Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
98%
921
2 minutes
20
Background: Genome-wide association studies (GWAS) have identified that a ∼1 M region centromeric to the MYC oncogene on chromosome 8q24.21 harbors at least five independent loci associated with prostate cancer risk and additional loci associated with cancers of breast, colon, bladder, and chronic lymphocytic leukemia (CLL). Because GWAS identify genetic markers that may be indirectly associated with disease, fine-mapping based on sequence analysis provides important insights into patterns of linkage disequilibrium (LD) and is critical in defining the optimal variants to nominate for biological follow-up.
Methods: To catalog variation in individuals of African ancestry, we resequenced a region (250 kb; chr8:128,050, 768–128, 300,801, hg19) containing several prostate cancer susceptibility loci as well as a locus associated with CLL. Our samples included 78 individuals from Ghana and 47 of African-Americans from Johns Hopkins University.
Results: After quality control metrics were applied to next-generation sequence data, 1,838 SNPs were identified. Of these, 285 were novel and not yet reported in any public database. Using genotypes derived from sequencing, we refined the LD and recombination hotspots within the region and determined a set of tag SNPs to be used in future fine-mapping studies. Based on LD, we annotated putative risk loci and their surrogates using ENCODE data, which should help guide laboratory studies.
Conclusions: In comparison to the 1000 Genome Project data, we have identified additional variants that could be important in establishing priorities for future functional work designed to explain the biological basis of associations between SNPs and both prostate cancer and CLL.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4199861 | PMC |
http://dx.doi.org/10.1002/pros.22726 | DOI Listing |