Volcanic ash supply to the surface ocean – remote sensing of biological responses and their wider biogeochemical significance

Transient micronutrient enrichment of the surface ocean can enhance phytoplankton growth rates and alter microbial community structure with an ensuing spectrum of biogeochemical feedbacks. Strong phytoplankton responses to micronutrients supplied by volcanic ash have been reported recently. Here we: (i) synthesize findings from these recent studies; (ii) report the results of a new remote sensing study of ash fertilization; and (iii) calculate theoretical bounds of ash-fertilized carbon export. Our synthesis highlights that phytoplankton responses to ash do not always simply mimic that of iron amendment; the exact mechanisms for this are likely biogeochemically important but are not yet well understood. Inherent optical properties of ash-loaded seawater suggest rhyolitic ash biases routine satellite chlorophyll-a estimation upwards by more than an order of magnitude for waters with 0.5 mg chlorophyll-a m-3. For this reason post-ash-deposition chlorophyll-a changes in oligotrophic waters detected via standard Case 1 (open ocean) algorithms should be interpreted with caution. Remote sensing analysis of historic events with a bias less than a factor of 2 provided limited stand-alone evidence for ash-fertilization. Confounding factors were poor coverage, incoherent ash dispersal, and ambiguity ascribing biomass changes to ash supply over other potential drivers. Using current estimates of iron release and carbon export efficiencies, uncertainty bounds of ash-fertilized carbon export for 3 events are presented. Patagonian iron supply to the Southern Ocean from volcanic eruptions is less than that of windblown dust on thousand year timescales but can dominate supply at shorter timescales. Reducing uncertainties in remote sensing of phytoplankton response and nutrient release from ash are avenues for enabling assessment of the oceanic response to large-scale transient nutrient enrichment

Lipoprotein ability to exchange and remove lipids from model membranes as a function of fatty acid saturation and presence of cholesterol

Lipoproteins play a central role in the development of atherosclerosis. High and low-density lipoproteins (HDL and LDL), known as 'good' and 'bad' cholesterol, respectively, remove and/or deposit lipids into the artery wall. Hence, insight into lipid exchange processes between lipoproteins and cell membranes is of particular importance in understanding the onset and development of cardiovascular disease. In order to elucidate the impact of phospholipid tail saturation and the presence of cholesterol in cell membranes on these processes, neutron reflection was employed in the present investigation to follow lipid exchange with both HDL and LDL against model membranes. Mirroring clinical risk factors for the development of atherosclerosis, lower exchange was observed in the presence of cholesterol, as well as for an unsaturated phospholipid, compared to faster exchange when using a fully saturated phospholipid. These results highlight the importance of membrane composition on the interaction with lipoproteins, chiefly the saturation level of the lipids and presence of cholesterol, and provide novel insight into factors of importance for build-up and reversibility of atherosclerotic plaque. In addition, the correlation between the results and well-established clinical risk factors suggests that the approach taken can be employed also for understanding a broader set of risk factors including, e.g., effects of triglycerides and oxidative stress, as well as local effects of drugs on atherosclerotic plaque formation

Accelerating Haplotype-Based Genome-Wide Association Study Using Perfect Phylogeny and Phase-Known Reference Data

The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype inference, also known as phasing. In this study, we developed an efficient approach to haplotype-based analysis in GWAS. By using a reference panel, our method accelerated the phasing process and reduced the potential bias generated by unrealistic assumptions in phasing process. The haplotype-based approach delivers great power and no type I error inflation for association studies. With only a medium-size reference panel, phasing error in our method is comparable to the genotyping error afforded by commercial genotyping solutions

Body composition and body fat distribution are related to cardiac autonomic control in non-alcoholic fatty liver disease patients

BACKGROUND/OBJECTIVES: Heart rate recovery (HRR), a cardiac autonomic control marker, was shown to be related to body composition (BC), yet this was not tested in non-alcoholic fatty liver disease (NAFLD) patients. The aim of this study was to determine if, and to what extent, markers of BC and body fat (BF) distribution are related to cardiac autonomic control in NAFLD patients. SUBJECTS/METHODS: BC was assessed with dual-energy X-ray absorptiometry in 28 NAFLD patients (19 men, 51±13 years, and 9 women, 47±13 years). BF depots ratios were calculated to assess BF distribution. Subjects’ HRR was recorded 1 (HRR1) and 2 min (HRR2) immediately after a maximum graded exercise test. RESULTS: BC and BF distribution were related to HRR; particularly weight, trunk BF and trunk BF-to-appendicular BF ratio showed a negative relation with HRR1 (r 1⁄4 0.613, r 1⁄4 0.597 and r 1⁄4 0.547, respectively, Po0.01) and HRR2 (r 1⁄4 0.484, r 1⁄4 0.446, Po0.05, and r 1⁄4 0.590, Po0.01, respectively). Age seems to be related to both HRR1 and HRR2 except when controlled for BF distribution. The preferred model in multiple regression should include trunk BF-to-appendicular BF ratio and BF to predict HRR1 (r2 1⁄4 0.549; Po0.05), and trunk BF-to-appendicular BF ratio alone to predict HRR2 (r2 1⁄4 0.430; Po0.001). CONCLUSIONS: BC and BF distribution were related to HRR in NAFLD patients. Trunk BF-to-appendicular BF ratio was the best independent predictor of HRR and therefore may be best related to cardiovascular increased risk, and possibly act as a mediator in age-related cardiac autonomic control variation.info:eu-repo/semantics/publishedVersio

Fractures are common within 18 months following first-line R-CHOP in older patients with diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) and osteoporotic fracture are both more common in older patients. Exposure to R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) is likely to increase the risk of fracture, but evidence is lacking to define fracture incidence in this group. Data on consecutive patients with DLBCL aged ≥70 years treated with 1 to 8 cycles of full or attenuated R-CHOP were retrospectively collected across 10 UK centers (2009-2019). Patients were followed up from starting R-CHOP for a minimum of 6 months and censored at 18 months; at last follow-up if <18 months; or at progression or death. Of 877 patients identified, 148 were excluded: 121 had progression or died before 6 months; 23 had follow-up <6 months. Across 729 remaining patients, the median age was 77 years, and 68% had an Eastern Cooperative Oncology Group performance status of 0 to 1. Eighty-one fractures occurred within 18 months of follow-up; 42 were symptomatic, including 30 requiring hospital attendance or admission. The cumulative fracture incidence was 6.2% (95% confidence interval [CI], 4.7-8.2) at 6 months; 9.7% (95% CI, 7.8-12.1) at 12 months; and 11.4% (95% CI, 9.3-14.0) at 18 months. Multivariate analysis identified a predisposing history (osteoporosis, osteopenia, prior fracture, and rheumatoid arthritis [RhA]), DLBCL bone involvement at baseline, and receipt of prephase steroids as independent risk factors for fracture. There is a clinically relevant fracture risk and significant associated morbidity in older patients receiving R-CHOP. Careful attention to bone health is warranted in older patients receiving R-CHOP. Randomized studies are required to better define the most effective strategies to reduce fracture risk

Genome-wide analyses for personality traits identify six genomic loci and show correlations with psychiatric disorders

Personality is influenced by genetic and environmental factors1 and associated with mental health. However, the underlying genetic determinants are largely unknown. We identified six genetic loci, including five novel loci2,3, significantly associated with personality traits in a meta-analysis of genome-wide association studies (N = 123,132–260,861). Of these genomewide significant loci, extraversion was associated with variants in WSCD2 and near PCDH15, and neuroticism with variants on chromosome 8p23.1 and in L3MBTL2. We performed a principal component analysis to extract major dimensions underlying genetic variations among five personality traits and six psychiatric disorders (N = 5,422–18,759). The first genetic dimension separated personality traits and psychiatric disorders, except that neuroticism and openness to experience were clustered with the disorders. High genetic correlations were found between extraversion and attention-deficit– hyperactivity disorder (ADHD) and between openness and schizophrenia and bipolar disorder. The second genetic dimension was closely aligned with extraversion–introversion and grouped neuroticism with internalizing psychopathology (e.g., depression or anxiety)

Using Extended Genealogy to Estimate Components of Heritability for 23 Quantitative and Dichotomous Traits

Important knowledge about the determinants of complex human phenotypes can be obtained from the estimation of heritability, the fraction of phenotypic variation in a population that is determined by genetic factors. Here, we make use of extensive phenotype data in Iceland, long-range phased genotypes, and a population-wide genealogical database to examine the heritability of 11 quantitative and 12 dichotomous phenotypes in a sample of 38,167 individuals. Most previous estimates of heritability are derived from family-based approaches such as twin studies, which may be biased upwards by epistatic interactions or shared environment. Our estimates of heritability, based on both closely and distantly related pairs of individuals, are significantly lower than those from previous studies. We examine phenotypic correlations across a range of relationships, from siblings to first cousins, and find that the excess phenotypic correlation in these related individuals is predominantly due to shared environment as opposed to dominance or epistasis. We also develop a new method to jointly estimate narrow-sense heritability and the heritability explained by genotyped SNPs. Unlike existing methods, this approach permits the use of information from both closely and distantly related pairs of individuals, thereby reducing the variance of estimates of heritability explained by genotyped SNPs while preventing upward bias. Our results show that common SNPs explain a larger proportion of the heritability than previously thought, with SNPs present on Illumina 300K genotyping arrays explaining more than half of the heritability for the 23 phenotypes examined in this study. Much of the remaining heritability is likely to be due to rare alleles that are not captured by standard genotyping arrays

Homozygosity Mapping on Homozygosity Haplotype Analysis to Detect Recessive Disease-Causing Genes from a Small Number of Unrelated, Outbred Patients

Genes involved in disease that are not common are often difficult to identify; a method that pinpoints them from a small number of unrelated patients will be of great help. In order to establish such a method that detects recessive genes identical-by-descent, we modified homozygosity mapping (HM) so that it is constructed on the basis of homozygosity haplotype (HM on HH) analysis. An analysis using 6 unrelated patients with Siiyama-type α1-antitrypsin deficiency, a disease caused by a founder gene, the correct gene locus was pinpointed from data of any 2 patients (length: 1.2–21.8 centimorgans, median: 1.6 centimorgans). For a test population in which these 6 patients and 54 healthy subjects were scrambled, the approach accurately identified these 6 patients and pinpointed the locus to a 1.4-centimorgan fragment. Analyses using synthetic data revealed that the analysis works well for IBD fragment derived from a most recent common ancestor (MRCA) who existed less than 60 generations ago. The analysis is unsuitable for the genes with a frequency in general population more than 0.1. Thus, HM on HH analysis is a powerful technique, applicable to a small number of patients not known to be related, and will accelerate the identification of disease-causing genes for recessive conditions

Rapid and Accurate Multiple Testing Correction and Power Estimation for Millions of Correlated Markers

With the development of high-throughput sequencing and genotyping technologies, the number of markers collected in genetic association studies is growing rapidly, increasing the importance of methods for correcting for multiple hypothesis testing. The permutation test is widely considered the gold standard for accurate multiple testing correction, but it is often computationally impractical for these large datasets. Recently, several studies proposed efficient alternative approaches to the permutation test based on the multivariate normal distribution (MVN). However, they cannot accurately correct for multiple testing in genome-wide association studies for two reasons. First, these methods require partitioning of the genome into many disjoint blocks and ignore all correlations between markers from different blocks. Second, the true null distribution of the test statistic often fails to follow the asymptotic distribution at the tails of the distribution. We propose an accurate and efficient method for multiple testing correction in genome-wide association studies—SLIDE. Our method accounts for all correlation within a sliding window and corrects for the departure of the true null distribution of the statistic from the asymptotic distribution. In simulations using the Wellcome Trust Case Control Consortium data, the error rate of SLIDE's corrected p-values is more than 20 times smaller than the error rate of the previous MVN-based methods' corrected p-values, while SLIDE is orders of magnitude faster than the permutation test and other competing methods. We also extend the MVN framework to the problem of estimating the statistical power of an association study with correlated markers and propose an efficient and accurate power estimation method SLIP. SLIP and SLIDE are available at http://slide.cs.ucla.edu

Inference of Relationships in Population Data Using Identity-by-Descent and Identity-by-State

It is an assumption of large, population-based datasets that samples are annotated accurately whether they correspond to known relationships or unrelated individuals. These annotations are key for a broad range of genetics applications. While many methods are available to assess relatedness that involve estimates of identity-by-descent (IBD) and/or identity-by-state (IBS) allele-sharing proportions, we developed a novel approach that estimates IBD0, 1, and 2 based on observed IBS within windows. When combined with genome-wide IBS information, it provides an intuitive and practical graphical approach with the capacity to analyze datasets with thousands of samples without prior information about relatedness between individuals or haplotypes. We applied the method to a commonly used Human Variation Panel consisting of 400 nominally unrelated individuals. Surprisingly, we identified identical, parent-child, and full-sibling relationships and reconstructed pedigrees. In two instances non-sibling pairs of individuals in these pedigrees had unexpected IBD2 levels, as well as multiple regions of homozygosity, implying inbreeding. This combined method allowed us to distinguish related individuals from those having atypical heterozygosity rates and determine which individuals were outliers with respect to their designated population. Additionally, it becomes increasingly difficult to identify distant relatedness using genome-wide IBS methods alone. However, our IBD method further identified distant relatedness between individuals within populations, supported by the presence of megabase-scale regions lacking IBS0 across individual chromosomes. We benchmarked our approach against the hidden Markov model of a leading software package (PLINK), showing improved calling of distantly related individuals, and we validated it using a known pedigree from a clinical study. The application of this approach could improve genome-wide association, linkage, heterozygosity, and other population genomics studies that rely on SNP genotype data