Using genetic markers to orient the edges in quantitative trait networks: The NEO software

<p>Abstract</p> <p>Background</p> <p>Systems genetic studies have been used to identify genetic loci that affect transcript abundances and clinical traits such as body weight. The pairwise correlations between gene expression traits and/or clinical traits can be used to define undirected trait networks. Several authors have argued that genetic markers (e.g expression quantitative trait loci, eQTLs) can serve as causal anchors for orienting the edges of a trait network. The availability of hundreds of thousands of genetic markers poses new challenges: how to relate (anchor) traits to multiple genetic markers, how to score the genetic evidence in favor of an edge orientation, and how to weigh the information from multiple markers.</p> <p>Results</p> <p>We develop and implement Network Edge Orienting (NEO) methods and software that address the challenges of inferring unconfounded and directed gene networks from microarray-derived gene expression data by integrating mRNA levels with genetic marker data and Structural Equation Model (SEM) comparisons. The NEO software implements several manual and automatic methods for incorporating genetic information to anchor traits. The networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose Local SEM-based Edge Orienting (LEO) scores that compare the fit of several competing causal graphs. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate the utility of NEO by recovering known causal relationships in the sterol homeostasis pathway using liver gene expression data from an F2 mouse cross. Further, we use NEO to study the relationship between a disease gene and a biologically important gene co-expression module in liver tissue.</p> <p>Conclusion</p> <p>The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored to genetic marker data. R software tutorials, data, and supplementary material can be downloaded from: <url>http://www.genetics.ucla.edu/labs/horvath/aten/NEO</url>.</p

Genetic and Genomic Analysis of a Fat Mass Trait with Complex Inheritance Reveals Marked Sex Specificity

The integration of expression profiling with linkage analysis has increasingly been used to identify genes underlying complex phenotypes. The effects of gender on the regulation of many physiological traits are well documented; however, “genetical genomic” analyses have not yet addressed the degree to which their conclusions are affected by sex. We constructed and densely genotyped a large F2 intercross derived from the inbred mouse strains C57BL/6J and C3H/HeJ on an apolipoprotein E null (ApoE(−/−)) background. This BXH.ApoE(−/−) population recapitulates several “metabolic syndrome” phenotypes. The cross consists of 334 animals of both sexes, allowing us to specifically test for the dependence of linkage on sex. We detected several thousand liver gene expression quantitative trait loci, a significant proportion of which are sex-biased. We used these analyses to dissect the genetics of gonadal fat mass, a complex trait with sex-specific regulation. We present evidence for a remarkably high degree of sex-dependence on both the cis and trans regulation of gene expression. We demonstrate how these analyses can be applied to the study of the genetics underlying gonadal fat mass, a complex trait showing significantly female-biased heritability. These data have implications on the potential effects of sex on the genetic regulation of other complex traits

Chromatin variation associated with liver metabolism is mediated by transposable elements

Additional file 1: Supplementary methods. Figure S1. Phenotypic diversity in different inbred strains. Figure S2. Chromatin variability across inbred strains of mice. Figure S3. Association between chromatin variation and SNPs. Figure S4. Accessible chromatin sites and TE sequences. Figure S5. DNA transposons and chromatin accessibility variation. Figure S6. Differential accessibility at young L1Md subfamilies across different strains. Figure S7. Chromatin accessibility at younger L1Md subfamilies in recombinant inbred strains. Figure S8. Chromatin variability and age of LINE subfamilies. Figure S9. Differential chromatin accessibility profile at younger and older LINEs in A/J mice liver. Figure S10. Accessibility and transcription of LINE subfamilies. Figure S11. CRISPR-Cas9 deletion of additional TEs. Figure S12. Guide RNA and genotyping primers used for CRISPR-Cas9 genome editing. Figure S13. Genotyping for TE deletions. Figure S14. Example of an eQTL that associated with variable chromatin accessibility at a LTR. Table S1. Summary of FAIRE-seq data sets in all the strains in this study. Table S3. Enriched biological process from GREAT analysis of accessible chromatin sites. Table S4. Sequences used in this study

Genomic analysis of metabolic pathway gene expression in mice

BACKGROUND: A segregating population of (C57BL/6J × DBA/2J)F2 intercross mice was studied for obesity-related traits and for global gene expression in liver. Quantitative trait locus analyses were applied to the subcutaneous fat-mass trait and all gene-expression data. These data were then used to identify gene sets that are differentially perturbed in lean and obese mice. RESULTS: We integrated global gene-expression data with phenotypic and genetic segregation analyses to evaluate metabolic pathways associated with obesity. Using two approaches we identified 13 metabolic pathways whose genes are coordinately regulated in association with obesity. Four genomic regions on chromosomes 3, 6, 16, and 19 were found to control the coordinated expression of these pathways. Using criteria that included trait correlation, differential gene expression, and linkage to genomic regions, we identified novel genes potentially associated with the identified pathways. CONCLUSION: This study demonstrates that genetic and gene-expression data can be integrated to identify pathways associated with clinical traits and their underlying genetic determinants

Mapping the genetic architecture of gene expression in human liver

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process. © 2008 Schadt et al

Dietary and Pharmacologic Manipulations of Host Lipids and Their Interaction with the Gut Microbiome in Non-Human Primates

The gut microbiome influences nutrient processing as well as host physiology. Plasma lipid levels have been associated with the microbiome, although the underlying mechanisms are largely unknown, and the effects of dietary lipids on the gut microbiome in humans are not well-studied. We used a compilation of four studies utilizing non-human primates (Chlorocebus aethiops and Macaca fascicularis) with treatments that manipulated plasma lipid levels using dietary and pharmacological techniques, and characterized the microbiome using 16S rDNA. High-fat diets significantly reduced alpha diversity (Shannon) and the Firmicutes/Bacteroidetes ratio compared to chow diets, even when the diets had different compositions and were applied in different orders. When analyzed for differential abundance using DESeq2, Bulleidia, Clostridium, Ruminococcus, Eubacterium, Coprocacillus, Lachnospira, Blautia, Coprococcus, and Oscillospira were greater in both chow diets while Succinivibrio, Collinsella, Streptococcus, and Lactococcus were greater in both high-fat diets (oleic blend or lard fat source). Dietary cholesterol levels did not affect the microbiome and neither did alterations of plasma lipid levels through treatments of miR-33 antisense oligonucleotide (anti-miR-33), Niemann–Pick C1-Like 1 (NPC1L1) antisense oligonucleotide (ASO), and inducible degrader of LDLR (IDOL) ASO. However, a liver X receptor (LXR) agonist shifted the microbiome and decreased bile acid levels. Fifteen genera increased with the LXR agonist, while seven genera decreased. Pseudomonas increased on the LXR agonist and was negatively correlated to deoxycholic acid, cholic acid, and total bile acids while Ruminococcus was positively correlated with taurolithocholic acid and taurodeoxycholic acid. Seven of the nine bile acids identified in the feces significantly decreased due to the LXR agonist, and total bile acids (nmol/g) was reduced by 62%. These results indicate that plasma lipid levels have, at most, a modest effect on the microbiome, whereas bile acids, derived in part from plasma lipids, are likely responsible for the indirect relationship between lipid levels and the microbiome

Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight

Systems biology approaches that are based on the genetics of gene expression have been fruitful in identifying genetic regulatory loci related to complex traits. We use microarray and genetic marker data from an F2 mouse intercross to examine the large-scale organization of the gene co-expression network in liver, and annotate several gene modules in terms of 22 physiological traits. We identify chromosomal loci (referred to as module quantitative trait loci, mQTL) that perturb the modules and describe a novel approach that integrates network properties with genetic marker information to model gene/trait relationships. Specifically, using the mQTL and the intramodular connectivity of a body weight–related module, we describe which factors determine the relationship between gene expression profiles and weight. Our approach results in the identification of genetic targets that influence gene modules (pathways) that are related to the clinical phenotypes of interest

Dosage compensation is less effective in birds than in mammals

<p>Abstract</p> <p>Background</p> <p>In animals with heteromorphic sex chromosomes, dosage compensation of sex-chromosome genes is thought to be critical for species survival. Diverse molecular mechanisms have evolved to effectively balance the expressed dose of X-linked genes between XX and XY animals, and to balance expression of X and autosomal genes. Dosage compensation is not understood in birds, in which females (ZW) and males (ZZ) differ in the number of Z chromosomes.</p> <p>Results</p> <p>Using microarray analysis, we compared the male:female ratio of expression of sets of Z-linked and autosomal genes in two bird species, zebra finch and chicken, and in two mammalian species, mouse and human. Male:female ratios of expression were significantly higher for Z genes than for autosomal genes in several finch and chicken tissues. In contrast, in mouse and human the male:female ratio of expression of X-linked genes is quite similar to that of autosomal genes, indicating effective dosage compensation even in humans, in which a significant percentage of genes escape X-inactivation.</p> <p>Conclusion</p> <p>Birds represent an unprecedented case in which genes on one sex chromosome are expressed on average at constitutively higher levels in one sex compared with the other. Sex-chromosome dosage compensation is surprisingly ineffective in birds, suggesting that some genomes can do without effective sex-specific sex-chromosome dosage compensation mechanisms.</p