Ina Hoeschele

Discovery of meaningful associations in genomic data using partial correlation coefficients

MOTIVATION A major challenge of systems biology is to infer biochemical interactions from large-scale observations, such as transcriptomics, proteomics and metabolomics. We propose to use a partial correlation analysis to construct approximate Undirected Dependency Graphs from such large-scale biochemical data. This approach enables a distinction between direct and indirect interactions of biochemical compounds, thereby inferring the underlying network topology. RESULTS The method is first thoroughly evaluated with a large set of simulated data. Results indicate that the approach has good statistical power and a low False Discovery Rate even in the presence of noise in the data. We then applied the method to an existing data set of yeast gene expression. Several small gene networks were inferred and found to contain genes known to be collectively involved in particular biochemical processes. In some of these networks there are also uncharacterized ORFs present, which lead to hypotheses about their functions. AVAILABILITY Programs running in MS-Windows and Linux for applying zeroth, first, second and third order partial correlation analysis can be downloaded at: http://mendes.vbi.vt.edu/tiki-index.php?page=Software. SUPPLEMENTARY INFORMATION Supplementary information can be found at: URL to be decided.

Gene Network Inference via Structural Equation Modeling in Genetical Genomics Experiments

Our goal is gene network inference in genetical genomics or systems genetics experiments. For species where sequence information is available, we first perform expression quantitative trait locus (eQTL) mapping by jointly utilizing cis-, cis–trans-, and trans-regulation. After using local structural models to identify regulator–target pairs for each eQTL, we construct an encompassing directed network (EDN) by assembling all retained regulator–target relationships. The EDN has nodes corresponding to expressed genes and eQTL and directed edges from eQTL to cis-regulated target genes, from cis-regulated genes to cis–trans-regulated target genes, from trans-regulator genes to target genes, and from trans-eQTL to target genes. For network inference within the strongly constrained search space defined by the EDN, we propose structural equation modeling (SEM), because it can model cyclic networks and the EDN indeed contains feedback relationships. On the basis of a factorization of the likelihood and the constrained search space, our SEM algorithm infers networks involving several hundred genes and eQTL. Structure inference is based on a penalized likelihood ratio and an adaptation of Occams window model selection. The SEM algorithm was evaluated using data simulated with nonlinear ordinary differential equations and known cyclic network topologies and was applied to a real yeast data set.

Identification of Differentially Expressed Genes in Individual Bovine Preimplantation Embryos Produced by Nuclear Transfer: Improper Reprogramming of Genes Required for Development

Abstract Using an interwoven-loop experimental design in conjunction with highly conservative linear mixed model methodology using estimated variance components, 18 genes differentially expressed between nuclear transfer (NT)- and in vitro fertilization (IVF)-produced embryos were identified. The set is comprised of three intermediate-filament protein genes (cytokeratin 8, cytokeratin 19, and vimentin), three metabolic genes (phosphoribosyl pyrophosphate synthetase 1, mitochondrial acetoacetyl-coenzyme A thiolase, and α-glucosidase), two lysosomal-related genes (prosaposin and lysosomal-associated membrane protein 2), and a gene associated with stress responses (heat shock protein 27) along with major histocompatibility complex class I, nidogen 2, a putative transport protein, heterogeneous nuclear ribonuclear protein K, mitochondrial 16S rRNA, and ES1 (a zebrafish orthologue of unknown function). The three remaining genes are novel. To our knowledge, this is the first report comparing individual embryos produced by NT and IVF using cDNA microarray technology for any species, and it uses a rigorous experimental design that emphasizes statistical significance to identify differentially expressed genes between NT and IVF embryos in cattle.

Genetic evaluation methods for populations with dominance and inbreeding

SummaryThe effect of inbreeding on mean and genetic covariance matrix for a quantitative trait in a population with additive and dominance effects is shown. This genetic covariance matrix is a function of five relationship matrices and five genetic parameters describing the population. Elements of the relationship matrices are functions of Gillois (1964) identity coefficients for the four genes at a locus in two individuals. The equivalence of the path coefficient method (Jacquard 1966) and the tabular method (Smith and Mäki-Tanila 1990) to compute the covariance matrix of additive and dominance effects in a population with inbreeding is shown. The tabular method is modified to compute relationship matrices rather than the covariance matrix, which is trait dependent. Finally, approximate and exact Best Linear Unbiased Predictions (BLUP) of additive and dominance effects are compared using simulated data with inbreeding but no directional selection. The trait simulated was affected by 64 unlinked biallelic loci with equal effect and complete dominance. Simulated average inbreeding levels ranged from zero in generation one to 0.35 in generation five. The approximate method only accounted for the effect of inbreeding on mean and additive genetic covariance matrix, whereas the exact accounted for all of the changes in mean and genetic covariance matrix due to inbreeding. Approximate BLUP, which is computable for large populations where exact BLUP is not feasible, yielded unbiased predictions of additive and dominance effects in each generation with only slightly reduced accuracies relative to exact BLUP.

Age-related variations in the methylome associated with gene expression in human monocytes and t cells

Age-related variations in DNA methylation have been reported; however, the functional relevance of these differentially methylated sites (age-dMS) are unclear. Here we report potentially functional age-dMS, defined as age- and cis-gene expression-associated methylation sites (age-eMS), identified by integrating genome-wide CpG methylation and gene expression profiles collected ex vivo from circulating T cells (227 CD4+ samples) and monocytes (1,264 CD14+ samples, age range: 55–94 years). None of the age-eMS detected in 227 T cell samples are detectable in 1,264 monocyte samples, in contrast to the majority of age-dMS detected in T cells that replicated in monocytes. Age-eMS tend to be hypomethylated with older age, located in predicted enhancers, and preferentially linked to expression of antigen processing and presentation genes. These results identify and characterize potentially functional age-related methylation in human T cells and monocytes, and provide novel insights into the role age-dMS may play in the aging process.

ATP-binding cassette transporter A1 locus is not a major determinant of HDL-C levels in a population at high risk for coronary heart disease

ATP-binding cassette transporter A1 (ABCA1) transports cellular cholesterol to lipid-poor apolipoproteins. Mutations in the ABCA1 gene are linked to rare phenotypes, familial hypoalphalipoproteinemia (FHA) and Tangier disease (TD), characterized by markedly decreased plasma high-density lipoprotein cholesterol (HDL-C) levels. The aim was to test if the ABCA1 locus is a major locus regulating HDL-C levels in the homogenous Finnish population with a high prevalence of coronary heart disease (CHD). Firstly, the ABCA1 locus was tested for linkage to HDL-C levels in 35 families with premature CHD and low HDL-C levels. Secondly, 62 men with low HDL-C levels and CHD were screened for the five mutations known to cause FHA. Thirdly, polymorphisms of the ABCA1 gene were tested for an association with HDL-C levels in a population sample of 515 subjects. The ABCA1 locus was not linked to HDL-C levels in the CHD families, and no carriers of the FHA mutations were found. The AA596 genotype was associated with higher HDL-C levels compared with the GG and GA genotypes in the women, but not in the men. The G596A genotypes explained 4% and the A2589G genotypes 3% of the variation in plasma HDL-C levels in women. The data suggest that the ABCA1 locus is of minor importance in the regulation of HDL-C in Finns.

Bayesian analysis of linkage between genetic markers and quantitative trait loci. I. Prior knowledge

SummaryPrior information on gene effects at individual quantitative trait loci (QTL) and on recombination rates between marker loci and QTL is derived. The prior distribution of QTL gene effects is assumed to be exponential with major effects less likely than minor ones. The prior probability of linkage between a marker and another single locus is a function of the number and length of chromosomes, and of the map function relating recombination rate to genetic distance among loci. The prior probability of linkage between a marker locus and a quantitative trait depends additionally on the number of detectable QTL, which may be determined from total additive genetic variance and minimum detectable QTL effect. The use of this prior information should improve linkage tests and estimates of QTL effects.

Bayesian analysis of linkage between genetic markers and quantitative trait loci. II. Combining prior knowledge with experimental evidence.

SummaryA Bayesian method was developed for identifying genetic markers linked to quantitative trait loci (QTL) by analyzing data from daughter or granddaughter designs and single markers or marker pairs. Traditional methods may yield unrealistic results because linkage tests depend on number of markers and QTL gene effects associated with selected markers are overestimated. The Bayesian or posterior probability of linkage combines information from a daughter or granddaughter design with the prior probability of linkage between a marker locus and a QTL. If the posterior probability exceeds a certain quantity, linkage is declared. Upon linkage acceptance, Bayesian estimates of marker-QTL recombination rate and QTL gene effects and frequencies are obtained. The Bayesian estimates of QTL gene effects account for different amounts of information by shrinking information from data toward the mean or mode of a prior exponential distribution of gene effects. Computation of the Bayesian analysis is feasible. Exact results are given for biallelic QTL, and extensions to multiallelic QTL are suggested.

Genetic evaluation with data presenting evidence of mixed major gene and polygenic inheritance.

SummaryA procedure for genetic evaluation with field data is proposed for situations in which there is mixed major gene and polygenic inheritance and the major genotype membership of some or of all individuals is unknown. Location parameters (fixed environmental, major genotype and polygenic effects), major genotype frequencies and variance components are estimated by the modal values of joint and marginal posterior distributions. The method is described for continuous and discontinuous data as well as for univariate and multivariate evaluations. Results from a simulation study are presented.

Alterations of a Cellular Cholesterol Metabolism Network Are a Molecular Feature of Obesity-Related Type 2 Diabetes and Cardiovascular Disease

Obesity is linked to type 2 diabetes (T2D) and cardiovascular diseases; however, the underlying molecular mechanisms remain unclear. We aimed to identify obesity-associated molecular features that may contribute to obesity-related diseases. Using circulating monocytes from 1,264 Multi-Ethnic Study of Atherosclerosis (MESA) participants, we quantified the transcriptome and epigenome. We discovered that alterations in a network of coexpressed cholesterol metabolism genes are a signature feature of obesity and inflammatory stress. This network included 11 BMI-associated genes related to sterol uptake (↑LDLR, ↓MYLIP), synthesis (↑SCD, FADS1, HMGCS1, FDFT1, SQLE, CYP51A1, SC4MOL), and efflux (↓ABCA1, ABCG1), producing a molecular profile expected to increase intracellular cholesterol. Importantly, these alterations were associated with T2D and coronary artery calcium (CAC), independent from cardiometabolic factors, including serum lipid profiles. This network mediated the associations between obesity and T2D/CAC. Several genes in the network harbored C-phosphorus-G dinucleotides (e.g., ABCG1/cg06500161), which overlapped Encyclopedia of DNA Elements (ENCODE)-annotated regulatory regions and had methylation profiles that mediated the associations between BMI/inflammation and expression of their cognate genes. Taken together with several lines of previous experimental evidence, these data suggest that alterations of the cholesterol metabolism gene network represent a molecular link between obesity/inflammation and T2D/CAC.