Causal graphical models in systems genetics: A unified framework for joint inference of causal network and genetic architecture for correlated phenotypes

Causal inference approaches in systems genetics exploit quantitative trait loci (QTL) genotypes to infer causal relationships among phenotypes. The genetic architecture of each phenotype may be complex, and poorly estimated genetic architectures may compromise the inference of causal relationships among phenotypes. Existing methods assume QTLs are known or inferred without regard to the phenotype network structure. In this paper we develop a QTL-driven phenotype network method (QTLnet) to jointly infer a causal phenotype network and associated genetic architecture for sets of correlated phenotypes. Randomization of alleles during meiosis and the unidirectional influence of genotype on phenotype allow the inference of QTLs causal to phenotypes. Causal relationships among phenotypes can be inferred using these QTL nodes, enabling us to distinguish among phenotype networks that would otherwise be distribution equivalent. We jointly model phenotypes and QTLs using homogeneous conditional Gaussian regression models, and we derive a graphical criterion for distribution equivalence. We validate the QTLnet approach in a simulation study. Finally, we illustrate with simulated data and a real example how QTLnet can be used to infer both direct and indirect effects of QTLs and phenotypes that co-map to a genomic region.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS288 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Transcriptomic profiling reveals extraordinary diversity of venom peptides in unexplored predatory gastropods of the genus Clavus

Predatory gastropods of the superfamily Conoidea number over 12,000 living species. The evolutionary success of this lineage can be explained by the ability of conoideans to produce complex venoms for hunting, defense and competitive interactions. Whereas venoms of cone snails (family Conidae) have become increasingly well studied, the venoms of most other conoidean lineages remain largely uncharacterized. In the present study we present the venom gland transcriptomes of two species of the genus Clavus that belong to the family Drilliidae. Venom gland transcriptomes of two specimens of Clavus canalicularis, and two specimens of Cv. davidgilmouri were analyzed, leading to the identification of a total of 1,176 putative venom peptide toxins ( drillipeptides ). Based on the combined evidence of secretion signal sequence identity, entire precursor similarity search (BLAST), and the orthology inference, putative Clavus toxins were assigned to 158 different gene families. The majority of identified transcripts comprise signal, pro-, mature peptide, and post- regions, with a typically short ( \u3c 50 amino acids) and cysteine-rich mature peptide region. Thus drillipeptides are structurally similar to conotoxins. However, convincing homology with known groups of Conus toxins was only detected for very few toxin families. Among these are Clavus counterparts of Conus venom insulins (drillinsulins), porins (drilliporins), highly diversified lectins (drillilectins). The short size of most drillipeptpides and structural similarity to conotoxins was unexpected, given that most related conoidean gastropod families (Terebridae and Turridae) possess longer mature peptide regions. Our findings indicate that, similar to conotoxins, drillipeptides may represent a valuable resource for future pharmacological exploration

Lipidomic QTL in Diversity Outbred mice identifies a novel function for α/β hydrolase domain 2 (Abhd2) as an enzyme that metabolizes phosphatidylcholine and cardiolipin.

We and others have previously shown that genetic association can be used to make causal connections between gene loci and small molecules measured by mass spectrometry in the bloodstream and in tissues. We identified a locus on mouse chromosome 7 where several phospholipids in liver showed strong genetic association to distinct gene loci. In this study, we integrated gene expression data with genetic association data to identify a single gene at the chromosome 7 locus as the driver of the phospholipid phenotypes. The gene encodes α/β-hydrolase domain 2 (Abhd2), one of 23 members of the ABHD gene family. We validated this observation by measuring lipids in a mouse with a whole-body deletion of Abhd2. The Abhd2KO mice had a significant increase in liver levels of phosphatidylcholine and phosphatidylethanolamine. Unexpectedly, we also found a decrease in two key mitochondrial lipids, cardiolipin and phosphatidylglycerol, in male Abhd2KO mice. These data suggest that Abhd2 plays a role in the synthesis, turnover, or remodeling of liver phospholipids

Epistatic and Combinatorial Effects of Pigmentary Gene Mutations in the Domestic Pigeon

SummaryUnderstanding the molecular basis of phenotypic diversity is a critical challenge in biology, yet we know little about the mechanistic effects of different mutations and epistatic relationships among loci that contribute to complex traits. Pigmentation genetics offers a powerful model for identifying mutations underlying diversity and for determining how additional complexity emerges from interactions among loci. Centuries of artificial selection in domestic rock pigeons (Columba livia) have cultivated tremendous variation in plumage pigmentation through the combined effects of dozens of loci. The dominance and epistatic hierarchies of key loci governing this diversity are known through classical genetic studies [1–6], but their molecular identities and the mechanisms of their genetic interactions remain unknown. Here we identify protein-coding and cis-regulatory mutations in Tyrp1, Sox10, and Slc45a2 that underlie classical color phenotypes of pigeons and present a mechanistic explanation of their dominance and epistatic relationships. We also find unanticipated allelic heterogeneity at Tyrp1 and Sox10, indicating that color variants evolved repeatedly though mutations in the same genes. These results demonstrate how a spectrum of coding and regulatory mutations in a small number of genes can interact to generate substantial phenotypic diversity in a classic Darwinian model of evolution [7]

Transposable element islands facilitate adaptation to novel environments in an invasive species

Peer reviewe

Genetic determinants of gut microbiota composition and bile acid profiles in mice.

The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ~22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2. Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions

Genome-Wide Analysis of Human Disease Alleles Reveals That Their Locations Are Correlated in Paralogous Proteins

The millions of mutations and polymorphisms that occur in human populations are potential predictors of disease, of our reactions to drugs, of predisposition to microbial infections, and of age-related conditions such as impaired brain and cardiovascular functions. However, predicting the phenotypic consequences and eventual clinical significance of a sequence variant is not an easy task. Computational approaches have found perturbation of conserved amino acids to be a useful criterion for identifying variants likely to have phenotypic consequences. To our knowledge, however, no study to date has explored the potential of variants that occur at homologous positions within paralogous human proteins as a means of identifying polymorphisms with likely phenotypic consequences. In order to investigate the potential of this approach, we have assembled a unique collection of known disease-causing variants from OMIM and the Human Genome Mutation Database (HGMD) and used them to identify and characterize pairs of sequence variants that occur at homologous positions within paralogous human proteins. Our analyses demonstrate that the locations of variants are correlated in paralogous proteins. Moreover, if one member of a variant-pair is disease-causing, its partner is likely to be disease-causing as well. Thus, information about variant-pairs can be used to identify potentially disease-causing variants, extend existing procedures for polymorphism prioritization, and provide a suite of candidates for further diagnostic and therapeutic purposes

Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire

Background: Pythium ultimum (P. ultimum) is a ubiquitous oomycete plant pathogen responsible for a variety of diseases on a broad range of crop and ornamental species. Results: The P. ultimum genome (42.8 Mb) encodes 15,290 genes and has extensive sequence similarity and synteny with related Phytophthora species, including the potato blight pathogen Phytophthora infestans. Whole transcriptome sequencing revealed expression of 86% of genes, with detectable differential expression of suites of genes under abiotic stress and in the presence of a host. The predicted proteome includes a large repertoire of proteins involved in plant pathogen interactions although surprisingly, the P. ultimum genome does not encode any classical RXLR effectors and relatively few Crinkler genes in comparison to related phytopathogenic oomycetes. A lower number of enzymes involved in carbohydrate metabolism were present compared to Phytophthora species, with the notable absence of cutinases, suggesting a significant difference in virulence mechanisms between P. ultimum and more host specific oomycete species. Although we observed a high degree of orthology with Phytophthora genomes, there were novel features of the P. ultimum proteome including an expansion of genes involved in proteolysis and genes unique to Pythium. We identified a small gene family of cadherins, proteins involved in cell adhesion, the first report in a genome outside the metazoans. Conclusions: Access to the P. ultimum genome has revealed not only core pathogenic mechanisms within the oomycetes but also lineage specific genes associated with the alternative virulence and lifestyles found within the pythiaceous lineages compared to the Peronosporaceae