Genz and Mendell-Elston Estimation of the High-Dimensional Multivariate Normal Distribution

Statistical analysis of multinomial data in complex datasets often requires estimation of the multivariate normal (MVN) distribution for models in which the dimensionality can easily reach 10–1000 and higher. Few algorithms for estimating the MVN distribution can offer robust and efficient performance over such a range of dimensions. We report a simulation-based comparison of two algorithms for the MVN that are widely used in statistical genetic applications. The venerable Mendell- Elston approximation is fast but execution time increases rapidly with the number of dimensions, estimates are generally biased, and an error bound is lacking. The correlation between variables significantly affects absolute error but not overall execution time. The Monte Carlo-based approach described by Genz returns unbiased and error-bounded estimates, but execution time is more sensitive to the correlation between variables. For ultra-high-dimensional problems, however, the Genz algorithm exhibits better scale characteristics and greater time-weighted efficiency of estimation

Transcriptome sequencing study implicates immune-related genes differentially expressed in schizophrenia: new data and a meta-analysis

We undertook an RNA sequencing (RNAseq)-based transcriptomic profiling study on lymphoblastoid cell lines of a European ancestry sample of 529 schizophrenia cases and 660 controls, and found 1058 genes to be differentially expressed by affection status. These differentially expressed genes were enriched for involvement in immunity, especially the 697 genes with higher expression in cases. Comparing the current RNAseq transcriptomic profiling to our previous findings in an array-based study of 268 schizophrenia cases and 446 controls showed a highly significant positive correlation over all genes. Fifteen (18%) of the 84 genes with significant (false discovery rateo0.05) expression differences between cases and controls in the previous study and analyzed here again were differentially expressed by affection status here at a genome-wide significance level (Bonferroni Po0.05 adjusted for 8141 analyzed genes in total, or Po ~ 6.1 × 10− 6), all with the same direction of effect, thus providing corroborative evidence despite each sample of fully independent subjects being studied by different technological approaches. Meta-analysis of the RNAseq and array data sets (797 cases and 1106 controls) showed 169 additional genes (besides those found in the primary RNAseq-based analysis) to be differentially expressed, and provided further evidence of immune gene enrichment. In addition to strengthening our previous array-based gene expression differences in schizophrenia cases versus controls and providing transcriptomic support for some genes implicated by other approaches for schizophrenia, our study detected new genes differentially expressed in schizophrenia. We highlight RNAseq-based differential expression of various genes involved in neurodevelopment and/or neuronal function, and discuss caveats of the approach

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20’s organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a randomeffect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era

Modeling methylation data as an additional genetic variance component

High-throughput platforms allow the characterization of thousands of previously known methylation sites. These platforms have great potential for investigating the epigenetic effects that are partially responsible for gene expression control. Methylation sites provide a bridge for the investigation of real-time environmental contributions on genomic events by the alteration of methylation status of those sites. Using the data provided by GAW20’s organization committee, we calculated the heritability estimates of each cytosine-phosphate-guanine (CpG) island before and after the use of fenofibrate, a lipid-control drug. Surprisingly, we detected substantially high heritability estimates before drug usage. This somewhat unexpected high sample correlation was corrected by the use of principal components and the distributions of heritability estimates before and after fenofibrate treatment, which made the distributions comparable. The methylation sites located near a gene were collected and a genetic relationship matrix estimated to represent the overall correlation between samples. We implemented a randomeffect association test to screen genes whose methylation patterns partially explain the observable high-density lipoprotein (HDL) heritability. Our leading association was observed for the TMEM52 gene that encodes a transmembrane protein, and is largely expressed in the liver, had not been previously associated with HDL until this manuscript. Using a variance component decomposition framework with the linear mixed model allows the integration of data from different sources, such as methylation, gene expression, metabolomics, and proteomics. The decomposition of the genetic variance component decomposition provides a flexible analytical approach for the challenges of this new omics era

Association of differential gene expression with imatinib mesylate and omacetaxine mepesuccinate toxicity in lymphoblastoid cell lines

BackgroundImatinib mesylate is currently the drug of choice to treat chronic myeloid leukemia. However, patient resistance and cytotoxicity make secondary lines of treatment, such as omacetaxine mepesuccinate, a necessity. Given that drug cytotoxicity represents a major problem during treatment, it is essential to understand the biological pathways affected to better predict poor drug response and prioritize a treatment regime.MethodsWe conducted cell viability and gene expression assays to determine heritability and gene expression changes associated with imatinib and omacetaxine treatment of 55 non-cancerous lymphoblastoid cell lines, derived from 17 pedigrees. In total, 48,803 transcripts derived from Illumina Human WG-6 BeadChips were analyzed for each sample using SOLAR, whilst correcting for kinship structure.ResultsCytotoxicity within cell lines was highly heritable following imatinib treatment (h2&thinsp;=&thinsp;0.60-0.73), but not omacetaxine treatment. Cell lines treated with an IC20 dose of imatinib or omacetaxine showed differential gene expression for 956 (1.96%) and 3,892 transcripts (7.97%), respectively; 395 of these (0.8%) were significantly influenced by both imatinib and omacetaxine treatment. k-means clustering and DAVID functional annotation showed expression changes in genes related to kinase binding and vacuole-related functions following imatinib treatment, whilst expression changes in genes related to cell division and apoptosis were evident following treatment with omacetaxine. The enrichment scores for these ontologies were very high (mostly &gt;10).ConclusionsInduction of gene expression changes related to different pathways following imatinib and omacetaxine treatment suggests that the cytotoxicity of such drugs may be differentially tolerated by individuals based on their genetic background.<br /

Whole genome sequence data implicate RBFOX1 in epilepsy risk in baboons

Background: Baboons exhibit a genetic generalized epilepsy (GGE) that resembles juvenile myoclonic epilepsy and may represent a suitable genetic model for human epilepsy. The genetic underpinnings of epilepsy were investigated in a baboon colony at the Southwest National Primate Research Center (San Antonio, TX) through the analysis of whole-genome sequence (WGS) data. Methods: Baboon WGS data were obtained for 38 cases and 19 healthy controls from the NCBI Sequence Read Archive and, after standard QC filtering, two subsets of variants were examined: (1) 20,881 SNPs from baboon homologs of 19 candidate GGE genes; and (2) 36,169 protein-altering SNPs. Association tests were conducted in SOLAR, and gene set enrichment analyses (GSEA) and protein-protein interaction (PPI) network construction were performed on genome-wide significant association results (Pn= 441 genes). Results: Heritability for epileptic seizure in the pedigreed baboon sample was estimated at 0.76 (SE=0.77; P=0.07). A significant association was detected for an intronic SNP in RBFOX1 (P=5.92 × 10-6; adjusted P=0.016). For protein-altering variants, GSEA revealed significant positive enrichment for genes involved in the extracellular matrix structure (ECM; FDR=0.0072) and collagen formation (FDR=0.017). Conclusions: SNP association results implicate RBFOX1 in baboon epilepsy, a gene that plays a key role in neuronal excitation and transcriptomic regulation, and has been previously linked to human epilepsy, both focal and generalized. Moreover, protein-damaging variants from across the baboon genome exhibit a wider pattern of association that links collagen-containing ECM to epilepsy risk. These findings suggest a shared genetic etiology between baboon and human forms of GGE

Dopamine perturbation of gene co-expression networks reveals differential response in schizophrenia for translational machinery

The dopaminergic hypothesis of schizophrenia (SZ) postulates that positive symptoms of SZ, in particular psychosis, are due to disturbed neurotransmission via the dopamine (DA) receptor D2 (DRD2). However, DA is a reactive molecule that yields various oxidative species, and thus has important non-receptor-mediated effects, with empirical evidence of cellular toxicity and neurodegeneration. Here we examine non-receptor-mediated effects of DA on gene co-expression networks and its potential role in SZ pathology. Transcriptomic profiles were measured by RNA-seq in B-cell transformed lymphoblastoid cell lines from 514 SZ cases and 690 controls, both before and after exposure to DA ex vivo (100 μM). Gene co-expression modules were identified using Weighted Gene Co-expression Network Analysis for both baseline and DA-stimulated conditions, with each module characterized for biological function and tested for association with SZ status and SNPs from a genome-wide panel. We identified seven co-expression modules under baseline, of which six were preserved in DA-stimulated data. One module shows significantly increased association with SZ after DA perturbation (baseline: P = 0.023; DA-stimulated: P = 7.8 × 10-5; ΔAIC = −10.5) and is highly enriched for genes related to ribosomal proteins and translation (FDR = 4 × 10−141), mitochondrial oxidative phosphorylation, and neurodegeneration. SNP association testing revealed tentative QTLs underlying module co-expression, notably at FASTKD2 (top P = 2.8 × 10−6), a gene involved in mitochondrial translation. These results substantiate the role of translational machinery in SZ pathogenesis, providing insights into a possible dopaminergic mechanism disrupting mitochondrial function, and demonstrates the utility of disease-relevant functional perturbation in the study of complex genetic etiologies

Transcriptomic signatures of schizophrenia revealed by dopamine perturbation in an ex vivo model

The dopaminergic hypothesis of schizophrenia (SZ) postulates that dopaminergic over activity causes psychosis, a central feature of SZ, based on the observation that blocking dopamine (DA) improves psychotic symptoms. DA is known to have both receptor- and non-receptor-mediated effects, including oxidative mechanisms that lead to apoptosis. The role of DA-mediated oxidative processes in SZ has been little studied. Here, we have used a cell perturbation approach and measured transcriptomic profiles by RNAseq to study the effect of DA exposure on transcription in B-cell transformed lymphoblastoid cell lines (LCLs) from 514 SZ cases and 690 controls. We found that DA had widespread effects on both cell growth and gene expression in LCLs. Overall, 1455 genes showed statistically significant differential DA response in SZ cases and controls. This set of differentially expressed genes is enriched for brain expression and for functions related to immune processes and apoptosis, suggesting that DA may play a role in SZ pathogenesis through modulating those systems. Moreover, we observed a non-significant enrichment of genes near genome-wide significant SZ loci and with genes spanned by SZ-associated copy number variants (CNVs), which suggests convergent pathogenic mechanisms detected by both genetic association and gene expression. The study suggests a novel role of DA in the biological processes of immune and apoptosis that may be relevant to SZ pathogenesis. Furthermore, our results show the utility of pathophysiologically relevant perturbation experiments to investigate the biology of complex mental disorders

Analysis of SLC16A11 Variants in 12,811 American Indians: Genotype-Obesity Interaction for Type 2 Diabetes and an Association With RNASEK Expression

Genetic variants in SLC16A11 were recently reported to be associated with type 2 diabetes in Mexican and other Latin American populations. The diabetes risk haplotype had a frequency of 50% in Native Americans from Mexico but was rare in Europeans and Africans. In the current study, we analyzed SLC16A11 in 12,811 North American Indians and found that the diabetes risk haplotype, tagged by the rs75493593 A allele, was nominally associated with type 2 diabetes (P = 0.001, odds ratio 1.11). However, there was a strong interaction with BMI (P = 5.1 × 10(-7)) such that the diabetes association was stronger in leaner individuals. rs75493593 was also strongly associated with BMI in individuals with type 2 diabetes (P = 3.4 × 10(-15)) but not in individuals without diabetes (P = 0.77). Longitudinal analyses suggest that this is due, in part, to an association of the A allele with greater weight loss following diabetes onset (P = 0.02). Analyses of global gene expression data from adipose tissue, skeletal muscle, and whole blood provide evidence that rs75493593 is associated with expression of the nearby RNASEK gene, suggesting that RNASEK expression may mediate the effect of genotype on diabetes