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Coexistent migraine affects relevant clinical features of patients with Ménière's disease (MD).Epidemiological studies have shown an association between migraine and MD. We sought to determine whether the coexistence of migraine affects any clinical features in patients with MD.In this retrospective case-control study of University Neurotology Clinic patients, 50 patients meeting 1995 AAO-HNS criteria for definite MD were compared to 18 patients meeting the same criteria in addition to the 2004 IHS criteria for migraine (MMD). All had typical low frequency sensorineural hearing loss and episodes of rotational vertigo. Outcome measures included: sex, age of onset of episodic vertigo or fluctuating hearing loss, laterality of hearing loss, aural symptoms, caloric responses, severity of hearing loss, and family history of migraine, episodic vertigo or hearing loss.Age of onset of episodic vertigo or fluctuating hearing loss was significantly lower in patients with MMD (mean +/- 1.96*SE = 37.2 +/- 6.3 years) than in those with MD (mean +/- 1.96*SE = 49.3 +/- 4.4 years). Concurrent bilateral aural symptoms and hearing loss were seen in 56% of MMD and 4% of MD patients. A family history of episodic vertigo was seen in 39% of MMD and 2% of MD patients.

Abnormalities in sleep and circadian rhythms are central features of bipolar disorder (BP), often persisting between episodes. We report here, to our knowledge, the first systematic analysis of circadian rhythm activity in pedigrees segregating severe BP (BP-I). By analyzing actigraphy data obtained from members of 26 Costa Rican and Colombian pedigrees [136 euthymic (i.e., interepisode) BP-I individuals and 422 non-BP-I relatives], we delineated 73 phenotypes, of which 49 demonstrated significant heritability and 13 showed significant trait-like association with BP-I. All BP-I-associated traits related to activity level, with BP-I individuals consistently demonstrating lower activity levels than their non-BP-I relatives. We analyzed all 49 heritable phenotypes using genetic linkage analysis, with special emphasis on phenotypes judged to have the strongest impact on the biology underlying BP. We identified a locus for interdaily stability of activity, at a threshold exceeding genome-wide significance, on chromosome 12pter, a region that also showed pleiotropic linkage to two additional activity phenotypes. National Institute of Health/[R01MH075007]/NIH/Estados Unidos National Institute of Health/[R01MH095454]/NIH/Estados Unidos National Institute of Health/[P30NS062691]/NIH/Estados Unidos National Institute of Health/[T32MH073526]/NIH/Estados Unidos National Institute of Health/[K23MH074644-01]/NIH/Estados Unidos National Institute of Health/[K08MH086786]/NIH/Estados Unidos UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biología Celular y Molecular (CIBCM)

We consider resequencing studies of associated loci and the problem of prioritizing sequence variants for functional follow-up. Working within the multivariate linear regression framework helps us to account for correlation across variants, and adopting a Bayesian approach naturally leads to posterior probabilities that incorporate all information about the variants' function. We describe two novel prior distributions that facilitate learning the role of each variant by borrowing evidence across phenotypes and across mutations in the same gene. We illustrate their potential advantages with simulations and re-analyzing a dataset of sequencing variants., Published at http://www.genetics.org/content/202/2/439 in GENETICS (http://www.genetics.org)

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 a wide range of BP-related 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.

Summary: Commonly used multiplicity adjustments fail to control the error rate for reported findings in many expression quantitative trait loci (eQTL) studies. TreeQTL implements a hierarchical multiple testing procedure which allows control of appropriate error rates defined relative to a grouping of the eQTL hypotheses. Availability and Implementation: The R package TreeQTL is available for download at http://bioinformatics.org/treeqtl. Contact: sabatti@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Recent theories regarding the pathophysiology of bipolar disorder suggest contributions of both neurodevelopmental and neurodegenerative processes. While structural neuroimaging studies indicate disease-associated neuroanatomical alterations, the behavioural correlates of these alterations have not been well characterized. Here, we investigated multi-generational families genetically enriched for bipolar disorder to: (i) characterize neurobehavioural correlates of neuroanatomical measures implicated in the pathophysiology of bipolar disorder; (ii) identify brain–behaviour associations that differ between diagnostic groups; (iii) identify neurocognitive traits that show evidence of accelerated ageing specifically in subjects with bipolar disorder; and (iv) identify brain–behaviour correlations that differ across the age span. Structural neuroimages and multi-dimensional assessments of temperament and neurocognition were acquired from 527 (153 bipolar disorder and 374 non-bipolar disorder) adults aged 18–87 years in 26 families with heavy genetic loading for bipolar disorder. We used linear regression models to identify significant brain–behaviour associations and test whether brain–behaviour relationships differed: (i) between diagnostic groups; and (ii) as a function of age. We found that total cortical and ventricular volume had the greatest number of significant behavioural associations, and included correlations with measures from multiple cognitive domains, particularly declarative and working memory and executive function. Cortical thickness measures, in contrast, showed more specific associations with declarative memory, letter fluency and processing speed tasks. While the majority of brain–behaviour relationships were similar across diagnostic groups, increased cortical thickness in ventrolateral prefrontal and parietal cortical regions was associated with better declarative memory only in bipolar disorder subjects, and not in non-bipolar disorder family members. Additionally, while age had a relatively strong impact on all neurocognitive traits, the effects of age on cognition did not differ between diagnostic groups. Most brain–behaviour associations were also similar across the age range, with the exception of cortical and ventricular volume and lingual gyrus thickness, which showed weak correlations with verbal fluency and inhibitory control at younger ages that increased in magnitude in older subjects, regardless of diagnosis. Findings indicate that neuroanatomical traits potentially impacted by bipolar disorder are significantly associated with multiple neurobehavioural domains. Structure–function relationships are generally preserved across diagnostic groups, with the notable exception of ventrolateral prefrontal and parietal association cortex, volumetric increases in which may be associated with cognitive resilience specifically in individuals with bipolar disorder. Although age impacted all neurobehavioural traits, we did not find any evidence of accelerated cognitive decline specific to bipolar disorder subjects. Regardless of diagnosis, greater global brain volume may represent a protective factor for the effects of ageing on executive functioning. National Institute of Health/[R01MH075007]/NIH/Estados Unidos National Institute of Health/[R01MH095454]/NIH/Estados Unidos National Institute of Health/[P30NS062691]/NIH/Estados Unidos National Institute of Health/[K23MH074644-01]/NIH/Estados Unidos National Institute of Health/[R01HG006695]/NIH/Estados Unidos National Institute of Health/[K08MH086786]/NIH/Estados Unidos UCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigación en Biología Celular y Molecular (CIBCM)

We performed a whole genome microsatellite marker scan in six multiplex families with bipolar (BP) mood disorder ascertained in Antioquia, a historically isolated population from North West Colombia. These families were characterized clinically using the approach employed in independent ongoing studies of BP in the closely related population of the Central Valley of Costa Rica. The most consistent linkage results from parametric and non-parametric analyses of the Colombian scan involved markers on 5q31-33, a region implicated by the previous studies of BP in Costa Rica. Because of these concordant results, a follow-up study with additional markers was undertaken in an expanded set of Colombian and Costa Rican families; this provided a genome-wide significant evidence of linkage of BPI to a candidate region of approximately 10 cM in 5q31-33 (maximum non-parametric linkage score=4.395, P0.00004). Interestingly, this region has been implicated in several previous genetic studies of schizophrenia and psychosis, including disease association with variants of the enthoprotin and gamma-aminobutyric acid receptor genes.

We have ascertained in the Central Valley of Costa Rica a new kindred (CR201) segregating for severe bipolar disorder (BP-I). The family was identified by tracing genealogical connections among eight persons initially independently ascertained for a genome wide association study of BP-I. For the genome screen in CR201, we trimmed the family down to 168 persons (82 of whom are genotyped), containing 25 individuals with a best-estimate diagnosis of BP-I. A total of 4,690 SNP markers were genotyped. Analysis of the data was hampered by the size and complexity of the pedigree, which prohibited using exact multipoint methods on the entire kindred. Two-point parametric linkage analysis, using a conservative model of transmission, produced a maximum LOD score of 2.78 on chromosome 6, and a total of 39 loci with LOD scores >1.0. Multipoint parametric and non-parametric linkage analysis was performed separately on four sections of CR201, and interesting (nominal P-value from either analysis

Benign recurrent vertigo (BRV) is a common disorder affecting up to 2% of the adult population and may be etiologically related to migraine because of similarities in the clinical spectrum of the phenotypes and a high co-morbidity within families. Many families have multiple-affected genetically related individuals suggesting familial transmission of the disorder with moderate to high penetrance. While clinically similar to episodic ataxias, there are currently no genes identified that contribute to BRV and no systematic linkage studies performed. In an initial effort to genetically define BRV, we have selected from our Neurology Clinic population a subset of 20 multigenerational families with apparent autosomal dominant transmission, and performed genetic linkage mapping using both parametric and non-parametric linkage (NPL) approaches. The Affymetrix 10K SNP Mapping Assay was used for the genotyping. Heterogeneity LOD (HLOD) analysis reveals the evidence of genetic heterogeneity for BRV and evidence of linkage in a subset of the families to 22q12 (HLOD = 4.02). An additional region was identified by NPL analysis at 5p15 (LOD = 2.63). As migraine is observed substantially more commonly both within the BRV-affected individuals and the related family members, it is possible that a form of migraine is allelic to the BRV locus at 22q12. However, testing linkage or the chromosome 22q12 region to a broader migraine/vertigo phenotype by defining affectation status as either migrainous headaches or BRV greatly weakened the linkage signal, and no significant other peaks were detected. Thus, BRV and migraine does not appear to be allelic disorders within these families. We conclude that BRV is a heterogeneous genetic disorder, appears genetically distinct from migraine with aura and is linked to 22q12. Additional family and population-based linkage and association studies will be needed to determine the causative alleles.

Late endosomes and lysosomes of mammalian cells in interphase tend to concentrate in the perinuclear region that harbors the microtubule-organizing center. We have previously reported abnormal distribution of these organelles – as judged by reduced percentages of cells displaying pronounced perinuclear accumulation – in mutant fibroblasts lacking BLOC-3 (for `biogenesis of lysosome-related organelles complex 3'). BLOC-3 is a protein complex that contains the products of the genes mutated in Hermansky-Pudlak syndrome types 1 and 4. Here, we developed a method based on image analysis to estimate the extent of organelle clustering in the perinuclear region of cultured cells. Using this method, we corroborated that the perinuclear clustering of late endocytic organelles containing Lamp1 (for `lysosome-associated membrane protein 1') is reduced in BLOC-3-deficient murine fibroblasts, and found that it is apparently normal in fibroblasts deficient in BLOC-1 or BLOC-2, which are another two protein complexes associated with Hermansky-Pudlak syndrome. Wild-type and mutant fibroblasts were transfected to express human LAMP1 fused at its cytoplasmic tail to green fluorescence protein (GFP). At low expression levels, LAMP1-GFP was targeted correctly to late endocytic organelles in both wild-type and mutant cells. High levels of LAMP1-GFP overexpression elicited aberrant aggregation of late endocytic organelles, a phenomenon that probably involved formation of anti-parallel dimers of LAMP1-GFP as it was not observed in cells expressing comparable levels of a non-dimerizing mutant variant, LAMP1-mGFP. To test whether BLOC-3 plays a role in the movement of late endocytic organelles, time-lapse fluorescence microscopy experiments were performed using live cells expressing low levels of LAMP1-GFP or LAMP1-mGFP. Although active movement of late endocytic organelles was observed in both wild-type and mutant fibroblasts, quantitative analyses revealed a relatively lower frequency of microtubule-dependent movement events, either towards or away from the perinuclear region, within BLOC-3-deficient cells. By contrast, neither the duration nor the speed of these microtubule-dependent events seemed to be affected by the lack of BLOC-3 function. These results suggest that BLOC-3 function is required, directly or indirectly, for optimal attachment of late endocytic organelles to microtubule-dependent motors.

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.

Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, non-overlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements.

The genetic programs underlying neural stem cell (NSC) proliferation and pluripotentiality have only been partially elucidated. We compared the gene expression profile of proliferating neural stem cell cultures (NS) with cultures differentiated for 24 h (DC) to identify functionally coordinated alterations in gene expression associated with neural progenitor proliferation. The majority of differentially expressed genes (65%) were upregulated in NS relative to DC. Microarray analysis of this in vitro system was followed by high throughput screening in situ hybridization to identify genes enriched in the germinal neuroepithelium, so as to distinguish those expressed in neural progenitors from those expressed in more differentiated cells in vivo. NS cultures were characterized by the coordinate upregulation of genes involved in cell cycle progression, DNA synthesis, and metabolism, not simply related to general features of cell proliferation, since many of the genes identified were highly enriched in the CNS ventricular zones and not widely expressed in other proliferating tissues. Components of specific metabolic and signal transduction pathways, and several transcription factors, including Sox3, FoxM1, and PTTG1, were also enriched in neural progenitor cultures. We propose a putative network of gene expression linking cell cycle control to cell fate pathways, providing a framework for further investigations of neural stem cell proliferation and differentiation.

We explore the implications of the false discovery rate (FDR) controlling procedure in disease gene mapping. With the aid of simulations, we show how, under models commonly used, the simple step-down procedure introduced by Benjamini and Hochberg controls the FDR for the dependent tests on which linkage and association genome screens are based. This adaptive multiple comparison procedure may offer an important tool for mapping susceptibility genes for complex diseases.

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.

Objective: Obsessive-compulsive disorder (OCD) and Tourette's syndrome are highly heritable neurodevelopmental disorders that are thought to share genetic risk factors. However, the identification of definitive susceptibility genes for these etiologically complex disorders remains elusive. The authors report a combined genome-wide association study (GWAS) of Tourette's syndrome and OCD.Method: The authors conducted a GWAS in 2,723 cases (1,310 with OCD, 834 with Tourette's syndrome, 579 with OCD plus Tourette's syndrome/chronic tics), 5,667 ancestry-matched controls, and 290 OCD parent-child trios. GWAS summary statistics were examined for enrichment of functional variants associated with gene expression levels in brain regions. Polygenic score analyses were conducted to investigate the genetic architecture within and across the two disorders.Results: Although no individual single-nucleotide polymorphisms (SNPs) achieved genome-wide significance, the GWAS signals were enriched for SNPs strongly associated with variations in brain gene expression levels (expression quantitative loci, or eQTLs), suggesting the presence of true functional variants that contribute to risk of these disorders Polygenic score analyses identified a significant polygenic component for OCD (p=2x10(-4)), predicting 3.2% of the phenotypic variance in an independent data set. In contrast, Tourette's syndrome had a smaller, nonsignificant polygenic component, predicting only 0.6% of the phenotypic variance (p=0.06). No significant polygenic signal was detected across the two disorders, although the sample is likely underpowered to detect a modest shared signal. Furthermore, the OCD polygenic signal was significantly attenuated when cases with both OCD and co-occurring Tourette's syndrome/chronic tics were included in the analysis (p=0.01).Conclusions: Previous work has shown that Tourette's syndrome and OCD have some degree of shared genetic variation. However, the data from this study suggest that there are also distinct components to the genetic architectures of these two disorders. Furthermore, OCD with co-occurring burette's syndrome/chronic tics may have different underlying genetic susceptibility compared with OCD alone.

This paper surveys and extends models and algorithms for identifying binding sites in noncoding regions of DNA. Binding sites control the transcription of genes into messenger RNA in preparation for translation into proteins. The base sequence of most binding sites is not entirely fixed, with the different permitted spellings collectively constituting a "motif." After summarizing the underlying biological issues, we review three different models for binding site identification. Each model was developed with a different type of dataset as reference. We then present a unified model that borrows from the previous ones and integrates their main features. In our unified model, one can identify motifs and their unknown positions along a sequence. One can also fit the model to data using maximum likelihood and maximum a posteriori algorithms. These algorithms rely on recursive formulas and the maximization/minorization principle. Finally, we conclude with a prospectus of future data analyses and theoretical research.

Horizontal gaze palsy with progressive scoliosis (HGPS) is a rare, autosomal recessive disorder characterized by a congenital absence of conjugate horizontal eye movement, with progressive scoliosis developing in childhood or adolescence. The authors identified two unrelated consanguineous families with HGPS. Genomewide homozygosity mapping and linkage analysis mapped the disease locus to a 30-cM interval on chromosome 11q23-25 (combined maximum multipoint lod score Z = 5.46).

Volume tests in a regression context were introduced by Hotelling in 1939. The concept was revisited by Diaconis and Efron in 1985, this time in the case of contingency tables to refine the power of explanation of the χ2 test. This article considers volume tests as a measure of dependency for tables with fixed margins. Building on earlier contributions, this article suggests the use of the volume test statistic as a measure of dependence to be applied, for example, in the evaluation of linkage disequilibrium between markers.

We illustrate how homozygosity of haplotypes can be used to measure the level of disequilibrium between two or more markers. An excess of either homozygosity or heterozygosity signals a departure from the gametic phase equilibrium: We describe the specific form of dependence that is associated with high (low) homozygosity and derive various linkage disequilibrium measures. They feature a clear biological interpretation, can be used to construct tests, and are standardized to allow comparison across loci and populations. They are particularly advantageous to measure linkage disequilibrium between highly polymorphic markers.

Compared to mixed populations, population isolates such as Finland show distinct differences in the prevalence of disease mutations. However, little information exists of the differences on the prevalence of different disease alleles in regional populations with different history of multiple bottlenecks. We constructed a DNA-array and monitored the prevalence of 31 rare and common disease mutations underlying 27 clinical phenotypes in a large population-based study sample. Over 64 000 genotypes were assigned in 2151 samples from four geographical areas representing early and late settlement regions of Finland. Each sample was analyzed in duplicate and a total of 142 000 array-derived genotyping calls were made. On average one in three individuals was found to be a carrier of one of the 31 monitored mutations. This should remove fears of the stigmatizing effect of a carrier-screening program monitoring multiple diseases. Regional differences were found in the prevalence of mutations, providing molecular evidence for the deviating population histories of regional subisolates. The mutations introduced early into the population revealed relatively even distribution in different subregions. More recently introduced rare mutations showed local clustering of disease alleles, indicating the persistence of population subisolates and the effect of multiple bottlenecks in molding the population gene pool. Regional differences were observed also for common disease alleles. Such precise information of the carrier frequencies could form the basis for targeted genetic screens in this population. Our approach describes a general paradigm for large-scale carrier-screening programs also in other populations.

SUMMARY 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.

ImportanceGenetic factors contribute to risk for bipolar disorder (BP), but its pathogenesis remains poorly understood. A focus on measuring multisystem quantitative traits that may be components of BP psychopathology may enable genetic dissection of this complex disorder, and investigation of extended pedigrees from genetically isolated populations may facilitate the detection of specific genetic variants that affect BP as well as its component phenotypes.ObjectiveTo identify quantitative neurocognitive, temperament-related, and neuroanatomical phenotypes that appear heritable and associated with severe BP (bipolar I disorder [BP-I]) and therefore suitable for genetic linkage and association studies aimed at identifying variants contributing to BP-I risk.Design, setting, and participantsMultigenerational pedigree study in 2 closely related, genetically isolated populations: the Central Valley of Costa Rica and Antioquia, Colombia. A total of 738 individuals, all from Central Valley of Costa Rica and Antioquia pedigrees, participated; among them, 181 have BP-I.Main outcomes and measuresFamilial aggregation (heritability) and association with BP-I of 169 quantitative neurocognitive, temperament, magnetic resonance imaging, and diffusion tensor imaging phenotypes.ResultsOf 169 phenotypes investigated, 126 (75%) were significantly heritable and 53 (31%) were associated with BP-I. About one-quarter of the phenotypes, including measures from each phenotype domain, were both heritable and associated with BP-I. Neuroimaging phenotypes, particularly cortical thickness in prefrontal and temporal regions as well as volume and microstructural integrity of the corpus callosum, represented the most promising candidate traits for genetic mapping related to BP based on strong heritability and association with disease. Analyses of phenotypic and genetic covariation identified substantial correlations among the traits, at least some of which share a common underlying genetic architecture.Conclusions and relevanceTo our knowledge, this is the most extensive investigation of BP-relevant component phenotypes to date. Our results identify brain and behavioral quantitative traits that appear to be genetically influenced and show a pattern of BP-I association within families that is consistent with expectations from case-control studies. Together, these phenotypes provide a basis for identifying loci contributing to BP-I risk and for genetic dissection of the disorder.

We introduce a new estimator for the vector of coefficients $\beta$ in the linear model $y=X\beta+z$, where $X$ has dimensions $n\times p$ with $p$ possibly larger than $n$. SLOPE, short for Sorted L-One Penalized Estimation, is the solution to \[\min_{b\in\mathbb{R}^p}\frac{1}{2}\Vert y-Xb\Vert _{\ell_2}^2+\lambda_1\vert b\vert _{(1)}+\lambda_2\vert b\vert_{(2)}+\cdots+\lambda_p\vert b\vert_{(p)},\] where $\lambda_1\ge\lambda_2\ge\cdots\ge\lambda_p\ge0$ and $\vert b\vert_{(1)}\ge\vert b\vert_{(2)}\ge\cdots\ge\vert b\vert_{(p)}$ are the decreasing absolute values of the entries of $b$. This is a convex program and we demonstrate a solution algorithm whose computational complexity is roughly comparable to that of classical $\ell_1$ procedures such as the Lasso. Here, the regularizer is a sorted $\ell_1$ norm, which penalizes the regression coefficients according to their rank: the higher the rank - that is, stronger the signal - the larger the penalty. This is similar to the Benjamini and Hochberg [J. Roy. Statist. Soc. Ser. B 57 (1995) 289-300] procedure (BH) which compares more significant $p$-values with more stringent thresholds. One notable choice of the sequence $\{\lambda_i\}$ is given by the BH critical values $\lambda_{\mathrm {BH}}(i)=z(1-i\cdot q/2p)$, where $q\in(0,1)$ and $z(\alpha)$ is the quantile of a standard normal distribution. SLOPE aims to provide finite sample guarantees on the selected model; of special interest is the false discovery rate (FDR), defined as the expected proportion of irrelevant regressors among all selected predictors. Under orthogonal designs, SLOPE with $\lambda_{\mathrm{BH}}$ provably controls FDR at level $q$. Moreover, it also appears to have appreciable inferential properties under more general designs $X$ while having substantial power, as demonstrated in a series of experiments running on both simulated and real data., Comment: Published at http://dx.doi.org/10.1214/15-AOAS842 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF, Author Summary Abnormal serum levels of various metabolites, including measures relevant to cholesterol, other fats, and sugars, are known to be risk factors for cardiovascular disease and type 2 diabetes. Identification of the genes that play a role in generating such abnormalities could advance the development of new treatment and prevention strategies for these disorders. Investigations of common genetic variants carried out in large sets of research subjects have successfully pinpointed such genes within many regions of the human genome. However, these studies often have not led to the identification of the specific genetic variations affecting metabolic traits. To attempt to detect such causal variations, we sequenced genes in 17 genomic regions implicated in metabolic traits in >6,000 people from Finland. By conducting statistical analyses relating specific variations (individually and grouped by gene) to the measures for these metabolic traits observed in the study subjects, we added to our understanding of how genotypes affect these traits. Our findings support a long-held hypothesis that the unique history of the Finnish population provides important advantages for analyzing the relationship between genetic variations and biomedically important traits.

Item does not contain fulltext 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.

Temperament has a strongly heritable component, yet multiple independent genome-wide studies have failed to identify significant genetic associations. We have assembled the largest sample to date of persons with genome-wide genotype data, who have been assessed with Cloninger's Temperament and Character Inventory. Sum scores for novelty seeking, harm avoidance, reward dependence and persistence have been measured in over 11 000 persons collected in four different cohorts. Our study had >80% power to identify genome-wide significant loci (P

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 both false discovery rate (FDR) control, and misclassification minimizing pro- cedures. Finally, two empirical examples illustrate the practical advantages of this Bayesian estimation paradigm

We propose a dictionary model for haplotypes. According to the model, a haplotype is con- structed by randomly concatenating haplotype segments from a given dictionary of segments. A haplotype block is defined as a set of haplotype segments that begin and end with the same pair of markers. In this framework, haplotype blocks can overlap, and the model provides a setting for testing the accuracy of simpler models invoking only nonoverlapping blocks. Each haplotype segment in a dictionary has an assigned probability and alternate spellings that ac- count for genotyping errors and mutation. The model also allows for missing data, unphased genotypes, and prior distribution of parameters. Likelihood evaluations rely on forward and backward recurrences similar to the ones encountered in hidden Markov models. Parameter estimation is carried out with an EM algorithm. The search for the optimal dictionary is a particularly difficult because of the variable dimension of the model space. We define a mini- mum description length criteria to evaluate each dictionary and use a combination of greedy search and careful initialization to select a best dictionary for a given data set. Application of the model to simulated data gives encouraging results. In a real data set, we are able to reconstruct a parsimonious dictionary that captures patterns of linkage disequilibrium well.

In many organisms the expression levels of each gene are controlled by the activation levels of known "Transcription Factors" (TF). A problem of considerable interest is that of estimating the "Transcription Regulation Networks" (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses $L_1$ penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates., Published in at http://dx.doi.org/10.1214/10-AOAS350 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Previous studies have implicated DTNBP1 as a schizophrenia susceptibility gene and its encoded protein, dysbindin, as a potential regulator of synaptic vesicle physiology. In this study, we found that endogenous levels of the dysbindin protein in the mouse brain are developmentally regulated, with higher levels observed during embryonic and early postnatal ages than in young adulthood. We obtained biochemical evidence indicating that the bulk of dysbindin from brain exists as a stable component of biogenesis of lysosome-related organelles complex-1 (BLOC-1), a multi-subunit protein complex involved in intracellular membrane trafficking and organelle biogenesis. Selective biochemical interaction between brain BLOC-1 and a few members of the SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) superfamily of proteins that control membrane fusion, including SNAP-25 and syntaxin 13, was demonstrated. Furthermore, primary hippocampal neurons deficient in BLOC-1 displayed neurite outgrowth defects. Taken together, these observations suggest a novel role for the dysbindin-containing complex, BLOC-1, in neurodevelopment, and provide a framework for considering potential effects of allelic variants in DTNBP1--or in other genes encoding BLOC-1 subunits--in the context of the developmental model of schizophrenia pathogenesis.

Recent advances in genomics have underscored the surprising ubiquity of DNA copy number variation (CNV). Fortunately, modern genotyping platforms also detect CNVs with fairly high reliability. Hidden Markov models and algorithms have played a dominant role in the interpretation of CNV data. Here we explore CNV reconstruction via estimation with a fused-lasso penalty as suggested by Tibshirani and Wang [Biostatistics 9 (2008) 18--29]. We mount a fresh attack on this difficult optimization problem by the following: (a) changing the penalty terms slightly by substituting a smooth approximation to the absolute value function, (b) designing and implementing a new MM (majorization--minimization) algorithm, and (c) applying a fast version of Newton's method to jointly update all model parameters. Together these changes enable us to minimize the fused-lasso criterion in a highly effective way. We also reframe the reconstruction problem in terms of imputation via discrete optimization. This approach is easier and more accurate than parameter estimation because it relies on the fact that only a handful of possible copy number states exist at each SNP. The dynamic programming framework has the added bonus of exploiting information that the current fused-lasso approach ignores. The accuracy of our imputations is comparable to that of hidden Markov models at a substantially lower computational cost., Published in at http://dx.doi.org/10.1214/10-AOAS357 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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.

Contains fulltext : 71232.pdf (Publisher’s version ) (Closed access) Schizophrenia is a severe psychiatric disease with complex etiology, affecting approximately 1% of the general population. Most genetics studies so far have focused on disease association with common genetic variation, such as single-nucleotide polymorphisms (SNPs), but it has recently become apparent that large-scale genomic copy-number variants (CNVs) are involved in disease development as well. To assess the role of rare CNVs in schizophrenia, we screened 54 patients with deficit schizophrenia using Affymetrix's GeneChip 250K SNP arrays. We identified 90 CNVs in total, 77 of which have been reported previously in unaffected control cohorts. Among the genes disrupted by the remaining rare CNVs are MYT1L, CTNND2, NRXN1, and ASTN2, genes that play an important role in neuronal functioning but--except for NRXN1--have not been associated with schizophrenia before. We studied the occurrence of CNVs at these four loci in an additional cohort of 752 patients and 706 normal controls from The Netherlands. We identified eight additional CNVs, of which the four that affect coding sequences were found only in the patient cohort. Our study supports a role for rare CNVs in schizophrenia susceptibility and identifies at least three candidate genes for this complex disorder.

We consider the problem of controlling false discoveries in association studies. We assume that the design of the study is adequate so that the "false discoveries" are potentially only because of random chance, not to confounding or other flaws. Under this premise, we review the statistical framework for hypothesis testing and correction for multiple comparisons. We consider in detail the currently accepted strategies in linkage analysis. We then examine the underlying similarities and differences between linkage and association studies and document some of the most recent methodological developments for association mapping.

To investigate the clinical features and natural history of mal de debarquement (MdD).Retrospective case review with follow-up questionnaire and telephone interviews.University Neurotology Clinic.Patients seen between 1980 and 2006 who developed a persistent sensation of rocking or swaying for at least 3 days after exposure to passive motion.Clinical features,diagnostic testing, and questionnaire responses.Of 64 patients(75% women) identified with MdD, 34 completed follow-up questionnaires and interviews in 2006. Most patients had normal neurological exams, ENGs and brain MRIs. The average age of the first MdD episode was 39+/-13 years. A total of 206 episodes were experienced by 64 patients. Of these, 104 episodes (51%) lasted1 month; 18%,1 year; 15%,2 years; 12%,4 years, and 11%,5 years. Eighteen patients (28%) subsequently developed spontaneous episodes of MdD-like symptoms after the initial MdD episode.There was a much higher rate of migraine in patients who went onto develop spontaneous episodes(73%) than in those who did not(22%). Subsequent episodes were longer than earlier ones in most patients who had multiple episodes.Re-exposure to passive motion temporarily decreased symptoms in most patients (66%).Subjective intolerance to visual motion increased (10% to 66%)but self-motion sensitivity did not(37% to 50%) with onset of MdD.The majority of MdD episodes lasting longer than 3 days resolve in less than one year but the probability of resolution declines each year. Many patients experience multiple MdD episodes. Some patients develop spontaneous episodes after the initial motion-triggered episode with migraine being a risk factor.

Tourette disorder (TD) is a neuropsychiatric disorder with a complex mode of inheritance and is characterized by multiple waxing and waning motor and phonic tics. This article reports the results of the largest genetic linkage study yet undertaken for TD. The sample analyzed includes 238 nuclear families yielding 304 "independent" sibling pairs and 18 separate multigenerational families, for a total of 2,040 individuals. A whole-genome screen with the use of 390 microsatellite markers was completed. Analyses were completed using two diagnostic classifications: (1) only individuals with TD were included as affected and (2) individuals with either TD or chronic-tic (CT) disorder were included as affected. Strong evidence of linkage was observed for a region on chromosome 2p (- logP = 4.42, P = 3.8 × 10-5) in the analyses that included individuals with TD or CT disorder as affected. Results in several other regions also provide moderate evidence (- logP >2.0) of additional susceptibility loci for TD.

A semiblind deconvolution method of analysis for gene expression data was proposed recently in a series of articles appeared in PNAS. We illustrate here how similar goals can be achieved in a Bayesian framework and how necessary information on the presence of binding sites can be obtained with Vocabulon, an algorithm based on a stochastic dictionary model.

This chapter contains sections titled: Introduction Unified Model Algorithms for Likelihood Evaluation Parameter Estimation via Minorization-Maximization Algorithm Examples Discussion and Conclusion References ]]>

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The authors recently introduced a framework, named network component analysis (NCA), for the reconstruction of the dynamics of transcriptional regulators' activities from gene expression assays. The original formulation had certain shortcomings that limited NCA's application to a wide class of network dynamics reconstruction problems, either because of limitations in the sample size or because of the stringent requirements imposed by the set of identifiability conditions. In addition, the performance characteristics of the method for various levels of data noise or in the presence of model inaccuracies were never investigated. In this article, the following aspects of NCA have been addressed, resulting in a set of extensions to the original framework: 1) The sufficient conditions on the a priori connectivity information (required for successful reconstructions via NCA) are made less stringent, allowing easier verification of whether a network topology is identifiable, as well as extending the class of identifiable systems. Such a result is accomplished by introducing a set of identifiability requirements that can be directly tested on the regulatory architecture, rather than on specific instances of the system matrix. 2) The two-stage least square iterative procedure used in NCA is proven to identify stationary points of the likelihood function, under Gaussian noise assumption, thus reinforcing the statistical foundations of the method. 3) A framework for the simultaneous reconstruction of multiple regulatory subnetworks is introduced, thus overcoming one of the critical limitations of the original formulation of the decomposition, for example, occurring for poorly sampled data (typical of microarray experiments). A set of Monte Carlo simulations we conducted with synthetic data suggests that the approach is indeed capable of accurately reconstructing regulatory signals when these are the input of large-scale networks that satisfy the suggested identifiability criteria, even under fairly noisy conditions. The sensitivity of the reconstructed signals to inaccuracies in the hypothesized network topology is also investigated. We demonstrate the feasibility of our approach for the simultaneous reconstruction of multiple regulatory subnetworks from the same data set with a successful application of the technique to gene expression measurements of the bacterium Escherichia coli.

Rare sequence variants may be important in understanding the biology of common diseases, but clearly establishing their association with disease is often difficult. Association studies of such variants are becoming increasingly common as large-scale sequence analysis of candidate genes has become feasible. A recent report suggested SLITRK1 (Slit and Trk-like 1) as a candidate gene for Tourette Syndrome (TS). The statistical evidence for this suggestion came from association analyses of a rare 3'-UTR variant, var321, which was observed in two patients but not observed in more than 2000 controls. We genotyped 307 Costa Rican and 515 Ashkenazi individuals (TS probands and their parents) and observed var321 in five independent Ashkenazi parents, two of whom did not transmit this variant to their affected child. Furthermore, we identified var321 in one subject from an Ashkenazi control sample. Our findings do not support the previously reported association and suggest that var321 is overrepresented among Ashkenazi Jews compared with other populations of European origin. The results further suggest that overrepresentation of rare variants in a specific ethnic group may complicate the interpretation of association analyses of such variants, highlighting the particular importance of precisely matching case and control populations for association analyses of rare variants.