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1.
  总被引:16,自引:0,他引:16  
  相似文献   
2.
    
Genome-wide association studies (GWAS) have successfully identified common genetic variants that contribute to breast cancer risk. Discovering additional variants has become difficult, as power to detect variants of weaker effect with present sample sizes is limited. An alternative approach is to look for variants associated with quantitative traits that in turn affect disease risk. As exposure to high circulating estradiol and testosterone, and low sex hormone-binding globulin (SHBG) levels is implicated in breast cancer etiology, we conducted GWAS analyses of plasma estradiol, testosterone, and SHBG to identify new susceptibility alleles. Cancer Genetic Markers of Susceptibility (CGEMS) data from the Nurses' Health Study (NHS), and Sisters in Breast Cancer Screening data were used to carry out primary meta-analyses among ~1600 postmenopausal women who were not taking postmenopausal hormones at blood draw. We observed a genome-wide significant association between SHBG levels and rs727428 (joint β = -0.126; joint P = 2.09 × 10(-16)), downstream of the SHBG gene. No genome-wide significant associations were observed with estradiol or testosterone levels. Among variants that were suggestively associated with estradiol (P<10(-5)), several were located at the CYP19A1 gene locus. Overall results were similar in secondary meta-analyses that included ~900 NHS current postmenopausal hormone users. No variant associated with estradiol, testosterone, or SHBG at P<10(-5) was associated with postmenopausal breast cancer risk among CGEMS participants. Our results suggest that the small magnitude of difference in hormone levels associated with common genetic variants is likely insufficient to detectably contribute to breast cancer risk.  相似文献   
3.
Many popular methods for exploring gene-gene interactions, including the case-only approach, rely on the key assumption that physically distant loci are in linkage equilibrium in the underlying population. These methods utilize the presence of correlation between unlinked loci in a disease-enriched sample as evidence of interactions among the loci in the etiology of the disease. We use data from the CGEMS case-control genome-wide association study of breast cancer to demonstrate empirically that the case-only and related methods have the potential to create large-scale false positives because of the presence of population stratification (PS) that creates long-range linkage disequilibrium in the genome. We show that the bias can be removed by considering parametric and nonparametric methods that assume gene-gene independence between unlinked loci, not in the entire population, but only conditional on population substructure that can be uncovered based on the principal components of a suitably large panel of PS markers. Applications in the CGEMS study as well as simulated data show that the proposed methods are robust to the presence of population stratification and are yet much more powerful, relative to standard logistic regression methods that are also commonly used as robust alternatives to the case-only type methods.  相似文献   
4.
    

Background

Telomerase is an enzyme specialized in maintaining telomere lengths in highly proliferative cells. Loss-of-function mutations cause critical telomere shortening and are associated with the bone marrow failure syndromes dyskeratosis congenita and aplastic anemia and with idiopathic pulmonary fibrosis. Here, we sought to determine the spectrum of clinical manifestations associated with telomerase loss-of-function mutations.

Methodology/Principal Findings

Sixty-nine individuals from five unrelated families with a variety of hematologic, hepatic, and autoimmune disorders were screened for telomerase complex gene mutations; leukocyte telomere length was measured by flow fluorescence in situ hybridization in mutation carriers and some non-carriers; the effects of the identified mutations on telomerase activity were determined; and genetic and clinical data were correlated. In six generations of a large family, a loss-of-function mutation in the telomerase enzyme gene TERT associated with severe telomere shortening and a range of hematologic manifestations, from macrocytosis to acute myeloid leukemia, with severe liver diseases marked by fibrosis and inflammation, and one case of idiopathic pulmonary fibrosis but not with autoimmune disorders. Additionally, we identified four unrelated families in which loss-of-function TERC or TERT gene mutations tracked with marrow failure, pulmonary fibrosis, and a spectrum of liver disorders.

Conclusions/Significance

These results indicate that heterozygous telomerase loss-of-function mutations associate with but are not determinant of a large spectrum of hematologic and liver abnormalities, with the latter sometimes occurring in the absence of marrow failure. Our findings, along with the link between pulmonary fibrosis and telomerase mutations, also suggest a common pathogenic mechanism for fibrotic diseases in which defective telomere repair plays important role.  相似文献   
5.
Taylor-Robinson, David (National Institute of Allergy and Infectious Diseases, Bethesda, Md.), Otakar Sobeslavsky, and Robert M. Chanock. Relationship of Mycoplasma pneumoniae to other human Mycoplasma species studied by gel diffusion. J. Bacteriol. 90:1432-1437. 1965.-Conditions are presented for the production of four lines of precipitate between Mycoplasma pneumoniae antigen and homologous hyperimmune rabbit serum in double diffusion in agar. The specificity of the reaction was shown by the fact that M. pneumoniae antigen did not react with antisera to the other human mycoplasma species, nor did M. pneumoniae antiserum produce lines with antigens prepared from the other human mycoplasmas. In addition, there was no reduction in the number or intensity of precipitation lines after absorption of M. pneumoniae antiserum with heterotypic mycoplasma antigens, or after absorption of heterotypic mycoplasma antisera with M. pneumoniae antigen. These findings indicate that, of the human mycoplasma species so far studied, M. pneumoniae is antigenically the most distinct.  相似文献   
6.
7.

Background

A recent ovarian cancer genome-wide association study (GWAS) identified a locus on 9p22 associated with reduced ovarian cancer risk. The single nucleotide polymorphism (SNP) markers localize to the BNC2 gene, which has been associated with ovarian development.

Methods

We analyzed the association of 9p22 SNPs with transvaginal ultrasound (TVU) screening results and CA-125 blood levels from participants without ovarian cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO); 1,106 women with adequate ultrasound screening results and available genotyping information were included in the study.

Results

We observed a significantly increased risk of abnormal suspicious TVU results for seven SNPs on 9p22, with odds ratios between 1.68 (95% CI: 1.04–2.72) for rs4961501 and 2.10 (95% CI: 1.31–3.38) for rs12379183. Associations were restricted to abnormal suspicious findings at the first TVU screen. We did not observe an association between 9p22 SNPs and CA-125 levels.

Conclusions

Our findings suggest that 9p22 SNPs, which were found to be associated with decreased risk of ovarian cancer in a recent GWAS, are associated with sonographically detectable ovarian abnormalities. Our results corroborate the relevance of the 9p22 locus for ovarian biology. Further studies are required to understand the complex relationship between screening abnormalities and ovarian carcinogenesis and to evaluate whether this locus can influence the risk stratification of ovarian cancer screening.  相似文献   
8.
9.

Background  

Association testing is a powerful tool for identifying disease susceptibility genes underlying complex diseases. Technological advances have yielded a dramatic increase in the density of available genetic markers, necessitating an increase in the number of association tests required for the analysis of disease susceptibility genes. As such, multiple-tests corrections have become a critical issue. However the conventional statistical corrections on locus-specific multiple tests usually result in lower power as the number of markers increases. Alternatively, we propose here the application of the longest significant run (LSR) method to estimate a region-specific p-value to provide an index for the most likely candidate region.  相似文献   
10.
Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.  相似文献   
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