A note on generalized Genome Scan Meta-Analysis statistics

Background  

Wise et al. introduced a rank-based statistical technique for meta-analysis of genome scans, the Genome Scan Meta-Analysis (GSMA) method. Levinson et al. recently described two generalizations of the GSMA statistic: (i) a weighted version of the GSMA statistic, so that different studies could be ascribed different weights for analysis; and (ii) an order statistic approach, reflecting the fact that a GSMA statistic can be computed for each chromosomal region or bin width across the various genome scan studies.     

Data acquisition for meta-analysis of genome-wide linkage studies using the genome search meta-analysis method

BACKGROUND: The Genome Search Meta-Analysis (GSMA) method enables researchers to pool results across genome-wide linkage studies, to increase the power to detect linkage. Results from individual studies must be extracted, with the maximum evidence for linkage placed into bins, usually of 30 cM width, and ranked within the study. Ranks are then summed across studies, with high summed ranks potentially showing evidence for linkage in the meta-analysis. OBJECTIVES: In this paper we study the properties of the GSMA method considering two different issues: (1) data binning from genome-wide results when indexed markers or graphs are available, based on either predefined boundary markers, or equal-length bins; (2) the use of selected instead of genome-wide results, using simulation to estimate power and type I error rates of GSMA. This is relevant when published papers show only summary results (e.g. with NPL score >1). Results: Using digitizing software to extract linkage statistics from graphs and assigning equal bin length is accurate, with the resulting ranking of bins similar to those defined through boundary markers. Simulation results show that power can fall substantially when genome-wide results are not available, particularly when only results from a single marker are available in a linked region. However there is no increase in false positive findings. CONCLUSIONS: The GSMA method is robust across different bin definitions and methods of data presentation and extraction. Using studies based on only the top ranked bins does not produce false positive results, but lacks power to detect genes conferring a modest increase in risk. Therefore, we advise that effort should be made to obtain genome-wide results from investigators or from published papers to avoid limiting the utility of the GSMA.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part I: Methods and power analysis

This is the first of three articles on a meta-analysis of genome scans of schizophrenia (SCZ) and bipolar disorder (BPD) that uses the rank-based genome scan meta-analysis (GSMA) method. Here we used simulation to determine the power of GSMA to detect linkage and to identify thresholds of significance. We simulated replicates resembling the SCZ data set (20 scans; 1,208 pedigrees) and two BPD data sets using very narrow (9 scans; 347 pedigrees) and narrow (14 scans; 512 pedigrees) diagnoses. Samples were approximated by sets of affected sibling pairs with incomplete parental data. Genotypes were simulated and nonparametric linkage (NPL) scores computed for 20 180-cM chromosomes, each containing six 30-cM bins, with three markers/bin (or two, for some scans). Genomes contained 0, 1, 5, or 10 linked loci, and we assumed relative risk to siblings (lambda(sibs)) values of 1.15, 1.2, 1.3, or 1.4. For each replicate, bins were ranked within-study by maximum NPL scores, and the ranks were averaged (R(avg)) across scans. Analyses were repeated with weighted ranks ((sqrt)N[genotyped cases] for each scan). Two P values were determined for each R(avg): P(AvgRnk) (the pointwise probability) and P(ord) (the probability, given the bin's place in the order of average ranks). GSMA detected linkage with power comparable to or greater than the underlying NPL scores. Weighting for sample size increased power. When no genomewide significant P values were observed, the presence of linkage could be inferred from the number of bins with nominally significant P(AvgRnk), P(ord), or (most powerfully) both. The results suggest that GSMA can detect linkage across multiple genome scans.     

Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis

To date, several susceptibility loci for systemic lupus erythematosus (SLE) have been identified by individual genome-wide scans, but many of these loci have shown inconsistent results across studies. Additionally, many individual studies are at the lower limit of acceptable power recommended for declaring significant linkage. The genome search meta-analysis (GSMA) has been proposed as a valid and robust method for combining several genome scan results. The aim of this study is to investigate whether there is any consistent evidence of linkage across multiple studies, and to identify novel SLE susceptibility loci by using GSMA method. Twelve genome scan results generated from nine independent studies have been used for the present GSMA. All together, the data consists of 605 families with 1,355 SLE affected individuals from three self-reported ethnicities; Caucasian, African-American, and Hispanic. For each study, the genome was divided into 120 bins (30 cM) and ranked according to the maximum evidence of linkage within each bin. The ranks were summed and averaged across studies following which the significance was assessed by the permutation tests. The present study identified two genomic locations at 6p22.3–6p21.1 and 16p12.3–16q12.2 that met genome-wide significance (p<0.000417). The identified region at 6p22.3–6p21.1 contains the HLA region. The combined p-values using Fisher’s method also supported the significance in these regions. Clustering of significant adjacent bins was observed for chromosomes 6 and 16. Additionally, there are 12 other bins with two point-wise p-values (Psumrnk and Pord) <0.05, suggesting that these bin regions are highly likely to contain SLE susceptibility loci. Among them, present GSMA also identified two novel regions at 4q32.1–4q34.3 and 13q13.2–13q22.2. However, separate analysis using only Caucasian populations identified the strongest evidence for linkage at chromosome 6p21.1–6q15 (Psumrnk=0.00021). One interesting novel region suggests that 3q22.1–3q25.33 (Psumrnk=0.01376) may be an ethnicity-specific SLE linkage. In summary, the present GSMA have identified two statistically significant genomic regions that reconfirmed the SLE linkage at chromosomes 6 and 16.     

Novel non-HLA-susceptible regions determined by meta-analysis of four genomewide scans for ankylosing spondylitis

We identified novel non-HLA-susceptible regions for ankylosing spondylitis (AS) by applying the genome-search-metaanalysis (GSMA) method to combine the previous four AS genomewide scan studies including 479 families with 1175 affected individuals. Three original genomescans were mainly analysed for Caucasian families and one analysed for Han Mongolian families. Ten bins had both Psumrnk and Pord <0.05, suggesting these bins most likely contain AS-linked loci. The 10 bins are 6.2, 16.3, 6.1, 3.3, 6.3, 16.4, 10.5, 17.1, 2.5 and 2.9. The most significant result of linkage was on chromosome 6p22.3-p21.1 (bin 6.2, Psumrnk <0.000417), where HLA loci are located. By addition of a genome scan of Chinese origin, our GSMA result further confirmed the HLA loci as the greatest susceptible region to AS and suggested that non-HLA loci chromosome 16q, 3p, 10q, 2p, 2q and 17p, may also contain AS-linked loci. The novel loci identified in our result give hints to further studies.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part II: Schizophrenia

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk<.000417). Two aggregate criteria for linkage were also met (clusters of nominally significant P values that did not occur in 1,000 replicates of the entire data set with no linkage present): 12 consecutive bins with both P(AvgRnk) and P(ord)<.05, including regions of chromosomes 5q, 3p, 11q, 6p, 1q, 22q, 8p, 20q, and 14p, and 19 consecutive bins with P(ord)<.05, additionally including regions of chromosomes 16q, 18q, 10p, 15q, 6q, and 17q. There is greater consistency of linkage results across studies than has been previously recognized. The results suggest that some or all of these regions contain loci that increase susceptibility to schizophrenia in diverse populations.     

Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.     

Identifying genomic regions for fine-mapping using genome scan meta-analysis (GSMA) to identify the minimum regions of maximum significance (MRMS) across populations

In order to detect linkage of the simulated complex disease Kofendrerd Personality Disorder across studies from multiple populations, we performed a genome scan meta-analysis (GSMA). Using the 7-cM microsatellite map, nonparametric multipoint linkage analyses were performed separately on each of the four simulated populations independently to determine p-values. The genome of each population was divided into 20-cM bin regions, and each bin was rank-ordered based on the most significant linkage p-value for that population in that region. The bin ranks were then averaged across all four studies to determine the most significant 20-cM regions over all studies. Statistical significance of the averaged bin ranks was determined from a normal distribution of randomly assigned rank averages. To narrow the region of interest for fine-mapping, the meta-analysis was repeated two additional times, with each of the 20-cM bins offset by 7 cM and 13 cM, respectively, creating regions of overlap with the original method. The 6-7 cM shared regions, where the highest averaged 20-cM bins from each of the three offsets overlap, designated the minimum region of maximum significance (MRMS). Application of the GSMA-MRMS method revealed genome wide significance (p-values refer to the average rank assigned to the bin) at regions including or adjacent to all of the simulated disease loci: chromosome 1 (p < 0.0001 for 160-167 cM, including D1), chromosome 3 (p-value < 0.0000001 for 287-294 cM, including D2), chromosome 5 (p-value < 0.001 for 0-7 cM, including D3), and chromosome 9 (p-value < 0.05 for 7-14 cM, the region adjacent to D4). This GSMA analysis approach demonstrates the power of linkage meta-analysis to detect multiple genes simultaneously for a complex disorder. The MRMS method enhances this powerful tool to focus on more localized regions of linkage.     

Genome scan meta-analysis of schizophrenia and bipolar disorder,part III: Bipolar disorder

Genome scans of bipolar disorder (BPD) have not produced consistent evidence for linkage. The rank-based genome scan meta-analysis (GSMA) method was applied to 18 BPD genome scan data sets in an effort to identify regions with significant support for linkage in the combined data. The two primary analyses considered available linkage data for “very narrow” (i.e., BP-I and schizoaffective disorder–BP) and “narrow” (i.e., adding BP-II disorder) disease models, with the ranks weighted for sample size. A “broad” model (i.e., adding recurrent major depression) and unweighted analyses were also performed. No region achieved genomewide statistical significance by several simulation-based criteria. The most significant P values (<.01) were observed on chromosomes 9p22.3-21.1 (very narrow), 10q11.21-22.1 (very narrow), and 14q24.1-32.12 (narrow). Nominally significant P values were observed in adjacent bins on chromosomes 9p and 18p-q, across all three disease models on chromosomes 14q and 18p-q, and across two models on chromosome 8q. Relatively few BPD pedigrees have been studied under narrow disease models relative to the schizophrenia GSMA data set, which produced more significant results. There was no overlap of the highest-ranked regions for the two disorders. The present results for the very narrow model are promising but suggest that more and larger data sets are needed. Alternatively, linkage might be detected in certain populations or subsets of pedigrees. The narrow and broad data sets had considerable power, according to simulation studies, but did not produce more highly significant evidence for linkage. We note that meta-analysis can sometimes provide support for linkage but cannot disprove linkage in any candidate region.     

Identification of shared genetic susceptibility locus for coronary artery disease, type 2 diabetes and obesity: a meta-analysis of genome-wide studies

ABSTRACT: Type 2 diabetes (2DM), obesity, and coronary artery disease (CAD) are frequently coexisted being as key components of metabolic syndrome. Whether there is shared genetic background underlying these diseases remained unclear. We performed a meta-analysis of 35 genome screens for 2DM, 36 for obesity or body mass index (BMI)-defined obesity, and 21 for CAD using genome search meta-analysis (GSMA), which combines linkage results to identify regions with only weak evidence and provide genetic interactions among different diseases. For each study, 120 genomic bins of approximately 30 cM were defined and ranked according to the best linkage evidence within each bin. For each disease, bin 6.2 achieved genomic significanct evidence, and bin 9.3, 10.5, 16.3 reached suggestive level for 2DM. Bin 11.2 and 16.3, and bin 10.5 and 9.3, reached suggestive evidence for obesity and CAD respectively. In pooled all three diseases, bin 9.3 and 6.5 reached genomic significant and suggestive evidence respectively, being relatively much weaker for 2DM/CAD or 2DM/obesity or CAD/obesity. Further, genomewide significant evidence was observed of bin 16.3 and 4.5 for 2DM/obesity, which is decreased when CAD was added. These findings indicated that bin 9.3 and 6.5 are most likely to be shared by 2DM, obesity and CAD. And bin 16.3 and 4.5 are potentially common regions to 2DM and obesity only. The observed shared susceptibility regions imply a partly overlapping genetic aspects of disease development. Fine scanning of these regions will definitely identify more susceptibility genes and causal variants.     

Meta-analysis of genome-wide linkage studies of asthma and related traits

Background

Asthma and allergy are complex multifactorial disorders, with both genetic and environmental components determining disease expression. The use of molecular genetics holds great promise for the identification of novel drug targets for the treatment of asthma and allergy. Genome-wide linkage studies have identified a number of potential disease susceptibility loci but replication remains inconsistent. The aim of the current study was to complete a meta-analysis of data from genome-wide linkage studies of asthma and related phenotypes and provide inferences about the consistency of results and to identify novel regions for future gene discovery.

Methods

The rank based genome-scan meta-analysis (GSMA) method was used to combine linkage data for asthma and related traits; bronchial hyper-responsiveness (BHR), allergen positive skin prick test (SPT) and total serum Immunoglobulin E (IgE) from nine Caucasian asthma populations.

Results

Significant evidence for susceptibility loci was identified for quantitative traits including; BHR (989 pedigrees, n = 4,294) 2p12-q22.1, 6p22.3-p21.1 and 11q24.1-qter, allergen SPT (1,093 pedigrees, n = 4,746) 3p22.1-q22.1, 17p12-q24.3 and total IgE (729 pedigrees, n = 3,224) 5q11.2-q14.3 and 6pter-p22.3. Analysis of the asthma phenotype (1,267 pedigrees, n = 5,832) did not identify any region showing genome-wide significance.

Conclusion

This study represents the first linkage meta-analysis to determine the relative contribution of chromosomal regions to the risk of developing asthma and atopy. Several significant results were obtained for quantitative traits but not for asthma confirming the increased phenotype and genetic heterogeneity in asthma. These analyses support the contribution of regions that contain previously identified asthma susceptibility genes and provide the first evidence for susceptibility loci on 5q11.2-q14.3 and 11q24.1-qter.     

Availability Analysis for Multitask Production Systems

A multitask production system is one in which different subsets of the components can be used to perform distinct functions or tasks. For such a system, some of the components are used intermittently and some may be used continuously. This type of operational protocol is often applied to flexible manufacturing systems. In this paper, we develop models of the operational reliability and of the availability of multitask systems. We construct models for both the case in which preventive maintenance is used and the case in which preventive maintenance is not used. The models presented here constitute an extension to existing models to allow the study of the behavior of modern manufacturing equipment.     

基于小波和BP网络的视觉诱发电位分类识别

提出了小波分解与BP网络相结合的方法来识别视觉诱发电位（Visual Evoked Potential,VEP）。先用小波分解对VEP进行特征提取和降维。然后用BP网络进行分类识别。     

Surveillance in longitudinal models: detection of intrauterine growth restriction

A new methodology for online detection of intrauterine growth restriction (IUGR) is proposed where traditional methods for statistical surveillance are applied. Here, deficient growth rate is used to detect IUGR instead of the common surrogate measure "small for gestational age" (SGA). Fetal growth is estimated by repeated measurements of symphysis-fundus (SF) height. At each time point the new method, based on the Shiryaev-Roberts method, is used to evaluate the growth in SF height. We use Swedish data to model a normal growth pattern, which is used to evaluate the capability of the new method to detect IUGR in comparison with a method used in practice today. Results from simulations indicate that the new method performs considerably better than the method used today. We also illustrate the effect of some important factors which influence the detection ability and illuminate the tendency of the method used today to misclassify SGA cases as IUGR.     

巴戟天应用的名实沿革考

通过对古今文献的分析及实地考查认为:南北朝以前使用之主流巴戟天药材的原植物可能为五味子科植物铁箍散Schisandr a propinqua(Wall.)Baill.var.sinensis Oliv.,药材现称川巴戟或香巴戟;唐代至清末广为运用的品种为归州巴戟天,原植物为茜草科植物四川虎刺Dammacanthus officinarum Huang,药材现称鄂西巴戟天或恩施巴戟天;现今药典收载之巴戟天为清末发展的新品种,其原植物为茜草科植物巴戟天 Morinda officinalis How。     

Electrochemical concepts and techniques in the study of stainless steel ennoblement

Electrochemical theory and technique used to investigate microbially influenced corrosion is discussed with a focus on methods used to demonstrate the manganic-oxide mechanism of stainless steel Ennoblement. The concept of mixed potential and its relationship to the current-voltage behavior of stainless steel is developed. This concept is used to interpret microbially induced changes in corrosion potential, polarization behavior, surface-oxide abundance, and the redox environment at submerged metal surfaces. Microelectrode, capacitance, and coulometric methods are described that can be used to discriminate electrochemical effects caused by changes in solution properties from those caused by mineral deposition at the metal surface. The variety of electrochemical, wet-chemical, microbiological, and surface analytical techniques used to demonstrate the effect of biomineralized manganese dioxide on the electrochemical behavior of stainless steel are summarized.     

Ethonobotany and sustainable utilization of natural dye plants in Sierra Leone

An ethnobotanical survey of practicing dyers in the biodiversity rich ecosystems of Sierra Leone and of the literature was conducted on the common plant species used to produce natural dyes. In addition, the methods used to obtain these dyes and to dye fabric, together with the techniques used to produce various patterns on fabric were investigated. Although the thriving dye industry is now predominantly serviced by synthetic dyes, the knowledge of the plants used still survives among some dyers, who use plant dyes to some extent. However this indigenous knowledge is rapidly being lost as increasingly less of it is being passed on to succeeding generations. Several plants used in the dyeing process are documented, together with their taxonomic characteristics; local names; how the dyes are produced and fabric dyed; the colors obtained; in addition to how various patterns are designed. Sustainable utilization of this important renewable natural resource is discussed.     

Constructing dense genetic linkage maps

This paper describes a novel combination of techniques for the construction of dense genetic linkage maps. The construction of such maps is hampered by the occurrence of even small proportions of typing errors. Simulated annealing is used to obtain the best map according to the optimality criterion: the likelihood or the total number of recombination events. Spatial sampling of markers is used to obtain a framework map. The construction of a framework map is essential if the steps used for simulated annealing are required to be simple. For missing-data imputation the Gibbs sampler is used. Map construction using simulated annealing and missing-data imputation are used in an iterative way. In order to obtain some measure of precision of the genetic linkage map obtained, the Metropolis-Hastings algorithm is used to obtain posterior intervals for the positions of markers. The process of map construction is embedded in a framework of pre-mapping and post-mapping diagnostics. The techniques described are illustrated using a practical application. Received: 1 June 2000 / Accepted: 21 September 2000     

High performance thin layer chromatography (HPTLC), an improved technique for screening lichen substances

Abstract: High performance thin layer chromatography (HPTLC) is a method that can be used for screening lichen substances. It is as simple to use as standard TLC, but has many advantages: It is more sensitive, it is possible to run more samples in a shorter period of time, and the amount of solvent used is much smaller. The material needed and the methods used are described in detail. Horizontal chromatogram development was used. Since two of the solvents used in system B have been substituted, and since the properties of the HPTLC plates are slightly different, our results are not entirely in accordance with the standardized TLC method. A revised table for the identification of 69 lichen substances (obtained from 62 taxa) is accordingly presented.     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.