Occurrence and ecological risk assessment of 16 phthalates in surface water of the mainstream of the Yangtze River, China.

Phthalic acid esters (PAEs), a class of typical endocrine disruptors, have received considerable attention due to their widespread applications and adverse effects on biological health. In this study, 30 water samples, along the mainstream of the Yangtze River (YR), were collected from Chongqing (upper stream) to Shanghai (estuary) from May to June in 2019. The total concentrations of 16 targeted PAEs ranged from 0.437 to 20.5 µg/L, with an average of 1.93 µg/L, where dibutyl phthalate (DBP, 0.222-20.2 µg/L), bis (2-ethylhexyl) phthalate (DEHP, 0.254-7.03 µg/L), and diisobutyl phthalate (DIBP, 0.0645-0.621 µg/L) were the most abundant PAEs. According to the pollution level in the YR to assess the ecological risk posed by PAEs, the results showed medium risk level of PAEs in the YR, among which DBP and DEHP posed a high ecological risk to aquatic organisms. The optimal solution for DBP and DEHP is found in ten fitting curves. The PNECSSD of them is 2.50 µg/L and 0.34 µg/L, respectively.

Integration of Distinct Analysis Strategies Improves Tissue-Trait Association Identification.

Integrating genome-wide association studies (GWAS) with transcriptomic data, human complex traits and diseases have been linked to relevant tissues and cell types using different methods. However, different results from these methods generated confusion while no gold standard is currently accepted, making it difficult to evaluate the discoveries. Here, applying three methods on the same data source, we estimated the sensitivity and specificity of these methods in the absence of a gold standard. We established a more specific tissue-trait association atlas by combining the information captured by different methods. Our triangulation strategy improves the performance of existing methods in establishing tissue-trait associations. The results provide better etiological and functional insights for the tissues underlying different human complex traits and diseases.

Genetic Landscape of the ACE2 Coronavirus Receptor.

BACKGROUND: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood. METHODS: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data. RESULTS: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells. CONCLUSIONS: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.

Genetic and phenotypic links between obesity and extracellular vesicles.

Obesity has a highly complex genetic architecture, making it difficult to understand the genetic mechanisms, despite the large number of discovered loci via genome-wide association studies (GWAS). Omics techniques have provided a better resolution to view this problem. As a proxy of cell-level biology, extracellular vesicles (EVs) are useful for studying cellular regulation of complex phenotypes such as obesity. Here, in a well-established Scottish cohort, we utilized a novel technology to detect surface proteins across millions of single EVs in each individual's plasma sample. Integrating the results with established obesity GWAS, we inferred 78 types of EVs carrying one or two of 12 surface proteins to be associated with adiposity-related traits such as waist circumference. We then verified that particular EVs' abundance is negatively correlated with body adiposity, while no association with lean body mass. We also revealed that genetic variants associated with protein-specific EVs capture 2-4-fold heritability enrichment for blood cholesterol levels. Our findings provide evidence that EVs with specific surface proteins have phenotypic and genetic links to obesity and blood lipids, respectively, guiding future EV biomarker research.

Projected changes in terrestrial water storage and associated flood potential across the Yangtze River basin.

Terrestrial water storage is a crucial component in water cycle and plays an important role in flood formations process, particularly in a changing environment. In this study, we aim to examine the future variation of terrestrial water storage anomaly (TWSA) and associated flood potential in one of the most flood-prone regions, the Yangtze River basin in China. Using the Gravity Recovery and Climate Experiment (GRACE) data, we perform bias correction for seven general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 under three Shared Socio-economic Pathway (SSP) scenarios: SSP126, SSP245, and SSP585. The spatiotemporal characteristics of changes in future Flood Potential Index are projected and compared between the near (2031-2060) and far (2071-2100) future with reference to the historical period (1985-2014). The results show that GCMs-simulated TWSA generally agrees well with the GRACE results after downscaling and bias correction with the average correlation coefficient of 0.86, Nash-Sutcliffe efficiency of 0.73 and the root mean square error of 21.68 mm. We found that the total variance of projected TWSA is mainly sourced from the internal variability and model uncertainties, while the uncertainties in scenarios contribute relatively less. Moreover, the flood potential is projected to decline during the near future under various scenarios and even lower during the far future under SSP585 scenario. Our findings provide implications for flood control and management under climate change over high flood risk regions worldwide.

Study on the university students' satisfaction of the wisdom tree massive open online course platform based on parameter optimization intelligent algorithm.

INTRODUCTION: Curriculum learning through the wisdom tree massive open online course platform not only gets rid of the limitations of specialty, school and region, eliminates the limitations of time and space in traditional teaching, but also effectively solves the problem of educational equity. OBJECTIVES: This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. METHODS: This study takes the university students in Fuzhou city information management department as the survey object, and adopts the electronic questionnaire survey method. A total of 1136 formal questionnaires were responded, and 1028 valid questionnaires were obtained after data cleaning and deleting invalid questionnaires (the effective rate was 90.49%). In this paper, the reliability and validity of the questionnaire were tested by IBM SPSS-20.0 software, and six explanatory variables including function, achievement, exercise, quality, richness, and interaction were obtained by principal component analysis. Then, the questionnaire data is converted to CSV (comma separated values) format for analysis. This paper proposes an intelligent algorithm combining decision tree, support vector machine, and simulated annealing to obtain the best classification accuracy and decision rules for university students' satisfaction with the wisdom tree massive open online course platform. In this paper, the proposed algorithm is compared with decision tree, random forest, k-nearest neighbor, and support vector machine to verify its performance. RESULTS: The experimental results show that training set classification accuracy of decision tree, random forest, k-nearest neighbor, only support vector machine and the proposed algorithm (simulated annealing + support vector machine) are 92.21%, 96.10%, 95.67%, 97.29%, and 99.58%, respectively. CONCLUSION: The proposed algorithm simulated annealing + support vector machine does increase the classification accuracy. At the same time, the 11 decision rules generated by simulated annealing + decision tree can provide useful information for decision makers.

Improved Estimation of Phenotypic Correlations Using Summary Association Statistics.

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology and statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) and LD score regression intercept, were widely applied to estimate phenotypic correlations. Here, we propose an improved Z-score correlation strategy based on SNPs with low minor allele frequencies (MAFs), and show how this simple strategy can correct the bias generated by the current methods. The low MAF estimator improves phenotypic correlation estimation, thus it is beneficial for methods and applications using phenotypic correlations inferred from summary association statistics.

Total genetic contribution assessment across the human genome.

Quantifying the overall magnitude of every single locus' genetic effect on the widely measured human phenome is of great challenge. We introduce a unified modelling technique that can consistently provide a total genetic contribution assessment (TGCA) of a gene or genetic variant without thresholding genetic association signals. Genome-wide TGCA in five UK Biobank phenotype domains highlights loci such as the HLA locus for medical conditions, the bone mineral density locus WNT16 for physical measures, and the skin tanning locus MC1R and smoking behaviour locus CHRNA3 for lifestyle. Tissue-specificity investigation reveals several tissues associated with total genetic contributions, including the brain tissues for mental health. Such associations are driven by tissue-specific gene expressions, which share genetic basis with the total genetic contributions. TGCA can provide a genome-wide atlas for the overall genetic contributions in each particular domain of human complex traits.

Exploring the genotoxicity triggers in the MP UV/H2O2-chloramination treatment of bisphenol A through bioassay coupled with non-targeted analysis.

Bisphenol A (BPA) is a well-known xenoestrogen, and UV/H2O2 advanced oxidation process (AOP) is one of the most effective technologies to remove BPA from water. Using BPA spiked tap water, a batch-scale photochemical experiment was conducted to investigate whether BPA can pose a genotoxicity concern during the medium pressure (MP) UV/H2O2 treatment and the post-chloramination. Samples at different UV exposure and post-chloramination durations were collected and analyzed by CALUX® gene reporter assays regarding estrogen receptor α (ERα) and p53 transcriptional activity. MP UV/H2O2 process did not cause extra estrogenic effects from the degradation of BPA, whereas genotoxicity occurred when the treated water was exposed with monochloramine. Seven frequently reported nitrogenous disinfection byproducts (N-DBPs) were detected, but none of them were responsible for the observed genotoxicity. Employed with gas chromatography-quadrupole time-of-flight mass spectrometry (GC-QTOF-MS), four compounds possibly contributed to the genotoxicity were tentatively identified and two of them with aminooxy- or cyano- group were considered as "new" N-DBPs. This study demonstrated that by-products differ from their parent compounds in toxicity can be formed in the UV oxidation with post-disinfection process, which should become a cause for concern.

Nontrivial Replication of Loci Detected by Multi-Trait Methods.

The ever-growing genome-wide association studies (GWAS) have revealed widespread pleiotropy. To exploit this, various methods that jointly consider associations of a genetic variant with multiple traits have been developed. Most efforts have been made concerning improving GWAS discovery power. However, how to replicate these discovered pleiotropic loci has yet to be discussed thoroughly. Unlike a single-trait scenario, multi-trait replication is not trivial considering the underlying genotype-multi-phenotype map of the associations. Here, we evaluate four methods for replicating multi-trait associations, corresponding to four levels of replication strength. Weak replication cannot justify pleiotropic genetic effects, whereas strong replication using our developed correlation methods can inform consistent pleiotropic genetic effects across the discovery and replication samples. We provide a protocol for replicating multi-trait genetic associations in practice. The described methods are implemented in the free and open-source R package MultiABEL.

Slopes and intercepts from log-log correlations of gas/particle quotient and octanol-air partition coefficient (vapor-pressure) for semi-volatile organic compounds: II. Theoretical predictions vs. monitoring.

The logarithm of gas/particle (G/P) partition quotient (logKP) has been found to have a linear relationship with logKOA (octanol-air partition coefficient) with slope mo and intercept bo and logPL (subcooled liquid vapor pressure) with slope mp and intercept bp. In the sister paper of the present work, analytical equations to predict the slope mo and intercept bo based on logKOA and predict the slope mp and intercept bp based on logPL are developed using steady state theory. In this work, the equations are evaluated using world-wide monitoring data (262 pairs for mo and bo values and 292 pairs for mp and bp values produced from more than 10,000 monitiring data worldwide) for selected seven groups of semi-volatile organic compounds (SVOCs), including polybrominated diphenyl ethers (PBDEs), polychlorinated dibenzo-p-dioxins and polychorinated dibenzofurans (PCDD/Fs), polyclorinated biphenyl (PCBs), polycyclic aromatic hydrocarbons (PAHs), polychlorinated naphthalenes (PCNs), organochlorinated pesticides (OCPs), novel brominated flame retardants (NBFRs), and other selected halogenated flame retardants. The slopes and intercepts predicted by the steady state equations reproduce the trends observed in monitoring regression results for the seven SVOC groups, with 44.4% of the variation of monitoring mo values accounted for by logKOA and 48.2% of the variation of monitoring mp values accounted for by logPL. Theoretically, the values of mo can be any value between 0 and 1 dependent on the values of KOA, and are not constrained to 1 as in equilibrium theory. Likewise, the values of mp can be any value between 0 and -1 dependent on the values of PL, and not constrained to -1 predicted by the equilibrium theory. The influence of sampling artifacts on the G/P partitioning of SVOCs has most likely been overemphasized by the equilibrium theory. Thus, the equilibrium approach should be abandoned in favor of the steady state approach for calculating the G/P partition quotients for SVOCs with high KOA values (>1011.38) or low PL values (<10-4.92).

Stretchable, dynamic covalent polymers for soft, long-lived bioresorbable electronic stimulators designed to facilitate neuromuscular regeneration.

Bioresorbable electronic stimulators are of rapidly growing interest as unusual therapeutic platforms, i.e., bioelectronic medicines, for treating disease states, accelerating wound healing processes and eliminating infections. Here, we present advanced materials that support operation in these systems over clinically relevant timeframes, ultimately bioresorbing harmlessly to benign products without residues, to eliminate the need for surgical extraction. Our findings overcome key challenges of bioresorbable electronic devices by realizing lifetimes that match clinical needs. The devices exploit a bioresorbable dynamic covalent polymer that facilitates tight bonding to itself and other surfaces, as a soft, elastic substrate and encapsulation coating for wireless electronic components. We describe the underlying features and chemical design considerations for this polymer, and the biocompatibility of its constituent materials. In devices with optimized, wireless designs, these polymers enable stable, long-lived operation as distal stimulators in a rat model of peripheral nerve injuries, thereby demonstrating the potential of programmable long-term electrical stimulation for maintaining muscle receptivity and enhancing functional recovery.

Persistence of antibiotic resistance genes from river water to tap water in the Yangtze River Delta.

Antibiotic resistance genes (ARGs) raise public concern as emerging contaminants. The abundance and variation of 11 ARGs, intI1 and 16S rRNA gene were deciphered using quantitative PCR (qPCR) in two drinking water treatment systems that include river, wetland, drinking water treatment plants (DWTPs) and tap water from the Yangtze River Delta. The influencing factors for ARG abundance in river water were also explored. All investigated genes were detected in river water and there was no significant difference between the two systems, with sulfonamide ARGs occupying the highest abundance. Temperature had a significant effect on the ARG distribution based on permutational multivariate analysis of variance (PERMANOVA). Further Spearman analysis demonstrated that temperature was strongly correlated with the abundance of sul1, sul2, tetA and tetC, and these genes were significantly correlated with environmental factors (including temperature, total organic carbon (TOC) and dissolved oxygen (DO)). Considering the frequency and abundance of ARGs, as well as their correlation with other genes, sul1, sul2, tetA and tetC could be used as indicators of ARGs in river water. No significant reduction was noted for the absolute abundance of ARGs from river water to wetland water. Principle coordinates analysis (PCoA) combined with PERMANOVA revealed that drinking water treatment was responsible for reducing 16S rRNA gene and ARG abundance resulting in 3-log reductions. However, it should be noted that after transportation of distribution pipeline, both 16S rRNA gene and ARGs still detected in tap water, which indicated persistence of ARGs and will require further research.

High-definition likelihood inference of genetic correlations across human complex traits.

Genetic correlation is a central parameter for understanding shared genetic architecture between complex traits. By using summary statistics from genome-wide association studies (GWAS), linkage disequilibrium score regression (LDSC) was developed for unbiased estimation of genetic correlations. Although easy to use, LDSC only partially utilizes LD information. By fully accounting for LD across the genome, we develop a high-definition likelihood (HDL) method to improve precision in genetic correlation estimation. Compared to LDSC, HDL reduces the variance of genetic correlation estimates by about 60%, equivalent to a 2.5-fold increase in sample size. We apply HDL and LDSC to estimate 435 genetic correlations among 30 behavioral and disease-related phenotypes measured in the UK Biobank (UKBB). In addition to 154 significant genetic correlations observed for both methods, HDL identified another 57 significant genetic correlations, compared to only another 2 significant genetic correlations identified by LDSC. HDL brings more power to genomic analyses and better reveals the underlying connections across human complex traits.

Preparation and characterization of a novel colorimetric indicator film based on gelatin/polyvinyl alcohol incorporating mulberry anthocyanin extracts for monitoring fish freshness.

This work aimed to develop a novel colorimetric indicator film for monitoring of food freshness based on gelatin/polyvinyl alcohol matrix incorporated with anthocyanin extracts from mulberry. The color of anthocyanin extracts solutions obviously changed from bright red to dark green in the pH range of 2.0-11.0. FTIR spectra and isothermal titration calorimetry showed that the anthocyanin extracts were successfully combined with gelatin/polyvinyl alcohol matrix by hydrogen binding and electrostatic interaction, which enhanced the stability of anthocyanin. The scanning electric microscopy showed that the compatibility between polyvinyl alcohol and gelatin were improved owing to the addition of anthocyanin extracts. With the anthocyanin extracts addition from 0 to 45 mg/100 mL mixed solution, the tensile strength decreased from 30.80 to 21.01 MPa and the elongation at break increased from 589.22% to 905.86%. The color response of film in buffer solution of different pH were in accordance with anthocyanin extracts solutions, and its color changes were clearly visible with naked eye. Finally, the film was evaluated by a test on monitoring fish spoilage, which presented visible color changes due to volatile nitrogenous compounds formed over time. These results showed that this developed film could be used as an effective method for the monitoring of food freshness.

Measurement and modeling the gas/particle partitioning of organochlorine pesticides (OCPs) in atmosphere at low temperatures.

The gas/particle (G/P) partition of organochlorine pesticides (OCPs) has been widely investigated and well documented, but rare at low temperature. In this study, seventy-four pairs of air samples in two sampling sites in northeastern China at a wide ambient temperature range of ~63 °C (-40 to +23 °C) were simultaneously collected in both gaseous and particulate phases and eighteen OCPs in these samples were measured and analyzed, among which, partition quotient (KP) values for fifteen OCPs were determined. Seven models including those have never been used for OCPs were applied to predict the values of KP, and the results were compared with the monitoring data for the fifteen OCPs. It was found out that, L-M-Y model provided advantages over the other models, with the best agreement to the monitoring data for analyzed OCPs (90.1 ±â€¯11.1% data points within ±1 log unit, RMSE: 0.53 ±â€¯0.18). The predicted maximum partition (MP) domain for eleven OCPs was observed with high values of their logarithm of octanol-air partition coefficient (log KOA > 12.5), where the log KP values become a constant (-1.53), indicating that the G/P partition of OCPs is in steady state but not the equilibrium. The Li-Ma-Yang (L-M-Y model) model, considering the wet and dry depositions of particles, elucidates the necessity of non-equilibrium term for the OCPs at low temperature. These results indicate that the L-M-Y model is valid for OCPs, which renders it highly promising for describing the partition behaviors in atmosphere for SVOCs, particularly at low temperature. An equation to calculate the condensation temperature TC was also derived, which gave a new understanding on the situation of chemicals with equal distribution between gaseous and particulate phases of OCPs and other similar SVOCs, especially in Polar Regions.

Genomics of 1 million parent lifespans implicates novel pathways and common diseases and distinguishes survival chances.

We use a genome-wide association of 1 million parental lifespans of genotyped subjects and data on mortality risk factors to validate previously unreplicated findings near CDKN2B-AS1, ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31, and identify and replicate novel findings near ABO, ZC3HC1, and IGF2R. We also validate previous findings near 5q33.3/EBF1 and FOXO3, whilst finding contradictory evidence at other loci. Gene set and cell-specific analyses show that expression in foetal brain cells and adult dorsolateral prefrontal cortex is enriched for lifespan variation, as are gene pathways involving lipid proteins and homeostasis, vesicle-mediated transport, and synaptic function. Individual genetic variants that increase dementia, cardiovascular disease, and lung cancer - but not other cancers - explain the most variance. Resulting polygenic scores show a mean lifespan difference of around five years of life across the deciles. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).

Ageing happens to us all, and as the cabaret singer Maurice Chevalier pointed out, "old age is not that bad when you consider the alternative". Yet, the growing ageing population of most developed countries presents challenges to healthcare systems and government finances. For many older people, long periods of ill health are part of the end of life, and so a better understanding of ageing could offer the opportunity to prolong healthy living into old age. Ageing is complex and takes a long time to study ­ a lifetime in fact. This makes it difficult to discern its causes, among the countless possibilities based on an individual's genes, behaviour or environment. While thousands of regions in an individual's genetic makeup are known to influence their risk of different diseases, those that affect how long they will live have proved harder to disentangle. Timmers et al. sought to pinpoint such regions, and then use this information to predict, based on their DNA, whether someone had a better or worse chance of living longer than average. The DNA of over 500,000 people was read to reveal the specific 'genetic fingerprints' of each participant. Then, after asking each of the participants how long both of their parents had lived, Timmers et al. pinpointed 12 DNA regions that affect lifespan. Five of these regions were new and had not been linked to lifespan before. Across the twelve as a whole several were known to be involved in Alzheimer's disease, smoking-related cancer or heart disease. Looking at the entire genome, Timmers et al. could then predict a lifespan score for each individual, and when they sorted participants into ten groups based on these scores they found that top group lived five years longer than the bottom, on average. Many factors beside genetics influence how long a person will live and our lifespan cannot be read from our DNA alone. Nevertheless, Timmers et al. had hoped to narrow down their search and discover specific genes that directly influence how quickly people age, beyond diseases. If such genes exist, their effects were too small to be detected in this study. The next step will be to expand the study to include more participants, which will hopefully pinpoint further genomic regions and help disentangle the biology of ageing and disease.

A Selection Operator for Summary Association Statistics Reveals Allelic Heterogeneity of Complex Traits.

In recent years, as a secondary analysis in genome-wide association studies (GWASs), conditional and joint multiple-SNP analysis (GCTA-COJO) has been successful in allowing the discovery of additional association signals within detected loci. This suggests that many loci mapped in GWASs harbor more than a single causal variant. In order to interpret the underlying mechanism regulating a complex trait of interest in each discovered locus, researchers must assess the magnitude of allelic heterogeneity within the locus. We developed a penalized selection operator for jointly analyzing multiple variants (SOJO) within each mapped locus on the basis of LASSO (least absolute shrinkage and selection operator) regression derived from summary association statistics. We found that, compared to stepwise conditional multiple-SNP analysis, SOJO provided better sensitivity and specificity in predicting the number of alleles associated with complex traits in each locus. SOJO suggested causal variants potentially missed by GCTA-COJO. Compared to using top variants from genome-wide significant loci in GWAS, using SOJO increased the proportion of variance prediction for height by 65% without additional discovery samples or additional loci in the genome. Our empirical results indicate that human height is not only a highly polygenic trait, but also has high allelic heterogeneity within its established hundreds of loci.

Multivariate discovery and replication of five novel loci associated with Immunoglobulin G N-glycosylation.

Joint modeling of a number of phenotypes using multivariate methods has often been neglected in genome-wide association studies and if used, replication has not been sought. Modern omics technologies allow characterization of functional phenomena using a large number of related phenotype measures, which can benefit from such joint analysis. Here, we report a multivariate genome-wide association studies of 23 immunoglobulin G (IgG) N-glycosylation phenotypes. In the discovery cohort, our multi-phenotype method uncovers ten genome-wide significant loci, of which five are novel (IGH, ELL2, HLA-B-C, AZI1, FUT6-FUT3). We convincingly replicate all novel loci via multivariate tests. We show that IgG N-glycosylation loci are strongly enriched for genes expressed in the immune system, in particular antibody-producing cells and B lymphocytes. We empirically demonstrate the efficacy of multivariate methods to discover novel, reproducible pleiotropic effects.Multivariate analysis methods can uncover the relationship between phenotypic measures characterised by modern omic techniques. Here the authors conduct a multivariate GWAS on IgG N-glycosylation phenotypes and identify 5 novel loci enriched in immune system genes.