A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

As increasing evidence suggests that multiple correlated genetic variants could jointly influence the outcome, a multilocus test that aggregates association evidence across multiple genetic markers in a considered gene or a genomic region may be more powerful than a single-marker test for detecting susceptibility loci. We propose a multilocus test, AdaJoint, which adopts a variable selection procedure to identify a subset of genetic markers that jointly show the strongest association signal, and defines the test statistic based on the selected genetic markers. The P-value from the AdaJoint test is evaluated by a computationally efficient algorithm that effectively adjusts for multiple-comparison, and is hundreds of times faster than the standard permutation method. Simulation studies demonstrate that AdaJoint has the most robust performance among several commonly used multilocus tests. We perform multilocus analysis of over 26 000 genes/regions on two genome-wide association studies of pancreatic cancer. Compared with its competitors, AdaJoint identifies a much stronger association between the gene CLPTM1L and pancreatic cancer risk (6.0 × 10⁻⁸), with the signal optimally captured by two correlated single-nucleotide polymorphisms (SNPs). Finally, we show AdaJoint as a powerful tool for mapping cis-regulating methylation quantitative trait loci on normal breast tissues, and find many CpG sites whose methylation levels are jointly regulated by multiple SNPs nearby.     

A pure likelihood approach to the analysis of genetic association data: an alternative to Bayesian and frequentist analysis

Investigators performing genetic association studies grapple with how to measure strength of association evidence, choose sample size, and adjust for multiple testing. We apply the evidential paradigm (EP) to genetic association studies, highlighting its strengths. The EP uses likelihood ratios (LRs), as opposed to P-values or Bayes'' factors, to measure strength of association evidence. We derive EP methodology to estimate sample size, adjust for multiple testing, and provide informative graphics for drawing inferences, as illustrated with a Rolandic Epilepsy (RE) fine-mapping study. We focus on controlling the probability of observing weak evidence for or against association (W) rather than type I errors (M). For example, for LR⩾32 representing strong evidence, at one locus with n=200 cases, n=200 controls, W=0.134, whereas M=0.005. For n=300 cases and controls, W=0.039 and M=0.004. These calculations are based on detecting an OR=1.5. Despite the common misconception, one is not tied to this planning value for analysis; rather one calculates the likelihood at all possible values to assess evidence for association. We provide methodology to adjust for multiple tests across m loci, which adjusts M and W for m. We do so for (a) single-stage designs, (b) two-stage designs, and (c) simultaneously controlling family-wise error rate (FWER) and W. Method (c) chooses larger sample sizes than (a) or (b), whereas (b) has smaller bounds on the FWER than (a). The EP, using our innovative graphical display, identifies important SNPs in elongator protein complex 4 (ELP4) associated with RE that may not have been identified using standard approaches.     

A Regression-based Association Test for Case-control Studies that Uses Inferred Ancestral Haplotype Similarity

Association methods based on haplotype similarity (HS) can overcome power and stability issues encountered in standard haplotype analyses. Current HS methods can be generally classified into evolutionary and two-sample approaches. We propose a new regression-based HS association method for case-control studies that incorporates covariate information and combines the advantages of the two classes of approaches by using inferred ancestral haplotypes. We first estimate the ancestral haplotypes of case individuals and then, for each individual, an ancestral-haplotype-based similarity score is computed by comparing that individual's observed genotype with the estimated ancestral haplotypes. Trait values are then regressed on the similarity scores. Covariates can easily be incorporated into this regression framework. To account for the bias in the raw p-values due to the use of case data in constructing ancestral haplotypes, as well as to account for variation in ancestral haplotype estimation, a permutation procedure is adopted to obtain empirical p-values. Compared with the standard haplotype score test and the multilocus T² test, our method improves power when neither the allele frequency nor linkage disequilibrium between the disease locus and its neighboring SNPs is too low and is comparable in other scenarios. We applied our method to the Genetic Analysis Workshop 15 simulated SNP data and successfully pinpointed a stretch of SNPs that covers the fine-scale region where the causal locus is located.     

Multiple regression analysis of twin data: A model-fitting approach

The multiple regression methodology proposed by DeFries and Fulker (DF; 1985, 1988) for the analysis of twin data is compared with maximum-likelihood estimation of genetic and environmental parameters from covariance structure. Expectations for the regression coefficients from submodels omitting theh ² andc ² terms are derived. Model comparisons similar to those conducted using maximum-likelihood estimation procedures are illustrated using multiple regression. Submodels of the augmented DF model are shown to yield parameter estimates highly similar to those obtained from the traditional latent variable model. While maximum-likelihood estimation of covariance structure may be the optimal statistical method of estimating genetic and environmental parameters, the model-fitting approach we propose is a useful extension to the highly flexible and conceptually simple DF methodology.This research was supported in part by NICHD Grants HD-11681 and HD-27802. Analyses of the data were facilitated by BRSG Grant RR-07013-25 awarded to the University of Colorado by the Biomedical Research Support Grant Program, Division of Research Resources, National Institutes of Health. The article was written while the first author was supported in part by the Natural Sciences and Engineering Research Council of Canada.     

Joint Identification of Multiple Genetic Variants via Elastic‐Net Variable Selection in a Genome‐Wide Association Analysis

Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome‐wide association (GWA) studies have been conducted with large‐scale data sets of genetic variants. Most of those studies have relied on single‐marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more powerful and would provide better prediction on complex traits since it utilizes combined information across variants. Here we propose a multi‐stage approach for GWA analysis: (1) prescreening, (2) joint identification of putative SNPs based on elastic‐net variable selection, and (3) empirical replication using bootstrap samples. Our approach enables an efficient joint search for genetic associations in GWA analysis. The suggested empirical replication method can be beneficial in GWA studies because one can avoid a costly, independent replication study while eliminating false‐positive associations and focusing on a smaller number of replicable variants. We applied the proposed approach to a GWA analysis, and jointly identified 129 genetic variants having an association with adult height in a Korean population.     

A maximum likelihood test of the two locus model for coeliac disease

A segregation analysis of two-locus inheritance based on a maximum likelihood test is developed and applied to coeliac disease. The results clearly reject a dominant-recessive mode of inheritance, but do not reject a double recessive model. In addition, analysis of the available affected sib pair HLA data are shown to be inconsistent with simple (single-locus) dominant or recessive models with environ-mentally-caused reduced penetrance.     

幕上高血压脑出血预后影响因素的多重线性回归分析

目的探讨影响幕上高血压脑出血（SICH）患者预后的相关因素,以指导临床治疗和评估预后。方法回顾性分析符合本研究纳入标准的幕上高血压脑出血324例完整病历。以一般资料、起病症状、入院查体、影像学资料、治疗方式、并发症等43项为自变量,以发病后1个月后功能独立性评定评分（functional independence measure,FIM）为因变量,建立多重线性回归模型,筛选出对预后有影响的因素,并比较各因素的影响大小。结果经统计学处理发现血肿体积、入院时收缩期血压、GCS评分、脑室是否积血、血肿体积扩大、是否并发肺部感染和应激性溃疡等7项对预后有显著性的影响。结论血肿体积、入院GCS评分和脑室是否积血对预后影响有重要意义,可作为SICH患者预后的关键性指标。     

A maximum likelihood statistical method for analyzing frequency-dependent fitness experiments

Experiments on frequency-dependent fitness often consist of forming pairwise mixtures of distinguishable types at several frequency combinations. These mixtures are allowed to undergo competition, after which the performance of each type is enumerated. A statistical method for analyzing such experiments is described in this article. This method, suggested previously for other purposes, is superior to the statistical procedures now commonly employed. It involves the maximum likelihood estimation of parameters for two logistic regression models: one which assumes that fitness is frequency-dependent, the other that fitness is constant over changing frequency. Estimators for both models can be calculated without difficulty using an iterative numerical algorithm implemented in a Fortran computer program available from the authors. Fitting both models allows for the construction of a likelihood ratio statistical test for whichever model is more appropriate. The method is illustrated by application to publishedDrosophila data from differential mating success experiments.Published with the approval of the Director of the New Hampshire Agricultural Experiment Station as Scientific Contribution No. 924.     

The probability of pregnancy after embryo transfer is affected by the age of the patient, cause of infertility, number of embryos transferred and the average morphology score, as revealed by multiple logistic regression analysis

Because the process of conception is affected by many variables,a multiple logistic regression analysis was performed to assess(i) the impact and relative weight of both patient and embryovariables and (ii) their possible effects on the probabilityof a vital pregnancy after embryo transfer. A statistical modelwas constructed predicting the probability of pregnancy afterembryo transfer. The variables that contributed significantlyto the predictive value of the model were the age of the patient,the cause of infertility, the number of embryos transferredand the average morphology score of the transferred embryos.Embryo variables appeared to have a significant but modest valuein predicting the probability of pregnancy after embryo transfer.Other variables, such as the thickness of the endometrium, werefound to have no prognostic value. Moreover, we found that theireffect could be explained by the variables already includedin the model.     

Responses to phenelzine and amitriptyline absence of differential predictors by multiple regression analysis

Multiple regression analyses using initial symptoms to predict outcome were carried out on data from an outpatient controlled comparison of amitriptyline, phenelzine and placebo. Separate analyses were carried out in the three treatment groups and regression equations were compared. Significant prediction was obtained only for phenelzine. On only one of three outcome measures analysed, however were there significant differences between treatment groups in regressions. Individual predictors were not easily interpretable. There was little evidence for different clinical predictors or response to phenelzine and amitriptyline in this sample.     

A comprehensive approach to the development of magnetotherapeutic devices exemplified by the atos apparatus

State Scientific-Manufacturing Association Almaz, Saratov. Translated from Meditsinskaya Tekhnika, No. 4, pp. 32–35, July–August, 1995.     

A new algorithm for haplotype-based association analysis: the Stochastic-EM algorithm

It is now widely accepted that haplotypic information can be of great interest for investigating the role of a candidate gene in the etiology of complex diseases. In the absence of family data, haplotypes cannot be deduced from genotypes, except for individuals who are homozygous at all loci or heterozygous at only one site. Statistical methodologies are therefore required for inferring haplotypes from genotypic data and testing their association with a phenotype of interest. Two maximum likelihood algorithms are often used in the context of haplotype‐based association studies, the Newton‐Raphson (NR) and the Expectation‐Maximisation (EM) algorithms. In order to circumvent the limitations of both algorithms, including convergence to local minima and saddle points, we here described how a stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype‐phenotype association. Statistical properties of the SEM algorithm were investigated through a simulation study for a large range of practical situations, including small/large samples and rare/frequent haplotypes, and results were compared to those obtained by use of the standard NR algorithm. Our simulation study indicated that the SEM algorithm provides results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotype‐based association analysis, especially when the number of polymorphisms is quite large.     

Exome sequence analysis and follow up genotyping implicates rare ULK1 variants to be involved in susceptibility to schizophrenia

Schizophrenia (SCZ) is a severe, highly heritable psychiatric disorder. Elucidation of the genetic architecture of the disorder will facilitate greater understanding of the altered underlying neurobiological mechanisms. The aim of this study was to identify likely aetiological variants in subjects affected with SCZ. Exome sequence data from a SCZ cas–control sample from Sweden was analysed for likely aetiological variants using a weighted burden test. Suggestive evidence implicated the UNC‐51‐like kinase (ULK1) gene, and it was observed that four rare variants that were more common in the Swedish SCZ cases were also more common in UK10K SCZ cases, as compared to obesity cases. These three missense variants and one intronic variant were genotyped in the University College London cohort of 1304 SCZ cases and 1348 ethnically matched controls. All four variants were more common in the SCZ cases than controls and combining them produced a result significant at P = 0.02. The results presented here demonstrate the importance of following up exome sequencing studies using additional datasets. The roles of ULK1 in autophagy and mTOR signalling strengthen the case that these pathways may be important in the pathophysiology of SCZ. The findings reported here await independent replication.     

企业员工应对方式与心理健康之间的关系

目的：验证不同应对方式对心理健康的单独与共同影响。方法：对4745名被试用分层回归的方法分析不同应对方式对心理健康的单独影响和交互作用。结论：单独使用“消极应对”的应对方式,对心理健康水平存在不利影响;而单独使用“积极应对”的应对方式能在一定程度上改善心理健康水平。“消极应对”与“积极应对”两种应对方式结合使用能显著改善心理健康水平,比单独使用这两种应对方式对心理健康更有利。     

Mycobacterium avium genotype is associated with the therapeutic response to lung infection

Factors that can interfere with the successful treatment of Mycobacterium avium lung infection have been inadequately studied. To identify a potent predictor of therapeutic responses of M. avium lung infection, we analyzed variable number tandem repeats (VNTR) at 16 minisatellite loci of M. avium clinical isolates. Associations between the VNTR profiling data and a therapeutic response were evaluated in 59 subjects with M. avium lung infection. M. avium lung infection of 30 subjects in whom clarithromycin-containing regimens produced microbiological and radiographic improvement was defined as responsive disease, while that of the remaining 29 subjects was defined as refractory disease. In phylogenetic analysis using the genotypic distance aggregated from 16-dimensional VNTR data, 59 M. avium isolates were divided into three clusters, which showed a nearly significant association with therapeutic responses (p 0.06). We then subjected the raw 16-dimensional VNTR data directly to principal component analysis, and identified the genetic features that were significantly associated with the therapeutic response (p <0.05). By further analysis of logistic regression with a stepwise variable-selection, we constructed the highest likelihood multivariate model, adjusted for age, to predict a therapeutic response, using VNTR data from only four minisatellite loci. In conclusion, we identified four mycobacterial minisatellite loci that together were associated with the therapeutic response of M. avium lung infections.     

A wearable comprehensive data sampling system for gait analysis

Abstract

Gait analysis is important for lower limb movement evaluation and rehabilitation research. More and more laboratories focus on it. Researchers need biomechanical data sampling equipment to obtain original signals for their analysis, sometimes even need kinds of signals for data fusion processing. But, the market supply of relative products is very limited. Moreover, one device acquires only one kind of signal, and needs computer as the control centre. So, there are two problems: moving range limitation, and synchronisation in data fusion processing. Most researchers plan experiments only indoors, and sometimes need to do secondary development for data fusion synchronisation. This article represents a compact-embedded system for lower limb biomechanical signals acquisition. Four kinds of signals are collected: foot plantar pressure, inertial measurement, laser distance sensing and electromyography. The embedded circuit is powered by a lithium battery. All the signals are synchronised by the embedded clock, and stored in secure digital memory card for offline analysis. It is convenient to plan experiments in all kinds of terrains indoors or outdoors. It is unique for its wearable, low power and comprehensive characters. Experimental results show that it is a useful tool for gait analysing research.     

基因多态性与疾病相关性的遗传分析中某些值得注意的问题

本文对基因多态性与疾病相关性的遗传分析(包括连锁分析和群体关联分析)中某些值得注意的问题进行了讨论,提出了一些解决途径和建议。     

A simple algebraic demonstration of the validity of DeFries-Fulker analysis in unselected samples with multiple kinship levels

DeFries and Fulker's (Behav. Genet. 15, 467–473, 1985) regression procedure (DF analysis) to estimatec ² andh ² was originally applied to selected twin data. Since then, DF analysis has been applied more broadly in unselected data and with multiple (nontwin) kinship levels. Theoretical work based on the matrix algebra of variance-covariance matrices has shown that estimates ofc ² andh ² are unbiased in selected two-group settings. In this article, a simple proof is presented supporting the validity of DF analysis in broader settings. We use scalar algebra to show that parameter estimates ofh ² andc ² are unbiased in unselected settings with multiple (more than two) kinship levels. Caveats are offered, and other DF analysis problems are identified.     

A genetic analysis of serotonergic biosynthetic and metabolic enzymes in migraine using a DNA pooling approach

Migraine is a common debilitating primary headache disorder with significant mental, physical and social health implications. The brain neurotransmitter 5-hydroxytryptamine (5-HT; serotonin) is involved in nociceptive pathways and has been implicated in the pathophysiology of migraine. With few genetic studies investigating biosynthetic and metabolic enzymes governing the rate of 5-HT activity and their relationship to migraine, it was the objective of this study to assess genetic variants within the human tryptophan hydroxylase (TPH), amino acid decarboxylase (AADC) and monoamine oxidase A (MAOA) genes in migraine susceptibility. This objective was undertaken using a high-throughput DNA pooling experimental design, which proved to be a very accurate, sensitive and specific method of estimating allele frequencies for single nucleotide polymorphism, insertion deletion and variable number tandem repeat loci. Application of DNA pooling to a wide array of genetic loci provides greater scope in the assessment of population-based genetic association study designs. Despite the application of this high-throughput genotyping method, negative results from the two-stage DNA pooling design used to screen loci within the TPH, AADC and MAOA genes did not support their role in migraine susceptibility.