Integrative Analysis of DiseaseLand Omics Database for Disease Signatures and Treatments: A Bipolar Case Study.

Transcriptomics technologies such as next-generation sequencing and microarray platforms provide exciting opportunities for improving diagnosis and treatment of complex diseases. Transcriptomics studies often share similar hypotheses, but are carried out on different platforms, in different conditions, and with different analysis approaches. These factors, in addition to small sample sizes, can result in a lack of reproducibility. A clear understanding and unified picture of many complex diseases are still elusive, highlighting an urgent need to effectively integrate multiple transcriptomic studies for disease signatures. We have integrated more than 3,000 high-quality transcriptomic datasets in oncology, immunology, neuroscience, cardiovascular and metabolic disease, and from both public and internal sources (DiseaseLand database). We established a systematic data integration and meta-analysis approach, which can be applied in multiple disease areas to create a unified picture of the disease signature and prioritize drug targets, pathways, and compounds. In this bipolar case study, we provided an illustrative example using our approach to combine a total of 30 genome-wide gene expression studies using postmortem human brain samples. First, the studies were integrated by extracting raw FASTQ or CEL files, then undergoing the same procedures for preprocessing, normalization, and statistical inference. Second, both p-value and effect size based meta-analysis algorithms were used to identify a total of 204 differentially expressed (DE) genes (FDR < 0.05) genes in the prefrontal cortex. Among these were BDNF, VGF, WFS1, DUSP6, CRHBP, MAOA, and RELN, which have previously been implicated in bipolar disorder. Finally, pathway enrichment analysis revealed a role for GPCR, MAPK, immune, and Reelin pathways. Compound profiling analysis revealed MAPK and other inhibitors may modulate the DE genes. The ability to robustly combine and synthesize the information from multiple studies enables a more powerful understanding of this complex disease.

Plasma Protein and MicroRNA Biomarkers of Insulin Resistance: A Network-Based Integrative -Omics Analysis.

Although insulin resistance (IR) is a key pathophysiologic condition underlying various metabolic disorders, impaired cellular glucose uptake is one of many manifestations of metabolic derangements in the human body. To study the systems-wide molecular changes associated with obesity-dependent IR, we integrated information on plasma proteins and microRNAs in eight obese insulin-resistant (OIR, HOMA-IR > 2.5) and nine lean insulin-sensitive (LIS, HOMA-IR < 1.0) normoglycemic males. Of 374 circulating miRNAs we profiled, 65 species increased and 73 species decreased in the OIR compared to the LIS subjects, suggesting that the overall balance of the miRNA secretome is shifted in the OIR subjects. We also observed that 40 plasma proteins increased and 4 plasma proteins decreased in the OIR subjects compared to the LIS subjects, and most proteins are involved in metabolic and endocytic functions. We used an integrative -omics analysis framework called iOmicsPASS to link differentially regulated miRNAs with their target genes on the TargetScan map and the human protein interactome. Combined with tissue of origin information, the integrative analysis allowed us to nominate obesity-dependent and obesity-independent protein markers, along with potential sites of post-transcriptional regulation by some of the miRNAs. We also observed the changes in each -omics platform that are not linked by the TargetScan map, suggesting that proteins and microRNAs provide orthogonal information for the progression of OIR. In summary, our integrative analysis provides a network of elevated plasma markers of OIR and a global shift of microRNA secretome composition in the blood plasma.

Potential of a tomato MAGIC population to decipher the genetic control of quantitative traits and detect causal variants in the resequencing era.

Identification of the polymorphisms controlling quantitative traits remains a challenge for plant geneticists. Multiparent advanced generation intercross (MAGIC) populations offer an alternative to traditional linkage or association mapping populations by increasing the precision of quantitative trait loci (QTL) mapping. Here, we present the first tomato MAGIC population and highlight its potential for the valorization of intraspecific variation, QTL mapping and causal polymorphism identification. The population was developed by crossing eight founder lines, selected to include a wide range of genetic diversity, whose genomes have been previously resequenced. We selected 1536 SNPs among the 4 million available to enhance haplotype prediction and recombination detection in the population. The linkage map obtained showed an 87% increase in recombination frequencies compared to biparental populations. The prediction of the haplotype origin was possible for 89% of the MAGIC line genomes, allowing QTL detection at the haplotype level. We grew the population in two greenhouse trials and detected QTLs for fruit weight. We mapped three stable QTLs and six specific of a location. Finally, we showed the potential of the MAGIC population when coupled with whole genome sequencing of founder lines to detect candidate SNPs underlying the QTLs. For a previously cloned QTL on chromosome 3, we used the predicted allelic effect of each founder and their genome sequences to select putative causal polymorphisms in the supporting interval. The number of candidate polymorphisms was reduced from 12 284 (in 800 genes) to 96 (in 54 genes), including the actual causal polymorphism. This population represents a new permanent resource for the tomato genetics community.

AlphaMPSim: flexible simulation of multi-parent crosses.

UNLABELLED: Multi-parent crosses of recombinant inbred lines exist in many species for fine-scale analysis of genome structure and marker-trait association. These populations encompass a wide range of crossing designs with varying potential. AlphaMPSim is a flexible simulation program that is efficiently designed for comparison of alternative designs for traits with varying genetic architectures and biallelic markers with densities up to full sequence. A large pool of founder haplotypes can be supplied by the user, or generated via integration with external coalescent simulation programs such as MaCS. From these, diverse founders for multi-parent designs can be generated automatically, and users can compare designs generated from diverse pedigrees. Full tracking of identity by descent status of alleles within the pedigree is undertaken, and output files are compatible with commonly available analysis packages in R. AVAILABILITY AND IMPLEMENTATION: Executable versions of AlphaMPSim for Mac and Linux and a user manual are available at http://www.roslin.ed.ac.uk/john-hickey/software-packages/.

Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array.

High-density single nucleotide polymorphism (SNP) genotyping arrays are a powerful tool for studying genomic patterns of diversity, inferring ancestral relationships between individuals in populations and studying marker-trait associations in mapping experiments. We developed a genotyping array including about 90,000 gene-associated SNPs and used it to characterize genetic variation in allohexaploid and allotetraploid wheat populations. The array includes a significant fraction of common genome-wide distributed SNPs that are represented in populations of diverse geographical origin. We used density-based spatial clustering algorithms to enable high-throughput genotype calling in complex data sets obtained for polyploid wheat. We show that these model-free clustering algorithms provide accurate genotype calling in the presence of multiple clusters including clusters with low signal intensity resulting from significant sequence divergence at the target SNP site or gene deletions. Assays that detect low-intensity clusters can provide insight into the distribution of presence-absence variation (PAV) in wheat populations. A total of 46 977 SNPs from the wheat 90K array were genetically mapped using a combination of eight mapping populations. The developed array and cluster identification algorithms provide an opportunity to infer detailed haplotype structure in polyploid wheat and will serve as an invaluable resource for diversity studies and investigating the genetic basis of trait variation in wheat.

A multiparent advanced generation inter-cross population for genetic analysis in wheat.

We present the first results from a novel multiparent advanced generation inter-cross (MAGIC) population derived from four elite wheat cultivars. The large size of this MAGIC population (1579 progeny), its diverse genetic composition and high levels of recombination all contribute to its value as a genetic resource. Applications of this resource include interrogation of the wheat genome and the analysis of gene-trait association in agronomically important wheat phenotypes. Here, we report the utilization of a MAGIC population for the first time for linkage map construction. We have constructed a linkage map with 1162 DArT, single nucleotide polymorphism and simple sequence repeat markers distributed across all 21 chromosomes. We benchmark this map against a high-density DArT consensus map created by integrating more than 100 biparental populations. The linkage map forms the basis for further exploration of the genetic architecture within the population, including characterization of linkage disequilibrium, founder contribution and inclusion of an alien introgression into the genetic map. Finally, we demonstrate the application of the resource for quantitative trait loci mapping using the complex traits plant height and hectolitre weight as a proof of principle.