Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

A model-based approach for analysis of spatial structure in genetic data

Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.     

Loss-of-function mutations in TGFB2 cause a syndromic presentation of thoracic aortic aneurysm

Loeys-Dietz syndrome (LDS) associates with a tissue signature for high transforming growth factor (TGF)-β signaling but is often caused by heterozygous mutations in genes encoding positive effectors of TGF-β signaling, including either subunit of the TGF-β receptor or SMAD3, thereby engendering controversy regarding the mechanism of disease. Here, we report heterozygous mutations or deletions in the gene encoding the TGF-β2 ligand for a phenotype within the LDS spectrum and show upregulation of TGF-β signaling in aortic tissue from affected individuals. Furthermore, haploinsufficient Tgfb2(+/-) mice have aortic root aneurysm and biochemical evidence of increased canonical and noncanonical TGF-β signaling. Mice that harbor both a mutant Marfan syndrome (MFS) allele (Fbn1(C1039G/+)) and Tgfb2 haploinsufficiency show increased TGF-β signaling and phenotypic worsening in association with normalization of TGF-β2 expression and high expression of TGF-β1. Taken together, these data support the hypothesis that compensatory autocrine and/or paracrine events contribute to the pathogenesis of TGF-β-mediated vasculopathies.     

Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.     

Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits

We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.     

A genome-wide association study in Han Chinese identifies new susceptibility loci for ankylosing spondylitis

To identify susceptibility loci for ankylosing spondylitis, we performed a two-stage genome-wide association study in Han Chinese. In the discovery stage, we analyzed 1,356,350 autosomal SNPs in 1,837 individuals with ankylosing spondylitis and 4,231 controls; in the validation stage, we analyzed 30 suggestive SNPs in an additional 2,100 affected individuals and 3,496 controls. We identified two new susceptibility loci between EDIL3 and HAPLN1 at 5q14.3 (rs4552569; P = 8.77 × 10(-10)) and within ANO6 at 12q12 (rs17095830; P = 1.63 × 10(-8)). We also confirmed previously reported associations in Europeans within the major histocompatibility complex (MHC) region (top SNP, rs13202464; P < 5 × 10(-324)) and at 2p15 (rs10865331; P = 1.98 × 10(-8)). We show that rs13202464 within the MHC region mainly represents the risk effect of HLA-B*27 variants (including HLA-B*2704, HLA-B*2705 and HLA-B*2715) in Chinese. The two newly discovered loci implicate genes related to bone formation and cartilage development, suggesting their potential involvement in the etiology of ankylosing spondylitis.     

A genome-wide association study in Chinese men identifies three risk loci for non-obstructive azoospermia

Non-obstructive azoospermia (NOA) is one of the most severe forms of male infertility. Its pathophysiology is largely unknown, and few genetic influences have been defined. To identify common variants contributing to NOA in Han Chinese men, we performed a three-stage genome-wide association study of 2,927 individuals with NOA and 5,734 controls. The combined analyses identified significant (P < 5.0 × 10(-8)) associations between NOA risk and common variants near PRMT6 (rs12097821 at 1p13.3: odds ratio (OR) = 1.25, P = 5.7 × 10(-10)), PEX10 (rs2477686 at 1p36.32: OR = 1.39, P = 5.7 × 10(-12)) and SOX5 (rs10842262 at 12p12.1: OR = 1.23, P = 2.3 × 10(-9)). These findings implicate genetic variants at 1p13.3, 1p36.32 and 12p12.1 in the etiology of NOA in Han Chinese men.     

基于UDEC巷道掘进矿压显现的数值模拟

通过采用UDEC软件,依据钻孔柱状图和采掘应力影响范围建立了相应的几何模型,根据煤岩体物理力学性质建立相应的材料模型,在此基础上结合地应力数据,从而完成数值模型.通过模拟实际巷道开挖尺寸及方位,得出巷道前方的应力分布和巷道两帮位移分布,为巷道冲击地压显现防治提供了理论支持.     

重症急性胰腺炎病人外周血单个核细胞线粒体DNA拷贝数的意义

目的探讨重症急性胰腺炎患者外周血单个核细胞线粒体DNA拷贝数与患者病情严重程度、预后的相关性。方法分离研究对象（重症急性胰腺炎患者34名,健康对照组17名）外周血单个核细胞,采用荧光实时定量PCR检测各组PBMC线粒体DNA相对舍量,其与严重程度、预后的相关性。结果重症急性胰腺炎患者线粒体DNA含量较健康对照组明显降低（P〈0．05）。患者单个核细胞线粒体DNA拷贝教与APACHEⅡ评分呈负相关（r=-0．425）其中并发症组mtDNA拷贝数显著低于无并发症组（P〈0．05）。结论重症急性胰腺炎患者外周血单个核细胞中线粒体DNA含量显著下降,与患者APACHEⅡ评分呈负相关,且低水平的线粒体DNA拷贝数提示并发症发生率的升高,因此,线粒体DNA拷贝数可作为潜在评估患者病情危重程度及预后的指标,这一发现值得进一步大型,前瞻性的研究。