False Discovery Rate
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Epigenomics ◽  
2021 ◽  
Brian T Joyce ◽  
Huikun Liu ◽  
Leishen Wang ◽  
Jun Wang ◽  
Yinan Zheng ◽  

Background & objectives: Examine maternal gestational diabetes mellitus (GDM), macrosomia and DNA methylation in candidate genes IGF1, IGF2, H19, ARHGRF11, MEST, NR3C1, ADIPOQ and RETN. Materials & methods: 1145 Children (572 GDM cases and 573 controls) from The Tianjin GDM study, including 177 with macrosomia, had blood DNA collection at median age 5.9 (range: 3.1–10.0). We used logistic regression to screen for associations with GDM and model macrosomia on 37 CpGs, and performed mediation analysis. Results: One CpG was associated with macrosomia at false discovery rate (FDR) <0.05 (cg14428359 in MEST); two (cg19466922 in MEST and cg26263166 in IGF2) were associated at p < 0.05 but mediated 26 and 13%, respectively. Conclusion: MEST and IGF2 were previously identified for potential involvement in fetal growth and development ( Trial Registration number: NCT01554358 [ClinicalTrials.gov] ).

2021 ◽  
Hang Gao ◽  
Li Li Zhao ◽  
Qun Zhao ◽  
Hua Li Zhang ◽  
Feng Bao Zhao ◽  

Chemical crosslinking coupled with mass spectrometry (CXMS) has emerged as a powerful technique to capture the dynamic information of protein complexes with high sensitivity, throughput and sample universality. To advance the study of in-vivo protein structures and protein-protein interactions on the large scale, a new alkynyl-enrichable crosslinker was developed with high efficiency of membrane penetration, reactivity and enrichment. The crosslinker was successfully used for in-vivo crosslinking of intact human cells, resulting in 6820 non-redundant crosslinks identified at a false discovery rate (FDR) of 1% using pLink 2.0, which 4898 (71.8%) of the cross-links were assigned as intraprotein and 1922 (28.2%) were interprotein links. To our knowledge, this is also the first time to realize the in-vivo crosslinking with a non-cleavable cross-linker for homo species cells.

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1191
Patrick M. Carry ◽  
Elizabeth A. Terhune ◽  
George D. Trahan ◽  
Lauren A. Vanderlinden ◽  
Cambria I. Wethey ◽  

Epigenetic mechanisms may contribute to idiopathic scoliosis (IS). We identified 8 monozygotic twin pairs with IS, 6 discordant (Cobb angle difference >10°) and 2 concordant (Cobb angle difference ≤2°). Genome-wide methylation in blood was measured with the Infinium HumanMethylation EPIC Beadchip. We tested for differences in methylation and methylation variability between discordant twins and tested the association between methylation and curve severity in all twins. Differentially methylated region (DMR) analyses identified gene promoter regions. Methylation at cg12959265 (chr. 7 DPY19L1) was less variable in cases (false discovery rate (FDR) = 0.0791). We identified four probes (false discovery rate, FDR < 0.10); cg02477677 (chr. 17, RARA gene), cg12922161 (chr. 2 LOC150622 gene), cg08826461 (chr. 2), and cg16382077 (chr. 7) associated with curve severity. We identified 57 DMRs where hyper- or hypo-methylation was consistent across the region and 28 DMRs with a consistent association with curve severity. Among DMRs, 21 were correlated with bone methylation. Prioritization of regions based on methylation concordance in bone identified promoter regions for WNT10A (WNT signaling), NPY (regulator of bone and energy homeostasis), and others predicted to be relevant for bone formation/remodeling. These regions may aid in understanding the complex interplay between genetics, environment, and IS.

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1183
Jun Dai ◽  
Ming Leung ◽  
Weihua Guan ◽  
Han‐Tian Guo ◽  
Ruth E. Krasnow ◽  

Epigenetics is a mechanism underlying cardiovascular disease. It is unknown whether DNA hydroxymethylation is prospectively associated with the risk for cardiovascular death independent of germline and common environment. Male twin pairs middle-aged in 1969–1973 and discordant for cardiovascular death through December 31, 2014, were included. Hydroxymethylation was quantified in buffy coat DNA collected in 1986–1987. The 1893 differentially hydroxymethylated regions (DhMRs) were identified after controlling for blood leukocyte subtypes and age among 12 monozygotic (MZ) pairs (Benjamini–Hochberg False Discovery Rate < 0.01), of which the 102 DhMRs were confirmed with directionally consistent log2-fold changes and p < 0.01 among additional 7 MZ pairs. These signature 102 DhMRs, independent of the germline, were located on all chromosomes except for chromosome 21 and the Y chromosome, mainly within/overlapped with intergenic regions and introns, and predominantly hyper-hydroxymethylated. A binary linear classifier predicting cardiovascular death among 19 dizygotic pairs was identified and equivalent to that generated from MZ via the 2D transformation. Computational bioinformatics discovered pathways, phenotypes, and DNA motifs for these DhMRs or their subtypes, suggesting that hydroxymethylation was a pathophysiological mechanism underlying cardiovascular death that might be influenced by genetic factors and warranted further investigations of mechanisms of these signature regions in vivo and in vitro.

2021 ◽  
Vol 22 (1) ◽  
Daniel L. Cameron ◽  
Jonathan Baber ◽  
Charles Shale ◽  
Jose Espejo Valle-Inclan ◽  
Nicolle Besselink ◽  

AbstractGRIDSS2 is the first structural variant caller to explicitly report single breakends—breakpoints in which only one side can be unambiguously determined. By treating single breakends as a fundamental genomic rearrangement signal on par with breakpoints, GRIDSS2 can explain 47% of somatic centromere copy number changes using single breakends to non-centromere sequence. On a cohort of 3782 deeply sequenced metastatic cancers, GRIDSS2 achieves an unprecedented 3.1% false negative rate and 3.3% false discovery rate and identifies a novel 32–100 bp duplication signature. GRIDSS2 simplifies complex rearrangement interpretation through phasing of structural variants with 16% of somatic calls phasable using paired-end sequencing.

2021 ◽  
Vol 11 (1) ◽  
Stephan van Vliet ◽  
James R. Bain ◽  
Michael J. Muehlbauer ◽  
Frederick D. Provenza ◽  
Scott L. Kronberg ◽  

AbstractA new generation of plant-based meat alternatives—formulated to mimic the taste and nutritional composition of red meat—have attracted considerable consumer interest, research attention, and media coverage. This has raised questions of whether plant-based meat alternatives represent proper nutritional replacements to animal meat. The goal of our study was to use untargeted metabolomics to provide an in-depth comparison of the metabolite profiles a popular plant-based meat alternative (n = 18) and grass-fed ground beef (n = 18) matched for serving size (113 g) and fat content (14 g). Despite apparent similarities based on Nutrition Facts panels, our metabolomics analysis found that metabolite abundances between the plant-based meat alternative and grass-fed ground beef differed by 90% (171 out of 190 profiled metabolites; false discovery rate adjusted p < 0.05). Several metabolites were found either exclusively (22 metabolites) or in greater quantities in beef (51 metabolites) (all, p < 0.05). Nutrients such as docosahexaenoic acid (ω-3), niacinamide (vitamin B3), glucosamine, hydroxyproline and the anti-oxidants allantoin, anserine, cysteamine, spermine, and squalene were amongst those only found in beef. Several other metabolites were found exclusively (31 metabolites) or in greater quantities (67 metabolites) in the plant-based meat alternative (all, p < 0.05). Ascorbate (vitamin C), phytosterols, and several phenolic anti-oxidants such as loganin, sulfurol, syringic acid, tyrosol, and vanillic acid were amongst those only found in the plant-based meat alternative. Large differences in metabolites within various nutrient classes (e.g., amino acids, dipeptides, vitamins, phenols, tocopherols, and fatty acids) with physiological, anti-inflammatory, and/or immunomodulatory roles indicate that these products should not be viewed as truly nutritionally interchangeable, but could be viewed as complementary in terms of provided nutrients. The new information we provide is important for making informed decisions by consumers and health professionals. It cannot be determined from our data if either source is healthier to consume.

Emily S. Lau ◽  
Samantha M. Paniagua ◽  
Shahrooz Zarbafian ◽  
Udo Hoffman ◽  
Michelle T. Long ◽  

Background Obesity may be associated with a range of cardiometabolic manifestations. We hypothesized that proteomic profiling may provide insights into the biological pathways that contribute to various obesity‐associated cardiometabolic traits. We sought to identify proteomic signatures of obesity and examine overlap with related cardiometabolic traits, including abdominal adiposity, insulin resistance, and adipose depots. Methods and Results We measured 71 circulating cardiovascular disease protein biomarkers in 6981 participants (54% women; mean age, 49 years). We examined the associations of obesity, computed tomography measures of adiposity, cardiometabolic traits, and incident metabolic syndrome with biomarkers using multivariable regression models. Of the 71 biomarkers examined, 45 were significantly associated with obesity, of which 32 were positively associated and 13 were negatively associated with obesity (false discovery rate q <0.05 for all). There was significant overlap of biomarker profiles of obesity and cardiometabolic traits, but 23 biomarkers, including melanoma cell adhesion molecule (MCAM), growth differentiation factor‐15 (GDF15), and lipoprotein(a) (LPA) were unique to metabolic traits only. Using hierarchical clustering, we found that the protein biomarkers clustered along 3 main trait axes: adipose, metabolic, and lipid traits. In longitudinal analyses, 6 biomarkers were significantly associated with incident metabolic syndrome: apolipoprotein B (apoB), insulin‐like growth factor‐binding protein 2 (IGFBP2), plasma kallikrein (KLKB1), complement C2 (C2), fibrinogen (FBN), and N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP); false discovery rate q <0.05 for all. Conclusions We found that the proteomic architecture of obesity overlaps considerably with associated cardiometabolic traits, implying shared pathways. Despite overlap, hierarchical clustering of proteomic profiles identified 3 distinct clusters of cardiometabolic traits: adipose, metabolic, and lipid. Further exploration of these novel protein targets and associated pathways may provide insight into the mechanisms responsible for the progression from obesity to cardiometabolic disease.

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