Oestrogen receptor α gene haplotype and postmenopausal breast cancer risk: a case control study

INTRODUCTION: Oestrogen receptor α, which mediates the effect of oestrogen in target tissues, is genetically polymorphic. Because breast cancer development is dependent on oestrogenic influence, we have investigated whether polymorphisms in the oestrogen receptor α gene (ESR1) are associated with breast cancer risk. METHODS: We genotyped breast cancer cases and age-matched population controls for one microsatellite marker and four single-nucleotide polymorphisms (SNPs) in ESR1. The numbers of genotyped cases and controls for each marker were as follows: TA(n), 1514 cases and 1514 controls; c.454-397C → T, 1557 cases and 1512 controls; c.454-351A → G, 1556 cases and 1512 controls; c.729C → T, 1562 cases and 1513 controls; c.975C → G, 1562 cases and 1513 controls. Using logistic regression models, we calculated odds ratios (ORs) and 95% confidence intervals (CIs). Haplotype effects were estimated in an exploratory analysis, using expectation-maximisation algorithms for case-control study data. RESULTS: There were no compelling associations between single polymorphic loci and breast cancer risk. In haplotype analyses, a common haplotype of the c.454-351A → G or c.454-397C → T and c.975C → G SNPs appeared to be associated with an increased risk for ductal breast cancer: one copy of the c.454-351A → G and c.975C → G haplotype entailed an OR of 1.19 (95% CI 1.06–1.33) and two copies with an OR of 1.42 (95% CI 1.15–1.77), compared with no copies, under a model of multiplicative penetrance. The association with the c.454-397C → T and c.975C → G haplotypes was similar. Our data indicated that these haplotypes were more influential in women with a high body mass index. Adjustment for multiple comparisons rendered the associations statistically non-significant. CONCLUSION: We found suggestions of an association between common haplotypes in ESR1 and the risk for ductal breast cancer that is stronger in heavy women

Disease-Aging Network Reveals Significant Roles of Aging Genes in Connecting Genetic Diseases

One of the challenging problems in biology and medicine is exploring the underlying mechanisms of genetic diseases. Recent studies suggest that the relationship between genetic diseases and the aging process is important in understanding the molecular mechanisms of complex diseases. Although some intricate associations have been investigated for a long time, the studies are still in their early stages. In this paper, we construct a human disease-aging network to study the relationship among aging genes and genetic disease genes. Specifically, we integrate human protein-protein interactions (PPIs), disease-gene associations, aging-gene associations, and physiological system–based genetic disease classification information in a single graph-theoretic framework and find that (1) human disease genes are much closer to aging genes than expected by chance; and (2) diseases can be categorized into two types according to their relationships with aging. Type I diseases have their genes significantly close to aging genes, while type II diseases do not. Furthermore, we examine the topological characters of the disease-aging network from a systems perspective. Theoretical results reveal that the genes of type I diseases are in a central position of a PPI network while type II are not; (3) more importantly, we define an asymmetric closeness based on the PPI network to describe relationships between diseases, and find that aging genes make a significant contribution to associations among diseases, especially among type I diseases. In conclusion, the network-based study provides not only evidence for the intricate relationship between the aging process and genetic diseases, but also biological implications for prying into the nature of human diseases