The Pediatric Cell Atlas: defining the growth phase of human development at single-cell resolution

Single-cell gene expression analyses of mammalian tissues have uncovered profound stage-specific molecular regulatory phenomena that have changed the understanding of unique cell types and signaling pathways critical for lineage determination, morphogenesis, and growth. We discuss here the case for a Pediatric Cell Atlas as part of the Human Cell Atlas consortium to provide single-cell profiles and spatial characterization of gene expression across human tissues and organs. Such data will complement adult and developmentally focused HCA projects to provide a rich cytogenomic framework for understanding not only pediatric health and disease but also environmental and genetic impacts across the human lifespan

Materializing digital collecting: an extended view of digital materiality

If digital objects are abundant and ubiquitous, why should consumers pay for, much less collect them? The qualities of digital code present numerous challenges for collecting, yet digital collecting can and does occur. We explore the role of companies in constructing digital consumption objects that encourage and support collecting behaviours, identifying material configuration techniques that materialise these objects as elusive and authentic. Such techniques, we argue, may facilitate those pleasures of collecting otherwise absent in the digital realm. We extend theories of collecting by highlighting the role of objects and the companies that construct them in materialising digital collecting. More broadly, we extend theories of digital materiality by highlighting processes of digital material configuration that occur in the pre-objectification phase of materialisation, acknowledging the role of marketing and design in shaping the qualities exhibited by digital consumption objects and consequently related consumption behaviours and experiences

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation

DNA methylation quantitative trait locus (mQTL) analyses on 32,851 participants identify genetic variants associated with DNA methylation at 420,509 sites in blood, resulting in a database of >270,000 independent mQTLs.Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15-17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype-phenotype map than previously anticipated.Molecular Epidemiolog

Sequence variants affecting eosinophil numbers associate with asthma and myocardial infarction.

Eosinophils are pleiotropic multifunctional leukocytes involved in initiation and propagation of inflammatory responses and thus have important roles in the pathogenesis of inflammatory diseases. Here we describe a genome-wide association scan for sequence variants affecting eosinophil counts in blood of 9,392 Icelanders. The most significant SNPs were studied further in 12,118 Europeans and 5,212 East Asians. SNPs at 2q12 (rs1420101), 2q13 (rs12619285), 3q21 (rs4857855), 5q31 (rs4143832) and 12q24 (rs3184504) reached genome-wide significance (P = 5.3 x 10(-14), 5.4 x 10(-10), 8.6 x 10(-17), 1.2 x 10(-10) and 6.5 x 10(-19), respectively). A SNP at IL1RL1 associated with asthma (P = 5.5 x 10(-12)) in a collection of ten different populations (7,996 cases and 44,890 controls). SNPs at WDR36, IL33 and MYB that showed suggestive association with eosinophil counts were also associated with atopic asthma (P = 4.2 x 10(-6), 2.2 x 10(-5) and 2.4 x 10(-4), respectively). We also found that a nonsynonymous SNP at 12q24, in SH2B3, associated significantly (P = 8.6 x 10(-8)) with myocardial infarction in six different populations (6,650 cases and 40,621 controls)

Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index.

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown. We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores. We included 35,668 children from 20 studies in the discovery phase and 11,873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide-significance (P-value<5 x 10(-8)) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B, and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) (Standard Error (SE) 0.007), 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503, and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value=3.12 x 10(-10)) increase in childhood body mass index in a population of 1,955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in strength of the associations with body mass index

Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index

A large number of genetic loci are associated with adult body mass index. However, the genetics of childhood body mass index are largely unknown.We performed a meta-analysis of genome-wide association studies of childhood body mass index, using sex- and age-adjusted standard deviation scores.We included 35 668 children from 20 studies in the discovery phase and 11 873 children from 13 studies in the replication phase. In total, 15 loci reached genome-wide significance (P-value < 5 × 10-8) in the joint discovery and replication analysis, of which 12 are previously identified loci in or close to ADCY3, GNPDA2, TMEM18, SEC16B, FAIM2, FTO, TFAP2B, TNNI3K, MC4R, GPR61, LMX1B and OLFM4 associated with adult body mass index or childhood obesity. We identified three novel loci: rs13253111 near ELP3, rs8092503 near RAB27B and rs13387838 near ADAM23. Per additional risk allele, body mass index increased 0.04 Standard Deviation Score (SDS) [Standard Error (SE) 0.007], 0.05 SDS (SE 0.008) and 0.14 SDS (SE 0.025), for rs13253111, rs8092503 and rs13387838, respectively. A genetic risk score combining all 15 SNPs showed that each additional average risk allele was associated with a 0.073 SDS (SE 0.011, P-value = 3.12 × 10-10) increase in childhood body mass index in a population of 1955 children. This risk score explained 2% of the variance in childhood body mass index. This study highlights the shared genetic background between childhood and adult body mass index and adds three novel loci. These loci likely represent age-related differences in stren

Correction to: Is diet partly responsible for differences in COVID-19 death rates between and within countries? (Clinical and Translational Allergy, (2020), 10, 1, (16), 10.1186/s13601-020-00323-0)

Following publication of the original article [1], the authors identified an error in the affiliation list. The affiliation of author G. Walter Canonica should have been split up into two affiliations: • Personalized Medicine, Asthma and Allergy – Humanitas Clinical and Research Center – IRCCS, Rozzano (MI), Italy • Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (MI), Italy The corrected affiliation list is reflected in this Correction. © 2020, The Author(s)