DataSheet1_Morroniside ameliorates inflammatory skeletal muscle atrophy via inhibiting canonical and non-canonical NF-κB and regulating protein synthesis/degradation.xlsx

No drug options exist for skeletal muscle atrophy in clinical, which poses a huge socio-economic burden, making development on drug interventions a general wellbeing need. Patients with a variety of pathologic conditions associated with skeletal muscle atrophy have systemically elevated inflammatory factors. Morroniside, derived from medicinal herb Cornus officinalis, possesses anti-inflammatory effect. However, whether and how morroniside combat muscle atrophy remain unknown. Here, we identified crucial genetic associations between TNFα/NF-κB pathway and grip strength based on population using 377,807 European participants from the United Kingdom Biobank dataset. Denervation increased TNFα in atrophying skeletal muscles, which inhibited myotube formation in vitro. Notably, morroniside treatment rescued TNFα-induced myotube atrophy in vitro and impeded skeletal muscle atrophy in vivo, resulting in increased body/muscles weights, No. of satellite cells, size of type IIA, IIX and IIB myofibers, and percentage of type IIA myofibers in denervated mice. Mechanistically, in vitro and/or in vivo studies demonstrated that morroniside could not only inhibit canonical and non-canonical NF-κB, inflammatory mediators (IL6, IL-1b, CRP, NIRP3, PTGS2, TNFα), but also down-regulate protein degradation signals (Follistatin, Myostatin, ALK4/5/7, Smad7/3), ubiquitin-proteasome molecules (FoxO3, Atrogin-1, MuRF1), autophagy-lysosomal molecules (Bnip3, LC3A, and LC3B), while promoting protein synthesis signals (IGF-1/IGF-1R/IRS-1/PI3K/Akt, and BMP14/BMPR2/ALK2/3/Smad5/9). Moreover, morroniside had no obvious liver and kidney toxicity. This human genetic, cells and mice pathological evidence indicates that morroniside is an efficacious and safe inflammatory muscle atrophy treatment and suggests its translational potential on muscle wasting.</p

Performance of Genotype Imputation for Low Frequency and Rare Variants from the 1000 Genomes

<div><p>Genotype imputation is now routinely applied in genome-wide association studies (GWAS) and meta-analyses. However, most of the imputations have been run using HapMap samples as reference, imputation of low frequency and rare variants (minor allele frequency (MAF) < 5%) are not systemically assessed. With the emergence of next-generation sequencing, large reference panels (such as the 1000 Genomes panel) are available to facilitate imputation of these variants. Therefore, in order to estimate the performance of low frequency and rare variants imputation, we imputed 153 individuals, each of whom had 3 different genotype array data including 317k, 610k and 1 million SNPs, to three different reference panels: the 1000 Genomes pilot March 2010 release (1KGpilot), the 1000 Genomes interim August 2010 release (1KGinterim), and the 1000 Genomes phase1 November 2010 and May 2011 release (1KGphase1) by using IMPUTE version 2. The differences between these three releases of the 1000 Genomes data are the sample size, ancestry diversity, number of variants and their frequency spectrum. We found that both reference panel and GWAS chip density affect the imputation of low frequency and rare variants. 1KGphase1 outperformed the other 2 panels, at higher concordance rate, higher proportion of well-imputed variants (info>0.4) and higher mean info score in each MAF bin. Similarly, 1M chip array outperformed 610K and 317K. However for very rare variants (MAF≤0.3%), only 0–1% of the variants were well imputed. We conclude that the imputation of low frequency and rare variants improves with larger reference panels and higher density of genome-wide genotyping arrays. Yet, despite a large reference panel size and dense genotyping density, very rare variants remain difficult to impute.</p></div

The proportion of well-imputed SNPs (info>0.4) in different MAF bins across imputation reference panels (Panel A is for the 317K genotypic array, Panel B is for 610K genotypic array, and Panel C is for 1M genotypic array).

<p>Panel D, E and F is a comparison of median info score across 3 reference panels for 317K, 610K and 1M genotypic array respectively.</p

The proportion of variants by Minor Allele Frequency (MAF) across imputation reference panels.

<p>The proportion of variants by Minor Allele Frequency (MAF) across imputation reference panels.</p

Overview of the imputation performances for the 3 genome-wide genotype arrays based on different reference panels.

<p>* SNP QC was done</p><p>** Well-imputed SNPs were those with proper info ≥ 0.4</p><p>Overview of the imputation performances for the 3 genome-wide genotype arrays based on different reference panels.</p

Concordance of the 9 imputation scenarios.

<p>Concordance of the 9 imputation scenarios.</p

The MAF distribution of the genotyped variants.

<p>* SNP QC was done</p><p>The MAF distribution of the genotyped variants.</p

Additional file 1: of Effect of CD14 polymorphisms on the risk of cardiovascular disease: evidence from a meta-analysis

Table S1. The information of publication bias in all Polymorphisms. Figure S1. Forest plot for the meta-analysis of the association between CD14 gene polymorphisms and CVD under the Dominant and Recessive models. Figure S2. Begg’s funnel plot of publication bias in the meta-analysis of the association of CD14 polymorphisms with CVD risk. Figure S3. Sensitivity analysis to assess the stability of the meta-analysis. (PDF 576 kb

Genome-wide association study meta-analysis for quantitative ultrasound parameters of bone identifies five novel loci for broadband ultrasound attenuation.

Osteoporosis is a common and debilitating bone disease that is characterised by low bone mineral density, typically assessed using dual-energy X-ray absorptiometry. Quantitative ultrasound (QUS), commonly utilising the two parameters velocity of sound (VOS) and broadband ultrasound attenuation (BUA), is an alternative technology used to assess bone properties at peripheral skeletal sites. The genetic influence on the bone qualities assessed by QUS remains an under-studied area. We performed a comprehensive GWAS including low-frequency variants (MAF ≥0.005) for BUA and VOS using a discovery population of individuals with whole-genome sequence (WGS) data from the UK10K project (n=1,268). These results were then meta-analysed with those from two deeply imputed GWAS replication cohorts (n=1,610 and 13,749). In the gender-combined analysis, we identified eight loci associated with BUA and five with VOS at the genome-wide significance level, including three novel loci for BUA at 8p23.1 (PPP1R3B), 11q23.1 (LOC387810) and 22q11.21 (SEPT5) (P = 2.4 × 10-8-1.6 × 10-9). Gene-based association testing in the gender-combined dataset revealed eight loci associated with BUA and seven with VOS at the genome-wide significance level, with one novel locus for BUA at FAM167A (8p23.1) (P = 1.4 × 10-6). An additional novel locus for BUA was seen in the male-specific analysis at DEFB103B (8p23.1) (P = 1.8 × 10-6). Fracture analysis revealed significant associations between variation at the WNT16 and RSPO3 loci and fracture risk (P = 0.004 and 4.0 × 10-4 respectively). In conclusion, by performing a large GWAS meta-analysis for QUS parameters of bone using a combination of WGS and deeply imputed genotype data, we have identified five novel genetic loci associated with BUA

Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel.

Imputing genotypes from reference panels created by whole-genome sequencing (WGS) provides a cost-effective strategy for augmenting the single-nucleotide polymorphism (SNP) content of genome-wide arrays. The UK10K Cohorts project has generated a data set of 3,781 whole genomes sequenced at low depth (average 7x), aiming to exhaustively characterize genetic variation down to 0.1% minor allele frequency in the British population. Here we demonstrate the value of this resource for improving imputation accuracy at rare and low-frequency variants in both a UK and an Italian population. We show that large increases in imputation accuracy can be achieved by re-phasing WGS reference panels after initial genotype calling. We also present a method for combining WGS panels to improve variant coverage and downstream imputation accuracy, which we illustrate by integrating 7,562 WGS haplotypes from the UK10K project with 2,184 haplotypes from the 1000 Genomes Project. Finally, we introduce a novel approximation that maintains speed without sacrificing imputation accuracy for rare variants