Dataset Information

Familial aggregation analysis of gene expressions.

ABSTRACT: 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.

SUBMITTER: Rao SQ

PROVIDER: S-EPMC2367533 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature

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Familial aggregation analysis of gene expressions.

Rao Shao-Qi SQ Xu Liang-De LD Zhang Guang-Mei GM Li Xia X Li Lin L Shen Gong-Qing GQ Jiang Yang Y Yang Yue-Ying YY Gong Bin-Sheng BS Jiang Wei W Zhang Fan F Xiao Yun Y Wang Qing K QK

BMC proceedings 20071218

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 ...[more]

PMID: 18466548

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Project description:In more than 70% of families with a strong history of breast and ovarian cancers, pathogenic mutation in BRCA1 or BRCA2 cannot be identified, even though hereditary factors are expected to be involved. It has been proposed that tumors with similar molecular phenotypes also share similar pathophysiological mechanisms. Grouping into molecularly homogeneous subsets may therefore be of potential value for further genetic analysis in order to identify new high penetrance breast cancer genes. In the current study, the aim was to investigate if global RNA profiling can be used to identify functional subgroups within breast tumors from families tested negative for BRCA1/2 germline mutations and how these subgroupings relate to different breast cancer patients within the same family. By analyzing a collection of 70 breast tumor biopsies from 58 families, we show that distinct functional subgroupings, similar to the intrinsic molecular breast cancer subtypes, exist. The distribution of subtypes was markedly different from the distribution found among BRCA1/2 mutation carriers. From 11 breast cancer families, breast tumor biopsies from more than one affected family member were included in the study. Notably, in 8 of these families we found that patients from the same family shared the same tumor subtype, showing a tendency of familial aggregation of tumor subtypes (p-value = 1.7e-3). Our finding indicates involvement of hereditary factors in these families in which family members may carry genetic susceptibility not just to breast cancer but to a particular subtype of breast cancer. Using our previously developed BRCA1/2-signatures, we identified 7 non-BRCA1/2 tumors with a BRCA1-like molecular phenotype and provide evidence for epigenetic inactivation of BRCA1 in three of the tumors. In addition, 7 BRCA2-like tumors were found. This is the first study to provide a biological link between breast cancers from family members of high risk non-BRCA1/2 families in a systematic manner, suggesting that future genetic analysis may benefit from subgrouping families into molecularly homogeneous subtypes in order to identify new high penetrance susceptibility genes. Gene expression profiling of 253 breast tumor samples. Breast tumor tissue from 125 patients with germline mutations in BRCA1 (n = 33) or BRCA2 (n = 22) or with no detectable germline mutation in BRCA1 or BRCA2 (n = 70) were included in the study. Serving as a representative control group, primary breast tumor samples (n = 128) were randomly selected among available samples originating from the same department and time period as for the hereditary samples. The study was conducted using Agilent-029949 Custom SurePrint G3 Human GE 8x60K Microarray platform.

Dataset Information

Familial aggregation analysis of gene expressions.

Publications

Familial aggregation analysis of gene expressions.

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