Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures

Generalized linear mixed-effects models in the context of genome-wide association studies (GWAS) represent a formidable computational challenge: the solution of millions of correlated generalized least-squares problems, and the processing of terabytes of data. We present high performance in-core and out-of-core shared-memory algorithms for GWAS: By taking advantage of domain-specific knowledge, exploiting multi-core parallelism, and handling data efficiently, our algorithms attain unequalled performance. When compared to GenABEL, one of the most widely used libraries for GWAS, on a 12-core processor we obtain 50-fold speedups. As a consequence, our routines enable genome studies of unprecedented size

Algorithms for Large-scale Whole Genome Association Analysis

In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for millions of polymorphisms. When handling these datasets, which exceed the main memory of contemporary computers, one faces two distinct challenges: 1) Millions of polymorphisms come at the cost of hundreds of Gigabytes of genotype data, which can only be kept in secondary storage; 2) the relatedness of the test population is represented by a covariance matrix, which, for large populations, can only fit in the combined main memory of a distributed architecture. In this paper, we present solutions for both challenges: The genotype data is streamed from and to secondary storage using a double buffering technique, while the covariance matrix is kept across the main memory of a distributed memory system. We show that these methods sustain high-performance and allow the analysis of enormous datase

Application of introduced nano-diamonds for the study of carbon condensation during detonation of high explosives

This paper describes the experimental studies of the formation of nano-diamonds during detonation of TNT/RDX 50/50 mixture with small-angle x-ray scattering (SAXS) method at a synchrotron radiation beam on VEPP-3 accelerator. A new experimental method with introduction of nano-diamonds into the explosive has been applied. Inclusion of the diamonds obtained after detonation into the TNT and RDX explosives allows modelling of the case of instant creation of nano-diamonds during detonation.Comment: Latex, 4 pages, 2 figures (proc. of SR-2008

Energy calibration of the NaI(Tl) calorimeter of the SND detector using cosmic muons

The general purpose spherical nonmagnetic detector (SND) is now taking data at VEPP-2M $e^+e^-$ collider in BINP (Novosibirsk) in the centre of mass energy range of $0.2 \div 1.4$ GeV. The energy calibration of the NaI(Tl) calorimeter of the SND detector with cosmic muons is described. Using this method, the energy resolution of $5.5$ for 500 MeV photons was achieved.Comment: 15 pages, Latex, 11 figures (.EPS

Energy calibration of the NaI(Tl) calorimeter of the SND detector using e+e−→e+e−e^+e^- \to e^+e^- events

Calibration of the three layer NaI(Tl) spherical calorimeter of the SND detector using electron -- positron scattering events is described. Energy resolution of $5$ for 500 MeV photons was achieved.Comment: 12 pages, Latex, 8 figures (.EPS

ρ→4π\rho\to 4\pi DECAY

The decay modes $\rho^0 \to 2\pi^+ 2\pi^-$ and $\rho^0 \to 2\pi^0 \pi^+ \pi^-$ are considered in the framework of the low energy effective chiral Lagrangian. The obtained values of the decay widths $\Gamma(\rho^0\to 2 \pi^+ 2 \pi^-)=$ $(16 \pm 1){\rm keV}$ and $\Gamma(\rho^0\to 2 \pi^0 \pi^+ \pi^-)= (6.0 \pm 0.2)$keV do not contradict the existing upper limits and seem to be big enough for the corresponding processes to be observed in future high luminosity experiments.Comment: 6 pages, LaTeX, without figure. Submitted to Phys. Lett.

A Genomic Background Based Method for Association Analysis in Related Individuals

Background. Feasibility of genotyping of hundreds and thousands of single nucleoticle polymorphisms (SNPs) in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population). Of the available methods that account for relatedness, the Measured Genotype (MG) approach is considered the 'gold standard'. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR) for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG) approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. Methodology. In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GCC) approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy. Conclusion. Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the 'gold standard' MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis in related individuals

PredictABEL: an R package for the assessment of risk prediction models

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL (http://www.genabel.org) and CRAN (http://cran.r-project.org/)