Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications

Low-density (LD) single nucleotide polymorphism (SNP) arrays provide a cost-effective solution for genomic prediction and selection, but algorithms and computational tools are needed for the optimal design of LD SNP chips. A multiple-objective, local optimization (MOLO) algorithm was developed for design of optimal LD SNP chips that can be imputed accurately to medium-density (MD) or high-density (HD) SNP genotypes for genomic prediction. The objective function facilitates maximization of non-gap map length and system information for the SNP chip, and the latter is computed either as locus-averaged (LASE) or haplotype-averaged Shannon entropy (HASE) and adjusted for uniformity of the SNP distribution. HASE performed better than LASE with more computing time. Nevertheless, the differences diminished when \u3e5,000 SNPs were selected. Optimization was accomplished conditionally on the presence of SNPs that were obligated to each chromosome. The frame location of SNPs on a chip can be either uniform (evenly spaced) or non-uniform. For the latter design, a tunable empirical Beta distribution was used to guide location distribution of frame SNPs such that both ends of each chromosome were enriched with SNPs. The SNP distribution on each chromosome was finalized through the objective function that was locally and empirically maximized. This MOLO algorithm was capable of selecting a set of approximately evenly-spaced and highly-informative SNPs, which in turn led to increased imputation accuracy compared with selection solely of evenly-spaced SNPs. Imputation accuracy increased with LD chip size, and imputation error rate was extremely low for chips with \u3e3,000 SNPs. Assuming that genotyping or imputation error occurs at random, imputation error rate can be viewed as the upper limit for genomic prediction error. Our results show that about 25% of imputation error rate was propagated to genomic prediction in an Angus population. The utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal

LQTS Gene LOVD Database

The Long QT Syndrome (LQTS) is a group of genetically heterogeneous disorders that predisposes young individuals to ventricular arrhythmias and sudden death. LQTS is mainly caused by mutations in genes encoding subunits of cardiac ion channels (KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2). Many other genes involved in LQTS have been described recently (KCNJ2, AKAP9, ANK2, CACNA1C, SCNA4B, SNTA1, and CAV3). We created an online database (http://www.genomed.org/LOVD/introduction.html) that provides information on variants in LQTS-associated genes. As of February 2010, the database contains 1738 unique variants in 12 genes. A total of 950 variants are considered pathogenic, 265 are possible pathogenic, 131 are unknown/unclassified, and 292 have no known pathogenicity. In addition to these mutations collected from published literature, we also submitted information on gene variants, including one possible novel pathogenic mutation in the KCNH2 splice site found in ten Chinese families with documented arrhythmias. The remote user is able to search the data and is encouraged to submit new mutations into the database. The LQTS database will become a powerful tool for both researchers and clinicians. © 2010 Wiley-Liss, Inc

A Phase Ib Study of the Simmitecan Single Agent and in Combination With 5-Fluorouracil/Leucovorin or Thalidomide in Patients With Advanced Solid Tumor

Background: Simmitecan is a potent inhibitor of topoisomerase I with anti-tumor activity. This phase Ib trial was conducted to investigate the safety and anti-tumor effect of simmitecan alone or in combination with other drugs.Methods: Eligible patients with advanced solid tumor had no further standard treatment options. Patients were allocated to receive simmitecan alone, simmitecan in combination with 5-fluorouracil (5-FU)/leucovorin (LV), or simmitecan in combination with thalidomide, 14 days a cycle, until disease progression or unacceptable toxicity occurred.Results: A total of 41 patients were enrolled, with a median age of 55 (range 29–69) years. Among them, 13 patients received simmitecan monotherapy, 10 received simmitecan + 5-FU/LV, and 18 received simmitecan + thalidomide. No dose-limiting toxicity occurred. Overall, the most common grade 3/4 adverse event (AE) was neutropenia (46.2, 70.0, and 88.9%, respectively, in simmitecan, simmitecan + 5-FU/LV, and simmitecan + thalidomide cohorts), and treatment-related severe AEs included anemia and febrile neutropenia (7.7% each in simmitecan cohort), diarrhea (10% in simmitecan +5-FU/LV cohort), and febrile neutropenia (5.6% in simmitecan + thalidomide cohort). The majority of patients (24/41, 58.3%) had progressed on prior irinotecan; nevertheless, partial response was achieved in one colorectal cancer patients treated with simmitecan + thalidomide. The disease control rates of simmitecan, simmitecan + 5-FU/LV, and simmitecan + thalidomide cohorts were 46.2, 80.0, and 61.1%, respectively.Conclusion: This study demonstrated a manageable safety profile of simmitecan as a single agent or as part of a combination therapy. There have not been any safety concerns with simmitecan in combination when compared to simmitecan alone. Simmitecan + 5-FU/LV regimen seemed to have a better efficacy. Nonetheless, the efficacy of this regimen needs to be further explored in the subsequent study