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Glycans are an essential structural component of Immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation). We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in thein vitroIgG expression system. Up- and downregulation of three genes,EEF1A1, MANBAandTNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in thisin vitroexpression system.

It is often difficult to be certain which genes underlie the effects seen in association studies. However, variants that disrupt the protein, such as predicted loss of function (pLoF) and missense variants, provide a shortcut to identify genes with a clear biological link to the phenotype of interest. Glycosylation is one of the most common post-translationalmodifications of proteins, and an important biomarker of both disease and its progression. Here, we utilised the power of genetic isolates, gene-based aggregation tests and intermediate phenotypes to assess the effect of rare (MAF−8) and for IgG glycan traits at 4 genes (p−7). Associations in three of these genes (FUT8, MGAT3andRFXAP) are driven by multiple rare variants simultaneously contributing to protein glycosylation. Association atST6GAL1, with a 300-fold up-drifted variant in the Orkney Islands, was detectable by a single-point exome-wide association analysis. Glycome-associated aggregate associations are located in genes already known to have a biological link to protein glycosylation (FUT6, FUT8for transferrin;FUT8, MGAT3andST6GAL1for IgG) but also in genes which have not been previously reported (e.g.RFXAPfor IgG). To assess the potential impact of rare variants associated with glycosylation on other traits, we queried public repositories of gene-based tests, discovering a potential connection between transferrin glycosylation,MSR1, galectin-3, insulin-like growth factor 1 and diabetes. However, the exact mechanism behind these connections requires further elucidation.

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.

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.

Adoption of diets based on some cereals, especially on rice, signified an iconic change in nutritional habits for many Asian populations and a relevant challenge for their capability to maintain glucose homeostasis. Indeed, rice shows the highest carbohydrates content and glycaemic index among the domesticated cereals and its usual ingestion represents a potential risk factor for developing insulin resistance and related metabolic diseases. Nevertheless, type 2 diabetes and obesity epidemiological patterns differ among Asian populations that rely on rice as a staple food, with higher diabetes prevalence and increased levels of central adiposity observed in people of South Asian ancestry rather than in East Asians. This may be at least partly due to the fact that populations from East Asian regions where wild rice or other cereals such as millet have been already consumed before their cultivation and/or were early domesticated have relied on these nutritional resources for a period long enough to have possibly evolved biological adaptations that counteract their detrimental side effects. To test such a hypothesis, we compared adaptive evolution of these populations with that of control groups from regions where the adoption of cereal-based diets occurred many thousand years later and which were identified from a genome-wide dataset including 2,379 individuals from 124 East Asian and South Asian populations. This revealed selective sweeps and polygenic adaptive mechanisms affecting functional pathways involved in fatty acids metabolism, cholesterol/triglycerides biosynthesis from carbohydrates, regulation of glucose homeostasis and production of retinoic acid in Chinese Han and Tujia ethnic groups, as well as in people of Korean and Japanese ancestry. Accordingly, long-standing rice- and/or millet-based diets have possibly contributed to trigger the evolution of such biological adaptations, which might represent one of the factors that play a role in mitigating the metabolic risk of these East Asian populations. 1 Introduction 2 Methods 2.1 Samples collection and genotyping 2.2 Data curation and assembly of a Pan-Asian dataset 2.3 Population structure analyses 2.4 Selection scans on the identified population clusters 2.5 Shortlisting of the most informative candidate adaptive genes 2.6 Gene network analyses aimed at testing for polygenic adaptation 3 Results 3.1 Exploring population structure in the assembled Pan-Asian dataset 3.2 Identification of genetically homogeneous population clusters 3.3 Detection of genomic regions enriched for selective sweeps in each population cluster 3.4 Fine mapping of the most informative candidate adaptive genes 3.5 Investigation of polygenic adaptive events in the Han-Tujia and Japanese clusters 4 Discussion 4.1 Population clusters within the South Asianand East Asian genomic landscapes 4.2 Distinctive patterns of adaptive evolution in candidate and control population clusters

Post-translational modifications 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 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 identify 10 significantly associated loci, 9 of which were not reported previously. 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. Glycosylation of these proteins is thus genetically regulated by both shared and protein-specific mechanisms.