On the use of the Gram matrix for multivariate functional principal components analysis

Dimension reduction is crucial in functional data analysis (FDA). The key tool to reduce the dimension of the data is functional principal component analysis. Existing approaches for functional principal component analysis usually involve the diagonalization of the covariance operator. With the increasing size and complexity of functional datasets, estimating the covariance operator has become more challenging. Therefore, there is a growing need for efficient methodologies to estimate the eigencomponents. Using the duality of the space of observations and the space of functional features, we propose to use the inner-product between the curves to estimate the eigenelements of multivariate and multidimensional functional datasets. The relationship between the eigenelements of the covariance operator and those of the inner-product matrix is established. We explore the application of these methodologies in several FDA settings and provide general guidance on their usability.Comment: 23 pages, 12 figure

Exploration of a polygenic risk score for alcohol consumption:A longitudinal analysis from the ALSPAC cohort

BACKGROUND: Uncertainty remains about the true extent by which alcohol consumption causes a number of health outcomes. Genetic variants, or combinations of variants built into a polygenic risk score (PGRS), can be used in an instrumental variable framework to assess causality between a phenotype and disease outcome of interest, a method known as Mendelian randomisation (MR). We aimed to identify genetic variants involved in the aetiology of alcohol consumption, and develop a PGRS for alcohol. METHODS: Repeated measures of alcohol consumption from mothers and their offspring were collected as part of the Avon Longitudinal Study of Parents and Children. We tested the association between 89 SNPs (identified from either published GWAS data or from functional literature) and repeated measures of alcohol consumption, separately in mothers (from ages 28-48) and offspring (from ages 15-21) who had ever reported drinking. We modelled log units of alcohol using a linear mixed model and calculated beta coefficients for each SNP separately. Cross-validation was used to determine an allelic score for alcohol consumption, and the AVENGEME algorithm employed to estimate variance of the trait explained. RESULTS: Following correction for multiple testing, one SNP (rs1229984) showed evidence for association with alcohol consumption (β = -0.177, SE = 0.042, p = <0.0001) in the mothers. No SNPs showed evidence for association in the offspring after correcting for multiple testing. The optimal allelic score was generated using p-value cut offs of 0.5 and 0.05 for the mothers and offspring respectively. These scores explained 0.3% and 0.7% of the variance. CONCLUSION: Our PGRS explains a modest amount of the variance in alcohol consumption and larger sample sizes would be required to use our PGRS in an MR framework

Epigenetic clocks for gestational age:statistical and study design considerations

Abstract In this letter to the editor, we highlight some concerns with a recently published method to estimate gestational age at delivery from DNA methylation data. We conduct novel analyses to highlight the implications of different choices in study design and statistical methods for the prediction of phenotypes from methylation data

Assessment of Offspring DNA Methylation across the Lifecourse Associated with Prenatal Maternal Smoking Using Bayesian Mixture Modelling

A growing body of research has implicated DNA methylation as a potential mediator of the effects of maternal smoking in pregnancy on offspring ill-health. Data were available from a UK birth cohort of children with DNA methylation measured at birth, age 7 and 17. One issue when analysing genome-wide DNA methylation data is the correlation of methylation levels between CpG sites, though this can be crudely bypassed using a data reduction method. In this manuscript we investigate the effect of sustained maternal smoking in pregnancy on longitudinal DNA methylation in their offspring using a Bayesian hierarchical mixture model. This model avoids the data reduction used in previous analyses. Four of the 28 previously identified, smoking related CpG sites were shown to have offspring methylation related to maternal smoking using this method, replicating findings in well-known smoking related genes MYO1G and GFI1. Further weak associations were found at the AHRR and CYP1A1 loci. In conclusion, we have demonstrated the utility of the Bayesian mixture model method for investigation of longitudinal DNA methylation data and this method should be considered for use in whole genome applications

Perioperative Levosimendan Infusion in Patients With End-Stage Heart Failure Undergoing Left Ventricular Assist Device Implantation

Left ventricular assist device (LVAD) therapy has been instrumental in saving lives of patients with end-stage heart failure (HF). Recent generation devices have short-to-mid-term survival rates close to heart transplantation. Unfortunately, up to 1 in 4 patients develop a life-threatening right-sided HF (RHF) early post LVAD implantation, with high morbidity and mortality rate, necessitating prolonged ICU stay, prolonged inotropic support, and implantation of a right-ventricular assist device. Pre-operative optimization of HF therapy could help in prevention, and/or mitigation of RHF. Levosimendan (LEVO) is a non-conventional inotropic agent that works by amplifying calcium sensitivity of troponin C in cardiac myocytes, without increasing the intra-cellular calcium or exacerbating ischemia. LEVO acts as an inodilator, which reduces the cardiac pre-, and after-load. LEVO administration is associated with hemodynamic improvements. Despite decades long of the use of LVAD and more than two decades of the use of LEVO for HF, the literature on LEVO use in LVAD is very limited. In this paper, we sought to conduct a systematic review to synthesize evidence related to the use of LEVO for the mitigation and/or prevention of RHF in patients undergoing LVAD implantation

Association between breastfeeding and DNA methylation over the life course:findings from the Avon Longitudinal Study of Parents and Children (ALSPAC)

Background: Breastfeeding is associated with short and long-term health benefits. Long-term effects might be mediated by epigenetic mechanisms, yet the literature on this topic is scarce. We performed the first epigenome-wide association study of infant feeding, comparing breastfed vs non-breastfed children. We measured DNA methylation in children from peripheral blood collected in childhood (age 7 years, N = 640) and adolescence (age 15&ndash;17 years, N = 709) within the Accessible Resource for Integrated Epigenomic Studies (ARIES) project, part of the larger Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Cord blood methylation (N = 702) was used as a negative control for potential pre-natal residual confounding. Results: Two differentially-methylated sites presented directionally-consistent associations with breastfeeding at ages 7 and 15&ndash;17 years, but not at birth. Twelve differentially-methylated regions in relation to breastfeeding were identified, and for three of them there was evidence of directional concordance between ages 7 and 15&ndash;17 years, but not between birth and age 7 years. Conclusions: Our findings indicate that DNA methylation in childhood and adolescence may be predicted by breastfeeding, but further studies with sufficiently large samples for replication are required to identify robust associations

Serine-arginine protein kinase 1 (SRPK1), a determinant of angiogenesis, is upregulated in prostate cancer and correlates with disease stage and invasion

Vascular endothelial growth factor (VEGF) undergoes alternative splicing to produce both proangiogenic and antiangiogenic isoforms. Preferential splicing of proangiogenic VEGF is determined by serine-arginine protein kinase 1 (SRPK1), which is upregulated in a number of cancers. In the present study, we aimed to investigate SRPK1 expression in prostate cancer (PCa) and its association with cancer progression. SRPK1 expression was assessed using immunohistochemistry of PCa tissue extracted from radical prostatectomy specimens of 110 patients. SRPK1 expression was significantly higher in tumour compared with benign tissue (p<0.00001) and correlated with higher pT stage (p=0.004), extracapsular extension (p=0.003) and extracapsular perineural invasion (p=0.008). Interestingly, the expression did not correlate with Gleason grade (p=0.21), suggesting that SRPK1 facilitates the development of a tumour microenvironment that favours growth and invasion (possibly through stimulating angiogenesis) while having little bearing on the morphology or function of the tumour cells themselves