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We multiply ascertained the BRCA1 pathogenic missense variant c.5207T > C; p.Val1736Ala (V1736A) in clinical investigation of breast and ovarian cancer families from Orkney in the Northern Isles of Scotland, UK. We sought to investigate the frequency and clinical relevance of this variant in those of Orcadian ancestry as an exemplar of the value of population cohorts in clinical care, especially in isolated populations. Oral history and birth, marriage and death registrations indicated genealogical linkage of the clinical cases to ancestors from the Isle of Westray, Orkney. Further clinical cases were identified through targeted testing for V1736A in women of Orcadian ancestry attending National Health Service (NHS) genetic clinics for breast and ovarian cancer family risk assessments. The variant segregates with female breast and ovarian cancer in clinically ascertained cases. Separately, exome sequence data from 2088 volunteer participants with three or more Orcadian grandparents, in the ORCADES research cohort, was interrogated to estimate the population prevalence of V1736A in Orcadians. The effects of the variant were assessed using Electronic Health Record (EHR) linkage. Twenty out of 2088 ORCADES research volunteers (~1%) carry V1736A, with a common haplotype around the variant. This allele frequency is ~480-fold higher than in UK Biobank participants. Cost-effectiveness of population screening for BRCA1 founder pathogenic variants has been demonstrated at a carrier frequency below the ~1% observed here. Thus we suggest that Orcadian women should be offered testing for the BRCA1 V1736A founder pathogenic variant, starting with those with known Westray ancestry.

Aims To identify a group of metabolites associated with incident cardiovascular disease (CVD) in people with type 2 diabetes and assess its predictive performance over-and-above a current CVD risk score (QRISK3). Methods and results A panel of 228 serum metabolites was measured at baseline in 1066 individuals with type 2 diabetes (Edinburgh Type 2 Diabetes Study) who were then followed up for CVD over the subsequent 10 years. We applied 100 repeats of Cox least absolute shrinkage and selection operator to select metabolites with frequency >90% as components for a metabolites-based risk score (MRS). The predictive performance of the MRS was assessed in relation to a reference model that was based on QRISK3 plus prevalent CVD and statin use at baseline. Of 1021 available individuals, 255 (25.0%) developed CVD (median follow-up: 10.6 years). Twelve metabolites relating to fluid balance, ketone bodies, amino acids, fatty acids, glycolysis, and lipoproteins were selected to construct the MRS that showed positive association with 10-year cardiovascular risk following adjustment for traditional risk factors [hazard ratio (HR) 2.67; 95% confidence interval (CI) 1.96, 3.64]. The c-statistic was 0.709 (95%CI 0.679, 0.739) for the reference model alone, increasing slightly to 0.728 (95%CI 0.700, 0.757) following addition of the MRS. Compared with the reference model, the net reclassification index and integrated discrimination index for the reference model plus the MRS were 0.362 (95%CI 0.179, 0.506) and 0.041 (95%CI 0.020, 0.071), respectively. Conclusion Metabolomics data might improve predictive performance of current CVD risk scores based on traditional risk factors in people with type 2 diabetes. External validation is warranted to assess the generalizability of improved CVD risk prediction using the MRS.

Background: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance. Methods: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins. Results: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F). Conclusion: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.

Changes in the N-glycosylation of immunoglobulin G (IgG) are often observed in pathological states, such as autoimmune, inflammatory, neurodegenerative, cardiovascular diseases and some types of cancer. However, in most cases, it is not clear if the disease onset causes these changes, or if the changes in IgG N-glycosylation are among the risk factors for the diseases. The aim of this study was to investigate the casual relationships between IgG N-glycosylation traits and 12 diseases, in which the alterations of IgG N-glycome were previously reported, using two sample Mendelian randomization (MR) approach. We have performed two sample MR using publicly available summary statistics of genome-wide association studies of IgG N-glycosylation and disease risks. Our results indicate positive causal effect of systemic lupus erythematosus (SLE) on the abundance of N-glycans with bisecting N-acetylglucosamine in the total IgG N-glycome. Therefore, we suggest regarding this IgG glycosylation trait as a biomarker of SLE. We also emphasize the need for more powerful GWAS studies of IgG N-glycosylation to further elucidate the causal effect of IgG N-glycome on the diseases.

The BRCA1 pathogenic missense variant c.5207T>C; p.Val1736Ala (V1736A) was multiply ascertained in the clinical investigation of breast and ovarian cancer families from Orkney in the Northern Isles of Scotland, UK. Oral history and birth, marriage and death registrations indicated genealogical linkage of the clinical cases to ancestors from the Isle of Westray, Orkney. Further clinical cases were identified through targeted testing for V1736A in women of Orcadian ancestry attending National Health Service (NHS) genetic clinics for breast and ovarian cancer family risk assessments. Fourteen mutation carriers were identified from predictive tests in relatives of affected members of the index kindred, nine more from NHS diagnostic testing plus a further six obligate carrier females. The variant segregates with female breast and ovarian cancer in clinically ascertained cases. Separately, exome sequence data from 2,090 volunteer participants with three or more Orcadian grandparents, in the ORCADES research cohort, was interrogated to estimate the population prevalence of V1736A in Orcadians. The effects of the variant were assessed using Electronic Health Record (EHR) linkage. Twenty out of 2,090 ORCADES research volunteers (∼1%) carry V1736A, with a common haplotype around the variant. This allele frequency is ∼480-fold higher than in UK Biobank participants. Cost-effectiveness of population screening for a single BRCA1 founder mutation has been demonstrated at a carrier frequency below the ∼1% observed here. Thus we suggest that Orcadian women should be offered testing for the BRCA1 V1736A founder mutation, starting with those with ancestry from Westray.

Defining the genetic components that control glycosylation of the human immunoglobulin G (IgG) is an ongoing effort, which has so far been addressed by means of heritability, linkage and genome-wide association studies (GWAS). Unlike the synthesis of proteins, N-glycosylation biosynthesis is not a template-driven process, but rather a complex process regulated by both genetic and environmental factors. Current heritability studies have shown that while up to 75% of the variation in levels of some IgG glycan traits can be explained by genetics, some glycan traits are completely defined by environmental influences. Advances in both high-throughput genotyping and glycan quantification methods have enabled genome-wide association studies that are increasingly used to estimate associations of millions of single-nucleotide polymorphisms and glycosylation traits. Using this method, 18 genomic regions have so far been robustly associated with IgG N-glycosylation, discovering associations with genes encoding glycosyltransferases, but also transcription factors, co-factors, membrane transporters and other genes with no apparent role in IgG glycosylation. Further computational analyses have shown that IgG glycosylation is likely to be regulated through the expression of glycosyltransferases, but have also for the first time suggested which transcription factors are involved in the process. Moreover, it was also shown that IgG glycosylation and inflammatory diseases share common underlying causal genetic variants, suggesting that studying genetic regulation of IgG glycosylation helps not only to better understand this complex process but can also contribute to understanding why glycans are changed in disease. However, further studies are needed to unravel whether changes in IgG glycosylation are causing these diseases or the changes in the glycome are caused by the disease.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. Supplementary Information The online version contains supplementary material available at 10.1007/s00439-021-02397-7.

Critical illness in COVID-19 is caused by inflammatory lung injury, mediated by the host immune system. We and others have shown that host genetic variation influences the development of illness requiring critical care1or hospitalisation2;3;4following SARS-Co-V2 infection. The GenOMICC (Genetics of Mortality in Critical Care) study recruits critically-ill cases and compares their genomes with population controls in order to find underlying disease mechanisms.Here, we use whole genome sequencing and statistical fine mapping in 7,491 critically-ill cases compared with 48,400 population controls to discover and replicate 22 independent variants that significantly predispose to life-threatening COVID-19. We identify 15 new independent associations with critical COVID-19, including variants within genes involved in interferon signalling (IL10RB, PLSCR1), leucocyte differentiation (BCL11A), and blood type antigen secretor status (FUT2). Using transcriptome-wide association and colocalisation to infer the effect of gene expression on disease severity, we find evidence implicating expression of multiple genes, including reduced expression of a membrane flippase (ATP11A), and increased mucin expression (MUC1), in critical disease.We show that comparison between critically-ill cases and population controls is highly efficient for genetic association analysis and enables detection of therapeutically-relevant mechanisms of disease. Therapeutic predictions arising from these findings require testing in clinical trials.

Background: Lung function is highly heritable and differs between the sexes throughout life. However, little is known about sex-differential genetic effects on lung function. We aimed to conduct the first genome-wide genotype-by-sex interaction study on lung function to identify genetic effects that differ between males and females. Methods: We tested for interactions between 7,745,864 variants and sex on spirometry-based measures of lung function in UK Biobank (N=303,612), and sought replication in 75,696 independent individuals from the SpiroMeta consortium. Results: Five independent single-nucleotide polymorphisms (SNPs) showed genome-wide significant (P-8) interactions with sex on lung function, and 21 showed suggestive interactions (P-6). The strongest signal, from rs7697189 (chr4:145436894) on forced expiratory volume in 1 second (FEV1) (P=3.15x10-15), was replicated (P=0.016) in SpiroMeta. The C allele increased FEV1 more in males (untransformed FEV1 β=0.028 [SE 0.0022] litres) than females (β=0.009 [SE 0.0014] litres), and this effect was not accounted for by differential effects on height, smoking or pubertal age. rs7697189 resides upstream of the hedgehog-interacting protein (HHIP) gene and was previously associated with lung function and HHIP lung expression. We found HHIP expression was significantly different between the sexes (P=6.90x10-6), but we could not detect sex differential effects of rs7697189 on expression. Conclusions: We identified a novel genotype-by-sex interaction at a putative enhancer region upstream of the HHIP gene. Establishing the mechanism by which HHIP SNPs have different effects on lung function in males and females will be important for our understanding of lung health and diseases in both sexes.

Post-translational modifications (PTMs) diversify protein functions and dynamically coordinate their signalling networks, influencing most aspects of cell physiology. Nevertheless, their genetic regulation or influence on complex traits is not fully understood. Here, we compare for the first time the genetic regulation of the same PTM of two proteins – glycosylation of transferrin and immunoglobulin G (IgG). By performing genome-wide association analysis of transferrin glycosylation, we identified 10 significantly associated loci, all novel. Comparing these with IgG glycosylation-associated genes, we note protein-specific associations with genes encoding glycosylation enzymes (transferrin - MGAT5, ST3GAL4, B3GAT1; IgG - MGAT3, ST6GAL1) as well as shared associations (FUT6, FUT8). Colocalisation analyses of the latter suggest that different causal variants in the FUT genes regulate fucosylation of the two proteins. We propose that they affect the binding of different transcription factors in different tissues, with fucosylation of IgG being regulated by IKZF1 in B-cells and of transferrin by HNF1A in liver.

Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at theFASlocus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

Biological age (BA), a measure of functional capacity and prognostic of health outcomes that discriminates between individuals of the same chronological age (chronAge), has been estimated using a variety of biomarkers. Previous comparative studies have mainly used epigenetic models (clocks), we use ~1000 participants to create eleven omics ageing clocks, with correlations of 0.45-0.97 with chronAge, even with substantial sub-setting of biomarkers. These clocks track common aspects of ageing with 94% of the variance in chronAge being shared among clocks. The difference between BA and chronAge - omics clock age acceleration (OCAA) - often associates with health measures. One year’s OCAA typically has the same effect on risk factors/10-year disease incidence as 0.46/0.45 years of chronAge. Epigenetic and IgG glycomics clocks appeared to track generalised ageing while others capture specific risks. We conclude BA is measurable and prognostic and that future work should prioritise health outcomes over chronAge.

Defining the genetic components that control glycosylation of the human immunoglobulin G (IgG) is an ongoing effort, which has so far been addressed by means of heritability, linkage and genome-wide association studies (GWAS). Unlike the synthesis of proteins, N-glycosylation biosynthesis is not a template-driven process, but rather a complex process regulated by both genetic and environmental factors. Current heritability studies have shown that while up to 75% of the variation in levels of some IgG glycan traits can be explained by genetics, some glycan traits are completely defined by environmental influences. Advances in both high-throughput genotyping and glycan quantification methods have enabled genome-wide association studies that are increasingly used to estimate associations of millions of single-nucleotide polymorphisms and glycosylation traits. Using this method, 18 genomic regions have so far been robustly associated with IgG N-glycosylation, discovering associations with genes encoding glycosyltransferases, but also transcription factors, co-factors, membrane transporters and other genes with no apparent role in IgG glycosylation. Further computational analyses have shown that IgG glycosylation is likely to be regulated through the expression of glycosyltransferases, but have also for the first time suggested which transcription factors are involved in the process. Moreover, it was also shown that IgG glycosylation and inflammatory diseases share common underlying causal genetic variants, suggesting that studying genetic regulation of IgG glycosylation helps not only to better understand this complex process but can also contribute to understanding why glycans are changed in disease. However, further studies are needed to unravel whether changes in IgG glycosylation are causing these diseases or the changes in the glycome are caused by the disease.

The N-glycosylation of immunoglobulin G (IgG) affects its structure and function. It has been demonstrated that IgG N-glycosylation patterns are inherited as complex quantitative traits. Genome-wide association studies identified loci harboring genes encoding enzymes directly involved in protein glycosylation as well as loci likely to be involved in regulation of glycosylation biochemical pathways. Many of these loci could be linked to immune functions and risk of inflammatory and autoimmune diseases. The aim of the present study was to discover and replicate new loci associated with IgG N-glycosylation and to investigate possible pleiotropic effects of these loci onto immune function and the risk of inflammatory and autoimmune diseases. We conducted a multivariate genome-wide association analysis of 23 IgG N-glycosylation traits measured in 8090 individuals of European ancestry. The discovery stage was followed up by replication in 3147 people and in silico functional analysis. Our study increased the total number of replicated loci from 22 to 29. For the discovered loci, we suggest a number of genes potentially involved in the control of IgG N-glycosylation. Among the new loci, two (near RNF168 and TNFRSF13B) were previously implicated in rare immune deficiencies and were associated with levels of circulating immunoglobulins. For one new locus (near AP5B1/OVOL1), we demonstrated a potential pleiotropic effect on the risk of asthma. Our findings underline an important link between IgG N-glycosylation and immune function and provide new clues to understanding their interplay.

The subset of patients who develop critical illness in Covid-19 have extensive inflammation affecting the lungs1 and are strikingly different from other patients: immunosuppressive therapy benefits critically-ill patients, but may harm some non-critical cases.2 Since susceptibility to life-threatening infections and immune-mediated diseases are both strongly heritable traits, we reasoned that host genetic variation may identify mechanistic targets for therapeutic development in Covid-19.3GenOMICC (Genetics Of Mortality In Critical Care, genomicc.org) is a global collaborative study to understand the genetic basis of critical illness. Here we report the results of a genome-wide association study (GWAS) in 2244 critically-ill Covid-19 patients from 208 UK intensive care units (ICUs), representing >95% of all ICU beds. Ancestry-matched controls were drawn from the UK Biobank population study and results were confirmed in GWAS comparisons with two other population control groups: the 100,000 genomes project and Generation Scotland.We identify and replicate three novel genome-wide significant associations, at chr19p13.3 (rs2109069, p = 3.98 × 10−12), within the gene encoding dipeptidyl peptidase 9 (DPP9), at chr12q24.13 (rs10735079, p =1.65 × 10−8) in a gene cluster encoding antiviral restriction enzyme activators (OAS1, OAS2, OAS3), and at chr21q22.1 (rs2236757, p = 4.99 × 10−8) in the interferon receptor gene IFNAR2. Consistent with our focus on extreme disease in younger patients with less comorbidity, we detect a stronger signal at the known 3p21.31 locus than previous studies (rs73064425, p = 4.77 × 10−30).We identify potential targets for repurposing of licensed medications. Using Mendelian randomisation we found evidence in support of a causal link from low expression of IFNAR2, and high expression of TYK2, to life-threatening disease. Transcriptome-wide association in lung tissue revealed that high expression of the monocyte/macrophage chemotactic receptor CCR2 is associated with severe Covid-19.Our results identify robust genetic signals relating to key host antiviral defence mechanisms, and mediators of inflammatory organ damage in Covid-19. Both mechanisms may be amenable to targeted treatment with existing drugs. Large-scale randomised clinical trials will be essential before any change to clinical practice.

Genome-wide association studies have led to a significant progress in identification of genomic loci affecting coronary artery disease (CAD) risk. However, revealing the causal genes responsible for the observed associations is challenging. In the present study, we aimed to prioritize CAD-relevant genes based on cumulative evidence from the published studies and our own study of colocalization between eQTLs and loci associated with CAD using SMR/HEIDI approach. Prior knowledge of candidate genes was extracted from both experimental and in silico studies, employing different prioritization algorithms. Our review systematized information for a total of 51 CAD-associated loci. We pinpointed 37 genes in 36 loci. For 27 genes we infer they are causal for CAD, and for 10 further genes we judge them most likely causal. Colocalization analysis showed that for 18 out of these loci, association with CAD can be explained by changes in gene expression in one or more CAD-relevant tissues. Furthermore, for 8 out of 36 loci, existing evidence suggested additional CAD-associated genes. For the remaining 15 loci, we concluded that evidence for gene prioritization remains inconsistent, insufficient, or absent. Our results provide deeper insights into the genetic etiology of CAD and demonstrate knowledge gaps where further research is warranted.

Background: Lung function is highly heritable and differs between the sexes throughout life. However, little is known about sex-differential genetic effects on lung function. We aimed to conduct the first genome-wide genotype-by-sex interaction study on lung function to identify genetic effects that differ between males and females. Methods: We tested for interactions between 7,745,864 variants and sex on spirometry-based measures of lung function in UK Biobank (N=303,612), and sought replication in 75,696 independent individuals from the SpiroMeta consortium. Results: Five independent single-nucleotide polymorphisms (SNPs) showed genome-wide significant (P

BACKGROUND: Type 2 diabetes results from interplay between genetic and acquired factors. Glycans on proteins reflect genetic, metabolic and environmental factors. However, associations of IgG glycans with type 2 diabetes have not been described. We compared IgG N-glycan patterns in type 2 diabetes with healthy subjects.METHODS: In the DiaGene study, a population-based case-control study, (1886 cases and 854 controls) 58 IgG glycan traits were analyzed. Findings were replicated in the population-based CROATIA-Korcula-CROATIA-Vis-ORCADES studies (162 cases and 3162 controls), and meta-analyzed. AUCs of ROC-curves were calculated using 10-fold cross-validation for clinical characteristics, IgG glycans and their combination.RESULTS: After correction for extensive clinical covariates, 5 IgG glycans and 13 derived traits significantly associated with type 2 diabetes in meta-analysis (after Bonferroni correction). Adding IgG glycans to age and sex increased the AUC from 0.542 to 0.734. Adding them to the extensive model did not substantially improve the AUC. The AUC for IgG glycans alone was 0.729.CONCLUSIONS: Several IgG glycans and traits firmly associate with type 2 diabetes, reflecting a pro-inflammatory and biologically-aged state. IgG glycans showed limited improvement of AUCs. However, IgG glycans showed good prediction alone, indicating they may capture information of combined covariates. The associations found may yield insights in type 2 diabetes pathophysiology.GENERAL SIGNIFICANCE: This work shows that IgG glycomic changes have biomarker potential and may yield important insights into pathophysiology of complex public health diseases, illustrated here for the first time in type 2 diabetes.

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The Viking Health Study Shetland is a population-based research cohort of 2,122 volunteer participants with ancestry from the Shetland Isles in northern Scotland. The high kinship and detailed phenotype data support a range of approaches for associating rare genetic variants, enriched in this isolate population, with quantitative traits and diseases. As an exemplar, the c.1750G > A; p.Gly584Ser variant within the coding sequence of the KCNH2 gene implicated in Long QT Syndrome (LQTS), which occurred once in 500 whole genome sequences from this population, was investigated. Targeted sequencing of the KCNH2 gene in family members of the initial participant confirmed the presence of the sequence variant and identified two further members of the same family pedigree who shared the variant. Investigation of these three related participants for whom single nucleotide polymorphism (SNP) array genotypes were available allowed a unique shared haplotype of 1.22 Mb to be defined around this locus. Searching across the full cohort for this haplotype uncovered two additional apparently unrelated individuals with no known genealogical connection to the original kindred. All five participants with the defined haplotype were shown to share the rare variant by targeted Sanger sequencing. If this result were verified in a healthcare setting, it would be considered clinically actionable, and has been actioned in relatives ascertained independently through clinical presentation. The General Practitioners of four study participants with the rare variant were alerted to the research findings by letters outlining the phenotype (prolonged electrocardiographic QTc interval). A lack of detectable haplotype sharing between c.1750G > A; p.Gly584Ser chromosomes from previously reported individuals from Finland and those in this study from Shetland suggests that this mutation has arisen more than once in human history. This study showcases the potential value of isolate population-based research resources for genomic medicine. It also illustrates some challenges around communication of actionable findings in research participants in this context.

Adult lung function is highly heritable and 279 genetic loci were recently reported as associated with spirometry-based measures of lung function. Though lung development and function differ between males and females throughout life, there has been no genome-wide study to identify genetic variants with differential effects on lung function in males and females. Here, we present the first genome-wide genotype-by-sex interaction study on four lung function traits in 303,612 participants from the UK Biobank. We detected five SNPs showing genome-wide significant (P−8) interactions with sex on lung function, as well as 21 suggestively significant interactions (P−6). The strongest sex interaction signal came from rs7697189 at 4:145436894 on forced expiratory volume in 1 second (FEV1) (P = 3.15 × 10−15), and was replicated (P = 0.016) in 75,696 individuals in the SpiroMeta consortium. Sex-stratified analyses demonstrated that the minor (C) allele of rs7697189 increased lung function to a greater extent in males than females (untransformed FEV1β = 0.028 [SE 0.0022] litres in males vs β = 0.009 [SE 0.0014] litres in females), and this effect was not accounted for by differential effects on height, smoking or age at puberty. This SNP resides upstream of the gene encoding hedgehog-interacting protein (HHIP) and has previously been reported for association with lung function andHHIPexpression in lung tissue. In our analyses, whileHHIPexpression in lung tissue was significantly different between the sexes with females having higher expression (most significant probeset P=6.90 × 10−6) after adjusting for age and smoking, rs7697189 did not demonstrate sex differential effects on expression. Establishing the mechanism by whichHHIPSNPs have different effects on lung function in males and females will be important for our understanding of lung health and diseases, such as chronic obstructive pulmonary disease (COPD), in both sexes.

Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3,811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated twelve loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other, more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH, and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area.

Variation in key transcription factors and glycogenes modifies IgG glycosylation and has an influence on inflammatory diseases., Effector functions of immunoglobulin G (IgG) are regulated by the composition of a glycan moiety, thus affecting activity of the immune system. Aberrant glycosylation of IgG has been observed in many diseases, but little is understood about the underlying mechanisms. We performed a genome-wide association study of IgG N-glycosylation (N = 8090) and, using a data-driven network approach, suggested how associated loci form a functional network. We confirmed in vitro that knockdown of IKZF1 decreases the expression of fucosyltransferase FUT8, resulting in increased levels of fucosylated glycans, and suggest that RUNX1 and RUNX3, together with SMARCB1, regulate expression of glycosyltransferase MGAT3. We also show that variants affecting the expression of genes involved in the regulation of glycoenzymes colocalize with variants affecting risk for inflammatory diseases. This study provides new evidence that variation in key transcription factors coupled with regulatory variation in glycogenes modifies IgG glycosylation and has influence on inflammatory diseases.

Background With the exponential growth in available biomedical data, there is a need for data integration methods that can extract information about relationships between the data sets. However, these data sets might have very different characteristics. For interpretable results, data-specific variation needs to be quantified. For this task, Two-way Orthogonal Partial Least Squares (O2PLS) has been proposed. To facilitate application and development of the methodology, free and open-source software is required. However, this is not the case with O2PLS. Results We introduce OmicsPLS, an open-source implementation of the O2PLS method in R. It can handle both low- and high-dimensional datasets efficiently. Generic methods for inspecting and visualizing results are implemented. Both a standard and faster alternative cross-validation methods are available to determine the number of components. A simulation study shows good performance of OmicsPLS compared to alternatives, in terms of accuracy and CPU runtime. We demonstrate OmicsPLS by integrating genetic and glycomic data. Conclusions We propose the OmicsPLS R package: a free and open-source implementation of O2PLS for statistical data integration. OmicsPLS is available at https://cran.r-project.org/package=OmicsPLS and can be installed in R via install.packages(“OmicsPLS”). Electronic supplementary material The online version of this article (10.1186/s12859-018-2371-3) contains supplementary material, which is available to authorized users.

Glycosylation is a common post-translational modification of proteins. It is known, that glycans are directly involved in the pathophysiology of every major disease. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here, we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people. We discovered and replicated twelve loci. This allowed us to demonstrate a clear overlap in genetic control between total plasma and IgG glycosylation. Majority of loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5). We, however, also found loci that are likely to reflect other, more complex, aspects of plasma glycosylation process. Functional genomic annotation suggested the role of DERL3, which potentially highlights the role of glycoprotein degradation pathway, and such transcription factor as IKZF1.

OBJECTIVE Poorer glycemic control in type 1 diabetes may alter N-glycosylation patterns on circulating glycoproteins, and these alterations may be linked with diabetic kidney disease (DKD). We investigated associations between N-glycans and glycemic control and renal function in type 1 diabetes. RESEARCH DESIGN AND METHODS Using serum samples from 818 adults who were considered to have extreme annual loss in estimated glomerular filtration rate (eGFR; i.e., slope) based on retrospective clinical records, from among 6,127 adults in the Scottish Diabetes Research Network Type 1 Bioresource Study, we measured total and IgG-specific N-glycan profiles. This yielded a relative abundance of 39 total (GP) and 24 IgG (IGP) N-glycans. Linear regression models were used to investigate associations between N-glycan structures and HbA1c, albumin-to-creatinine ratio (ACR), and eGFR slope. Models were adjusted for age, sex, duration of type 1 diabetes, and total serum IgG. RESULTS Higher HbA1c was associated with a lower relative abundance of simple biantennary N-glycans and a higher relative abundance of more complex structures with more branching, galactosylation, and sialylation (GP12, 26, 31, 32, and 34, and IGP19 and 23; all P < 3.79 × 10−4). Similar patterns were seen for ACR and greater mean annual loss of eGFR, which were also associated with fewer of the simpler N-glycans (all P < 3.79 × 10−4). CONCLUSIONS Higher HbA1c in type 1 diabetes is associated with changes in the serum N-glycome that have elsewhere been shown to regulate the epidermal growth factor receptor and transforming growth factor-β pathways that are implicated in DKD. Furthermore, N-glycans are associated with ACR and eGFR slope. These data suggest that the role of altered N-glycans in DKD warrants further investigation.

Aging is a complex process of accumulation of molecular, cellular, and organ damage, leading to loss of function and increased vulnerability to disease and finally to death (1). It is well known that lifestyle choices such as smoking and physical activity can hasten or delay the aging process (2). Such observations have led to the search for molecular markers of age that can be used to predict, monitor, and provide insight into age-associated physiological decline and disease. Protein structure is defined by the sequence of nucleotides in the corresponding genes, thus the polypeptide sequence of a protein cannot change with age. However, an important structural and functional element of the majority of proteins are the glycans that participate in virtually all physiological processes (3). Glycans are product of a complex pathway that involves hundreds of different proteins and are encoded in a complex dynamic network of hundreds of genes (4). Epigenetic regulation of gene expression is expected to affect protein glycosylation and several publications recently reported this effect (5–8). Changes in glycosylation with age have been shown over 20 years ago (9) and have also replicated in recent large population studies (10–13). Immunoglobulin G (IgG) is an excellent model glycoprotein because its glycosylation has been well defined (Figure 1), and many important functional effects of alternative IgG glycosylation have been described (14). For example, glycosylation acts as a switch between pro- and anti-inflammatory IgG functionality. Most of the IgG molecules are not sialylated and are proinflammatory. Terminal α2,6-sialylation of IgG glycans decreases the ability of IgG to bind to activating FcγRs and promotes recognition by DC-SIGN, which increases expression of inhibitory FcγRIIB and is anti-inflammatory (15). Another fascinating example is the role of core fucose in the modulation of antibody-dependent cellular cytotoxicity: IgG-containing glycans that lack core fucose have 100-fold increased affinity for FcγRIIIA and are therefore much more efficient in activating antibody-dependent cellular cytotoxicity than fucosylated glycoforms of the same molecule (16). On average, 95% of the IgG population is core fucosylated (12); thus, most of the immunoglobulins have a “safety switch,” which prevents them from activating antibody-dependent cellular cytotoxicity. Malfunction of this system appears to be associated with autoimmune diseases as indicated by both pleiotropic effects of genes that associate with IgG glycosylation on different inflammatory and autoimmune diseases, and the observed alterations in IgG glycosylation in systemic lupus erythematous (17) and many inflammatory diseases (18). Figure 1. UPLC analysis of immunoglobulin G (IgG) glycosylation. Each IgG contains one conserved N-glycosylation site on Asn197 of its heavy chain. Different glycans can be attached to this site and the process seems to be highly regulated. UPLC analysis can reveal ... Interindividual variability of IgG glycosylation in a population is large (12) and it appears to be affected by both variation in DNA sequence (19) and environmental factors (11). Most of the studies that investigated glycosylation changes with age were either of limited size or were performed on the total plasma glycome; thus, in addition to changes in glycosylation, the observed differences reflected changes in the concentration of individual plasma proteins. In this study, we focused on glycosylation of IgG and analyzed more than 5,000 individuals from four different European populations to provide definitive data about changes in IgG glycosylation through the lifetime.

More than half of all known proteins, and almost all membrane and extra-cellular proteins have oligosaccharide structures or glycans attached to them. Defects in glycosylation pathways are directly involved in at least 30 severe human diseases.A multiple center cross-sectional study (China, Croatia, and Scotland) was carried out to investigate the possible association between hypertension and IgG glycosylation. A hydrophilic interaction chromatography of fluorescently labeled glycans was used to analyze N-glycans attached to IgG in plasma samples from a total of 4757 individuals of Chinese Han, Croatian, and Scottish ethnicity.Five glycans (IgG with digalactosylated glycans) significantly differed in participants with prehypertension or hypertension compared to those with normal blood pressure, while additional 17 glycan traits were only significantly differed in participants with hypertension compared to those of normal blood pressure. These glycans were also significant correlated with systolic blood pressure (SBP) or diastolic blood pressure (DBP).The present study demonstrated for the 1st time an association between hypertension and IgG glycome composition. These findings suggest that the individual variation in N-glycosylation of IgG contributes to pathogenesis of hypertension, presumably via its effect on pro- and/or anti-inflammatory pathways.

Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach. Validation is performed using data from a GWAS and results from three in vitro experiments. We show that one predicted reaction is enzymatically feasible and that one rejected reaction does not occur in vitro. Moreover, in contrast to previous knowledge, enzymes involved in our predictions colocalize in the Golgi of two cell lines, further confirming the in silico predictions., IgG glycosylation is an important factor in immune function, yet the molecular details of protein glycosylation remain poorly understood. The data-driven approach presented here uses large-scale plasma IgG mass spectrometry measurements to infer new biochemical reactions in the glycosylation pathway.

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies. © 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

BACKGROUND: Glycosylation is one of the most common post-translation modifications with large influences on protein structure and function. The effector function of immunoglobulin G (IgG) alters between pro- and anti-inflammatory, based on its glycosylation. IgG glycan synthesis is highly complex and dynamic.METHODS: With the use of two different analytical methods for assessing IgG glycosylation, we aim to elucidate the link between DNA methylation and glycosylation of IgG by means of epigenome-wide association studies. In total, 3000 individuals from 4 cohorts were analyzed.RESULTS: The overlap of the results from the two glycan measurement panels yielded DNA methylation of 7 CpG-sites on 5 genomic locations to be associated with IgG glycosylation: cg25189904 (chr.1, GNG12); cg05951221, cg21566642 and cg01940273 (chr.2, ALPPL2); cg05575921 (chr.5, AHRR); cg06126421 (6p21.33); and cg03636183 (chr.19, F2RL3). Mediation analyses with respect to smoking revealed that the effect of smoking on IgG glycosylation may be at least partially mediated via DNA methylation levels at these 7 CpG-sites.CONCLUSION: Our results suggest the presence of an indirect link between DNA methylation and IgG glycosylation that may in part capture environmental exposures.GENERAL SIGNIFICANCE: An epigenome-wide analysis conducted in four population-based cohorts revealed an association between DNA methylation and IgG glycosylation patterns. Presumably, DNA methylation mediates the effect of smoking on IgG glycosylation.