harbin 150080, People's Republic of China.
Li
Lin
L
Departments of Molecular Cardiology and Cardiovascular Medicine, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.
Shen
Gong-Qing
GQ
Departments of Molecular Cardiology and Cardiovascular Medicine, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.
Jiang
Yang
Y
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Yang
Yue-Ying
YY
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Gong
Bin-Sheng
BS
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Jiang
Wei
W
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Zhang
Fan
F
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Xiao
Yun
Y
Department of Bioinformatics, Harbin Medical University, Harbin 150086, People's Republic of China.
Wang
Qing K
QK
Departments of Molecular Cardiology and Cardiovascular Medicine, The Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA.