Identifying causal rare variants of disease through family-based analysis of Genetics Analysis Workshop 17 data set

Linkage- and association-based methods have been proposed for mapping disease-causing rare variants. Based on the family information provided in the Genetic Analysis Workshop 17 data set, we formulate a two-pronged approach that combines both methods. Using the identity-by-descent information provided for eight extended pedigrees (n = 697) and the simulated quantitative trait Q1, we explore various traditional nonparametric linkage analysis methods; the best result is obtained by assuming between-family heterogeneity and applying the Haseman-Elston regression to each pedigree separately. We discover strong signals from two genes in two different families and weaker signals for a third gene from two other families. As an exploratory approach, we apply an association test based on a modified family-based association test statistic to all rare variants (frequency < 1% or < 3%) designated as causal for Q1. Family-based association tests correctly identified causal single-nucleotide polymorphisms for four genes (KDR, VEGFA, VEGFC, and FLT1). Our results suggest that both linkage and association tests with families show promise for identifying rare variants

Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes

<p>Abstract</p> <p>Background</p> <p>Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.</p> <p>Results</p> <p>Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most relevant to disease status. The method can also be generalized to model differential gene connectivity patterns within previously defined gene sets, gene networks and pathways. We demonstrate that the GGM method reliably detects differences in network connectivity patterns in datasets of varying sample size. Applying this method to two independent breast cancer expression data sets, we identified numerous reproducible differences in network connectivity across histological grades of breast cancer, including several published gene sets and pathways. Most notably, our model identified two gene hubs (MMP12 and CXCL13) that each exhibited differential connectivity to more than 30 transcripts in both datasets. Both genes have been previously implicated in breast cancer pathobiology, but themselves are not differentially expressed by histologic grade in either dataset, and would thus have not been identified using traditional differential gene expression testing approaches. In addition, 16 curated gene sets demonstrated significant differential connectivity in both data sets, including the matrix metalloproteinases, PPAR alpha sequence targets, and the PUFA synthesis pathway.</p> <p>Conclusions</p> <p>Our results suggest that GGM can be used to formally evaluate differences in global interactome connectivity across disease states, and can serve as a powerful tool for exploring the molecular events that contribute to disease at a systems level.</p

The Genetics of Pneumothorax.

A genetic influence on spontaneous pneumothoraces-those occurring without a traumatic or iatrogenic cause-is supported by several lines of evidence: 1) pneumothorax can cluster in families (i.e., familial spontaneous pneumothorax), 2) mutations in the FLCN gene have been found in both familial and sporadic cases, and 3) pneumothorax is a known complication of several genetic syndromes. Herein, we review known genetic contributions to both sporadic and familial pneumothorax. We summarize the pneumothorax-associated genetic syndromes, including Birt-Hogg-Dubé syndrome, Marfan syndrome, vascular (type IV) Ehlers-Danlos syndrome, alpha-1 antitrypsin deficiency, tuberous sclerosis complex/lymphangioleiomyomatosis, Loeys-Dietz syndrome, cystic fibrosis, homocystinuria, and cutis laxa, among others. At times, pneumothorax is their herald manifestation. These syndromes have serious potential extrapulmonary complications (e.g., malignant renal tumors in Birt-Hogg-Dubé syndrome), and surveillance and/or treatment is available for most disorders; thus, establishing a diagnosis is critical. To facilitate this, we provide an algorithm to guide the clinician in discerning which cases of spontaneous pneumothorax may have a genetic or familial contribution, which cases warrant genetic testing, and which cases should prompt an evaluation by a geneticist

Using Canonical Correlation Analysis to Discover Genetic Regulatory Variants

Background: Discovering genetic associations between genetic markers and gene expression levels can provide insight into gene regulation and, potentially, mechanisms of disease. Such analyses typically involve a linkage or association analysis in which expression data are used as phenotypes. This approach leads to a large number of multiple comparisons and may therefore lack power. We assess the potential of applying canonical correlation analysis to partitioned genomewide data as a method for discovering regulatory variants. Methodology/Principal Findings: Simulations suggest that canonical correlation analysis has higher power than standard pairwise univariate regression to detect single nucleotide polymorphisms when the expression trait has low heritability. The increase in power is even greater under the recessive model. We demonstrate this approach using the Childhood Asthma Management Program data. Conclusions/Significance: Our approach reduces multiple comparisons and may provide insight into the complex relationships between genotype and gene expression

Importin-13 genetic variation is associated with improved airway responsiveness in childhood asthma

Background: Glucocorticoid function is dependent on efficient translocation of the glucocorticoid receptor (GR) from the cytoplasm to the nucleus of cells. Importin-13 (IPO13) is a nuclear transport receptor that mediates nuclear entry of GR. In airway epithelial cells, inhibition of IPO13 expression prevents nuclear entry of GR and abrogates anti-inflammatory effects of glucocorticoids. Impaired nuclear entry of GR has been documented in steroid-non-responsive asthmatics. We hypothesize that common IPO13 genetic variation influences the anti-inflammatory effects of inhaled corticosteroids for the treatment of asthma, as measured by change in methacholine airway hyperresponsiveness (AHR-PC20). Methods: 10 polymorphisms were evaluated in 654 children with mild-to-moderate asthma participating in the Childhood Asthma Management Program (CAMP), a clinical trial of inhaled anti-inflammatory medications (budesonide and nedocromil). Population-based association tests with repeated measures of PC20 were performed using mixed models and confirmed using family-based tests of association. Results: Among participants randomized to placebo or nedocromil, IPO13 polymorphisms were associated with improved PC20 (i.e. less AHR), with subjects harboring minor alleles demonstrating an average 1.51–2.17 fold increase in mean PC20 at 8-months post-randomization that persisted over four years of observation (p = 0.01–0.005). This improvement was similar to that among children treated with long-term inhaled corticosteroids. There was no additional improvement in PC20 by IPO13 variants among children treated with inhaled corticosteroids. Conclusion: IPO13 variation is associated with improved AHR in asthmatic children. The degree of this improvement is similar to that observed with long-term inhaled corticosteroid treatment, suggesting that IPO13 variation may improve nuclear bioavailability of endogenous glucocorticoids

Soluble toll-like receptor 2 is a biomarker for sepsis in critically ill patients with multi-organ failure within 12 h of ICU admission

Soluble TLR2 levels are elevated in infective and inflammatory conditions, but its diagnostic value with sepsis-induced multi-organ failure has not been evaluated. 37 patients with a diagnosis of severe sepsis/septic shock (sepsis) and 27 patients with organ failure without infection (SIRS) were studied. Median (IQR) plasma sTLR2 levels were 2.7 ng/ml (1.4–6.1) in sepsis and 0.6 ng/ml (0.4–0.9) in SIRS p < 0.001. sTLR2 showed good diagnostic value for sepsis at cut-off of 1.0 ng/ml, AUC:0.959. We report the ability of sTLR2 levels to discriminate between sepsis and SIRS within 12 h of ICU admission in patients with multi-organ failure

Copy number variation genotyping using family information

BACKGROUND: In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. RESULTS: To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. CONCLUSIONS: In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs

Copy number variation genotyping using family information

Abstract Background In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. Results To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. Conclusions In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.http://deepblue.lib.umich.edu/bitstream/2027.42/112374/1/12859_2012_Article_5896.pd

Expression of SMARCD1 interacts with age in association with asthma control on inhaled corticosteroid therapy.

BackgroundGlobal gene expression levels are known to be highly dependent upon gross demographic features including age, yet identification of age-related genomic indicators has yet to be comprehensively undertaken in a disease and treatment-specific context.MethodsWe used gene expression data from CD4+ lymphocytes in the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), an open-access collection of subjects participating in genetic studies of asthma with available gene expression data. Replication population participants were Puerto Rico islanders recruited as part of the ongoing Genes environments &amp; Admixture in Latino Americans (GALA II), who provided nasal brushings for transcript sequencing. The main outcome measure was chronic asthma control as derived by questionnaires. Genomic associations were performed using regression of chronic asthma control score on gene expression with age in years as a covariate, including a multiplicative interaction term for gene expression times age.ResultsThe SMARCD1 gene (SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily D member 1) interacted with age to influence chronic asthma control on inhaled corticosteroids, with a doubling of expression leading to an increase of 1.3 units of chronic asthma control per year (95% CI [0.86, 1.74], p = 6 × 10- 9), suggesting worsening asthma control with increasing age. This result replicated in GALA II (p = 3.8 × 10- 8). Cellular assays confirmed the role of SMARCD1 in glucocorticoid response in airway epithelial cells.ConclusionFocusing on age-dependent factors may help identify novel indicators of asthma medication response. Age appears to modulate the effect of SMARCD1 on asthma control with inhaled corticosteroids