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The Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia
(2019)
The impact of many unfavorable childhood traits or diseases, such as low birth weight and mental disorders, is not limited to childhood and adolescence, as they are also associated with poor outcomes in adulthood, such as cardiovascular disease. Insight into the genetic etiology of childhood and adolescent traits and disorders may therefore provide new perspectives, not only on how to improve wellbeing during childhood, but also how to prevent later adverse outcomes. To achieve the sample sizes required for genetic research, the Early Growth Genetics (EGG) and EArly Genetics and Lifecourse Epidemiology (EAGLE) consortia were established. The majority of the participating cohorts are longitudinal population-based samples, but other cohorts with data on early childhood phenotypes are also involved. Cohorts often have a broad focus and collect(ed) data on various somatic and psychiatric traits as well as environmental factors. Genetic variants have been successfully identified for multiple traits, for example, birth weight, atopic dermatitis, childhood BMI, allergic sensitization, and pubertal growth. Furthermore, the results have shown that genetic factors also partly underlie the association with adult traits. As sample sizes are still increasing, it is expected that future analyses will identify additional variants. This, in combination with the development of innovative statistical methods, will provide detailed insight on the mechanisms underlying the transition from childhood to adult disorders. Both consortia welcome new collaborations. Policies and contact details are available from the corresponding authors of this manuscript and/or the consortium websites.
This article documents the addition of 283 microsatellite marker loci to the Molecular Ecology Resources Database. Loci were developed for the following species: Agalinis acuta; Ambrosia artemisiifolia; Berula erecta; Casuarius casuarius; Cercospora zeae-maydis; Chorthippus parallelus; Conyza canadensis; Cotesia sesamiae; Epinephelus acanthistius; Ficedula hypoleuca; Grindelia hirsutula; Guadua angustifolia; Leucadendron rubrum; Maritrema novaezealandensis; Meretrix meretrix; Nilaparvata lugens; Oxyeleotris marmoratus; Phoxinus neogaeus; Pristomyrmex punctatus; Pseudobagrus brevicorpus; Seiridium cardinale; Stenopsyche marmorata; Tetranychus evansi and Xerus inauris. These loci were cross-tested on the following species: Agalinis decemloba; Agalinis tenella; Agalinis obtusifolia; Agalinis setacea; Agalinis skinneriana; Cercospora zeina; Cercospora kikuchii; Cercospora sorghi; Mycosphaerella graminicola; Setosphaeria turcica; Magnaporthe oryzae; Cotesia flavipes; Cotesia marginiventris; Grindelia Xpaludosa; Grindelia chiloensis; Grindelia fastigiata; Grindelia lanceolata; Grindelia squarrosa; Leucadendron coniferum; Leucadendron salicifolium; Leucadendron tinctum; Leucadendron meridianum; Laodelphax striatellus; Sogatella furcifera; Phoxinus eos; Phoxinus rigidus; Phoxinus brevispinosus; Phoxinus bicolor; Tetranychus urticae; Tetranychus turkestani; Tetranychus ludeni; Tetranychus neocaledonicus; Tetranychus amicus; Amphitetranychus viennensis; Eotetranychus rubiphilus; Eotetranychus tiliarium; Oligonychus perseae; Panonychus citri; Bryobia rubrioculus; Schizonobia bundi; Petrobia harti; Xerus princeps; Spermophilus tridecemlineatus and Sciurus carolinensis.
Data integration has become a useful strategy for uncovering new insights into complex biological networks. We studied whether this approach can help to delineate the signal transducer and activator of transcription 6 (STAT6)-mediated transcriptional network driving T helper (Th) 2 cell fate decisions. To this end, we performed an integrative analysis of publicly available RNA-seq data of Stat6-knockout mouse studies together with STAT6 ChIP-seq data and our own gene expression time series data during Th2 cell differentiation. We focused on transcription factors (TFs), cytokines, and cytokine receptors and delineated 59 positively and 41 negatively STAT6-regulated genes, which were used to construct a transcriptional network around STAT6. The network illustrates that important and well-known TFs for Th2 cell differentiation are positively regulated by STAT6 and act either as activators for Th2 cells (e.g., Gata3, Atf3, Satb1, Nfil3, Maf, and Pparg) or as suppressors for other Th cell subpopulations such as Th1 (e.g., Ar), Th17 (e.g., Etv6), or iTreg (e.g., Stat3 and Hifla) cells. Moreover, our approach reveals 11 TFs (e.g., Atf5, Creb3l2, and Asb2) with unknown functions in Th cell differentiation. This fact together with the observed enrichment of asthma risk genes among those regulated by STAT6 underlines the potential value of the data integration strategy used here. Thus, our results clearly support the opinion that data integration is a useful tool to delineate complex physiological processes.