Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.

AIMS/HYPOTHESIS: Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts. METHODS: Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD. RESULTS: There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable. CONCLUSIONS/INTERPRETATION: This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

A role for heritable transcriptomic variation in maize adaptation to temperate environments.

BACKGROUND: Transcription bridges genetic information and phenotypes. Here, we evaluated how changes in transcriptional regulation enable maize (Zea mays), a crop originally domesticated in the tropics, to adapt to temperate environments. RESULT: We generated 572 unique RNA-seq datasets from the roots of 340 maize genotypes. Genes involved in core processes such as cell division, chromosome organization and cytoskeleton organization showed lower heritability of gene expression, while genes involved in anti-oxidation activity exhibited higher expression heritability. An expression genome-wide association study (eGWAS) identified 19,602 expression quantitative trait loci (eQTLs) associated with the expression of 11,444 genes. A GWAS for alternative splicing identified 49,897 splicing QTLs (sQTLs) for 7614 genes. Genes harboring both cis-eQTLs and cis-sQTLs in linkage disequilibrium were disproportionately likely to encode transcription factors or were annotated as responding to one or more stresses. Independent component analysis of gene expression data identified loci regulating co-expression modules involved in oxidation reduction, response to water deprivation, plastid biogenesis, protein biogenesis, and plant-pathogen interaction. Several genes involved in cell proliferation, flower development, DNA replication, and gene silencing showed lower gene expression variation explained by genetic factors between temperate and tropical maize lines. A GWAS of 27 previously published phenotypes identified several candidate genes overlapping with genomic intervals showing signatures of selection during adaptation to temperate environments. CONCLUSION: Our results illustrate how maize transcriptional regulatory networks enable changes in transcriptional regulation to adapt to temperate regions.

Druggable cancer secretome: neoplasm-associated traits.

BACKGROUND: The genome association databases provide valuable clues to identify novel targets for cancer diagnosis and therapy. Genes harboring phenotype-associated polymorphisms for neoplasm traits can be identified using diverse bioinformatics tools. The recent availability of various protein expression datasets from normal human tissues, including the body fluids, enables for baseline expression profiling of the cancer secretome. Chemoinformatics approaches can help identify drug-like compounds from the protein 3D structures. MATERIALS AND METHODS: The National Center for Biotechnology Information (NCBI) Phenome Genome Integrator (PheGenI) tool was enriched for neoplasm-associated traits. The neoplasm genes were characterized using diverse bioinformatics tools for pathways, gene ontology, genome-wide association, protein expression and functional class. Chemogenomics analysis was performed using the canSAR protein annotation tool. RESULTS: The neoplasm-associated traits segregated into 1,305 genes harboring 2,837 single nucleotide polymorphisms (SNPs). Also identified were 65 open reading frames (ORFs) encompassing 137 SNPs. The neoplasm genes and the associated SNPs were classified into distinct tumor types. Protein expression in the secretome was seen for 913 of the neoplasm-associated genes, including 17 novel uncharacterized ORFs. Druggable proteins, including enzymes, transporters, channel proteins and receptors, were detected. Thirty-four novel druggable lead genes emerged from these studies, including seven cancer lead targets. Chemogenomics analysis using the canSAR protein annotation tool identified 168 active compounds (<1 µM) for the neoplasm genes in the body fluids. Among these, 7 most active lead compounds with drug-like properties (1-600 nM) were identified for the cancer lead targets, encompassing enzymes and receptors. CONCLUSION: Over seventy percent of the neoplasm trait-associated genes were detected in the body fluids, such as ascites, blood, tear, milk, semen, urine, etc. Ligand-based druggabililty analysis helped establish lead prioritization. The association of these proteins with diverse cancer types and other diseases provides a framework to develop novel diagnosis and therapy targets.

Phenome-genome association studies of pancreatic cancer: new targets for therapy and diagnosis.

BACKGROUND: Pancreatic cancer, has a very high mortality rate and requires novel molecular targets for diagnosis and therapy. Genetic association studies over databases offer an attractive starting point for gene discovery. MATERIALS AND METHODS: The National Center for Biotechnology Information (NCBI) Phenome Genome Integrator (PheGenI) tool was enriched for pancreatic cancer-associated traits. The genes associated with the trait were characterized using diverse bioinformatics tools for Genome-Wide Association (GWA), transcriptome and proteome profile and protein classes for motif and domain. RESULTS: Two hundred twenty-six genes were identified that had a genetic association with pancreatic cancer in the human genome. This included 25 uncharacterized open reading frames (ORFs). Bioinformatics analysis of these ORFs identified putative druggable proteins and biomarkers including enzymes, transporters and G-protein-coupled receptor signaling proteins. Secreted proteins including a neuroendocrine factor and a chemokine were identified. Five out of these ORFs encompassed non coding RNAs. The ORF protein expression was detected in numerous body fluids, such as ascites, bile, pancreatic juice, milk, plasma, serum and saliva. Transcriptome and proteome analyses showed a correlation of mRNA and protein expression for nine ORFs. Analysis of the Catalogue of Somatic Mutations in Cancer (COSMIC) database revealed a strong correlation across copy number variations and mRNA over-expression for four ORFs. Mining of the International Cancer Gene Consortium (ICGC) database identified somatic mutations in a significant number of pancreatic patients' tumors for most of these ORFs. The pancreatic cancer-associated ORFs were also found to be genetically associated with other neoplasms, including leukemia, malignant melanoma, neuroblastoma and prostate carcinomas, as well as other unrelated diseases and disorders, such as Alzheimer's disease, Crohn's disease, coronary diseases, attention deficit disorder and addiction. CONCLUSION: Based on Genome-Wide Association Studies (GWAS), copy number variations, somatic mutational status and correlation of gene expression in pancreatic tumors at the mRNA and protein level, expression specificity in normal tissues and detection in body fluids, six ORFs emerged as putative leads for pancreatic cancer. These six targets provide a basis for accelerated drug discovery and diagnostic marker development for pancreatic cancer.