A common genetic variant in the 15q24 nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) is associated with a reduced ability of women to quit smoking in pregnancy

Maternal smoking during pregnancy is associated with low birth weight and adverse pregnancy outcomes. Women are more likely to quit smoking during pregnancy than at any other time in their lives, but some pregnant women continue to smoke. A recent genome-wide association study demonstrated an association between a common polymorphism (rs1051730) in the nicotinic acetylcholine receptor gene cluster (CHRNA5–CHRNA3–CHRNB4) and both smoking quantity and nicotine dependence. We aimed to test whether the same polymorphism that predisposes to greater cigarette consumption would also reduce the likelihood of smoking cessation in pregnancy. We studied 7845 pregnant women of European descent from the South-West of England. Using 2474 women who smoked regularly immediately pre-pregnancy, we analysed the association between the rs1051730 risk allele and both smoking cessation during pregnancy and smoking quantity. Each additional copy of the risk allele was associated with a 1.27-fold higher odds (95% CI 1.11–1.45) of continued smoking during pregnancy (P = 0.0006). Adjustment for pre-pregnancy smoking quantity weakened, but did not remove this association [odds ratio (OR) 1.20 (95% CI 1.03–1.39); P = 0.018]. The same risk allele was also associated with heavier smoking before pregnancy and in the first, but not the last, trimester [OR for smoking 10+ cigarettes/day versus 1–9/day in first trimester = 1.30 (95% CI 1.13–1.50); P = 0.0003]. To conclude, we have found strong evidence of association between the rs1051730 variant and an increased likelihood of continued smoking in pregnancy and have confirmed the previously observed association with smoking quantity. Our data support the role of genetic factors in influencing smoking cessation during pregnancy

Vascularized tissue‐engineered chambers promote survival and function of transplanted islets and improve glycemic control

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154402/1/fsb2fj054879fje.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154402/2/fsb2fj054879fje-sup-0001.pd

Adult height variants affect birth length and growth rate in children

Previous studies identified 180 single nucleotide polymorphisms (SNPs) associated with adult height, explaining ∼10% of the variance. The age at which these begin to affect growth is unclear. We modelled the effect of these SNPs on birth length and childhood growth. A total of 7768 participants in the Avon Longitudinal Study of Parents and Children had data available. Individual growth trajectories from 0 to 10 years were estimated using mixed-effects linear spline models and differences in trajectories by individual SNPs and allelic score were determined. The allelic score was associated with birth length (0.026 cm increase per ‘tall’ allele, SE = 0.003, P = 1 × 10−15, equivalent to 0.017 SD). There was little evidence of association between the allelic score and early infancy growth (0–3 months), but there was evidence of association between the allelic score and later growth. This association became stronger with each consecutive growth period, per ‘tall’ allele per month effects were 0.015 SD (3 months–1 year, SE = 0.004), 0.023 SD (1–3 years, SE = 0.003) and 0.028 SD (3–10 years, SE = 0.003). By age 10, the mean height difference between individuals with ≤170 versus ≥191 ‘tall’ alleles (the top and bottom 10%) was 4.7 cm (0.8 SD), explaining ∼5% of the variance. There was evidence of associations with specific growth periods for some SNPs (rs3791675, EFEMP1 and rs6569648, L3MBTL3) and supportive evidence for previously reported age-dependent effects of HHIP and SOCS2 SNPs. SNPs associated with adult height influence birth length and have an increasing effect on growth from late infancy through to late childhood. By age 10, they explain half the height variance (∼5%) of that explained in adults (∼10%)

The functional "KL-VS" variant of KLOTHO is not associated with type 2 diabetes in 5028 UK Caucasians

BACKGROUND: Klotho has an important role in insulin signalling and the development of ageing-like phenotypes in mice. The common functional "KL-VS" variant in the KLOTHO (KL) gene is associated with longevity in humans but its role in type 2 diabetes is not known. We performed a large case-control and family-based study to test the hypothesis that KL-VS is associated with type 2 diabetes in a UK Caucasian population. METHODS: We genotyped 1793 cases, 1619 controls and 1616 subjects from 509 families for the single nucleotide polymorphism (SNP) F352V (rs9536314) that defines the KL-VS variant. Allele and genotype frequencies were compared between cases and controls. Family-based analysis was used to test for over- or under-transmission of V352 to affected offspring. RESULTS: Despite good power to detect odds ratios of 1.2, there were no significant associations between alleles or genotypes and type 2 diabetes (V352 allele: odds ratio = 0.96 (0.84–1.09)). Additional analysis of quantitative trait data in 1177 healthy control subjects showed no association of the variant with fasting insulin, glucose, triglycerides, HDL- or LDL-cholesterol (all P > 0.05). However, the HDL-cholesterol levels observed across the genotype groups showed a similar, but non-significant, pattern to previously reported data. CONCLUSION: This is the first large-scale study to examine the association between common functional variation in KL and type 2 diabetes risk. We have found no evidence that the functional KL-VS variant is a risk factor for type 2 diabetes in a large UK Caucasian case-control and family-based study

A study of association between common variation in the growth hormone-chorionic somatomammotropin hormone gene cluster and adult fasting insulin in a UK Caucasian population

BACKGROUND: Reduced growth during infancy is associated with adult insulin resistance. In a UK Caucasian cohort, the CSH1.01 microsatellite polymorphism in the growth hormone-chorionic somatomammotropin hormone gene cluster was recently associated with increases in adult fasting insulin of approximately 23 pmol/l for TT homozygote males compared to D1D1 or D2D2 homozygotes (P = 0.001 and 0.009; n = 206 and 92, respectively), but not for females. TT males additionally had a 547-g lower weight at 1 year (n = 270; P = 0.008) than D2D2 males. We sought to replicate these data in healthy UK Caucasian subjects. We genotyped 1396 subjects (fathers, mothers and children) from a consecutive birth study for the CSH1.01 marker and analysed genotypes for association with 1-year weight in boys and fasting insulin in fathers. RESULTS: We found no evidence for association of CSH1.01 genotype with adult male fasting insulin concentrations (TT/D1D1 P = 0.38; TT/D2D2 P = 0.18) or weight at 1 year in boys (TT/D1D1 P = 0.76; TT/D2D2 P = 0.85). For fasting insulin, our data can exclude the previously observed effect sizes as the 95 % confidence intervals for the differences observed in our study exclude increases in fasting insulin of 9.0 and 12.6 pmol/l for TT relative to D1D1 and D2D2 homozygotes, respectively. Whilst we have fewer data on boys' 1-year weight than the original study, our data can exclude a reduction in 1-year weight greater than 557 g for TT relative to D2D2 homozygotes. CONCLUSION: We have not found association of the CSH1.01 genotype with fasting insulin or weight at 1 year. We conclude that the original study is likely to have over-estimated the effect size for fasting insulin, or that the difference in results reflects the younger age of subjects in this study relative to those in the previous study

SARS-CoV-2 RNA detected in blood products from patients with COVID-19 is not associated with infectious virus

Background: Laboratory diagnosis of SARS-CoV-2 infection (the cause of COVID-19) uses PCR to detect viral RNA (vRNA) in respiratory samples. SARS-CoV-2 RNA has also been detected in other sample types, but there is limited understanding of the clinical or laboratory significance of its detection in blood. Methods: We undertook a systematic literature review to assimilate the evidence for the frequency of vRNA in blood, and to identify associated clinical characteristics. We performed RT-PCR in serum samples from a UK clinical cohort of acute and convalescent COVID-19 cases (n=212), together with convalescent plasma samples collected by NHS Blood and Transplant (NHSBT) (n=462 additional samples). To determine whether PCR-positive blood samples could pose an infection risk, we attempted virus isolation from a subset of RNA-positive samples. Results: We identified 28 relevant studies, reporting SARS-CoV-2 RNA in 0-76% of blood samples; pooled estimate 10% (95%CI 5-18%). Among serum samples from our clinical cohort, 27/212 (12.7%) had SARS-CoV-2 RNA detected by RT-PCR. RNA detection occurred in samples up to day 20 post symptom onset, and was associated with more severe disease (multivariable odds ratio 7.5). Across all samples collected ≥28 days post symptom onset, 0/494 (0%, 95%CI 0-0.7%) had vRNA detected. Among our PCR-positive samples, cycle threshold (ct) values were high (range 33.5-44.8), suggesting low vRNA copy numbers. PCR-positive sera inoculated into cell culture did not produce any cytopathic effect or yield an increase in detectable SARS-CoV-2 RNA. Conclusions: vRNA was detectable at low viral loads in a minority of serum samples collected in acute infection, but was not associated with infectious SARS-CoV-2 (within the limitations of the assays used). This work helps to inform biosafety precautions for handling blood products from patients with current or previous COVID-19

Anatomy of STEM Teaching in American Universities: A Snapshot from a Large-Scale Observation Study

National and local initiatives focused on the transformation of STEM teaching in higher education have multiplied over the last decade. These initiatives often focus on measuring change in instructional practices, but it is difficult to monitor such change without a national picture of STEM educational practices, especially as characterized by common observational instruments. We characterized a snapshot of this landscape by conducting the first large scale observation-based study. We found that lecturing was prominent throughout the undergraduate STEM curriculum, even in classrooms with infrastructure designed to support active learning, indicating that further work is required to reform STEM education. Additionally, we established that STEM faculty’s instructional practices can vary substantially within a course, invalidating the commonly-used teaching evaluations based on a one-time observation

Integrating data types to estimate spatial patterns of avian migration across the Western Hemisphere

For many avian species, spatial migration patterns remain largely undescribed, especially across hemispheric extents. Recent advancements in tracking technologies and high-resolution species distribution models (i.e., eBird Status and Trends products) provide new insights into migratory bird movements and offer a promising opportunity for integrating independent data sources to describe avian migration. Here, we present a three-stage modeling framework for estimating spatial patterns of avian migration. First, we integrate tracking and band re-encounter data to quantify migratory connectivity, defined as the relative proportions of individuals migrating between breeding and nonbreeding regions. Next, we use estimated connectivity proportions along with eBird occurrence probabilities to produce probabilistic least-cost path (LCP) indices. In a final step, we use generalized additive mixed models (GAMMs) both to evaluate the ability of LCP indices to accurately predict (i.e., as a covariate) observed locations derived from tracking and band re-encounter data sets versus pseudo-absence locations during migratory periods and to create a fully integrated (i.e., eBird occurrence, LCP, and tracking/band re-encounter data) spatial prediction index for mapping species-specific seasonal migrations. To illustrate this approach, we apply this framework to describe seasonal migrations of 12 bird species across the Western Hemisphere during pre- and postbreeding migratory periods (i.e., spring and fall, respectively). We found that including LCP indices with eBird occurrence in GAMMs generally improved the ability to accurately predict observed migratory locations compared to models with eBird occurrence alone. Using three performance metrics, the eBird + LCP model demonstrated equivalent or superior fit relative to the eBird-only model for 22 of 24 species–season GAMMs. In particular, the integrated index filled in spatial gaps for species with over-water movements and those that migrated over land where there were few eBird sightings and, thus, low predictive ability of eBird occurrence probabilities (e.g., Amazonian rainforest in South America). This methodology of combining individual-based seasonal movement data with temporally dynamic species distribution models provides a comprehensive approach to integrating multiple data types to describe broad-scale spatial patterns of animal movement. Further development and customization of this approach will continue to advance knowledge about the full annual cycle and conservation of migratory birds