Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels

Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets

The impact of redefining affection status for alcoholism on affected- sib-pair analysis

The analysis of a complex disease such as alcohol dependence requires a more precise definition of affection status. Collaborative Study on the Genetics of Alcoholism (COGA) provided a variety of qualitative and quantitative measures as well as genotype information, in addition to two criteria of affection status. To identify two groups of phenotypically 'more homogeneous' individuals among alcoholics (COGA criterion), we redefined affection status by using cluster analysis and classification and regression tree, incorporating some important covariates such as event related potentials, monoamine oxidase B activity, status of smoking, age of onset, three variables of personality assessed with the Tridimensional Personality Questionnaire and three latent class variables. With redefined affection status, we repeated nonparametric analysis by three sib pair analysis programs (SIBPAL, SIBPAIR, and BETA) using nine candidate DNA markers identified by Reich et al. [1998] and Long et al. [1998]. The goals of our analysis are 1) to confirm previous results for these nine markers with redefined affection status and 2) to compare the performance from these three programs.</p

On the use of multifactor dimensionality reduction (MDR) and classification and regression tree (CART) to identify haplotype-haplotype interactions in genetic studies

[[abstract]]Haplotype-based approaches may have greater power than single-locus analyses when the SNPs are in strong linkage disequilibrium with the risk locus. To overcome potential complexities owing to large numbers of haplotypes in genetic studies, we evaluated two data mining approaches, multifactor dimensionality reduction (MDR) and classification and regression tree (CART), with the concept of haplotypes considering their haplotype uncertainty to detect haplotype–haplotype (HH) interactions. In evaluation of performance for detecting HH interactions, MDR had higher power than CART, but MDR gave a slightly higher type I error. Additionally, we performed an HH interaction analysis with a publicly available dataset of Parkinson's disease and confirmed previous findings that the RET proto-oncogene is associated with the disease. In this study, we showed that using HH interaction analysis is possible to assist researchers in gaining more insight into identifying genetic risk factors for complex diseases.[[notice]]補正完

Use of a failure probability constraint to suggest an initial dose in a phase I cancer clinical trial

The primary objective of a Phase I cancer clinical trial is to determine the maximum tolerated dose of a drug. The “failure probability” was proposed and used as a constraint to help identify a suitable initial dose range. The maximum tolerated dose was then determined based on a 3 + 3 cohort-based escalation scheme. Multiple simulations were conducted, and the method was evaluated according to the required sample size and accuracy and precision of maximum tolerated dose estimate. The results indicated that the median of the initial dose range suggested using a failure probability is a suitable initial dose regardless of the dose escalation sequence used for a cancer Phase I study. This initial dose required a smaller sample size and resulted in less bias of the estimated maximum tolerated dose compared with a commonly used initial dose, that is, 10% of the lethal dose. We tested our approach using real dose and toxicity outcome data from two published Phase I studies. These results indicate that adding a failure probability constraint into the calculation of the initial dose range will improve the efficiency of Phase I cancer trials