Loss of δ-catenin function in severe autism.

Autism is a multifactorial neurodevelopmental disorder affecting more males than females; consequently, under a multifactorial genetic hypothesis, females are affected only when they cross a higher biological threshold. We hypothesize that deleterious variants at conserved residues are enriched in severely affected patients arising from female-enriched multiplex families with severe disease, enhancing the detection of key autism genes in modest numbers of cases. Here we show the use of this strategy by identifying missense and dosage sequence variants in the gene encoding the adhesive junction-associated δ-catenin protein (CTNND2) in female-enriched multiplex families and demonstrating their loss-of-function effect by functional analyses in zebrafish embryos and cultured hippocampal neurons from wild-type and Ctnnd2 null mouse embryos. Finally, through gene expression and network analyses, we highlight a critical role for CTNND2 in neuronal development and an intimate connection to chromatin biology. Our data contribute to the understanding of the genetic architecture of autism and suggest that genetic analyses of phenotypic extremes, such as female-enriched multiplex families, are of innate value in multifactorial disorders.

An enhancer polymorphism at the cardiomyocyte intercalated disc protein NOS1AP locus is a major regulator of the QT interval.

QT interval variation is assumed to arise from variation in repolarization as evidenced from rare Na- and K-channel mutations in Mendelian QT prolongation syndromes. However, in the general population, common noncoding variants at a chromosome 1q locus are the most common genetic regulators of QT interval variation. In this study, we use multiple human genetic, molecular genetic, and cellular assays to identify a functional variant underlying trait association: a noncoding polymorphism (rs7539120) that maps within an enhancer of NOS1AP and affects cardiac function by increasing NOS1AP transcript expression. We further localized NOS1AP to cardiomyocyte intercalated discs (IDs) and demonstrate that overexpression of NOS1AP in cardiomyocytes leads to altered cellular electrophysiology. We advance the hypothesis that NOS1AP affects cardiac electrical conductance and coupling and thereby regulates the QT interval through propagation defects. As further evidence of an important role for propagation variation affecting QT interval in humans, we show that common polymorphisms mapping near a specific set of 170 genes encoding ID proteins are significantly enriched for association with the QT interval, as compared to genome-wide markers. These results suggest that focused studies of proteins within the cardiomyocyte ID are likely to provide insights into QT prolongation and its associated disorders.

Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations.

Blood pressure (BP) is a heritable, quantitative trait with intraindividual variability and susceptibility to measurement error. Genetic studies of BP generally use single-visit measurements and thus cannot remove variability occurring over months or years. We leveraged the idea that averaging BP measured across time would improve phenotypic accuracy and thereby increase statistical power to detect genetic associations. We studied systolic BP (SBP), diastolic BP (DBP), mean arterial pressure (MAP), and pulse pressure (PP) averaged over multiple years in 46,629 individuals of European ancestry. We identified 39 trait-variant associations across 19 independent loci (p < 5 × 10(-8)); five associations (in four loci) uniquely identified by our LTA analyses included those of SBP and MAP at 2p23 (rs1275988, near KCNK3), DBP at 2q11.2 (rs7599598, in FER1L5), and PP at 6p21 (rs10948071, near CRIP3) and 7p13 (rs2949837, near IGFBP3). Replication analyses conducted in cohorts with single-visit BP data showed positive replication of associations and a nominal association (p < 0.05). We estimated a 20% gain in statistical power with long-term average (LTA) as compared to single-visit BP association studies. Using LTA analysis, we identified genetic loci influencing BP. LTA might be one way of increasing the power of genetic associations for continuous traits in extant samples for other phenotypes that are measured serially over time.

Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.

Blood pressure is a heritable trait influenced by several biological pathways and responsive to environmental stimuli. Over one billion people worldwide have hypertension (≥140 mm Hg systolic blood pressure or ≥90 mm Hg diastolic blood pressure). Even small increments in blood pressure are associated with an increased risk of cardiovascular events. This genome-wide association study of systolic and diastolic blood pressure, which used a multi-stage design in 200,000 individuals of European descent, identified sixteen novel loci: six of these loci contain genes previously known or suspected to regulate blood pressure (GUCY1A3-GUCY1B3, NPR3-C5orf23, ADM, FURIN-FES, GOSR2, GNAS-EDN3); the other ten provide new clues to blood pressure physiology. A genetic risk score based on 29 genome-wide significant variants was associated with hypertension, left ventricular wall thickness, stroke and coronary artery disease, but not kidney disease or kidney function. We also observed associations with blood pressure in East Asian, South Asian and African ancestry individuals. Our findings provide new insights into the genetics and biology of blood pressure, and suggest potential novel therapeutic pathways for cardiovascular disease prevention.

Effects of rare and common blood pressure gene variants on essential hypertension: results from the Family Blood Pressure Program, CLUE, and Atherosclerosis Risk in Communities studies.

RATIONALE: Hypertension affects ≈30% of adults in industrialized countries and is the major risk factor for cardiovascular disease. OBJECTIVE: We sought to study the genetic effect of coding and conserved noncoding variants in syndromic hypertension genes on systolic blood pressure (BP) and diastolic BP to assess their overall impact on essential hypertension. METHODS AND RESULTS: We resequenced 11 genes (AGT, CYP11B1, CYP17A1, HSD11B2, NR3C1, NR3C2, SCNN1A, SCNN1B, SCNN1G, WNK1, and WNK4) in 560 European American (EA) and African American ancestry GenNet participants with extreme systolic BP. We investigated genetic associations of 2535 variants with BP in 19997 EAs and in 6069 African Americans in 3 types of analyses. First, we studied the combined effects of all variants in GenNet. Second, we studied 1000 Genomes imputed polymorphic variants in 9747 EA and 3207 African American Atherosclerosis Risk in Communities subjects. Finally, we genotyped 37 missense and common noncoding variants in 6591 EAs and in 6521 individuals (3659 EA/2862 African American) from the CLUE and Family Blood Pressure Program studies, respectively. None of the variants individually reached significant false-discovery rates ≤0.05 for systolic BP and diastolic BP. However, on pooling all coding and noncoding variants, we identified at least 5 loci (AGT, CYP11B1, NR3C2, SCNN1G, and WNK1) with higher association at evolutionary conserved sites. CONCLUSIONS: Both rare and common variants at these genes affect BP in the general population with modest effects sizes (<0.05 standard deviation units), and much larger sample sizes are required to assess the impact of individual genes. Collectively, conserved noncoding variants affect BP to a greater extent than missense mutations.

Gene expression anti-profiles as a basis for accurate universal cancer signatures.

BACKGROUND: Early screening for cancer is arguably one of the greatest public health advances over the last fifty years. However, many cancer screening tests are invasive (digital rectal exams), expensive (mammograms, imaging) or both (colonoscopies). This has spurred growing interest in developing genomic signatures that can be used for cancer diagnosis and prognosis. However, progress has been slowed by heterogeneity in cancer profiles and the lack of effective computational prediction tools for this type of data. RESULTS: We developed anti-profiles as a first step towards translating experimental findings suggesting that stochastic across-sample hyper-variability in the expression of specific genes is a stable and general property of cancer into predictive and diagnostic signatures. Using single-chip microarray normalization and quality assessment methods, we developed an anti-profile for colon cancer in tissue biopsy samples. To demonstrate the translational potential of our findings, we applied the signature developed in the tissue samples, without any further retraining or normalization, to screen patients for colon cancer based on genomic measurements from peripheral blood in an independent study (AUC of 0.89). This method achieved higher accuracy than the signature underlying commercially available peripheral blood screening tests for colon cancer (AUC of 0.81). We also confirmed the existence of hyper-variable genes across a range of cancer types and found that a significant proportion of tissue-specific genes are hyper-variable in cancer. Based on these observations, we developed a universal cancer anti-profile that accurately distinguishes cancer from normal regardless of tissue type (ten-fold cross-validation AUC > 0.92). CONCLUSIONS: We have introduced anti-profiles as a new approach for developing cancer genomic signatures that specifically takes advantage of gene expression heterogeneity. We have demonstrated that anti-profiles can be successfully applied to develop peripheral-blood based diagnostics for cancer and used anti-profiles to develop a highly accurate universal cancer signature. By using single-chip normalization and quality assessment methods, no further retraining of signatures developed by the anti-profile approach would be required before their application in clinical settings. Our results suggest that anti-profiles may be used to develop inexpensive and non-invasive universal cancer screening tests.

An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data.

BACKGROUND: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal in any given classification application. In addition, for most classification problems, selecting the best performing classification algorithm amongst a number of competing algorithms is a difficult task for various reasons. As for example, the order of performance may depend on the performance measure employed for such a comparison. In this work, we present a novel adaptive ensemble classifier constructed by combining bagging and rank aggregation that is capable of adaptively changing its performance depending on the type of data that is being classified. The attractive feature of the proposed classifier is its multi-objective nature where the classification results can be simultaneously optimized with respect to several performance measures, for example, accuracy, sensitivity and specificity. We also show that our somewhat complex strategy has better predictive performance as judged on test samples than a more naive approach that attempts to directly identify the optimal classifier based on the training data performances of the individual classifiers. RESULTS: We illustrate the proposed method with two simulated and two real-data examples. In all cases, the ensemble classifier performs at the level of the best individual classifier comprising the ensemble or better. CONCLUSIONS: For complex high-dimensional datasets resulting from present day high-throughput experiments, it may be wise to consider a number of classification algorithms combined with dimension reduction techniques rather than a fixed standard algorithm set a priori.

Developmental microRNA expression profiling of murine embryonic orofacial tissue.

BACKGROUND: Orofacial development is a multifaceted process involving precise, spatio-temporal expression of a panoply of genes. MicroRNAs (miRNAs), the largest family of noncoding RNAs involved in gene silencing, represent critical regulators of cell and tissue differentiation. MicroRNA gene expression profiling is an effective means of acquiring novel and valuable information regarding the expression and regulation of genes, under the control of miRNA, involved in mammalian orofacial development. METHODS: To identify differentially expressed miRNAs during mammalian orofacial ontogenesis, miRNA expression profiles from gestation day (GD) -12, -13 and -14 murine orofacial tissue were compared utilizing miRXplore microarrays from Miltenyi Biotech. Quantitative real-time PCR was utilized for validation of gene expression changes. Cluster analysis of the microarray data was conducted with the clValid R package and the UPGMA clustering method. Functional relationships between selected miRNAs were investigated using Ingenuity Pathway Analysis. RESULTS: Expression of over 26% of the 588 murine miRNA genes examined was detected in murine orofacial tissues from GD-12-GD-14. Among these expressed genes, several clusters were seen to be developmentally regulated. Differential expression of miRNAs within such clusters wereshown to target genes encoding proteins involved in cell proliferation, cell adhesion, differentiation, apoptosis and epithelial-mesenchymal transformation, all processes critical for normal orofacial development. CONCLUSIONS: Using miRNA microarray technology, unique gene expression signatures of hundreds of miRNAs in embryonic orofacial tissue were defined. Gene targeting and functional analysis revealed that the expression of numerous protein-encoding genes, crucial to normal orofacial ontogeny, may be regulated by specific miRNAs.

RankAggreg, an R package for weighted rank aggregation.

BACKGROUND: Researchers in the field of bioinformatics often face a challenge of combining several ordered lists in a proper and efficient manner. Rank aggregation techniques offer a general and flexible framework that allows one to objectively perform the necessary aggregation. With the rapid growth of high-throughput genomic and proteomic studies, the potential utility of rank aggregation in the context of meta-analysis becomes even more apparent. One of the major strengths of rank-based aggregation is the ability to combine lists coming from different sources and platforms, for example different microarray chips, which may or may not be directly comparable otherwise. RESULTS: The RankAggreg package provides two methods for combining the ordered lists: the Cross-Entropy method and the Genetic Algorithm. Two examples of rank aggregation using the package are given in the manuscript: one in the context of clustering based on gene expression, and the other one in the context of meta-analysis of prostate cancer microarray experiments. CONCLUSION: The two examples described in the manuscript clearly show the utility of the RankAggreg package in the current bioinformatics context where ordered lists are routinely produced as a result of modern high-throughput technologies.

Reconstruction of genetic association networks from microarray data: a partial least squares approach.

MOTIVATION: Gene association/interaction networks provide vast amounts of information about essential processes inside the cell. A complete picture of gene-gene associations/interactions would open new horizons for biologists, ranging from pure appreciation to successful manipulation of biological pathways for therapeutic purposes. Therefore, identification of important biological complexes whose members (genes and their products proteins) interact with each other is of prime importance. Numerous experimental methods exist but, for the most part, they are costly and labor intensive. Computational techniques, such as the one proposed in this work, provide a quick 'budget' solution that can be used as a screening tool before more expensive techniques are attempted. Here, we introduce a novel computational method based on the partial least squares (PLS) regression technique for reconstruction of genetic networks from microarray data. RESULTS: The proposed PLS method is shown to be an effective screening procedure for the detection of gene-gene interactions from microarray data. Both simulated and real microarray experiments show that the PLS-based approach is superior to its competitors both in terms of performance and applicability. AVAILABILITY: R code is available from the supplementary web-site whose URL is given below.

Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach.

MOTIVATION: Biologists often employ clustering techniques in the explorative phase of microarray data analysis to discover relevant biological groupings. Given the availability of numerous clustering algorithms in the machine-learning literature, an user might want to select one that performs the best for his/her data set or application. While various validation measures have been proposed over the years to judge the quality of clusters produced by a given clustering algorithm including their biological relevance, unfortunately, a given clustering algorithm can perform poorly under one validation measure while outperforming many other algorithms under another validation measure. A manual synthesis of results from multiple validation measures is nearly impossible in practice, especially, when a large number of clustering algorithms are to be compared using several measures. An automated and objective way of reconciling the rankings is needed. RESULTS: Using a Monte Carlo cross-entropy algorithm, we successfully combine the ranks of a set of clustering algorithms under consideration via a weighted aggregation that optimizes a distance criterion. The proposed weighted rank aggregation allows for a far more objective and automated assessment of clustering results than a simple visual inspection. We illustrate our procedure using one simulated as well as three real gene expression data sets from various platforms where we rank a total of eleven clustering algorithms using a combined examination of 10 different validation measures. The aggregate rankings were found for a given number of clusters k and also for an entire range of k. AVAILABILITY: R code for all validation measures and rank aggregation is available from the authors upon request. SUPPLEMENTARY INFORMATION: Supplementary information are available at http://www.somnathdatta.org/Supp/RankCluster/supp.htm.

The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.

To dissect the genetic architecture of blood pressure and assess effects on target organ damage, we analyzed 128,272 SNPs from targeted and genome-wide arrays in 201,529 individuals of European ancestry, and genotypes from an additional 140,886 individuals were used for validation. We identified 66 blood pressure-associated loci, of which 17 were new; 15 harbored multiple distinct association signals. The 66 index SNPs were enriched for cis-regulatory elements, particularly in vascular endothelial cells, consistent with a primary role in blood pressure control through modulation of vascular tone across multiple tissues. The 66 index SNPs combined in a risk score showed comparable effects in 64,421 individuals of non-European descent. The 66-SNP blood pressure risk score was significantly associated with target organ damage in multiple tissues but with minor effects in the kidney. Our findings expand current knowledge of blood pressure-related pathways and highlight tissues beyond the classical renal system in blood pressure regulation.

MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data.

BACKGROUND: MicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. Microarray expression profiling of miRNAs is an effective means of acquiring genome-level information of miRNA activation and inhibition, as well as the potential regulatory role that these genes play within a biological system. As with mRNA expression profiling arrays, miRNA microarrays come in a variety of platforms from numerous manufacturers, and there are a multitude of techniques available for reducing and analyzing these data. RESULTS: In this paper, we present an analysis of a typical two-color miRNA microarray experiment using publicly available packages from R and Bioconductor, the open-source software project for the analysis of genomic data. Covered topics include visualization, normalization, quality checking, differential expression, cluster analysis, miRNA target identification, and gene set enrichment analysis. Many of these tools carry-over from the analysis of mRNA microarrays, but with some notable differences that require special attention. The paper is presented as a "compendium" which, along with the accompanying R package MmPalateMiRNA, contains all of the experimental data and source code to reproduce the analyses contained in the paper. CONCLUSIONS: The compendium presented in this paper will provide investigators with an access point for applying the methods available in R and Bioconductor for analysis of their own miRNA array data.

Meta analysis of Chronic Fatigue Syndrome through integration of clinical, gene expression, SNP and proteomic data.

We start by constructing gene-gene association networks based on about 300 genes whose expression values vary between the groups of CFS patients (plus control). Connected components (modules) from these networks are further inspected for their predictive ability for symptom severity, genotypes of two single nucleotide polymorphisms (SNP) known to be associated with symptom severity, and intensity of the ten most discriminative protein features. We use two different network construction methods and choose the common genes identified in both for added validation. Our analysis identified eleven genes which may play important roles in certain aspects of CFS or related symptoms. In particular, the gene WASF3 (aka WAVE3) possibly regulates brain cytokines involved in the mechanism of fatigue through the p38 MAPK regulatory pathway.

Quantifying and modeling birth order effects in autism.

Autism is a complex genetic disorder with multiple etiologies whose molecular genetic basis is not fully understood. Although a number of rare mutations and dosage abnormalities are specific to autism, these explain no more than 10% of all cases. The high heritability of autism and low recurrence risk suggests multifactorial inheritance from numerous loci but other factors also intervene to modulate risk. In this study, we examine the effect of birth rank on disease risk which is not expected for purely hereditary genetic models. We analyzed the data from three publicly available autism family collections in the USA for potential birth order effects and studied the statistical properties of three tests to show that adequate power to detect these effects exist. We detect statistically significant, yet varying, patterns of birth order effects across these collections. In multiplex families, we identify V-shaped effects where middle births are at high risk; in simplex families, we demonstrate linear effects where risk increases with each additional birth. Moreover, the birth order effect is gender-dependent in the simplex collection. It is currently unknown whether these patterns arise from ascertainment biases or biological factors. Nevertheless, further investigation of parental age-dependent risks yields patterns similar to those observed and could potentially explain part of the increased risk. A search for genes considering these patterns is likely to increase statistical power and uncover novel molecular etiologies.

TGFß-1 and Wnt-3a interact to induce unique gene expression profiles in murine embryonic palate mesenchymal cells.

Development of the secondary palate in mammals is a complex process under the control of numerous growth and differentiation factors that regulate key processes such as cell proliferation, synthesis of extracellular matrix molecules, and epithelial-mesenchymal transdifferentiation. Alterations in any one of these processes either through genetic mutation or environmental insult have the potential to lead to clefts of the secondary palate. Members of the TGFß family of cytokines are crucial mediators of these processes and emerging evidence supports a pivotal role for members of the Wnt family of secreted growth and differentiation factors. Previous work in this laboratory demonstrated cross-talk between the Wnt and TGFß signaling pathways in cultured mouse embryonic palate mesenchymal cells. In the current study we tested the hypothesis that unique gene expression profiles are induced in murine embryonic palate mesenchymal cells as a result of this cross-talk between the TGFß and Wnt signal transduction pathways.

No association between variant N-acetyltransferase genes, cigarette smoking and Prostate Cancer susceptibility among men of African descent.

OBJECTIVE: We evaluated the individual and combination effects of NAT1, NAT2 and tobacco smoking in a case-control study of 219 incident prostate cancer (PCa) cases and 555 disease-free men. METHODS: Allelic discriminations for 15 NAT1 and NAT2 loci were detected in germ-line DNA samples using TaqMan polymerase chain reaction (PCR) assays. Single gene, gene-gene and gene-smoking interactions were analyzed using logistic regression models and multi-factor dimensionality reduction (MDR) adjusted for age and subpopulation stratification. MDR involves a rigorous algorithm that has ample statistical power to assess and visualize gene-gene and gene-environment interactions using relatively small samples sizes (i.e., 200 cases and 200 controls). RESULTS: Despite the relatively high prevalence of NAT1*10/*10 (40.1%), NAT2 slow (30.6%), and NAT2 very slow acetylator genotypes (10.1%) among our study participants, these putative risk factors did not individually or jointly increase PCa risk among all subjects or a subset analysis restricted to tobacco smokers. CONCLUSION: Our data do not support the use of N-acetyltransferase genetic susceptibilities as PCa risk factors among men of African descent; however, subsequent studies in larger sample populations are needed to confirm this finding.

Feature selection and machine learning with mass spectrometry data.

Mass spectrometry has been used in biochemical research for a long time. However, its potential for discovering proteomic biomarkers using protein mass spectra has aroused tremendous interest in the last few years. In spite of its potential for biomarker discovery, it is recognized that the identification of meaningful proteomic features from mass spectra needs careful evaluation. Hence, extracting meaningful features and discriminating the samples based on these features are still open areas of research. Several research groups are actively involved in making the process as perfect as possible. In this chapter, we provide a review of major contributions toward feature selection and classification of proteomic mass spectra involving MALDI-TOF and SELDI-TOF technology.

Epidemiologic associations of carpal tunnel syndrome and sleep position: Is there a case for causation?

INTRODUCTION: Carpal tunnel syndrome (CTS) is thought to be due to compression of the median nerve in the carpal tunnel. It is known that carpal tunnel pressures are elevated in wrist postures of flexion and extension and in those patients with CTS. Classic symptoms of CTS include night waking with pain, tingling, and numbness. These classic symptoms stimulated our interest in the relationship of sleep to the development of CTS. METHOD: We reviewed the literature surrounding the epidemiology of CTS and the literature regarding sleep disturbances. Through careful distillation of these studies and a process of reasoning, we have developed a hypothesis for a causal mechanism of CTS. RESULTS: Epidemiologically, it has been shown that CTS is associated with age, gender, increased body mass index (BMI), diabetes, pregnancy, and is more common in some populations. The same associations noted above for CTS are strongly associated with sleep disturbances. Sleep disturbances due to age, gender, BMI, pregnancy, and population variations are all associated with sleeping in the lateral position supporting the hypothesis that a common causative mechanism of CTS is sleeping in a lateral position. DISCUSSION: We believe that the epidemiologic associations with CTS act through a common causative mechanism, increased sleeping in the lateral position which puts the wrist at increased risk of flexion or extension, compressing the median nerve in the carpal tunnel. This hypothesis is simple and explains the connection between a previously unconnected group of epidemiologic associations. This realization has real clinical significance in that it focuses our attention on the early disorder when it is completely reversible. It clarifies previously confused clinical circumstances, creates research questions that can be tested, and it invites us to change our clinical perspective in this most common form of nerve compression.