Gene- or region-based association study via kernel principal component analysis.

BACKGROUND: In genetic association study, especially in GWAS, gene- or region-based methods have been more popular to detect the association between multiple SNPs and diseases (or traits). Kernel principal component analysis combined with logistic regression test (KPCA-LRT) has been successfully used in classifying gene expression data. Nevertheless, the purpose of association study is to detect the correlation between genetic variations and disease rather than to classify the sample, and the genomic data is categorical rather than numerical. Recently, although the kernel-based logistic regression model in association study has been proposed by projecting the nonlinear original SNPs data into a linear feature space, it is still impacted by multicolinearity between the projections, which may lead to loss of power. We, therefore, proposed a KPCA-LRT model to avoid the multicolinearity. RESULTS: Simulation results showed that KPCA-LRT was always more powerful than principal component analysis combined with logistic regression test (PCA-LRT) at different sample sizes, different significant levels and different relative risks, especially at the genewide level (1E-5) and lower relative risks (RR = 1.2, 1.3). Application to the four gene regions of rheumatoid arthritis (RA) data from Genetic Analysis Workshop16 (GAW16) indicated that KPCA-LRT had better performance than single-locus test and PCA-LRT. CONCLUSIONS: KPCA-LRT is a valid and powerful gene- or region-based method for the analysis of GWAS data set, especially under lower relative risks and lower significant levels.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Vomiting and wasting disease associated with hemagglutinating encephalomyelitis viruses infection in piglets in jilin, china

One coronavirus strain was isolated from brain tissues of ten piglets with evident clinical manifestations of vomiting, diarrhea and dyskinesia in Jilin province in China. Antigenic and genomic characterizations of the virus (isolate PHEV-JLsp09) were based on multiplex PCR and negative staining electron microscopy and sequence analysis of the Hemagglutinin-esterase (HE) gene. These piglets were diagnosed with Porcine hemagglutinating encephalomyelitis virus (PHEV)

High Altitude test of RPCs for the ARGO-YBJ experiment

A 50 m**2 RPC carpet was operated at the YangBaJing Cosmic Ray Laboratory (Tibet) located 4300 m a.s.l. The performance of RPCs in detecting Extensive Air Showers was studied. Efficiency and time resolution measurements at the pressure and temperature conditions typical of high mountain laboratories, are reported.Comment: 16 pages, 10 figures, submitted to Nucl. Instr. Met

Accelerating Haplotype-Based Genome-Wide Association Study Using Perfect Phylogeny and Phase-Known Reference Data

The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype inference, also known as phasing. In this study, we developed an efficient approach to haplotype-based analysis in GWAS. By using a reference panel, our method accelerated the phasing process and reduced the potential bias generated by unrealistic assumptions in phasing process. The haplotype-based approach delivers great power and no type I error inflation for association studies. With only a medium-size reference panel, phasing error in our method is comparable to the genotyping error afforded by commercial genotyping solutions

Sciences of the USA 1418 -1421 ͉ PNAS

The discovery of the block-like structure of linkage disequilibrium (LD) in human populations holds the promise of delineating the etiology of common diseases. However, understanding the magnitude, mechanism, and utility of between-population LD sharing is critical for future genome-wide association studies. In this study, substantial LD sharing between six non-African populations was observed, although much less between African-American and non-African, based on 20,000 SNPs of chromosome 21. We also demonstrated the respective roles of recombination and demographic events in shaping LD sharing. Furthermore, we showed that the haplotype-tagged SNPs chosen from one population are portable to the others in East Asia. Therefore, we concluded that the magnitude of LD sharing between human populations justifies the use of representative populations for selecting haplotypetagged SNPs in genome-wide association studies of complex diseases. bottleneck ͉ genetic distance ͉ association study ͉ common disease ͉ genetic variant C omprehensive testing of the association between genetic variations in the human genome and common diseases holds the promise of delineating the genetic architecture of these diseases (1-5). Substantial sharing of the boundaries and specific haplotypes of linkage disequilibrium (LD) blocks between populations was observed (6). However, variations of haplotype and LD across populations were also reported, raising concerns on its practical hindrance for genomewide testing of association (7-9). Conflicting observations on the magnitude of LD sharing between human populations, therefore, call for a careful examination of the following three questions, which are fundamental in developing strategies for genomewide testing of association. First, measurement of LD sharing between populations should be made independent of the definition of LD blocks, which introduce inconsistent block boundaries (10). Second, the mechanisms that shape LD sharing between populations are yet to be fully explored although the roles of recombination hotspots and demographic events have been implicated To address the aforementioned questions, we typed Ͼ20,000 SNPs on chromosome 21 in seven populations: three representative continental populations [African-American (AFR), European (EUR), and Han Chinese (HAN)] and four other major East Asian (EA) populations. This design allows a close examination of LD sharing between continental groups as well as those within East Asia. In this report, we measured the LD sharing between populations independent of the definition of LD block; and we showed that bottleneck events play a critical role in shaping the LD sharing between Africans and nonAfricans, but much less so between non-Africans. An important question for applying HapMap results to disease studies is how tagSNPs selected from a HapMap population will be ported to disease studies performed in other populations. In this study, we showed that tagSNPs selected from representative continental populations are indeed portable to the others in the same continent for association studies, at least in East Asia, with reasonable efficiency. In addition, we proposed a simple guideline that allows a quick evaluation of the portability of tagSNPs between populations by typing a small number of SNPs. Results Overall 26,112 SNPs were selected and typed in this study, and the data from 19,060 SNPs passed the quality control criteria and were used for further analyses. The SNPs and quality control criteria for SNP selection are described in Materials and Methods. Seven world populations, including EUR, AFR, and five EA populations, were studied. The five EA populations, i.e., HAN, Miao (HMJ), Zhuang (CCY), Wa (WBM), and Uighur (UIG), represent five major linguistic families spoken in East Asia. Preservation of LD between populations, i.e., LD sharing (S, or S AB when the population A was given as reference), is measured by the proportion of SNP pairs in LD in one population (population A or the reference) that are also in LD in another (population B). In this study, LD sharing was estimated without invoking the inference of haplotype blocks; therefore, the measure is independent of the definition of haplotype blocks. LD between two loci was measured in r 2 (16). Detail for the measure of LD sharing is described in Materials and Methods. LD sharing between EAs ranges from 63-74% for r 2 Ն 0.1 and 70-84% for r 2 Ն 0.5 (se

A Monte Carlo permutation test for random mating using genome sequences.

Testing for random mating of a population is important in population genetics, because deviations from randomness of mating may indicate inbreeding, population stratification, natural selection, or sampling bias. However, current methods use only observed numbers of genotypes and alleles, and do not take advantage of the fact that the advent of sequencing technology provides an opportunity to investigate this topic in unprecedented detail. To address this opportunity, a novel statistical test for random mating is required in population genomics studies for which large sequencing datasets are generally available. Here, we propose a Monte-Carlo-based-permutation test (MCP) as an approach to detect random mating. Computer simulations used to evaluate the performance of the permutation test indicate that its type I error is well controlled and that its statistical power is greater than that of the commonly used chi-square test (CHI). Our simulation study shows the power of our test is greater for datasets characterized by lower levels of migration between subpopulations. In addition, test power increases with increasing recombination rate, sample size, and divergence time of subpopulations. For populations exhibiting limited migration and having average levels of population divergence, the statistical power approaches 1 for sequences longer than 1 Mbp and for samples of 400 individuals or more. Taken together, our results suggest that our permutation test is a valuable tool to detect random mating of populations, especially in population genomics studies

Research on the Diffusion Behavior of Cu in Low-Carbon Steel under High Temperatures

The effective diffusion of Cu in Fe is the key to forming a stable transition layer between copper and low-carbon steel, but it is seriously affected by several factors, especially temperature, and the diffusion of Cu can only be completed at high temperatures. In order to analyze the diffusion coefficient of Cu in low-carbon steel under high temperatures, and to obtain the best diffusion temperature range of Cu in steel, the electrodeposition method was used to prepare the diffusion couple of copper and low-carbon steel, which would be annealed under different temperatures for 6 h; meanwhile, the MD models were also used to analyze the diffusion behavior of Cu in Fe at different temperatures. The results show that the diffusion of Cu in low-carbon steel could be realized by high-temperature annealing, and as the temperature increases, the thickness of the Cu/low-carbon steel transition layer shows an increasing trend. When the annealing temperature is between 900 °C and 1000 °C, the thickness of the transition layer increases the fastest. The results of the MD models show that, when the temperature is in the phase transition zone, the main restrictive link for the diffusion of Cu in Fe is the phase transition process of Fe; additionally, when the temperature is higher, the main restrictive link for the diffusion of Cu in Fe is the activity of the atom