From QTLs for enzyme activity to candidate genes in maize

In order to facilitate the search for genes underlying QTLs (Quantitative Trait Loci), the activities of key enzymes of the carbohydrate metabolism in maize, and the concentration of their substrates or products were used as quantitative traits. For each of the chosen enzyme, i.e. ADPglucose pyrophosphorylase, sucrose-phosphate-synthase and invertases, the corresponding cDNA was available. Since biochemical traits are more closely related to gene expression than agronomic traits, co-locations could be expected between an enzyme structural gene and a QTL for its enzyme activity, and/or the corresponding product or substrate content. This approach was applied using recombinant inbred lines on leaves at 3- or 4-leaf stage, under control and water stress conditions and on grain, at maturity. Several QTLs were detected for each trait, particularly for two enzyme activities measured in mature leaves. Apparent co-locations between QTL for activity and structural locus were observed for sucrose-phosphate-synthase (chromosome 8) and acid-soluble invertase (chromosome 2 and 5). Leaf acid-soluble (vacuolar) invertase provided an interesting case since QTL, on chromosome 5, explaining 17% of variability was apparently co-located with the Ivr2 gene encoding a vacuolar invertase protein which was strongly water-stress inducible. Similarly, in grain, an amylose QTL co-located with the Sh2 gene of ADPglucose pyrophosphorylase. The reliability of this candidate was further tested through the examination of Sh2 DNA polymorphism in 46 genetically unrelated lines. A correlation was obtained between this polymorphism and kernel starch content, which further validated Sh2 as a candidate. Some improvements or alternatives to this strategy are briefly discussed.      

Revealing allele-specific gene expression by single-cell transcriptomics

Single-cell sequencing has emerged as a revolutionary method that reveals biological processes with unprecedented resolution and scale, and has already greatly impacted biology and medicine. To investigate processes such as alternative splicing, novel exon detection and allele-specific expression (ASE), full-length based single-cell RNA-seq methods are required for broad sequence coverage and single nucleotide polymorphism (SNP) identification. In this review, we revisit recent achievements from studies that used single-cell RNA-seq to advance our understanding of ASE in the context of both autosomal and X-chromosome genes. We also recapitulate useful bioinformatic tools developed to identify haplotype phase.     

Pathway deregulation and expression QTLs in response to Actinobacillus pleuropneumoniae infection in swine

Actinobacillus (A.) pleuropneumoniae is among the most important pathogens in pig. The agent causes severe economic losses due to decreased performance, the occurrence of acute or chronic pleuropneumonia, and an increase in death incidence. Since therapeutics cannot be used in a sustainable manner, and vaccination is not always available, new prophylactic measures are urgently needed. Recent research has provided evidence for a genetic predisposition in susceptibility to A. pleuropneumoniae in a Hampshire × German Landrace F2 family with 170 animals. The aim of the present study is to characterize the expression response in this family in order to unravel resistance and susceptibility mechanisms and to prioritize candidate genes for future fine mapping approaches. F2 pigs differed distinctly in clinical, pathological, and microbiological parameters after challenge with A. pleuropneumoniae. We monitored genome-wide gene expression from the 50 most and 50 least susceptible F2 pigs and identified 171 genes differentially expressed between these extreme phenotypes. We combined expression QTL analyses with network analyses and functional characterization using gene set enrichment analysis and identified a functional hotspot on SSC13, including 55 eQTL. The integration of the different results provides a resource for candidate prioritization for fine mapping strategies, such as TF, TFRC, RUNX1, TCN1, HP, CD14, among others.     

Meeting report for the 4th Annual Complex Trait Consortium Meeting: From QTLs to Systems Genetics

The fourth annual meeting of the Complex Trait Consortium (CTC) was held in Groningen, the Netherlands on June 26–29, 2005. This meeting, which set a new attendance record, followed three previous and highly successful meetings. The focus at this meeting was on continued resource development and the exiting new field of systems genetics, the integration and anchoring of multi-dimensional data-types to underlying genetic variation. A new aspect at the meeting was the three-minute, ‘come see my poster’ presentations that generated significant interaction at the poster sessions. If the 2005 meeting is an indicator of things to come, future meetings promise to offer even more exciting research efforts to integrate high-throughput biological measurements with genetic variation to unravel the mechanisms responsible for inter-individual variation.     

From RNA-seq reads to differential expression results

Many methods and tools are available for preprocessing high-throughput RNA sequencing data and detecting differential expression.     

Treatment- and population-dependent activity patterns of behavioral and expression QTLs

Genetic control of gene expression and higher-order phenotypes is almost invariably dependent on environment and experimental conditions. We use two families of recombinant inbred strains of mice (LXS and BXD) to study treatment- and genotype-dependent control of hippocampal gene expression and behavioral phenotypes. We analyzed responses to all combinations of two experimental perturbations, ethanol and restraint stress, in both families, allowing for comparisons across 8 combinations of treatment and population. We introduce the concept of QTL activity patterns to characterize how associations between genomic loci and traits vary across treatments. We identified several significant behavioral QTLs and many expression QTLs (eQTLs). The behavioral QTLs are highly dependent on treatment and population. We classified eQTLs into three groups: cis-eQTLs (expression variation that maps to within 5 Mb of the cognate gene), syntenic trans-eQTLs (the gene and the QTL are on the same chromosome but not within 5 Mb), and non-syntenic trans-eQTLs (the gene and the QTL are on different chromosomes). We found that most non-syntenic trans-eQTLs were treatment-specific whereas both classes of syntenic eQTLs were more conserved across treatments. We also found there was a correlation between regions along the genome enriched for eQTLs and SNPs that were conserved across the LXS and BXD families. Genes with eQTLs that co-localized with the behavioral QTLs and displayed similar QTL activity patterns were identified as potential candidate genes associated with the phenotypes, yielding identification of novel genes as well as genes that have been previously associated with responses to ethanol.     

From modules to medicine: How modular domains and their associated networks can enable personalized medicine

Unveiling of cancer genomes is unleashing new therapeutic strategies for cancer. With cancer parts lists in hand, new approaches to personalized medicine can be developed by understanding the assembly of cancer machines using modular domains in proteins and their associated networks. Using the Src-homology-2 (SH2) domain as an example, new profiling approaches can discern global patterns of tyrosine phosphorylation in cancer cells that can enable molecular cancer medicine. Identifying and quantifying protein-protein interactions also has the potential to subtype tumors and guide clinical decision making. These approaches should extend the impact of genomics through viewing the architecture of cancer systems and improve predictions of patient outcome and therapeutic response, as well as guide combination therapy approaches that attack cancer systems.