A-6G and A-20C Polymorphisms in the Angiotensinogen Promoter and Hypertension Risk in Chinese: A Meta-Analysis

BACKGROUND: Numerous studies in Chinese populations have evaluated the association between the A-6G and A-20C polymorphisms in the promoter region of angiotensinogen gene and hypertension. However, the results remain conflicting. We carried out a meta-analysis for these associations. METHODS AND RESULTS: Case-control studies in Chinese and English publications were identified by searching the MEDLINE, EMBASE, CNKI, Wanfang, CBM, and VIP databases. The random-effects model was applied for dichotomous outcomes to combine the results of the individual studies. We finally selected 24 studies containing 5932 hypertensive patients and 5231 normotensive controls. Overall, we found significant association between the A-6G polymorphism and the decreased risk of hypertension in the dominant genetic model (AA+AG vs. GG: P=0.001, OR=0.71, 95%CI 0.57-0.87, P(heterogeneity)=0.96). The A-20C polymorphism was significantly associated with the increased risk for hypertension in the allele comparison (C vs. A: P=0.03, OR=1.14, 95%CI 1.02-1.27, P(heterogeneity)=0.92) and recessive genetic model (CC vs. CA+AA: P=0.005, OR=1.71, 95%CI 1.18-2.48, P(heterogeneity)=0.99). In the subgroup analysis by ethnicity, significant association was also found among Han Chinese for both A-6G and A-20C polymorphisms. A borderline significantly decreased risk of hypertension between A-6G and Chinese Mongolian was seen in the allele comparison (A vs. G: P=0.05, OR=0.79, 95%CI 0.62-1.00, P(heterogeneity)=0.84). CONCLUSION: Our meta-analysis indicated significant association between angiotensinogen promoter polymorphisms and hypertension in the Chinese populations, especially in Han Chinese

A Powerful Test of Parent-of-Origin Effects for Quantitative Traits Using Haplotypes

Imprinting is an epigenetic phenomenon where the same alleles have unequal transcriptions and thus contribute differently to a trait depending on their parent of origin. This mechanism has been found to affect a variety of human disorders. Although various methods for testing parent-of-origin effects have been proposed in linkage analysis settings, only a few are available for association analysis and they are usually restricted to small families and particular study designs. In this study, we develop a powerful maximum likelihood test to evaluate the parent-of-origin effects of SNPs on quantitative phenotypes in general family studies. Our method incorporates haplotype distribution to take advantage of inter-marker LD information in genome-wide association studies (GWAS). Our method also accommodates missing genotypes that often occur in genetic studies. Our simulation studies with various minor allele frequencies, LD structures, family sizes, and missing schemes have uniformly shown that using the new method significantly improves the power of detecting imprinted genes compared with the method using the SNP at the testing locus only. Our simulations suggest that the most efficient strategy to investigate parent-of-origin effects is to recruit one parent and as many offspring as possible under practical constraints. As a demonstration, we applied our method to a dataset from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) to test the parent-of-origin effects of the SNPs within the PPARGC1A, MTP and FABP2 genes on diabetes-related phenotypes, and found that several SNPs in the MTP gene show parent-of-origin effects on insulin and glucose levels

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Product hierarchy-based customer profiles for electronic commerce recommendation

Personalized service is becoming a key strategy in electronic commerce. Traditional personalization techniques such as collaborative filtering and rule-based method have many drawbacks, including lack of scalability, reliance on subjective user rating or static profiles, and the inability to capture a richer set of semantic relationships among objects. In this paper, we present a new approach, building customer profiles based on product hierarchy for more effective personalization in electronic commerce. We divide each customer profile into three parts: basic profile, preference profile, and rule profile. Based on the customer profiles, two kinds of recommendations can be generated, which are interest recommendation and association recommendation. We also propose a special data structure: Profile Tree for effective searching and matching. In terms of our method, customer profiles can be constructed online, and realtime recommendations can be implemented. In the end, we conduct experiments to validate our methods, using real data