ABSTRACT
BACKGROUND: Previous observational studies have provided conflicting results on the association between serum iron status and the risk of breast cancer. Considering the relevance of this relationship to breast cancer prevention, its elucidation is warranted. OBJECT: We used a two-sample Mendelian randomisation (MR) study to explore the causal relationship between serum iron status and the risk of breast cancer. METHOD: To select single nucleotide polymorphisms (SNPs) that could be used as instrumental variables for iron status, we used the Genetics of Iron Status consortium, which includes 11 discovery and 8 replication cohorts, encompassing 48,972 individuals of European descent. Moreover, we used the OncoArray network to select SNPs that could be considered instrumental variables for the outcome of interest (breast cancer); this dataset included 122,977 individuals of European descent with breast cancer and 105,974 peers without breast cancer. Both conservative (SNPs associated with overall iron status markers) and liberal (SNPs associated with the levels of at least one iron status marker) approaches were used as part of the MR analysis. For the former, we used an inverse-variance weighted (IVW) method, whereas for the latter, we used the IVW, MR-Egger regression, weighted median and simple mode methods. RESULTS: When the conservative approach was used, iron status showed no significant association with the risk of breast cancer or any of its subtypes. However, when the liberal approach was used, transferrin levels were found to be positively associated with the risk of ER-negative breast cancer based on the simple mode method (OR for MR, 1.225; 95% CI, 1.064, 1.410; P = 0.030). Nevertheless, the levels of the other iron status markers showed no association with the risk of breast cancer or its subtypes (P > 0.05). CONCLUSION: In our MR study, the liberal approach suggested that changes in the concentration of transferrin could increase the risk of ER-negative breast cancer, although the levels of other iron status markers had no effect on the risk of breast cancer or its subtypes. This should be verified in future studies.
ABSTRACT
BACKGROUND: Trachypithecus leucocephalus, the white-headed langur, is a critically endangered primate that is endemic to the karst mountains in the southern Guangxi province of China. Studying the genomic and transcriptomic mechanisms underlying its local adaptation could help explain its persistence within a highly specialized ecological niche. RESULTS: In this study, we used PacBio sequencing and optical assembly and Hi-C analysis to create a high-quality de novo assembly of the T. leucocephalus genome. Annotation and functional enrichment revealed many genes involved in metabolism, transport, and homeostasis, and almost all of the positively selected genes were related to mineral ion binding. The transcriptomes of 12 tissues from three T. leucocephalus individuals showed that the great majority of genes involved in mineral absorption and calcium signaling were expressed, and their gene families were significantly expanded. For example, FTH1 primarily functions in iron storage and had 20 expanded copies. CONCLUSIONS: These results increase our understanding of the evolution of alkali tolerance and other traits necessary for the persistence of T. leucocephalus within an ecologically unique limestone karst environment.
Subject(s)
Colobinae , Alkalies , Animals , China , Genome , Presbytini , TranscriptomeABSTRACT
BACKGROUND: Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. RESULT: A total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson's test, Jaccard's index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10- 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10- 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes. CONCLUSION: All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.
