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Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the international classification of diseases (ICD), the directed acyclic graph structure of the gene ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypotheses can create redundancies among rejections that hinder interpretability. This leads to the practice of filtering rejection sets obtained from multiple testing procedures, which may in turn invalidate their inferential guarantees. We propose Focused BH, a simple, flexible, and principled methodology to adjust for the application of any prespecified filter. We prove that Focused BH controls the false discovery rate under various conditions, including when the filter satisfies an intuitive monotonicity property and the p-values are positively dependent. We demonstrate in simulations that Focused BH performs well across a variety of settings, and illustrate this method's practical utility via analyses of real datasets based on ICD and GO. [ABSTRACT FROM AUTHOR]

Recent advances in genome sequencing and imputation technologies provide an exciting opportunity to comprehensively study the contribution of genetic variants to complex phenotypes. However, our ability to translate genetic discoveries into mechanistic insights remains limited at this point. In this paper, we propose an efficient knockoff-based method, GhostKnockoff, for genome-wide association studies (GWAS) that leads to improved power and ability to prioritize putative causal variants relative to conventional GWAS approaches. The method requires only Z-scores from conventional GWAS and hence can be easily applied to enhance existing and future studies. The method can also be applied to meta-analysis of multiple GWAS allowing for arbitrary sample overlap. We demonstrate its performance using empirical simulations and two applications: (1) a meta-analysis for Alzheimer's disease comprising nine overlapping large-scale GWAS, whole-exome and whole-genome sequencing studies and (2) analysis of 1403 binary phenotypes from the UK Biobank data in 408,961 samples of European ancestry. Our results demonstrate that GhostKnockoff can identify putatively functional variants with weaker statistical effects that are missed by conventional association tests. The authors present GhostKnockoff, a method for genome-wide association studies which can be applied to enhance existing and future studies to identify functional variants with weaker statistical effects that might be missed by conventional association tests. [ABSTRACT FROM AUTHOR]

We present a comprehensive statistical framework to analyze data from genome-wide association studies of polygenic traits, producing interpretable findings while controlling the false discovery rate. In contrast with standard approaches, our method can leverage sophisticated multivariate algorithms but makes no parametric assumptions about the unknown relation between genotypes and phenotype. Instead, we recognize that genotypes can be considered as a random sample from an appropriate model, encapsulating our knowledge of genetic inheritance and human populations. This allows the generation of imperfect copies (knockoffs) of these variables that serve as ideal negative controls, correcting for linkage disequilibrium and accounting for unknown population structure, which may be due to diverse ancestries or familial relatedness. The validity and effectiveness of our method are demonstrated by extensive simulations and by applications to the UK Biobank data. These analyses confirm our method is powerful relative to state-of-the-art alternatives, while comparisons with other studies validate most of our discoveries. Finally, fast software is made available for researchers to analyze Biobank-scale datasets. [ABSTRACT FROM AUTHOR]

We introduce a multiple testing procedure that controls global error rates at multiple levels of resolution. Conceptually, we frame this problem as the selection of hypotheses that are organized hierarchically in a tree structure. We describe a fast algorithm and prove that it controls relevant error rates given certain assumptions on the dependence between the |$p$| -values. Through simulations, we demonstrate that the proposed procedure provides the desired guarantees under a range of dependency structures and that it has the potential to gain power over alternative methods. Finally, we apply the method to studies on the genetic regulation of gene expression across multiple tissues and on the relation between the gut microbiome and colorectal cancer. [ABSTRACT FROM AUTHOR]

Background: Disturbed sleep and activity are prominent features of bipolar disorder type I (BP-I). However, the relationship of sleep and activity characteristics to brain structure and behavior in euthymic BP-I patients and their non-BP-I relatives is unknown. Additionally, underlying genetic relationships between these traits have not been investigated. Methods: Relationships between sleep and activity phenotypes, assessed using actigraphy, with structural neuroimaging (brain) and cognitive and temperament (behavior) phenotypes were investigated in 558 euthymic individuals from multi-generational pedigrees including at least one member with BP-I. Genetic correlations between actigraphy-brain and actigraphy-behavior associations were assessed, and bivariate linkage analysis was conducted for trait pairs with evidence of shared genetic influences. Results: More physical activity and longer awake time were significantly associated with increased brain volumes and cortical thickness, better performance on neurocognitive measures of long-term memory and executive function, and less extreme scores on measures of temperament (impulsivity, cyclothymia). These associations did not differ between BP-I patients and their non-BP-I relatives. For nine activity-brain or activity-behavior pairs there was evidence for shared genetic influence (genetic correlations); of these pairs, a suggestive bivariate quantitative trait locus on chromosome 7 for wake duration and verbal working memory was identified. Conclusions: Our findings indicate that increased physical activity and more adequate sleep are associated with increased brain size, better cognitive function and more stable temperament in BP-I patients and their non-BP-I relatives. Additionally, we found evidence for pleiotropy of several actigraphy-behavior and actigraphy-brain phenotypes, suggesting a shared genetic basis for these traits. [ABSTRACT FROM AUTHOR]

The goal of expression quantitative trait loci (eQTL) studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20 000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor: eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues as brain, liver, etc.-often the organs of most immediate medical relevance. Given the high-dimensional nature of these datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures. These testing procedures primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated with these variants, in a way that accounts for the considerable amount of selection. Yet, given the difficulty of procuring additional samples, this challenge is of practical importance. We illustrate in this work how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy with a 2-fold contribution: (1) it reflects the selection steps typically adopted in state of the art investigations and (2) it introduces the use of randomness instead of data-splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated. [ABSTRACT FROM AUTHOR]

We enjoyed reading Professor Efron's (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. Interestingly, Efron puts the pure predictive algorithms to the test in a scenario where the sample size is extremely moderate by today's standards ( I n i = 102). In the knockoff framework, one can use I any i black-box (any predictive model) to identify any kind possible association - linear or not - between a large set of explanatory variables HT ht and an outcome I Y i . This framework rigorously tests whether the conditional distribution HT ht actually depends on any of the variables I X SB j sb i without assuming any model for how I X i and I Y i are connected (Candès et al. 2018). [Extracted from the article]

In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings. GWAS analysis currently relies mostly on linear mixed models, which do not account for linkage disequilibrium (LD) between tested variants. Here, Sesia et al. propose KnockoffZoom, a non-parametric statistical method for the simultaneous discovery and fine-mapping of causal variants, assuming only that LD is described by hidden Markov models (HMMs). [ABSTRACT FROM AUTHOR]

We enjoyed reading Professor Efron's (Brad) paper just as much as we enjoyed listening to his June 2019 lecture in Leiden. Interestingly, Efron puts the pure predictive algorithms to the test in a scenario where the sample size is extremely moderate by today's standards ( I n i = 102). In the knockoff framework, one can use I any i black-box (any predictive model) to identify any kind possible association - linear or not - between a large set of explanatory variables Graph HT ht and an outcome I Y i . This framework rigorously tests whether the conditional distribution Graph HT ht actually depends on any of the variables I X SB j sb i without assuming any model for how I X i and I Y i are connected (Candès et al. [4]). [Extracted from the article]

Background: DNA methylation of CpG sites on genetic loci has been linked to increased risk of asthma in children exposed to elevated ambient air pollutants (AAPs). Further identification of specific CpG sites and the pollutants that are associated with methylation of these CpG sites in immune cells could impact our understanding of asthma pathophysiology. In this study, we sought to identify some CpG sites in specific genes that could be associated with asthma regulation (Foxp3 and IL10) and to identify the different AAPs for which exposure prior to the blood draw is linked to methylation levels at these sites. We recruited subjects from Fresno, California, an area known for high levels of AAPs. Blood samples and responses to questionnaires were obtained (n = 188), and in a subset of subjects (n = 33), repeat samples were collected 2 years later. Average measures of AAPs were obtained for 1, 15, 30, 90, 180, and 365 days prior to each blood draw to estimate the short-term vs. long-term effects of the AAP exposures. Results: Asthma was significantly associated with higher differentially methylated regions (DMRs) of the Foxp3 promoter region (p = 0.030) and the IL10 intronic region (p = 0.026). Additionally, at the 90-day time period (90 days prior to the blood draw), Foxp3 methylation was positively associated with NO2, CO, and PM2.5 exposures (p = 0.001, p = 0.001, and p = 0.012, respectively). In the subset of subjects retested 2 years later (n = 33), a positive association between AAP exposure and methylation was sustained. There was also a negative correlation between the average Foxp3 methylation of the promoter region and activated Treg levels (p = 0.039) and a positive correlation between the average IL10 methylation of region 3 of intron 4 and IL10 cytokine expression (p = 0.030). Conclusions: Short-term and long-term exposures to high levels of CO, NO2, and PM2.5 were associated with alterations in differentially methylated regions of Foxp3. IL10 methylation showed a similar trend. For any given individual, these changes tend to be sustained over time. In addition, asthma was associated with higher differentially methylated regions of Foxp3 and IL10. [ABSTRACT FROM AUTHOR]

The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information. [ABSTRACT FROM AUTHOR]

Genomic copy number variations (CNVs) and increased parental age are both associated with the risk to develop a variety of clinical neuropsychiatric disorders such as autism, schizophrenia and bipolar disorder. At the same time, it has been shown that the rate of transmitted de novo single nucleotide mutations is increased with paternal age. To address whether paternal age also affects the burden of structural genomic deletions and duplications, we examined various types of CNV burden in a large population sample from the Netherlands. Healthy participants with parental age information ( n = 6,773) were collected at different University Medical Centers. CNVs were called with the PennCNV algorithm using Illumina genome-wide SNP array data. We observed no evidence in support of a paternal age effect on CNV load in the offspring. Our results were negative for global measures as well as several proxies for de novo CNV events in this unique sample. While recent studies suggest de novo single nucleotide mutation rate to be dominated by the age of the father at conception, our results strongly suggest that at the level of global CNV burden there is no influence of increased paternal age. While it remains possible that local genomic effects may exist for specific phenotypes, this study indicates that global CNV burden and increased father's age may be independent disease risk factors. [ABSTRACT FROM AUTHOR]

Background: Variations in DNA copy number carry information on the modalities of genome evolution and mis-regulation of DNA replication in cancer cells. Their study can help localize tumor suppressor genes, distinguish different populations of cancerous cells, and identify genomic variations responsible for disease phenotypes. A number of different high throughput technologies can be used to identify copy number variable sites, and the literature documents multiple effective algorithms. We focus here on the specific problem of detecting regions where variation in copy number is relatively common in the sample at hand. This problem encompasses the cases of copy number polymorphisms, related samples, technical replicates, and cancerous sub-populations from the same individual. Results: We present a segmentation method named generalized fused lasso (GFL) to reconstruct copy number variant regions. GFL is based on penalized estimation and is capable of processing multiple signals jointly. Our approach is computationally very attractive and leads to sensitivity and specificity levels comparable to those of state-of-the-art specialized methodologies. We illustrate its applicability with simulated and real data sets. Conclusions: The flexibility of our framework makes it applicable to data obtained with a wide range of technology. Its versatility and speed make GFL particularly useful in the initial screening stages of large data sets. [ABSTRACT FROM AUTHOR]

Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. [ABSTRACT FROM AUTHOR]

Down Syndrome cell adhesion molecule (Dscam) genes encode neuronal cell recognition proteins of the immunoglobulin superfamily. In Drosophila, Dscam1 generates 19,008 different ectodomains by alternative splicing of three exon clusters, each encoding half or a complete variable immunoglobulin domain. Identical isoforms bind to each other, but rarely to isoforms differing at any one of the variable immunoglobulin domains. Binding between isoforms on opposing membranes promotes repulsion. Isoform diversity provides the molecular basis for neurite self-avoidance. Self-avoidance refers to the tendency of branches from the same neuron (self-branches) to selectively avoid one another. To ensure that repulsion is restricted to self-branches, different neurons express different sets of isoforms in a biased stochastic fashion. Genetic studies demonstrated that Dscam1 diversity has a profound role in wiring the fly brain. Here we show how many isoforms are required to provide an identification system that prevents non-self branches from inappropriately recognizing each other. Using homologous recombination, we generated mutant animals encoding 12, 24, 576 and 1,152 potential isoforms. Mutant animals with deletions encoding 4,752 and 14,256 isoforms were also analysed. Branching phenotypes were assessed in three classes of neurons. Branching patterns improved as the potential number of isoforms increased, and this was independent of the identity of the isoforms. Although branching defects in animals with 1,152 potential isoforms remained substantial, animals with 4,752 isoforms were indistinguishable from wild-type controls. Mathematical modelling studies were consistent with the experimental results that thousands of isoforms are necessary to ensure acquisition of unique Dscam1 identities in many neurons. We conclude that thousands of isoforms are essential to provide neurons with a robust discrimination mechanism to distinguish between self and non-self during self-avoidance. [ABSTRACT FROM AUTHOR]

Background/Aims: Illumina genotyping arrays provide information on DNA copy number. Current methodology for their analysis assumes linkage equilibrium across adjacent markers. This is unrealistic, given the markers high density, and can result in reduced specificity. Another limitation of current methods is that they cannot be directly applied to the analysis of multiple samples with the goal of detecting copy number polymorphisms and their association with traits of interest. Methods: We propose a new Hidden Markov Model for Illumina genotype data, that takes into account linkage disequilibrium between adjacent loci. Our framework also allows for location specific deletion/duplication rates. When multiple samples are available, we describe a methodology for their analysis that simultaneously reconstructs the copy number states in each sample and identifies genomic locations with increased variability in copy number in the population. This approach can be extended to test association between copy number variants and a disease trait. Results and Conclusions: We show that taking into account linkage disequilibrium between adjacent markers can increase the specificity of a HMM in reconstructing copy number variants, especially single copy deletions. Our multisample approach is computationally practical and can increase the power of association studies. Copyright © 2009 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) of longitudinal birth cohorts enable joint investigation of environmental and genetic influences on complex traits. We report GWAS results for nine quantitative metabolic traits (triglycerides, high-density lipoprotein, low-density lipoprotein, glucose, insulin, C-reactive protein, body mass index, and systolic and diastolic blood pressure) in the Northern Finland Birth Cohort 1966 (NFBC1966), drawn from the most genetically isolated Finnish regions. We replicate most previously reported associations for these traits and identify nine new associations, several of which highlight genes with metabolic functions: high-density lipoprotein with NR1H3 (LXRA), low-density lipoprotein with AR and FADS1-FADS2, glucose with MTNR1B, and insulin with PANK1. Two of these new associations emerged after adjustment of results for body mass index. Gene–environment interaction analyses suggested additional associations, which will require validation in larger samples. The currently identified loci, together with quantified environmental exposures, explain little of the trait variation in NFBC1966. The association observed between low-density lipoprotein and an infrequent variant in AR suggests the potential of such a cohort for identifying associations with both common, low-impact and rarer, high-impact quantitative trait loci. [ABSTRACT FROM AUTHOR]

To investigatethe clinical features and naturalhistory of mal de debarquement(MdD). Retrospective casereview with follow-up questionnaireand telephone interviews. University Neurotology Clinic. Patients seen between1980 and 2006 who developed apersistent sensation of rocking orswaying for at least 3 days after exposureto passive motion. Clinical features,diagnostic testing, and questionnaireresponses. Of 64 patients(75 % women) identifiedwith MdD, 34 completed follow-upquestionnaires and interviews in2006. Most patients had normalneurological exams, ENGs andbrain MRIs. The average age of thefirst MdD episode was 39 ± 13years. A total of 206 episodes wereexperienced by 64 patients. Ofthese, 104 episodes (51 %) lasted> 1 month; 18 %, > 1 year; 15 %, > 2years; 12 %, > 4 years, and 11 %, > 5years. Eighteen patients (28 %) subsequentlydeveloped spontaneousepisodes of MdD-like symptomsafter the initial MdD episode.There was a much higher rate ofmigraine in patients who went onto develop spontaneous episodes(73 %) than in those who did not(22 %). Subsequent episodes werelonger than earlier ones in mostpatients who had multiple episodes.Re-exposure to passivemotion temporarily decreasedsymptoms in most patients (66 %).Subjective intolerance to visualmotion increased (10 % to 66 %)but self-motion sensitivity did not(37 % to 50 %) with onset of MdD. The majority of MdDepisodes lasting longer than 3 daysresolve in less than one year but theprobability of resolution declineseach year. Many patients experiencemultiple MdD episodes. Somepatients develop spontaneousepisodes after the initial motiontriggeredepisode with migrainebeing a risk factor. [ABSTRACT FROM AUTHOR]

Affymetrix's SNP (single-nucleotide polymorphism) genotyping chips have increased the scope and decreased the cost of gene-mapping studies. Because each SNP is queried by multiple DNA probes, the chips present interesting challenges in genotype calling. Traditional clustering methods distinguish the three genotypes of an SNP fairly well given a large enough sample of unrelated individuals or a training sample of known genotypes. This article describes our attempt to improve genotype calling by constructing Gaussian mixture models with empirically derived priors. The priors stabilize parameter estimation and borrow information collectively gathered on tens of thousands of SNPs. When data from related family members are available, our models capture the correlations in signals between relatives. With these advantages in mind, we apply the models to Affymetrix probe intensity data on 10,000 SNPs gathered on 63 genotyped individuals spread over eight pedigrees. We integrate the genotype-calling model with pedigree analysis and examine a sequence of symmetry hypotheses involving the correlated probe signals. The symmetry hypotheses raise novel mathematical issues of parameterization. Using the Bayesian information criterion, we select the best combination of symmetry assumptions. Compared to Affymetrix's software, our model leads to a reduction in no-calls with little sacrifice in overall calling accuracy. [ABSTRACT FROM AUTHOR]

Objective: Analyze the information contained in homozygous haplotypes detected with high density genotyping. Methods: We analyze the genotypes of ∼2,500 markers on chr 22 in 12 population samples, each including 200 individuals. We develop a measure of disequilibrium based on haplotype homozygosity and an algorithm to identify genomic segments characterized by non-random homozygosity (NRH), taking into account allele frequencies, missing data, genotyping error, and linkage disequilibrium. Results: We show how our measure of linkage disequilibrium based on homozygosity leads to results comparable to those of R2, as well as the importance of correcting for small sample variation when evaluating D′. We observe that the regions that harbor NRH segments tend to be consistent across populations, are gene rich, and are characterized by lower recombination. Conclusions: It is crucial to take into account LD patterns when interpreting long stretches of homozygous markers. Copyright © 2006 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Gene microarray technology is often used to compare the expression of thousand of genes in two different cell lines. Typically, one does not expect measurable changes in transcription amounts for a large number of genes; furthermore, the noise level of array experiments is rather high in relation to the available number of replicates. For the purpose of statistical analysis, inference on the "population" difference in expression for genes across the two cell lines is often cast in the framework of hypothesis testing, with the null hypothesis being no change in expression. Given that thousands of genes are investigated at the same time, this requires some multiple comparison correction procedure to be in place. We argue that hypothesis testing, with its emphasis on type I error and family analogues, may not address the exploratory nature of most microarray experiments. We instead propose viewing the problem as one of estimation of a vector known to have a large number of zero components. In a Bayesian framework, we describe the prior knowledge on expression changes using mixture priors that incorporate a mass at zero, and we choose a loss function that favors the selection of sparse solutions. We consider two different models applicable to the microarray problem, depending on the nature of replicates available, and show how to explore the posterior distributions of the parameters using MCMC. Simulations show an interesting connection between this Bayesian estimation framework and false discovery rate (FDR) control. Finally, two empirical examples illustrate the practical advantages of this Bayesian estimation paradigm. [ABSTRACT FROM PUBLISHER]

Efforts to identify gene variants associated with susceptibility to common diseases use three approaches: pedigree and affected sib-pair linkage studies and association studies of population samples. The different aims of these study designs reflect their derivation from biological versus epidemiological traditions. Similar principles regarding determination of the evidence levels required to consider the results statistically significant apply to both linkage and association studies, however. Such determination requires explicit attention to the prior probability of particular findings, as well as appropriate correction for multiple comparisons. For most common diseases, increasing the sample size in a study is a crucial step in achieving statistically significant genetic mapping results. Recent studies suggest that the technology and statistical methodology will soon be available to make well-powered studies feasible using any of these approaches. [ABSTRACT FROM AUTHOR]

Bacteria respond to environmental changes through a variety of sensor proteins, which eventually relay the signals to corresponding DNA binding proteins to modulate transcription. The DNA binding transcription regulators, or transcription factors (TFs), typically require posttranscriptional modification or ligand binding to assume an active conformation, which may bind to DNA and either positively or negatively regulate transcription. Here, the activity of a TF is defined as the concentration of its subpopulation capable of DNA binding. The collective activities of TFs can thus be regarded as the physiological state of the cell.

High-dimensional data sets generated by high-throughput technologies, such as DNA microarray, are often the outputs of complex networked systems driven by hidden regulatory signals. Traditional statistical methods for computing low-dimensional or hidden representations of these data sets, such as principal component analysis and independent component analysis, ignore the underlying network structures and provide decompositions based purely on a priori statistical constraints on the computed component signals. The resulting decomposition thus provides a phenomenological model for the observed data and does not necessarily contain physically or biologically meaningful signals. Here, we develop a method, called network component analysis, for uncovering hidden regulatory signals from outputs of networked systems, when only a partial knowledge of the underlying network topology is available. The a priori network structure information is first tested for compliance with a set of identifiability criteria. For networks that satisfy the criteria, the signals from the regulatory nodes and their strengths of influence on each output node can be faithfully reconstructed. This method is first validated experimentally by using the absorbance spectra of a network of various hemoglobin species. The method is then applied to microarray data generated from yeast Saccharamyces cerevisiae and the activities of various transcription factors during cell cycle are reconstructed by using recently discovered connectivity information for the underlying transcriptional regulatory networks. [ABSTRACT FROM AUTHOR]

A principal goal of genetic research is to identify specific genotypes that are associated with human phenotypes. It will soon be possible to conduct genome-wide genotyping on a massive scale. Our current approaches for defining and assaying phenotypes may be inadequate for making optimal use of such genotypic data. We propose an international effort to create phenomic databases, that is, comprehensive assemblages of systematically collected phenotypic information, and to develop new approaches for analyzing such phenotypic data. We term this effort the Human Phenome Project and suggest a scientific and organizational scope for the project. [ABSTRACT FROM AUTHOR]

Introduces the volume tests within a regression context. Evaluation of linkage disequilibrium between markers; Ways of measuring bivariate distribution; Use of geometrical properties to test statistical hypothesis.

Although Monte Carlo methods have frequently been applied with success, indiscriminate use of Markov chain Monte Carlo leads to unsatisfactory performances in numerous applications. We present a generalised version of the Gibbs sampler that is based on conditional moves along the traces of groups of transformations in the sample space. We explore its connection with the multigrid Monte Carlo method and its use in designing more efficient samplers. The generalised Gibbs sampler provides a framework encompassing a class of recently proposed tricks such as parameter expansion and reparameterisation. To illustrate, we apply this new method to Bayesian inference problems for nonlinear state-space models, ordinal data and stochastic differential equations with discrete observations. [ABSTRACT FROM PUBLISHER]

The article evaluates the genome-wide-association (GWA) studies of common diseases which aim to examine the variation in human genome. It states that GWA studies rely on linkage disequilibrium that enables single nucleotide polymorphism (SNP) to act as marker for association to other genetic variants. It stresses that GWA studies provides information not only about the new genes but also clarifies how human evolution made the diseases that are common today.

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]

This article discusses the article "Likelihood-Based Inference on Haplotype Effects in Genetic Association Studies," by D. Y. Lin and D. Zeng. It provides more genetical background to support Lin and Zeng's work which focuses on the estimation of haplotype effects. Modifications in DNA sequences can predispose carriers to develop diseases. The modifications have may have mutations, insertions or deletions in the gene sequence that can lead to a different protein to be synthesized. The variations may affect slicing patterns or expression levels by taking place in noncoding portions of DNA.

Presents the author's comments on the paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," by Jianqing Fan, Heng Peng and Tao Huang, published in the September 2005 issue of the "Journal of the American Statistical Association." Appreciation of the authors for their significant contribution in enlarging the bag of statistical tools available for normalization; Opinion on the normalization strategy described; Importance of the iterative procedure suggested; Views on the focus of the paper from a methodological standpoint; Contexts in which the methodology described may be applicable.

The article discusses the study "Distribution and Dynamics of Lamp1-Containing Endocytic Organelles in Fibroblasts Deficient in BLOC-3," by Ramin Nazarian, Chiara Sabatti et al., published in the "Journal of Cell Science." The researchers developed a method based on image analysis to estimate the extent of organelle clustering in the perinuclear region of cultured cells. The findings suggest that BLOC-3 function is required, directly or indirectly, for optimal attachment of late endocytic organelles to microtubule-dependent motors.

We describe a unique family in which several individual are affected with episodes of ataxia that best fit the phenotype of episodic ataxia type 2 (EA2). All of the affected family members had episodes typically lasting for several hours, and none of them had muscle abnormalities including myokymia. Episodic ataxia type 1 (EA1) was not considered initially as a clinical diagnosis for the affected individuals in this family. However, by linkage mapping, sequencing and polymorphism analysis, all affecteds were found to have a novel mutation in KCNA1. Numerous missense mutations have been described previously in KCNA1 that cause EA1. The mutation c.1025G>T replaces a highly conserved serine with isoleucine at position 342 (p.Ser342Ile) in the highly conserved fifth transmembrane domain of the KCNA1. This mutation leads to a distinct clinical phenotype without myokymia broadening the scope of clinical characteristics of EA1 and highlighting the heterogeneity of phenotypic effects from distinct missense mutations. © 2004 Wiley-Liss, Inc. [ABSTRACT FROM AUTHOR]