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Understanding the distribution of human genetic variation is an important foundation for research into the genetics of common diseases. Some of the alleles that modify common disease risk are themselves likely to be common and, thus, amenable to identification using gene-association methods. A problem with this approach is that the large sample sizes required for sufficient statistical power to detect alleles with moderate effect make gene-association studies susceptible to false-positive findings as the result of population stratification [1, 2]. Such type I errors can be eliminated by using either family-based association tests or methods that sufficiently adjust for population stratification [3–5]. These methods require the availability of genetic markers that can detect and, thus, control for sources of genetic stratification among populations. In an effort to investigate population stratification and identify appropriate marker panels, we have analysed 11,555 single nucleotide polymorphisms in 203 individuals from 12 diverse human populations. Individuals in each population cluster to the exclusion of individuals from other populations using two clustering methods. Higher-order branching and clustering of the populations are consistent with the geographic origins of populations and with previously published genetic analyses. These data provide a valuable resource for the definition of marker panels to detect and control for population stratification in population-based gene identification studies. Using three US resident populations (European-American, African-American and Puerto Rican), we demonstrate how such studies can proceed, quantifying proportional ancestry levels and detecting significant admixture structure in each of these populations.

Combined effects of hydrostatic pressure, temperature, and the addition of allyl isothiocyanate (AIT) on the inactivation of Escherichia coli, including type O157, were investigated. Inactivation to undetectable levels by hydrostatic pressure alone requires 400 to 600 MPa. E. coligrowth was delayed with increasing of applied pressure and the AIT concentration added subsequently. The antibacterial effects of AIT vapor increased on JCM 1649 and IFO 3301 after pressurization. The bactericidal effects of pressurization with the addition of AIT at 4°C or 40°C were greater than at 20°C, and all bacteria tested were effectively killed at 200 or 250 MPa with 10 to 80 μg/ml of AIT. We tried to apply the combined treatment to a food product “Asazuke” (low salt vegetables), and it was confirmed that E. coliinoculated into the product was inactivated the same as in the in vitro test. We also studied the inactivation mechanism behind pressurization with AIT from the relationship between pressure resistance and precultivation temperature, and it was suggested that destruction of membrane structure caused bacterial kill.

Gene delivery via the hematopoietic stem cell (HSC) offers an attractive means to introduce antiviral genes into both T cells and macrophages for acquired immunodeficiency syndrome (AIDS) gene therapy. An amphotropic retroviral vector encoding a bicistronic gene coexpressing RevM10 and the murine CD8α′ chain (lyt2) was developed to transduce HSC/progenitor cells. After transduction of CD34+ cells isolated from human umbilical cord blood, the lyt2 molecule detected by flow cytometry was used to monitor the level of gene transduction and expression and to enrich RevM10-expressing cells by cell sorting without drug selection. Using this quantitative method, high levels of gene transduction and expression (around 20%) were achieved by high-speed centrifugation of CD34+ cells with the retroviral supernatant (spinoculation). After reconstitution of human bone marrow implanted in SCID mice (SCID-hu bone) with the transduced HSC/progenitor cells, a significant number of donor-derived CD14+ bone marrow cells were found to express the RevM10/lyt2 gene. Finally, replication of a macrophage-tropic human immunodeficiency virus–type 1 (HIV-1) isolate was greatly inhibited in the lyt2+/CD14+ cells differentiated from transduced CD34+ cells after the enrichment of lyt2+ population. Thus, the RevM10 gene did not appear to inhibit the differentiation of HSC/progneitor cells into monocytes/macrophages. The level of retrovirus-mediated RevM10 expression in monocytes/macrophages derived from transduced HSCs is sufficient to suppress HIV-1 replication.

When Saccharomyces cerevisiae RNA polymerase (Pol) III transcribes the S. cerevisiae SUP4 tRNA^Tyr gene, it is obliged to navigate past a large, multi-subunit DNA-bound complex of proteins. We have analyzed individual steps of RNA chain elongation on this gene. Slow steps of transcriptional initiation were by-passed by forming 5'-end-labeled, arrested and precisely positioned transcription complexes. Synchronous resumption of chain elongation by these complexes allowed a single round of RNA synthesis and termination to be analyzed in detail. Results for synthesis at 20°C and 0°C, in the presence of 100 µM and 1 mM ribonucleoside triphosphates (NTPs) are presented. RNA chain elongation through assembled transcription complexes was uneven but relatively rapid: at 20°C with 1 mM NTPs, the fastest RNA chains elongated at an average rate of 29 nucleotides (nt)/second, and the median RNA chains elongated at 21 to 22 nt/second on average. These rates are comparable with a recent measurement of the average rate of chain elongation in vivo by Drosophila RNA polymerase II at 25°C. At 0°C, RNA chain elongation rates were, on average, approximately 30-fold slower. Quantitative analysis of the individual steps of RNA chain elongation showed that steps of adding U and A to U-terminated RNA chains tended to be relatively slow, and to be more strongly influenced by nucleotide concentration. Termination of transcription occurred in the sequence T₇GT₆ (in the non-template DNA strand) and was progressive. Transcripts with five, six and seven U residues were formed, and there was even slow readthrough of the T₇ stretch, with GU₃ adding rapidly, suggesting that incorporation of a single G into the RNA chain served to reset elongation rates substantially or entirely. Stripping transcription factor (TF) IIIC from transcription complexes did not substantially increase overall RNA chain growth rate, but did diminish pausing at a single site upstream of the boxB binding site of TFIIIC. The TFIIIC-generated delay at this single site was estimated to be only approximately 0·15 to 0·2 seconds at 20°C. Quantitative analysis of RNA chain elongation yielded kinetic parameters for the individual steps of nucleotide addition that were used in computer simulations of RNA chain growth. Elongation modeled as a simple sequence of pseudo-first-order reactions yielded computed RNA chain length distributions that remained relatively synchronous during elongation, while observed chain growth quickly became desynchronized. The discrepancy points to complexities of RNA chain elongation that are discussed. In particular, we explored the idea, that Pol III can switch repeatedly between rapidly stepping and slowly stepping states during the elongation of a single RNA molecule, by appropriate computer simulations. Copyright 1994, 1999 Academic Press

Copy number variants (CNVs) account for a large proportion of genetic variation in the genome. The initial discoveries of long (100 kb) CNVs in normal healthy individuals were made on BAC arrays and low resolution oligonucleotide arrays. Subsequent studies that used higher resolution microarrays and SNP genotyping arrays detected the presence of large numbers of CNVs that are < 100 kb, with median lengths of approximately 10 kb. More recently, whole genome sequencing of individuals has revealed an abundance of shorter CNVs with lengths < 1 kb.

Motivation: The identification of signatures of positive selection can provide important insights into recent evolutionary history in human populations. Current methods mostly rely on allele frequency determination or focus on one or a small number of candidate chromosomal regions per study. With the availability of large-scale genotype data, efficient approaches for an unbiased whole genome scan are becoming necessary. Methods: We have developed a new method, the whole genome long-range haplotype test (WGLRH), which uses genome-wide distributions to test for recent positive selection. Adapted from the long-range haplotype (LRH) test, the WGLRH test uses patterns of linkage disequilibrium (LD) to identify regions with extremely low historic recombination. Common haplotypes with significantly longer than expected ranges of LD given their frequencies are identified as putative signatures of recent positive selection. In addition, we have also determined the ancestral alleles of SNPs by genotyping chimpanzee and gorilla DNA, and have identified SNPs where the non-ancestral alleles have risen to extremely high frequencies in human populations, termed ‘flipped SNPs’. Combining the haplotype test and the flipped SNPs determination, the WGLRH test serves as an unbiased genome-wide screen for regions under putative selection, and is potentially applicable to the study of other human populations. Results: Using WGLRH and high-density oligonucleotide arrays interrogating 116 204 SNPs, we rapidly identified putative regions of positive selection in three populations (Asian, Caucasian, African-American), and extended these observations to a fourth population, Yoruba, with data obtained from the International HapMap consortium. We mapped significant regions to annotated genes. While some regions overlap with genes previously suggested to be under positive selection, many of the genes have not been previously implicated in natural selection and offer intriguing possibilities for further study. Availability: the programs for the WGLRH algorithm are freely available and can be downloaded at http://www.affymetrix.com/support/supplement/WGLRH_program.zip Contact: Giulia_Kennedy@affymetrix.com Supplementary Material: Three Supplementary tables

Motivation: A high density of single nucleotide polymorphism (SNP) coverage on the genome is desirable and often an essential requirement for population genetics studies. Region-specific or chromosome-specific linkage studies also benefit from the availability of as many high quality SNPs as possible. The availability of millions of SNPs from both Perlegen and the public domain and the development of an efficient microarray-based assay for genotyping SNPs has brought up some interesting analytical challenges. Effective methods for the selection of optimal subsets of SNPs spanning the genome and methods for accurately calling genotypes from probe hybridization patterns have enabled the development of a new microarray-based system for robustly genotyping over 100 000 SNPs per sample. Results: We introduce a new dynamic model-based algorithm (DM) for screening over 3 million SNPs and genotyping over 100 000 SNPs. The model is based on four possible underlying states: Null, A, AB and B for each probe quartet. We calculate a probe-level log likelihood for each model and then select between the four competing models with an SNP-level statistical aggregation across multiple probe quartets to provide a high-quality genotype call along with a quality measure of the call. We assess performance with HapMap reference genotypes, informative Mendelian inheritance relationship in families, and consistency between DM and another genotype classification method. At a call rate of 95.91% the concordance with reference genotypes from the HapMap Project is 99.81% based on over 1.5 million genotypes, the Mendelian error rate is 0.018% based on 10 trios, and the consistency between DM and MPAM is 99.90% at a comparable rate of 97.18%. We also develop methods for SNP selection and optimal probe selection. Availability: The DM algorithm is available in Affymetrix's Genotyping Tools software package and in Affymetrix's GDAS software package. See http://www.affymetrix.com for further information. 10K and 100K mapping array data are available on the Affymetrix website. Contact: xiaojun_di@affymetrix.com

Motivation: Analysis of many thousands of single nucleotide polymorphisms (SNPs) across whole genome is crucial to efficiently map disease genes and understanding susceptibility to diseases, drug efficacy and side effects for different populations and individuals. High density oligonucleotide microarrays provide the possibility for such analysis with reasonable cost. Such analysis requires accurate, reliable methods for feature extraction, classification, statistical modeling and filtering. Results: We propose the modified partitioning around medoids as a classification method for relative allele signals. We use the average silhouette width, separation and other quantities as quality measures for genotyping classification. We form robust statistical models based on the classification results and use these models to make genotype calls and calculate quality measures of calls. We apply our algorithms to several different genotyping microarrays. We use reference types, informative Mendelian relationship in families, and leave-one-out cross validation to verify our results. The concordance rates with the single base extension reference types are 99.36% for the SNPs on autosomes and 99.64% for the SNPs on sex chromosomes. The concordance of the leave-one-out test is over 99.5% and is 99.9% higher for AA, AB and BB cells. We also provide a method to determine the gender of a sample based on the heterozygous call rate of SNPs on the X chromosome. See http://www.affymetrix.com for further information. The microarray data will also be available from the Affymetrix web site. Availability: The algorithms will be available commercially in the Affymetrix software package.